A method and device for multi-stage closed-loop planning and scheduling evaluation of a source-network-load-storage integrated system

Abstract

The application discloses a kind of source network load storage integration system multi-stage closed loop planning scheduling evaluation method and device, belong to energy optimization technical field.Its method includes: obtaining operation benchmark data input to the energy system optimization model constructed in advance, obtains planning scheduling evaluation result;Energy system optimization model construction includes: constructing energy system modular component model and comprehensive power load correction model, establishes full life cycle economic low-carbon multi-objective optimization configuration objective function, sets safety domain operation space compulsory restraint rule and policy compliance compulsory restraint rule, forms planning stage model;Establish the optimization scheduling objective function for preventing uncertainty risk under market operation, and construct source load hyperrectangle uncertainty model, form scheduling stage model;Build the evaluation model including positive and negative trend benefit index single evaluation score and comprehensive multi-attribute evaluation.This application improves new energy consumption, reduces power purchase cost, enhances system robust operation ability.

Description

Technical Field

[0001] This invention relates to a multi-stage closed-loop planning, scheduling, and evaluation method and device for an integrated energy source, grid, load, and storage system, belonging to the field of energy optimization technology. Background Technology

[0002] With the increasing penetration rate of new energy sources, integrated energy systems (source-grid-load-storage) have gradually become a focus of attention in academia and industry due to their significant advantages in improving energy utilization efficiency, reducing carbon emissions, and enhancing system flexibility. However, how to achieve coordinated planning and configuration optimization of new energy and storage under a low-carbon economy, and how to optimize scheduling to mitigate uncertainty risks under a market-oriented operation model considering source-load uncertainties and electricity market factors, remain key challenges in current research. Industrial parks are characterized by load-intensive and highly volatile energy demand. In recent years, the rapid development of distributed new energy sources has provided technical support for the transformation of the energy structure in industrial parks. Against this backdrop, research on the planning and configuration of integrated energy systems (source-grid-load-storage) for industrial parks, the optimization scheduling to mitigate uncertainty risks under a market-oriented operation model, and the comprehensive multi-attribute evaluation of planning and scheduling effects have significant theoretical value and practical application significance.

[0003] Domestic and foreign scholars have made some basic achievements in the planning and optimization operation of integrated energy systems of source, grid, load and storage, mainly focusing on system modeling, planning optimization, optimization operation and economic dispatch. However, the existing research results and technologies still have the following technical shortcomings: (1) Most studies focus on a single objective such as economy or low carbon, and lack in-depth discussion and analysis on the synergistic optimization of the two; (2) There are few studies on specific scenarios of the "all self-generation and self-consumption" mode in the park, especially few studies on the planning and configuration optimization of incremental projects, and there is a lack of optimization dispatch verification and application results under the market-oriented operation model that considers the uncertainty of source and load and the factors of the power market after the planning configuration is realized; (3) The synergistic planning and configuration of new energy power generation and storage are mostly limited to short-term operation, and the analysis of seasonal adjustment capacity in the whole life cycle is insufficient. The mandatory binding rules of policy compliance are not comprehensively considered, resulting in poor planning configuration effect. There is a lack of comprehensive evaluation means of planning effect, and it is impossible to adjust the planning configuration parameters by using the evaluation score; (4) The system model unit is not refined enough, there is a lack of correction of historical data, and the mandatory binding rules of the system safety domain operation space are too loose and do not conform to the actual engineering optimization dispatch operation, resulting in poor optimization dispatch effect. There is a lack of comprehensive evaluation means of dispatch effect. Summary of the Invention

[0004] The technical problem to be solved by this invention is the following: existing technologies have multiple single-objective optimizations, insufficient research on incremental projects for self-use in industrial parks, shortcomings in the collaborative planning and system modeling of industrial park resources and storage, lack of a comprehensive evaluation system, and technical defects such as isolation between planning, scheduling and evaluation and lack of closed-loop feedback.

[0005] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution.

[0006] On the one hand, this invention provides a multi-stage closed-loop planning, scheduling, and evaluation method for an integrated source-grid-load-storage system, comprising:

[0007] Obtain operational baseline data for the integrated power generation, grid, load, and storage system of the industrial park;

[0008] The operational baseline data is input into a pre-built energy system optimization model for processing to obtain planning and scheduling evaluation results;

[0009] The energy system optimization model includes a planning phase model, a scheduling phase model, and an evaluation model. The construction steps of the energy system optimization model are as follows:

[0010] Construct a modular component model for the energy system and a comprehensive power load correction model, establish a multi-objective optimization configuration objective function for economic and low-carbon development throughout the entire life cycle, and set mandatory binding rules for the safe domain operation space and mandatory binding rules for policy compliance to form a planning stage model;

[0011] Establish an optimized scheduling objective function to prevent uncertainty risks under market-oriented operation, and construct a source-load hyperrectangular uncertainty model to form a scheduling phase model;

[0012] Based on the effects of the planning stage model or scheduling stage model, an evaluation model is constructed that includes individual evaluation scores for positive and negative trend benefit indicators and comprehensive multi-attribute evaluation.

[0013] The execution steps of the energy system optimization model include:

[0014] The operational baseline data is input into the planning stage model and the scheduling stage model. The planning stage model and the scheduling stage model respectively perform economic and low-carbon planning configuration and optimized scheduling of the integrated source-grid-load-storage system of the park. The evaluation model scores the economic and low-carbon planning configuration results and optimized scheduling results respectively. The planning stage model and the scheduling stage model decide whether to adjust the planning parameters and configuration based on their respective scores.

[0015] Output the economic low-carbon planning configuration results of the planning phase model, the optimized scheduling results of the scheduling phase model, and the scoring results of the evaluation model.

[0016] By constructing a multi-objective optimization system that coordinates economic and low-carbon development, the system achieves synergistic optimization of economic and low-carbon benefits in the allocation of power generation, grid, load, and storage in the industrial park. It introduces a super-rectangular uncertainty model for power generation and load and market-based risk optimization scheduling objectives to adapt to the planning scenario of incremental projects for full self-generation and self-consumption in the industrial park. Based on the entire life cycle of the system, it conducts configuration planning, adds mandatory constraints for policy compliance, and iteratively corrects configuration parameters using a comprehensive multi-attribute evaluation model. It adopts refined modular component modeling and sets up a comprehensive power load correction mechanism, configures strict system security domain constraints that fit the actual engineering situation, and combines them with a comprehensive evaluation mechanism for planning and scheduling effectiveness. This improves upon the shortcomings of traditional modeling units, which are coarse, have lenient security constraints, and have difficulty in quantifying and evaluating scheduling effects.

[0017] Preferably, the operating baseline data includes:

[0018] The input dataset for the planning phase model includes: actual operating wind speed, historical operating wind speed, and operating characteristic model parameters of wind power generation components; irradiance and model parameters of basic solar power generation components during operation and historical operation; loss rate, initial energy storage capacity, charging efficiency, and discharging efficiency of energy storage components; annual average power factor of components at the public grid connection point; maximum power capacity of power supply transformers; maximum power capacity of power supplied from the grid to the system and power fed back to the grid; historical data of comprehensive power load and calibration model parameters; step size of optimized operating data points; optimized operating cycle; maximum allowable wind curtailment rate and maximum solar curtailment rate within the optimized operating cycle; installed capacity limits of solar and wind power generation components; system demand capacity control and power feedback baseline control; and other parameters to be determined. The planning includes the maximum initial investment cost of the equipment, the area occupied by the equipment installation and the usable land area of ​​the park, the maximum allowable load ratio, the binary variable of interruptible state, the set of allowable interruption periods, the upper limit of the annual interruptible electricity consumption ratio of the total electricity consumption as stipulated by policies or agreements, the minimum continuous interruption duration and unit interruption power cost, the lower limit of the green electricity direct supply self-consumption ratio stipulated by policies, the upper limit of the on-grid electricity ratio, the lower limit of the renewable energy power generation ratio of the total electricity consumption ratio, the regional grid carbon emission factor corresponding to the park's purchase of electricity from the grid, the upper limit of annual energy consumption carbon emissions stipulated by policies, the unit electricity purchase price, the unit maintenance cost, the unit capacity installation cost, the capacity, the investment recovery coefficient, the discount rate, the working years of energy storage components, the annual salary and number of employees, and the unit power equivalent carbon emission coefficient.

[0019] The input dataset of the scheduling phase model includes: the step size of the optimized scheduling operation data points, the electricity price of the park purchasing electricity from the upper-level grid and selling electricity to the grid during the optimized scheduling, the operation and maintenance cost of the unit charging and discharging power of the energy storage components, the unit compensation cost of interruptible loads, the electricity market price for the park to increase and decrease reserve capacity, the time period set during which the park is allowed to sell electricity to the grid, interruptible loads, and provide upper and lower reserve capacity, and the maximum absolute value of the output deviation of the wind power generation components and solar power generation components during the optimized scheduling.

[0020] The planning and scheduling evaluation results include:

[0021] The economic and low-carbon planning configuration results of the planning phase model are as follows: predicted output power and corrected historical data of wind power generation components, predicted output power and corrected historical data of solar power generation components, corrected comprehensive power load data, capacity, charging power, and discharging power of energy storage components, output power of power supply transformers at public grid access points, power supply from the grid to the system, economic target result value, energy cost value, operation and maintenance cost value, equipment construction investment cost value, salary cost of full-time operation and management personnel, low-carbon target value, actual power input to the system from wind power generation and solar power generation, satisfaction with economic and low-carbon multi-objectives, actual wind curtailment rate, actual solar curtailment rate, energy storage capacity of optimized planning configuration of energy storage components, installed capacity of optimized planning configuration of solar power generation components and wind power generation components, total interruption cost of interruptible load throughout the year, and actual interrupted power of the park's energy system.

[0022] The optimized scheduling results of the scheduling phase model are: the total operating cost of the park during the optimized scheduling cycle, the active power of the park purchasing electricity from and selling electricity to the grid during the optimized scheduling, the upward and downward reserve capacity provided by the park to the grid, and the actual planned utilization output of wind power generation components, solar power generation components, and the actual planned utilization demand of comprehensive power load during the optimized scheduling.

[0023] The scoring results of the evaluation model are: single evaluation scores for positive trend benefit indicators, single evaluation scores for negative trend benefit indicators, and comprehensive multi-attribute evaluation scores.

[0024] Preferably, the modular component model of the energy system includes modular component models of wind power generation, solar power generation, energy storage, and public grid access points;

[0025] Constructing the modular component model for wind power generation includes:

[0026] ,

[0027] The constraints are:

[0028] ,

[0029] in, For modular components of wind power generation Predicted output power at time of day For modular components of wind power generation The actual operating wind speed at any given time. The minimum operating wind speed for the modular wind power generation components. The wind speed limit for safe operation of modular wind power generation components. The rated installed capacity of the modular wind power generation components. The rated operating wind speed for modular wind power generation components, For wind energy Power at any moment The operating power factor of the wind turbine rotor. For wind-driven turbine operation characteristic coefficients, This refers to the air density in the environment where the wind power generation equipment is located. The effective area swept by the rotor of a wind power generation device. The upper limit of the polynomial power value for predicting the output curve of modular wind power generation components. The first predicted output curve for modular wind power generation components A number of constant fitting coefficients For modular components of wind power generation The actual operating wind speed at any given time Power value The constant term coefficients are used to fit the predicted output curve of the modular wind power generation module. , , , , , , , These are the different characteristic coefficients of the function relating the tip speed ratio and pitch angle of wind power generation equipment. For the tip speed ratio of wind power generation equipment, The pitch angle of the wind power generation equipment;

[0030] Constructing the modular solar power generation component model includes:

[0031] ,

[0032] in, Modular solar power generation module station Predicted power generation at any given time The coefficient for adjusting the temperature variation of power output in the basic unit of a modular solar power generation module. For generating electricity from basic units of modular solar power modules The ambient temperature during constant operation. The standard test environment temperature for the basic unit of the modular solar power generation module is set to ensure optimal power generation. For generating electricity from basic units of modular solar power modules The intensity of light during constant operation. The rated operating temperature for generating electricity in the basic unit of a modular solar power generation module. This refers to the temperature reference value for the basic unit of a modular solar power generation module in its actual operating environment. This serves as a benchmark value for the actual light intensity in the working environment of the basic unit of a modular solar power generation module. This refers to the total number of basic units of the modular solar power generation modules within the solar power generation module station. The average annual comprehensive power generation efficiency of the modular solar power generation station. The rated power output of a basic unit of a modular solar power generation module under standard testing conditions. The light intensity of the power generation working environment of the basic unit of the modular solar power generation module under the standard specification test environment of the basic unit of the modular solar power generation module;

[0033] Constructing the modular energy storage component model includes:

[0034] ,

[0035] in, Modular components for energy storage Energy stored at all times The loss rate of modular energy storage components. Modular components for energy storage Energy stored at all times and Modular energy storage components are respectively located in Time and Charging power at any time and These represent the charging efficiency and discharging efficiency of the modular energy storage components, respectively. and Modular energy storage components are respectively located in Time and Discharge power at any given time To optimize the step size of operational data points in the planning and configuration, The numerical value of the regular operating interval of the modular energy storage components. The energy stored in the modular energy storage component during its initial startup and operation is denoted by , and the discharge efficiency of the modular energy storage component is denoted by . The stored energy is the energy stored at the first moment of operation within the regular operating cycle of the modular energy storage component.

[0036] Constructing the modular component model of the public network access point includes:

[0037] The park's distributed solar power generation projects adopt either a fully self-consumption model or a self-consumption with surplus electricity fed into the grid model.

[0038] The modular component model of the public network access point under the self-generated and self-used mode is as follows:

[0039] ,

[0040] in, Power supply transformer for modular components of public network access points The power supplied by the power grid to the integrated energy system of source, grid, load and storage at any given time. Modular components for public network access points The power of the input power transformer at any given time. The maximum power capacity of the power supply transformer for the modular components of the public network access point. The annual average power factor of modular components for public network access points;

[0041] The modular component model of the public network access point under the self-generated and self-consumption surplus power grid connection mode is as follows:

[0042] ,

[0043] in, The upper limit of power supply capacity for modular components at public grid access points to the integrated energy system of source-grid-load-storage. Modular components for public network access points The power output of the integrated energy system (source, grid, load, and storage) fed back to the grid at any given time. The upper limit of the power capacity that a modular component-based integrated energy system for public grid access points can send back to the grid is defined;

[0044] Constructing the comprehensive power load correction model includes:

[0045] Based on seasonal anomaly detection, seasonal components are extracted and residual models are calculated:

[0046] ,

[0047] in, for Typical values ​​of seasonal components at any given time. for Before and after the moment Data from the same period of the day, This is the number of days offset. for Remove the seasonal residual components at all times. for Raw load data at any given time;

[0048] Determine the dynamic threshold, calculate local statistics, and determine the anomaly rule model:

[0049] ,

[0050] in, This is the local median. for Remove the seasonal residual components at all times. for Remove the seasonal residual components at all times. for Remove the seasonal residual components at all times. for Remove the seasonal residual components at all times. for Remove the seasonal residual components at all times. The bias is determined by the local median. This is the median operation function. The function for calculating the absolute deviation of the median. This is an exception indicator variable; it returns 1 when the data is determined to be abnormal, and 0 otherwise.

[0051] Construct a similarity measurement model for matching similar days:

[0052] ,

[0053] in, for Time and Similarity of time, It is a natural exponential function. for The date feature vector at any given time. for The date feature vector at any given time. for Vector and Euclidean distance between vectors The standard deviation of the distance;

[0054] Constructing a weighted least squares model for locally weighted regression:

[0055] ,

[0056] in, for Corrected load data at any given time. For loss function, To minimize the parameter function, for Most similar in time A set of indexes for each time point. For weighted interpolation weights, for Raw load data at any given time. Different auxiliary operation parameters for the loss function, For about variables kernel function, For about variables kernel function, for Time and Similarity of time, For bandwidth.

[0057] Preferably, the life-cycle economic and low-carbon multi-objective optimization configuration objective function includes the life-cycle economic optimization objective, the life-cycle low-carbon optimization objective, and the life-cycle economic and low-carbon multi-objective.

[0058] Constructing the economic optimization objective throughout the entire life cycle includes:

[0059] ,

[0060] The constraints are:

[0061] ,

[0062] in, To optimize the economic goals of an integrated energy system encompassing generation, grid, load, and storage during its planned and configured operational cycle, To optimize the energy costs of an integrated energy system (source, grid, load, and storage) during its planned and configured operation cycle, To optimize the operation and maintenance costs of integrated energy systems (source, grid, load, and storage) during their planned and configured operating cycles, To optimize the investment cost of integrated energy system construction for power generation, grid, load, and storage during the planned operation cycle, To optimize the allocation and operation cycle of an integrated energy system encompassing power generation, grid, load, and storage, the salary costs of dedicated operation and management personnel are planned. The total interruption cost of interruptible load throughout the year. for The unit price of electricity at any given time for The power purchased from the upper-level power grid by the integrated energy system of source, grid, load and storage. To optimize the step size of operational data points in the planning and configuration, For equipment The unit maintenance cost for Time device of effort, For equipment The unit capacity installation cost, For equipment capacity, This is the investment recovery factor. For the discount rate, Modular components for energy storage Years of service, For staff annual salary and wages, The number of employees is the fixed number.

[0063] The construction of the low-carbon optimization target throughout the entire life cycle includes:

[0064] ,

[0065] in, To achieve the low-carbon optimization goals of integrated energy systems (source, grid, load, and storage) under the planned and optimized operating cycle, For the integrated energy system of source, grid, load and storage The equivalent carbon emission factor per unit power of electricity purchased from the main power grid at all times. Modular components for public network access points The power supplied by the power grid to the integrated energy system of source, grid, load and storage at any given time. For wind power generation The actual power input to the park's integrated energy system at all times. For modular solar power generation components The actual power output input into the park's integrated energy system of source, grid, load and storage at all times;

[0066] The construction of the aforementioned multi-objective economic and low-carbon economy throughout the entire life cycle includes:

[0067] ,

[0068] The constraints are:

[0069] ,

[0070] For the economic and low-carbon multi-objective function throughout the entire life cycle, To achieve multi-objective satisfaction in economic low-carbon development, The minimum carbon emission value corresponding to the single-objective optimization for low-carbon operation goals. The maximum value of the corresponding carbon emissions when performing single-objective optimization for the goal of low-carbon operation. To quantify the level of satisfaction with carbon emission levels, To quantify the level of satisfaction with economic cost levels, The minimum economic value corresponding to the single-objective optimization of the economic operation objective. The maximum value of the corresponding economic value when optimizing a single objective for the purpose of optimizing economic operation.

[0071] The proposed low-carbon economic planning configuration achieves optimal allocation of energy sources and storage in the park, maximizing the reduction of power grid purchase costs and maximizing the green electricity ratio in the park. It also enables seamless business integration across the entire process from planning to evaluation, from planning to dispatch, and from dispatch to evaluation, thus solving the technical defects of isolated planning, dispatch, and evaluation without closed-loop feedback.

[0072] Preferably, the mandatory binding rules for the safe operating space include the safe operating space for wind power generation, the safe operating space for solar power generation, power supply and demand matching, the power distribution status of modular components at public grid access points, the safe operating space for modular energy storage components, the investment capacity of modular component resources, the building area occupied by modular component resources, and the mandatory binding rules for the interruptible regulation of comprehensive power load.

[0073] Constructing the mandatory binding rules for the wind power generation safety domain operation space includes:

[0074] ,

[0075] in, For wind power generation The actual power input to the park's integrated energy system at all times. For modular components of wind power generation Predicted output power at time of day This represents the maximum power generation capacity of wind power generation. To optimize the operational cycle for planning and configuration, To optimize the maximum allowable wind curtailment rate during the planned operation cycle;

[0076] Constructing the mandatory binding rules for the operating space of the solar power generation safety domain includes:

[0077] ,

[0078] in, For modular solar power generation components The actual power output input to the park's integrated energy system (source, grid, load, and storage) at any given time. Modular solar power generation module station Predicted power generation at any given time This represents the maximum power output of the modular solar power generation module. To optimize the maximum allowable curtailment rate during the planned operation cycle;

[0079] Constructing the power supply and demand matching mandatory binding rule includes:

[0080] ,

[0081] in, Modular components for public network access points The power supplied by the power grid to the integrated energy system of source, grid, load and storage at any given time. Modular components for energy storage Discharge power at any given time Modular components for public network access points The power output of the integrated energy system (source, grid, load, and storage) fed back to the grid at any given time. Modular components for energy storage Charging power at any time for Total electrical load at any given time In order to be in The actual electrical load power interrupted by the park's energy system at any given time;

[0082] Constructing the mandatory binding rules for the power distribution state of the modular components of the public network access point includes:

[0083] ,

[0084] in, This refers to the demand and capacity control of an integrated energy system encompassing power generation, grid, load, and storage. This refers to the set of demand and capacity control periods for an integrated energy system encompassing power generation, grid, load, and storage. This is the baseline control quantity for the power transmission from the integrated energy source-grid-load-storage system. The set of control periods for the power return baseline of the integrated energy source-grid-load-storage system;

[0085] Constructing the mandatory binding rules for the operating space of the security domain of the energy storage modular component includes:

[0086] ,

[0087] in, Modular components for energy storage The minimum proportion of energy stored in modular energy storage components relative to the equipment capacity. Modular components for energy storage capacity, Modular components for energy storage exist Energy stored at all times Modular components for energy storage The modular energy storage components account for the largest proportion of the equipment capacity. Modular components for energy storage exist Charging power at any time Modular components for energy storage Maximum charging, Modular components for energy storage exist Discharge power at any given time Modular components for energy storage The power of the energy release, Modular components for energy storage The stored energy at the beginning of a regular operating interval cycle. The energy matching degree at the beginning and end of the cycle of the modular energy storage component. Modular components for energy storage The stored energy at the end of a regular operating interval period. Modular components for energy storage exist The charging and discharging power value at any given time. Modular components for energy storage exist The charging / discharging state transition flag at any given time has a value of 1 indicating a state transition has occurred, and a value of 0 indicating that no state transition has been sent. Modular components for energy storage exist The charging and discharging power value at any given time. The maximum number of charge / discharge cycles is limited within the regular operating interval of modular energy storage components. The periodic value of the regular operation interval of the modular energy storage components;

[0088] The mandatory binding rules for the resource investment capacity of the modular components include:

[0089] ,

[0090] in, The investment cost of the equipment to be planned and configured. The maximum initial investment cost for the equipment to be planned and configured;

[0091] The rules for mandating the building area occupancy of the modular components are constructed as follows:

[0092] ,

[0093] in, The first modular component in the integrated energy system of source, grid, load and storage The installation area occupied by this type of equipment The land area available for the integrated energy system of source, grid, load and storage in the park;

[0094] Constructing the mandatory binding rules for the interruptible regulation of the integrated power load includes:

[0095] ,

[0096] in, In order to be in The actual electrical load power interrupted by the park's energy system at any given time. In order to be in The maximum allowable load percentage for interruption at any given time. for Total electrical load at any given time In order to be in The interruptibility status is a binary variable at any given time, where 1 indicates that interruption is allowed at that time, and 0 indicates that interruption is not allowed. For a predefined set of allowed interruption periods, To optimize the operational cycle for planning and configuration, To optimize the step size of operational data points in the planning and configuration, The annual interruptible electricity consumption as a percentage of total electricity consumption is the upper limit stipulated by policy or agreement. For internal summation index, The minimum duration of continuous interruption. In order to be in The interruptibility status is a binary variable at any given time, where 1 indicates that interruption is allowed at that time, and 0 indicates that interruption is not allowed. In order to be in The interruptible state binary variable at time point. The total interruption cost of interruptible load throughout the year. In order to be in The unit interrupt power cost at any given moment.

[0097] Preferably, the mandatory binding rules for policy compliance include: mandatory binding rules for the proportion of green electricity directly supplied for self-consumption, the proportion of electricity fed into the grid, the proportion of renewable energy power generation in total electricity consumption, energy consumption, carbon emissions, and green electricity penetration rate;

[0098] The mandatory binding rules for the proportion of green electricity directly supplied for self-consumption include:

[0099] ,

[0100] in, To optimize the operational cycle for planning and configuration, In order to be in The self-consumption capacity of renewable energy power generation in the park at any given time. To optimize the step size of operational data points in the planning and configuration, For modular solar power generation components The actual power output input to the park's integrated energy system (source, grid, load, and storage) at any given time. For wind power generation The actual power input to the park's integrated energy system at all times. This is the lower limit for the proportion of green electricity directly supplied for self-consumption as stipulated by policy;

[0101] The mandatory binding rule for the proportion of electricity consumed during internet access is constructed as follows:

[0102] ,

[0103] in, Modular components for public network access points The power output of the integrated energy system (source, grid, load, and storage) fed back to the grid at any given time. This is the upper limit of the proportion of electricity used for internet access as stipulated by policy.

[0104] Constructing a mandatory binding rule for the proportion of renewable energy generation to total electricity consumption includes:

[0105] ,

[0106] in, for Total electrical load at any given time This is the lower limit for the proportion of renewable energy power generation in total electricity consumption as stipulated by policy.

[0107] The mandatory binding rules for energy consumption and carbon emissions are constructed as follows:

[0108] ,

[0109] in, This represents the park's annual energy consumption and carbon emissions. The regional power grid carbon emission factor corresponding to the park's purchase of electricity from the power grid. Modular components for public network access points The power supplied by the power grid to the integrated energy system of source, grid, load and storage at any given time. The annual energy consumption carbon emission cap stipulated by policy;

[0110] The mandatory binding rule for green electricity penetration rate is constructed as follows:

[0111] ,

[0112] in, This is the lower limit for the annual green electricity penetration rate stipulated by policy.

[0113] By incorporating mandatory binding rules for policy compliance, the park's planning and scheduling schemes are ensured to seamlessly align with national and local policies on the integration of power generation, grid, load, and storage, thus mitigating compliance risks. At the same time, by leveraging mandatory binding rules for the safe operating space of the security domain, the collaborative and safe operation of multiple modules and components is guaranteed, resulting in strong engineering applicability and high promotional value.

[0114] Preferably, the objective function for optimizing scheduling to mitigate uncertainty risks under the market-based operation includes:

[0115] ,

[0116] The constraints are:

[0117] ,

[0118] in, To optimize for the maximum value within the range of source load uncertainties, To optimize the total operating cost within the scheduling cycle of the park, To optimize the scheduling operation cycle, To optimize scheduling The electricity price that the industrial park purchases from the upper-level power grid at any given time. To optimize scheduling The active power of electricity purchased by the industrial park from the upper-level power grid at any given time. To optimize scheduling and the step size of running data points, To optimize scheduling The variable represents the state of the park selling electricity to the grid at any given time. A value of 1 indicates that the park is allowed to sell electricity to the grid at that time, while a value of 0 indicates that the park is not allowed to sell electricity to the grid. To optimize scheduling The electricity price that the industrial park sells to the grid at any given time. To optimize scheduling The active power of electricity sold from the industrial park to the power grid at any given time. To optimize the operation and maintenance cost per unit charging power of modular energy storage components during scheduling, Modular components for energy storage Charging power at any time To optimize the operation and maintenance cost per unit discharge power of modular energy storage components during scheduling, Modular components for energy storage Discharge power at any given time To optimize scheduling The status of the park's interruptible load scenario at any given time is represented by a binary variable. A value of 1 indicates that the park's interruptible load is allowed at that time, while a value of 0 indicates that the park's interruptible load is not allowed. To optimize scheduling The unit compensation cost of a load that can be interrupted at any time. In order to be in The actual electrical load power interrupted by the park's energy system at any given time. To optimize scheduling The variable represents the scenario state of the reserve capacity provided by the park to the upper-level power grid at any given time. A value of 1 indicates that the park is allowed to provide reserve capacity to the upper-level power grid at that time, while a value of 0 indicates that the park is not allowed to provide reserve capacity to the upper-level power grid. To optimize scheduling The park is constantly raising the electricity market price for reserve capacity. To optimize scheduling The park is constantly increasing its reserve capacity to the higher-level power grid. To optimize scheduling The electricity market price for reserve capacity should be lowered at any time. To optimize scheduling The park is constantly providing reserve capacity to the higher-level power grid. For the set of uncertainties in the source load, To optimize scheduling The actual planned utilization of power output from the modular wind power generation components. To optimize scheduling The actual planned utilization of the power output of the modular solar power generation components at any given time. To optimize scheduling Real-time comprehensive power load actual planned utilization demand value, To optimize scheduling The lower limit of the output of modular wind power generation components at any given time. To optimize scheduling The upper limit of the output of modular wind power generation components at any given time. To optimize scheduling The lower limit of the output of modular solar power generation modules at any given time. To optimize scheduling The upper limit of the output of modular solar power generation modules at any given time. To optimize scheduling The lower limit of the overall power load demand at any given time. To optimize scheduling The upper limit of the overall power load demand at any given time. To optimize the set of time periods during which the park is allowed to sell electricity to the grid, To optimize scheduling, the set of time periods that allow interruptible loads in the park should be selected. To optimize scheduling, the set of time periods during which the park is allowed to provide upper and lower reserve capacity to the upper-level power grid.

[0119] The planning and scheduling model can effectively reflect the seasonality and intraday peak-valley characteristics of the park's operation. In terms of intraday peak-valley characteristics, the synergistic effect of wind, solar and energy storage is significant, the peak-valley arbitrage advantage is fully utilized, the potential for new energy consumption is fully released, and the potential for reducing electricity purchase costs is fully realized. In terms of seasonal characteristics, the optimized operation of the planning configuration is consistent with the seasonal energy consumption patterns of the park.

[0120] Preferably, the source-load superrectangular uncertainty model includes the power output of wind power generation modular components, the power output of solar power generation modular components, and the comprehensive power load demand superrectangular uncertainty model;

[0121] Constructing the superrectangular uncertainty model for the output of the modular wind power generation component includes:

[0122] ,

[0123] in, To optimize scheduling The actual planned utilization of power output from the modular wind power generation components. To optimize scheduling The lower limit of the output of modular wind power generation components at any given time. To optimize scheduling The upper limit of the output of modular wind power generation components at any given time. To optimize scheduling Predicted output of modular wind power generation components at any given time To optimize scheduling The absolute value of the maximum deviation in the output of the modular wind power generation components at any given moment;

[0124] Constructing the superrectangular uncertainty model for the output of the aforementioned modular solar power generation module includes:

[0125] ,

[0126] in, To optimize scheduling The actual planned utilization of the power output of the modular solar power generation components at any given time. To optimize scheduling The lower limit of the output of modular solar power generation modules at any given time. To optimize scheduling The upper limit of the output of modular solar power generation modules at any given time. To optimize scheduling Predicted output of modular solar power generation modules at any given time. To optimize scheduling The absolute value of the maximum deviation in the output of the modular solar power generation module at any given moment;

[0127] The superrectangular uncertainty model for the comprehensive power load demand is constructed as follows:

[0128] ,

[0129] in, To optimize scheduling Real-time comprehensive power load actual planned utilization demand value, To optimize scheduling The lower limit of the overall power load demand at any given time. To optimize scheduling The upper limit of the overall power load demand at any given time. To optimize scheduling The forecast value of comprehensive power load demand at all times. To optimize scheduling The maximum absolute value of the comprehensive power load demand at any given time.

[0130] Preferably, the step of constructing an evaluation model that includes individual evaluation scores for positive and negative trend benefit indicators and comprehensive multi-attribute evaluation includes:

[0131] Construct a scoring model for single-item evaluation of positive trend benefit indicators, including:

[0132] ,

[0133] in, For the first The individual evaluation scores for each positive trend benefit indicator, after being processed to be dimensionless, range from [60, 100]. The first one obtained during the planning or scheduling phase The actual result value of a positive trending benefit indicator For the first A standard reference benchmark value for a positive trending benefit indicator, corresponding to 60 points. For the first The full score reference value for each positive trend benefit indicator is 100 points. Index of positive trend benefit indicators. A subset is formed by positively trending benefit indicators. To perform the minimum value operation, This is for calculating the maximum value.

[0134] Construct a single-item evaluation scoring model for negative trend benefit indicators, including:

[0135] ,

[0136] in, For the first The individual evaluation scores for each negative trend benefit indicator, after being processed to be dimensionless, range from [60, 100]. For the first The standard reference value for a negative trend benefit indicator is 60 points. The first one obtained during the planning or scheduling phase The actual value of a negative-trend benefit indicator For the first The full score reference value for each negative trend benefit indicator is 100 points. A subset is formed by indicators with negative trends in efficiency. Index of negative trend benefit indicators;

[0137] Construct a comprehensive multi-attribute evaluation model, including:

[0138] ,

[0139] in, To comprehensively evaluate the scores based on multiple attributes, the value range is [60, 100]. For the first The importance weighting coefficient of the score of each positively trending benefit indicator. For the first The importance weighting coefficient of the score of each negative trend benefit indicator.

[0140] The optimized dispatching method for mitigating uncertainty risks under the market-oriented operation model of the integrated energy system of the park's source-grid-load-storage system comprehensively considers the uncertainty of source and load and electricity market factors. It achieves optimized dispatching to mitigate uncertainty risks under the market-oriented operation model of the park, significantly improves the robust operation capability of the system under scenarios such as new energy fluctuations and load forecast deviations, and ensures the park's energy supply security and market transaction benefits. It can provide reference and guidance for the park to participate in the formulation of market-oriented optimized dispatching plans and the avoidance of uncertainty risks.

[0141] On the other hand, the present invention provides a system based on the aforementioned multi-stage closed-loop planning, scheduling, and evaluation method for the integrated source-grid-load-storage system, comprising:

[0142] The data acquisition module is used to: acquire the operational baseline data of the integrated source-grid-load-storage system in the park;

[0143] The data processing module is used to: input the operating baseline data into a pre-built energy system optimization model for processing, and obtain planning and scheduling evaluation results;

[0144] The energy system optimization model construction module is used for: constructing a modular component model and a comprehensive power load correction model for the energy system; establishing a multi-objective optimization configuration objective function for economic and low-carbon development throughout the entire life cycle; setting mandatory binding rules for the safe domain operation space and mandatory binding rules for policy compliance to form a planning stage model; establishing an optimization scheduling objective function to prevent uncertainty risks under market-oriented operation; constructing a source-load super-rectangular uncertainty model to form a scheduling stage model; and constructing an evaluation model including single-item evaluation scores for positive and negative trend benefit indicators and comprehensive multi-attribute evaluation based on the effects of the planning stage model or scheduling stage model.

[0145] Energy system optimization model processing module: used to: input the operating baseline data into the planning stage model and the scheduling stage model, the planning stage model and the scheduling stage model respectively perform economic and low-carbon planning configuration and optimized scheduling of the integrated source-grid-load-storage system of the park, the evaluation model scores the economic and low-carbon planning configuration results and optimized scheduling results respectively, the planning stage model and the scheduling stage model decide whether to adjust the planning parameters and configuration based on their respective scores; output the economic and low-carbon planning configuration results of the planning stage model, the optimized scheduling results of the scheduling stage model and the scoring results of the evaluation model.

[0146] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0147] This invention proposes an economic and low-carbon planning configuration that achieves optimal allocation of energy sources and storage in the industrial park through coordinated planning. This maximizes the reduction of grid power purchase costs and maximizes the green electricity ratio of the park, enabling seamless business integration across the entire process from planning to evaluation, from planning to dispatch, and from dispatch to evaluation. It fully incorporates mandatory binding rules for policy compliance, while leveraging mandatory binding rules for the safe operating space of the safety domain to ensure the coordinated and safe operation of multiple modules and components. It has strong engineering applicability and high promotional value. The optimized dispatching under the market-oriented operation model of the integrated energy system of the park's energy sources, grid, load, and storage comprehensively considers the uncertainty of energy sources and loads, as well as electricity market factors, achieving optimized dispatching under the market-oriented operation model of the park to mitigate uncertainty risks. This significantly improves the system's robust operation capability under scenarios such as renewable energy fluctuations and load forecasting deviations. Attached Figure Description

[0148] Figure 1This is a flowchart illustrating the multi-stage closed-loop planning, scheduling, and evaluation method for the integrated source-grid-load-storage system shown in Embodiment 1 of the present invention.

[0149] Figure 2 This is a schematic diagram of the integrated energy system architecture for industrial and commercial parks, as shown in Embodiment 2 of the present invention.

[0150] Figure 3 This is a typical daytime source-load power curve of the park in spring, as shown in Embodiment 2 of the present invention.

[0151] Figure 4 This is a typical summer day source-load power curve of the park as shown in Embodiment 2 of the present invention;

[0152] Figure 5 This is a typical daytime source-load power curve of the park in autumn, as shown in Embodiment 2 of the present invention.

[0153] Figure 6 This is a typical daytime source-load power curve of the park during winter, as shown in Embodiment 2 of the present invention.

[0154] Figure 7 This is the park load curve diagram for the 1st of each month throughout the year, as shown in Embodiment 2 of the present invention;

[0155] Figure 8 This is the annual 8760-hour park load curve shown in Embodiment 2 of the present invention;

[0156] Figure 9 This is a graph showing the annual 8760-hour solar power generation modular component curve of the park, as shown in Embodiment 2 of the present invention.

[0157] Figure 10 This is the time-of-use electricity pricing diagram for the industrial park shown in Embodiment 2 of the present invention;

[0158] Figure 11 This is a diagram showing the annual power balance operation results of the park as shown in Embodiment 2 of the present invention;

[0159] Figure 12 This is a diagram illustrating the operational effect of a typical spring day's park planning and configuration, as shown in Embodiment 2 of the present invention.

[0160] Figure 13 This is a diagram illustrating the operational effect of the park's planning and configuration on a typical summer day, as shown in Embodiment 2 of the present invention.

[0161] Figure 14 This is a typical spring day solar-storage synergy effect diagram of the park, as shown in Embodiment 2 of the present invention.

[0162] Figure 15 This is a typical summer daytime solar-storage synergy effect diagram of a park, as shown in Embodiment 2 of the present invention.

[0163] Figure 16 This is a diagram illustrating the operational effect of the park's planning and configuration on a typical autumn day, as shown in Embodiment 2 of the present invention.

[0164] Figure 17 This is a diagram illustrating the operational effect of the park's planning and configuration on a typical winter day, as shown in Embodiment 2 of the present invention.

[0165] Figure 18 This is a typical autumn daytime solar-storage synergy effect diagram of an industrial park, as shown in Embodiment 2 of the present invention.

[0166] Figure 19 This is a typical daytime solar-storage synergy effect diagram of a solar power plant in winter, as shown in Embodiment 2 of the present invention.

[0167] Figure 20 This is a diagram illustrating the annual planned power load and curtailed solar power operation characteristics of the industrial park, as shown in Embodiment 2 of the present invention.

[0168] Figure 21 This is a characteristic diagram of the annual park planning configuration of source, grid, load and storage operation shown in Embodiment 2 of the present invention. Detailed Implementation

[0169] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0170] Example 1

[0171] This embodiment 1 introduces a multi-stage closed-loop planning, scheduling, and evaluation method for an integrated source-grid-load-storage system, including:

[0172] Obtain operational baseline data for the integrated power generation, grid, load, and storage system of the industrial park;

[0173] The operating baseline data is input into a pre-built energy system optimization model for processing to obtain the planning and scheduling evaluation results;

[0174] The energy system optimization model includes a planning phase model, a scheduling phase model, and an evaluation model. The construction steps of the energy system optimization model are as follows:

[0175] Construct a modular component model for the energy system and a comprehensive power load correction model, establish a multi-objective optimization configuration objective function for economic and low-carbon development throughout the entire life cycle, and set mandatory binding rules for the safe domain operation space and mandatory binding rules for policy compliance to form a planning stage model;

[0176] Establish an optimized scheduling objective function to prevent uncertainty risks under market-oriented operation, and construct a source-load hyperrectangular uncertainty model to form a scheduling phase model;

[0177] Based on the effects of the planning stage model or the scheduling stage model, construct an evaluation model that includes individual evaluation scores for positive and negative trend benefit indicators and comprehensive multi-attribute evaluation.

[0178] The execution steps of the energy system optimization model include:

[0179] The baseline data is input into the planning stage model and the scheduling stage model. The planning stage model and the scheduling stage model respectively perform economic and low-carbon planning configuration and optimized scheduling of the park's integrated source-grid-load-storage system. The evaluation model scores the economic and low-carbon planning configuration results and optimized scheduling results respectively. The planning stage model and the scheduling stage model decide whether to adjust the planning parameters and configuration based on their respective scores.

[0180] The output includes the economic and low-carbon planning configuration results of the planning phase model, the optimized scheduling results of the scheduling phase model, and the scoring results of the evaluation model.

[0181] Specifically, the steps of the multi-stage closed-loop planning, scheduling, and evaluation method for the integrated source-grid-load-storage system are as follows:

[0182] Step S1: Input data information of the park's integrated energy system (source, grid, load, and storage);

[0183] Step S2: Construct a modular component model of the integrated energy system (source, grid, load, and storage) for the park;

[0184] Step S3: Propose an objective function for the optimal configuration of the integrated energy system of the park's source-grid-load-storage;

[0185] Step S4: Set mandatory binding rules for the safe domain operation space of the integrated energy system of source, grid, load and storage in the park;

[0186] Step S5: Establish mandatory binding rules for policy compliance of the integrated energy system of source, grid, load and storage in the park;

[0187] Step S6: Propose an optimized scheduling objective function to mitigate uncertainty risks under the market-oriented operation model of the integrated energy system of the park's source-grid-load-storage system;

[0188] Step S7: Construct a source-load hyperrectangular uncertainty model under an optimized scheduling mode that mitigates uncertainty risks within a market-oriented operation model;

[0189] Step S8: Construct a comprehensive multi-attribute evaluation model for the planning and scheduling effect of the integrated energy system of source, grid, load and storage in the park;

[0190] Step S9: Output data information of the park's integrated energy system of source, grid, load and storage.

[0191] Specifically, such as Figure 1 As shown, the above method can be divided into four stages and two closed loops: The first stage is the planning stage, including steps S1, S2, S3, S4, S5, and S9, which realizes the economic and low-carbon planning and configuration of the integrated energy system of source, grid, load, and storage in the park; the second stage is the comprehensive multi-attribute evaluation stage of the planning effect, including steps S8 and S9, which realizes the comprehensive multi-attribute evaluation score of the planning effect of the integrated energy system of source, grid, load, and storage in the park; the third stage is the scheduling stage, including steps S1, S2, S4, S5, S6, S7, and S9, which realizes the optimized scheduling to prevent uncertainty risks under the market-oriented operation model of the integrated energy system of source, grid, load, and storage in the park; the fourth stage is the comprehensive multi-attribute evaluation stage of the scheduling effect, including steps S8 and S9, which realizes the comprehensive multi-attribute evaluation score of the optimized scheduling effect to prevent uncertainty risks under the market-oriented operation model of the integrated energy system of source, grid, load, and storage in the park.

[0192] Specifically, the first closed loop is to output the results of the second stage to the first stage, and to provide feedback to adjust the planning stage parameters and configurations. The first stage decides whether to adjust the planning stage parameters and configurations based on the score of the second stage.

[0193] Specifically, the second closed loop outputs the results of the fourth stage to the third stage, providing feedback to adjust the scheduling stage parameters and configurations. The third stage then decides whether to adjust the scheduling stage parameters and configurations based on the score of the fourth stage.

[0194] The benchmark data includes:

[0195] The input dataset for the planning phase model includes: actual operating wind speed, historical operating wind speed, and operating characteristic model parameters for wind power generation modules; irradiance and model parameters for basic solar power generation modules during operation and historical operation; loss rate, initial energy storage capacity, charging efficiency, and discharging efficiency of energy storage modules; annual average power factor of modules at the public grid connection point; maximum power capacity of power supply transformers; maximum power capacity of power supplied from the grid to the system and power fed back to the grid; historical data of integrated power load and calibration model parameters; step size of optimized operating data points; optimized operating cycle; maximum allowable wind curtailment rate and maximum solar curtailment rate within the optimized operating cycle; installed capacity limits for solar and wind power generation modules; system demand capacity control quantities; and power feedback baseline control quantities. The maximum initial investment cost of the planned equipment, the area occupied by equipment installation and the area of ​​land available in the park, the maximum allowable load ratio, the binary variable of interruptible state, the set of allowable interruption periods, the upper limit of the annual interruptible electricity consumption ratio of the total electricity consumption as stipulated by policies or agreements, the minimum duration of continuous interruption and the unit interruption power cost, the lower limit of the proportion of green electricity direct supply for self-consumption as stipulated by policies, the upper limit of the proportion of electricity fed into the grid, the lower limit of the proportion of renewable energy generation in the total electricity consumption, the regional grid carbon emission factor corresponding to the park's purchase of electricity from the grid, the upper limit of annual energy consumption carbon emissions as stipulated by policies, the unit electricity purchase price, the unit maintenance cost, the unit capacity installation cost, the capacity, the investment recovery coefficient, the discount rate, the working years of energy storage components, the annual salary and number of employees, and the equivalent carbon emission coefficient per unit power.

[0196] The input dataset for the scheduling phase model includes: the step size of the data points for optimized scheduling operation, the electricity price for the park to purchase electricity from the upper-level grid and sell electricity to the grid during optimized scheduling, the operation and maintenance cost of the unit charging and discharging power of the energy storage components, the unit compensation cost of interruptible loads, the electricity market price for the park to increase and decrease reserve capacity, the time period set during which the park is allowed to sell electricity to the grid, interruptible loads, and provide upper and lower reserve capacity, and the maximum absolute value of the output deviation of wind power generation components and solar power generation components during optimized scheduling;

[0197] The planning and scheduling evaluation results include:

[0198] The economic and low-carbon planning configuration results of the planning phase model include: predicted output power and corrected historical data of wind power generation components, predicted output power and corrected historical data of solar power generation components, corrected comprehensive power load data, capacity, charging power, and discharging power of energy storage components, output power of power supply transformers at public grid access points, power supply from the grid to the system, economic target results, energy cost, operation and maintenance cost, equipment construction investment cost, salary cost of full-time operation and management personnel, low-carbon target value, actual power input to the system from wind and solar power generation, satisfaction with economic and low-carbon multi-objectives, actual wind curtailment rate, actual solar curtailment rate, energy storage capacity of optimized energy storage component configuration, installed capacity of optimized solar and wind power generation components, total interruption cost of interruptible load throughout the year, and actual interrupted power load of the park's energy system.

[0199] The optimized scheduling results of the scheduling phase model are: the total operating cost of the park during the optimized scheduling cycle, the active power of the park purchasing electricity from and selling electricity to the grid during the optimized scheduling, the reserve capacity provided by the park to the grid for upward and downward adjustments, and the actual planned utilization output of wind power generation components, solar power generation components, and the actual planned utilization demand of comprehensive power load during the optimized scheduling.

[0200] The evaluation model's scoring results include: individual scores for positive trend benefit indicators, individual scores for negative trend benefit indicators, and comprehensive multi-attribute evaluation scores.

[0201] Modular component models for energy systems include modular component models for wind power generation, solar power generation, energy storage, and public grid connection points;

[0202] Specifically, the wind power generation modular component operation characteristic model can predict wind power generation based on the acquired wind speed and meteorological data; the wind power generation modular component operation characteristic model can correct missing and abnormal data in the historical data of the wind power generation modular component output to form new corrected wind power generation modular component output data. During the correction, the power load data in the comprehensive power load model is assigned as the wind power generation modular component output power data to complete the correction of the historical output data of the wind power generation modular component.

[0203] Constructing a modular component model for wind power generation, including:

[0204] ,

[0205] The constraints are:

[0206] ,

[0207] in, For modular components of wind power generation Predicted output power at time of day For modular components of wind power generation The actual operating wind speed at any given time. The minimum operating wind speed for the modular wind power generation components. The wind speed limit for safe operation of modular wind power generation components. The rated installed capacity of the modular wind power generation components. The rated operating wind speed for modular wind power generation components, For wind energy Power at any moment The operating power factor of the wind turbine rotor. For wind-driven turbine operation characteristic coefficients, This refers to the air density in the environment where the wind power generation equipment is located. The effective area swept by the rotor of a wind power generation device. The upper limit of the polynomial power value for predicting the output curve of modular wind power generation components. The first predicted output curve for modular wind power generation components A number of constant fitting coefficients For modular components of wind power generation The actual operating wind speed at any given time Power value The constant term coefficients are used to fit the predicted output curve of the modular wind power generation module. , , , , , , , These are the different characteristic coefficients of the function relating the tip speed ratio and pitch angle of wind power generation equipment. For the tip speed ratio of wind power generation equipment, This refers to the pitch angle of the wind turbine propeller.

[0208] Specifically, the solar power modular component operation characteristic model can predict solar power generation based on the acquired solar intensity, ambient temperature and meteorological data; the solar power modular component operation characteristic model can correct missing and abnormal data in the historical data of the solar power modular component output to form new corrected solar power modular component output data. During the correction, the power load data in the comprehensive power load model is assigned as the output power data of the solar power modular component, thus completing the correction of the historical output data of the solar power modular component.

[0209] Constructing a modular solar power generation component model, including:

[0210] ,

[0211] in, Modular solar power generation module station Predicted power generation at any given time The coefficient for adjusting the temperature variation of power output in the basic unit of a modular solar power generation module. For generating electricity from basic units of modular solar power modules The ambient temperature during constant operation. The standard test environment temperature for the basic unit of the modular solar power generation module is set to ensure optimal power generation. For generating electricity from basic units of modular solar power modules The intensity of light during constant operation. The rated operating temperature for generating electricity in the basic unit of a modular solar power generation module. This refers to the temperature reference value for the basic unit of a modular solar power generation module in its actual operating environment. This serves as a benchmark value for the actual light intensity in the working environment of the basic unit of a modular solar power generation module. This refers to the total number of basic units of the modular solar power generation modules within the solar power generation module station. The average annual comprehensive power generation efficiency of the modular solar power generation station. The rated power output of a basic unit of a modular solar power generation module under standard testing conditions. The light intensity of the basic unit of the modular solar power generation module under the standard specification test environment for the power generation operation environment of the basic unit of the modular solar power generation module.

[0212] Specifically, distributed new energy sources such as solar power generation can choose from three modes: full grid connection, full self-consumption, or self-consumption with surplus electricity fed to the grid. If a large-scale park solar power generation project chooses full grid connection, it can be registered and managed as a centralized solar power station, with separate meters and grid connection points, which is not within the scope of this invention. For distributed solar power generation projects in parks that adopt the full self-consumption mode or the self-consumption with surplus electricity fed to the grid mode, it is necessary to comprehensively consider the new energy grid connection mode and construct a suitable modular component model for the public grid access point.

[0213] Construct a modular component model for energy storage, including:

[0214] ,

[0215] in, Modular components for energy storage Energy stored at all times The loss rate of modular energy storage components. Modular components for energy storage Energy stored at all times and Modular energy storage components are respectively located in Time and Charging power at any time and These represent the charging efficiency and discharging efficiency of the modular energy storage components, respectively. and Modular energy storage components are respectively located in Time and Discharge power at any given time To optimize the step size of operational data points in the planning and configuration, The numerical value of the regular operating interval of the modular energy storage components. The energy stored in the modular energy storage component during its initial startup and operation is denoted by , and the discharge efficiency of the modular energy storage component is denoted by . The stored energy is the energy stored at the first moment of operation within the regular operating cycle of the modular energy storage component.

[0216] Construct a modular component model for public network access points, including:

[0217] The park's distributed solar power generation projects adopt either a fully self-consumption model or a self-consumption with surplus electricity fed into the grid model.

[0218] The modular component model of the public network access point under the all-self-use mode is as follows:

[0219] ,

[0220] in, Power supply transformer for modular components of public network access points The power supplied by the power grid to the integrated energy system of source, grid, load and storage at any given time. Modular components for public network access points The power of the input power transformer at any given time. The maximum power capacity of the power supply transformer for the modular components of the public network access point. The annual average power factor of modular components for public network access points;

[0221] The modular component model of the public network access point under the self-generated and self-consumed surplus electricity grid connection mode is as follows:

[0222] ,

[0223] in, The upper limit of power supply capacity for modular components at public grid access points to the integrated energy system of source-grid-load-storage. Modular components for public network access points The power output of the integrated energy system (source, grid, load, and storage) fed back to the grid at any given time. The upper limit of the power capacity that a modular component-based integrated energy system for public grid access points can send back to the grid is 100%.

[0224] Specifically, the comprehensive power load includes all power loads within the park, such as charging piles, air conditioners, production lines, and lighting, and is the sum of multiple types of comprehensive power loads. However, the collection and storage of different types of comprehensive power loads may result in missing or abnormal data. For the identified missing or abnormal data, it is necessary to fill in and correct them. The utilization of comprehensive power load data requires handling data missing and abnormal data. The handling of such data problems is collectively referred to as comprehensive power load correction. The comprehensive power load correction model includes an outlier detection model and a missing and outlier filling model.

[0225] Constructing a comprehensive power load correction model, including:

[0226] Based on seasonal anomaly detection, seasonal components are extracted and residual models are calculated:

[0227] ,

[0228] in, for Typical values ​​of seasonal components at any given time. for Before and after the moment Data from the same period of the day, This is the number of days offset. for Remove the seasonal residual components at all times. for Raw load data at any given time;

[0229] Determine the dynamic threshold, calculate local statistics, and determine the anomaly rule model:

[0230] ,

[0231] in, This is the local median. for Remove the seasonal residual components at all times. for Remove the seasonal residual components at all times. for Remove the seasonal residual components at all times. for Remove the seasonal residual components at all times. for Remove the seasonal residual components at all times. The bias is determined by the local median. This is the median operation function. The function for calculating the absolute deviation of the median. This is an exception indicator variable; it returns 1 when the data is determined to be abnormal, and 0 otherwise.

[0232] Specifically, A large value indicates that the original load data deviates from the normal range. arrive The values ​​are for 25 different times. Approximately equal to the standard deviation, in a normal distribution, exceeding... The probability of this range is only 0.3%, which is considered an anomaly; the coefficient 1.4826 is the adjustment coefficient under the normal distribution.

[0233] Construct a similarity measurement model for matching similar days:

[0234] ,

[0235] in, for Time and Similarity of time, It is a natural exponential function. for The date feature vector at any given time. for The date feature vector at any given time. for Vector and Euclidean distance between vectors The standard deviation of the distance;

[0236] Specifically, This includes whether it is a holiday (value 0 or 1), day of the week (value 1-7), season (value 1-4), and month (value 1-12). This includes whether it is a holiday (value 0 or 1), day of the week (value 1-7), season (value 1-4), and month (value 1-12).

[0237] Constructing a weighted least squares model for locally weighted regression:

[0238] ,

[0239] in, for Corrected load data at any given time. For loss function, To minimize the parameter function, for Most similar in time A set of indexes for each time point. For weighted interpolation weights, for Raw load data at any given time. Different auxiliary operation parameters for the loss function, For about variables kernel function, For about variables kernel function, for Time and Similarity of time, For bandwidth;

[0240] Specifically, The values ​​of the variables that minimize the following expression are respectively... Find the partial derivative ,right Find the partial derivative Solving the system of equations yields the optimal solution. and .

[0241] Specifically, modular energy storage components are an important part of the integrated energy system of power grid and industrial park, which can smooth load fluctuations, improve the absorption rate of wind power and solar power, and enhance the flexibility of the system. The charging and discharging operation of modular energy storage components is subject to mandatory rules based on energy and power.

[0242] The objective function for multi-objective optimization of economic and low-carbon allocation throughout the entire life cycle includes economic optimization objectives, low-carbon optimization objectives, and multiple economic and low-carbon objectives throughout the entire life cycle.

[0243] Construct economic optimization goals throughout the entire life cycle, including:

[0244] ,

[0245] The constraints are:

[0246] ,

[0247] in, To optimize the economic goals of an integrated energy system encompassing generation, grid, load, and storage during its planned and configured operational cycle, To optimize the energy costs of an integrated energy system (source, grid, load, and storage) during its planned and configured operation cycle, To optimize the operation and maintenance costs of integrated energy systems (source, grid, load, and storage) during their planned and configured operating cycles, To optimize the investment cost of integrated energy system construction for power generation, grid, load, and storage during the planned operation cycle, To optimize the allocation and operation cycle of an integrated energy system encompassing power generation, grid, load, and storage, the salary costs of dedicated operation and management personnel are planned. The total interruption cost of interruptible load throughout the year. for The unit price of electricity at any given time for The power purchased from the upper-level power grid by the integrated energy system of source, grid, load and storage. To optimize the step size of operational data points in the planning and configuration, For equipment The unit maintenance cost for Time device of effort, For equipment The unit capacity installation cost, For equipment capacity, This is the investment recovery factor. For the discount rate, Modular components for energy storage Years of service, For staff annual salary and wages, This refers to the number of employees.

[0248] Specifically, equipment This includes modular components for wind power generation, modular components for solar power generation, and modular components for energy storage; equipment. Equipment to be planned and configured.

[0249] Establish low-carbon optimization goals throughout the entire life cycle, including:

[0250] ,

[0251] in, To achieve the low-carbon optimization goals of integrated energy systems (source, grid, load, and storage) under the planned and optimized operating cycle, For the integrated energy system of source, grid, load and storage The equivalent carbon emission factor per unit power of electricity purchased from the main power grid at all times. Modular components for public network access points The power supplied by the power grid to the integrated energy system of source, grid, load and storage at any given time. For wind power generation The actual power input to the park's integrated energy system at all times. For modular solar power generation components The actual working power input at any time is fed into the power generation system of the park's integrated energy system of source, grid, load and storage.

[0252] Constructing a multi-objective framework for low-carbon and economic development throughout the entire life cycle, including:

[0253] ,

[0254] The constraints are:

[0255] ,

[0256] in, For the economic and low-carbon multi-objective function throughout the entire life cycle, To achieve multi-objective satisfaction in economic low-carbon development, The minimum carbon emission value corresponding to the single-objective optimization for low-carbon operation goals. The maximum value of the corresponding carbon emissions when performing single-objective optimization for the goal of low-carbon operation. To quantify the level of satisfaction with carbon emission levels, To quantify the level of satisfaction with economic cost levels, The minimum economic value corresponding to the single-objective optimization of the economic operation objective. The maximum value of the corresponding economic value when optimizing a single objective for the purpose of optimizing economic operation.

[0257] The mandatory binding rules for the safe operating space include the safe operating space for wind power generation, the safe operating space for solar power generation, power supply and demand matching, the power distribution status of modular components at public grid access points, the safe operating space for modular energy storage components, the investment capacity of modular component resources, the building area occupied by modular component resources, and the mandatory binding rules for the interruptible regulation of comprehensive power load.

[0258] Construct mandatory binding rules for the safe operating space of wind power generation, including:

[0259] ,

[0260] in, For wind power generation The actual power input to the park's integrated energy system at all times. For modular components of wind power generation Predicted output power at time of day This represents the maximum power generation capacity of wind power generation. To optimize the operational cycle for planning and configuration, To optimize the maximum allowable wind curtailment rate during the planned operation cycle.

[0261] Construct mandatory binding rules for the safe operation space of solar power generation, including:

[0262] ,

[0263] in, For modular solar power generation components The actual power output input to the park's integrated energy system (source, grid, load, and storage) at any given time. Modular solar power generation module station Predicted power generation at any given time This represents the maximum power output of the modular solar power generation module. To optimize the maximum allowable curtailment rate during the planned operation cycle.

[0264] Specifically, the maximum output of the solar power modular component at any given time depends on factors such as the light intensity, geographical location, and weather conditions at that time. In the model, the actual output of the solar power modular component does not exceed the maximum output, and the utilization rate of the solar power modular component is not lower than the set threshold under various operating scenarios.

[0265] Specifically, Considered as an installed capacity limit, it can be set as a variable to be optimized as needed during the planning and configuration optimization process.

[0266] Construct mandatory binding rules for power supply and demand matching, including:

[0267] ,

[0268] in, Modular components for public network access points The power supplied by the power grid to the integrated energy system of source, grid, load and storage at any given time. Modular components for energy storage Discharge power at any given time Modular components for public network access points The power output of the integrated energy system (source, grid, load, and storage) fed back to the grid at any given time. Modular components for energy storage Charging power at any time for Total electrical load at any given time In order to be in The actual electrical load power interrupted by the park's energy system at any given time.

[0269] Construct mandatory binding rules for the power distribution status of modular components at public network access points, including:

[0270] ,

[0271] in, This refers to the demand and capacity control of an integrated energy system encompassing power generation, grid, load, and storage. This refers to the set of demand and capacity control periods for an integrated energy system encompassing power generation, grid, load, and storage. This is the baseline control quantity for the power transmission from the integrated energy source-grid-load-storage system. This refers to the set of control periods for the power transmission baseline of the integrated energy system (source, grid, load, and storage).

[0272] Construct mandatory binding rules for the safety domain operating space of modular energy storage components, including:

[0273] ,

[0274] in, Modular components for energy storage The minimum proportion of energy stored in modular energy storage components relative to the equipment capacity. Modular components for energy storage capacity, Modular components for energy storage exist Energy stored at all times Modular components for energy storage The modular energy storage components account for the largest proportion of the equipment capacity. Modular components for energy storage exist Charging power at any time Modular components for energy storage Maximum charging, Modular components for energy storage exist Discharge power at any given time Modular components for energy storage The power of the energy release, Modular components for energy storage The stored energy at the beginning of a regular operating interval cycle. The energy matching degree at the beginning and end of the cycle of the modular energy storage component. Modular components for energy storage The stored energy at the end of a regular operating interval period. Modular components for energy storage exist The charging and discharging power value at any given time. Modular components for energy storage exist The charging / discharging state transition flag at any given time has a value of 1 indicating a state transition has occurred, and a value of 0 indicating that no state transition has been sent. Modular components for energy storage exist The charging and discharging power value at any given time. The maximum number of charge / discharge cycles is limited within the regular operating interval of modular energy storage components. The numerical value of the regular operating interval of the modular energy storage components.

[0275] Mandatory rules governing the investment capacity of modular component resources include:

[0276] ,

[0277] in, The investment cost of the equipment to be planned and configured. The maximum initial investment cost for the equipment to be planned and configured.

[0278] Establish mandatory rules for the building area occupied by modular component resources, including:

[0279] ,

[0280] in, The first modular component in the integrated energy system of source, grid, load and storage The installation area occupied by this type of equipment The land area available for the integrated energy system of source, grid, load and storage in the park;

[0281] Specifically, each device needs to take into account the mandatory binding rules of the dedicated land area and the geographical location of the integrated energy system construction of the park. For example, the installation of solar power generation modular components on the roof of a building needs to be subject to the mandatory binding rule of the maximum effective sunlight area of ​​the roof. The installation site of the energy storage modular components needs to be subject to the mandatory binding rule of the maximum effective site area that meets the safety protection requirements of the energy storage modular components.

[0282] Construct mandatory binding rules for integrated power load interruptibility control, including:

[0283] ,

[0284] in, In order to be in The actual electrical load power interrupted by the park's energy system at any given time. In order to be in The maximum allowable load percentage for interruption at any given time. for Total electrical load at any given time In order to be in The interruptibility status is a binary variable at any given time, where 1 indicates that interruption is allowed at that time, and 0 indicates that interruption is not allowed. For a predefined set of allowed interruption periods, To optimize the operational cycle for planning and configuration, To optimize the step size of operational data points in the planning and configuration, The annual interruptible electricity consumption as a percentage of total electricity consumption is the upper limit stipulated by policy or agreement. For internal summation index, The minimum duration of continuous interruption. In order to be in The interruptibility status is a binary variable at any given time, where 1 indicates that interruption is allowed at that time, and 0 indicates that interruption is not allowed. In order to be in The interruptible state binary variable at time point. The total interruption cost of interruptible load throughout the year. In order to be in The unit interrupt power cost at any given moment.

[0285] Specifically, at any given time, the actual interrupted load power must not exceed a certain percentage of the total electrical load at that time, and must also meet the physical limitations of the interruptible load capacity, which is the mandatory binding rule for the interruptible load power limit; interruptible loads can only be disconnected during the specified permitted interruption period, and the interruption power must be zero during other periods. A binary state variable is introduced to represent whether an interruption is allowed during any period, which is the mandatory binding rule for the availability of interruptible load periods; the total annual interrupted electricity must not exceed a certain percentage of the total annual electricity consumption of the park to ensure power supply reliability, which is the mandatory binding rule for the proportion of annual total electricity consumption of interruptible loads; to prevent frequent start-stop interruption operations, the duration of each interruption is usually required to be no less than the minimum continuous interruption duration, which is represented by an auxiliary variable to indicate the interruption start time, which is the mandatory binding rule for the minimum continuous interruption time of interruptible loads; the interruption cost is usually related to the interrupted electricity and interruption depth, and adopts a linear cost model function form. This cost is also included in the optimization objective function, which is the mandatory binding rule for the interruption cost model of interruptible loads.

[0286] Specifically, The value can vary over time, with higher costs during peak hours.

[0287] Mandatory rules for policy compliance include the proportion of green electricity directly supplied for self-consumption, the proportion of electricity supplied to the grid, the proportion of renewable energy generation in total electricity consumption, energy consumption, carbon emissions, and green electricity penetration rate.

[0288] Establish mandatory binding rules for the proportion of green electricity directly supplied to self-consumption, including:

[0289] ,

[0290] in, To optimize the operational cycle for planning and configuration, In order to be in The self-consumption capacity of renewable energy power generation in the park at any given time. To optimize the step size of operational data points in the planning and configuration, For modular solar power generation components The actual power output input to the park's integrated energy system (source, grid, load, and storage) at any given time. For wind power generation The actual power input to the park's integrated energy system at all times. This is the lower limit for the proportion of green electricity directly supplied for self-generation and self-consumption as stipulated by policy.

[0291] Establish mandatory rules for the proportion of electricity consumed during internet access, including:

[0292] ,

[0293] in, Modular components for public network access points The power output of the integrated energy system (source, grid, load, and storage) fed back to the grid at any given time. This is the upper limit of the proportion of electricity used for internet access as stipulated by policy.

[0294] Establish mandatory and binding rules on the proportion of renewable energy generation in total electricity consumption, including:

[0295] ,

[0296] in, for Total electrical load at any given time This is the lower limit for the proportion of renewable energy power generation in total electricity consumption as stipulated by policy.

[0297] Establish mandatory binding rules for energy consumption and carbon emissions, including:

[0298] ,

[0299] in, This represents the park's annual energy consumption and carbon emissions. The regional power grid carbon emission factor corresponding to the park's purchase of electricity from the power grid. Modular components for public network access points The power supplied by the power grid to the integrated energy system of source, grid, load and storage at any given time. This is the annual carbon emission cap for energy consumption stipulated by policy.

[0300] Establish mandatory binding rules for green electricity penetration rates, including:

[0301] ,

[0302] in, This is the lower limit for the annual green electricity penetration rate stipulated by policy.

[0303] Establish an optimal scheduling objective function to mitigate uncertainty risks under market-oriented operation, including:

[0304] ,

[0305] The constraints are:

[0306] ,

[0307] in, To optimize for the maximum value within the range of source load uncertainties, To optimize the total operating cost within the scheduling cycle of the park, To optimize the scheduling operation cycle, To optimize scheduling The electricity price that the industrial park purchases from the upper-level power grid at any given time. To optimize scheduling The active power of electricity purchased by the industrial park from the upper-level power grid at any given time. To optimize scheduling and the step size of running data points, To optimize scheduling The variable represents the state of the park selling electricity to the grid at any given time. A value of 1 indicates that the park is allowed to sell electricity to the grid at that time, while a value of 0 indicates that the park is not allowed to sell electricity to the grid. To optimize scheduling The electricity price that the industrial park sells to the grid at any given time. To optimize scheduling The active power of electricity sold from the industrial park to the power grid at any given time. To optimize the operation and maintenance cost per unit charging power of modular energy storage components during scheduling, Modular components for energy storage Charging power at any time To optimize the operation and maintenance cost per unit discharge power of modular energy storage components during scheduling, Modular components for energy storage Discharge power at any given time To optimize scheduling The status of the park's interruptible load scenario at any given time is represented by a binary variable. A value of 1 indicates that the park's interruptible load is allowed at that time, while a value of 0 indicates that the park's interruptible load is not allowed. To optimize scheduling The unit compensation cost of a load that can be interrupted at any time. In order to be in The actual electrical load power interrupted by the park's energy system at any given time. To optimize scheduling The variable represents the scenario state of the reserve capacity provided by the park to the upper-level power grid at any given time. A value of 1 indicates that the park is allowed to provide reserve capacity to the upper-level power grid at that time, while a value of 0 indicates that the park is not allowed to provide reserve capacity to the upper-level power grid. To optimize scheduling The park is constantly raising the electricity market price for reserve capacity. To optimize scheduling The park is constantly increasing its reserve capacity to the higher-level power grid. To optimize scheduling The electricity market price for reserve capacity should be lowered at any time. To optimize scheduling The park is constantly providing reserve capacity to the higher-level power grid. For the set of uncertainties in the source load, To optimize scheduling The actual planned utilization of power output from the modular wind power generation components. To optimize scheduling The actual planned utilization of the power output of the modular solar power generation components at any given time. To optimize scheduling Real-time comprehensive power load actual planned utilization demand value, To optimize scheduling The lower limit of the output of modular wind power generation components at any given time. To optimize scheduling The upper limit of the output of modular wind power generation components at any given time. To optimize scheduling The lower limit of the output of modular solar power generation modules at any given time. To optimize scheduling The upper limit of the output of modular solar power generation modules at any given time. To optimize scheduling The lower limit of the overall power load demand at any given time. To optimize scheduling The upper limit of the overall power load demand at any given time. To optimize the set of time periods during which the park is allowed to sell electricity to the grid, To optimize scheduling, the set of time periods that allow interruptible loads in the park should be selected. To optimize scheduling, the set of time periods during which the park is allowed to provide upper and lower reserve capacity to the upper-level power grid.

[0308] Specifically, It is the set of source-load uncertainties, containing all possible power realizations.

[0309] The source-load superrectangular uncertainty model includes the output of wind power generation modular components, the output of solar power generation modular components, and the comprehensive power load demand superrectangular uncertainty model.

[0310] Construct a superrectangular uncertainty model for the output of modular wind power generation components, including:

[0311] ,

[0312] in, To optimize scheduling The actual planned utilization of power output from the modular wind power generation components. To optimize scheduling The lower limit of the output of modular wind power generation components at any given time. To optimize scheduling The upper limit of the output of modular wind power generation components at any given time. To optimize scheduling Predicted output of modular wind power generation components at any given time To optimize scheduling The absolute value of the maximum deviation in the output of the modular wind power generation components at any given time.

[0313] Construct a superrectangular uncertainty model for the output of modular solar power generation modules, including:

[0314] ,

[0315] in, To optimize scheduling The actual planned utilization of the power output of the modular solar power generation components at any given time. To optimize scheduling The lower limit of the output of modular solar power generation modules at any given time. To optimize scheduling The upper limit of the output of modular solar power generation modules at any given time. To optimize scheduling Predicted output of modular solar power generation modules at any given time. To optimize scheduling The absolute value of the maximum deviation in output of the modular solar power generation components at any given time.

[0316] Construct a superrectangular uncertainty model for integrated electricity load demand, including:

[0317] ,

[0318] in, To optimize scheduling Real-time comprehensive power load actual planned utilization demand value, To optimize scheduling The lower limit of the overall power load demand at any given time. To optimize scheduling The upper limit of the overall power load demand at any given time. To optimize scheduling The forecast value of comprehensive power load demand at all times. To optimize scheduling The maximum absolute value of the comprehensive power load demand at any given time.

[0319] The steps for constructing an evaluation model that includes individual evaluation scores for positive and negative trend benefit indicators and comprehensive multi-attribute evaluations include:

[0320] Construct a scoring model for single-item evaluation of positive trend benefit indicators, including:

[0321] ,

[0322] in, For the first The individual evaluation scores for each positive trend benefit indicator, after being processed to be dimensionless, range from [60, 100]. The first one obtained during the planning or scheduling phase The actual result value of a positive trending benefit indicator For the first A standard reference benchmark value for a positive trending benefit indicator, corresponding to 60 points. For the first The full score reference value for each positive trend benefit indicator is 100 points. Index of positive trend benefit indicators. A subset is formed by positively trending benefit indicators. To perform the minimum value operation, This is for calculating the maximum value.

[0323] Construct a single-item evaluation scoring model for negative trend benefit indicators, including:

[0324] ,

[0325] in, For the first The individual evaluation scores for each negative trend benefit indicator, after being processed to be dimensionless, range from [60, 100]. For the first The standard reference value for a negative trend benefit indicator is 60 points. The first one obtained during the planning or scheduling phase The actual value of a negative-trend benefit indicator For the first The full score reference value for each negative trend benefit indicator is 100 points. A subset is formed by indicators with negative trends in efficiency. Index of negative trend benefit indicators.

[0326] Specifically, , The value range is [60, 100]. , Used to limit the evaluation score to the range of [60, 100].

[0327] Construct a comprehensive multi-attribute evaluation model, including:

[0328] ,

[0329] in, To comprehensively evaluate the scores based on multiple attributes, the value range is [60, 100]. For the first The importance weighting coefficient of the score of each positively trending benefit indicator. For the first The importance weighting coefficient of the score of each negative trend benefit indicator.

[0330] Specifically, a multi-attribute evaluation model for the planning and scheduling effect of the integrated energy system (source-grid-load-storage) in the park is established, and evaluation indicators for the planning and scheduling results of the integrated energy system (source-grid-load-storage) in the park are set. The set of all evaluation indicators is as follows: Among them, the result evaluation indicators are divided into positive trend benefit indicators and negative trend benefit indicators; the higher the positive trend benefit indicators, the better, and all positive trend benefit indicators constitute a subset. The smaller the negative trend benefit index, the better; all negative trend benefit indices constitute a subset. Each result evaluation indicator has a known standard reference benchmark value (passing benchmark value) and a full score reference benchmark value (full score benchmark value) set according to the requirements of the energy system. The actual planning and scheduling phases are carried out under the premise of meeting the mandatory binding rules, and the result evaluation indicator values ​​generated in the planning and scheduling phases are all better than the corresponding standard reference benchmark values ​​(passing benchmark values).

[0331] Specifically, the weighting coefficients of the score importance of the positive-trending benefit indicators. The importance weighting coefficient of the score of the negative tendency benefit indicator This reflects the relative importance of each benefit indicator in the comprehensive multi-attribute evaluation model. In practical engineering applications, the specific values ​​of the score importance weight coefficients can be determined by methods such as the analytic hierarchy process, entropy weight method, or expert experience.

[0332] Example 2

[0333] In this embodiment 2, a commercial and industrial park is selected as the application scenario. The multi-stage closed-loop planning, scheduling, and evaluation method for the integrated source-grid-load-storage system proposed in this invention is used for simulation verification. This embodiment constructs an integrated source-grid-load-storage energy system consisting of modular wind power generation components, modular solar power generation components, modular energy storage components, and diverse electrical loads such as charging piles, central air conditioning, production lines, and park lighting. The system topology is as follows: Figure 2 As shown, based on the hourly electricity load curve and new energy output curve of the park throughout the year (8760 hours), a full-time simulation operation was carried out to obtain the optimal configuration scheme of modular solar power generation and energy storage components with the best economic, low-carbon comprehensive benefits.

[0334] This embodiment uses the Gregorian calendar as the standard for seasonal division: spring is from March to May, summer is from June to August, autumn is from September to November, and winter is from December to February of the following year. The load curve of the integrated energy system (source-grid-load-storage) in industrial and commercial parks during spring is shown below. Figure 3 As shown, the summer load curve of the integrated energy system (source, grid, load, and storage) in the industrial and commercial park is as follows: Figure 4 As shown, the autumn load curve of the integrated energy system (source, grid, load, and storage) in the industrial and commercial park is as follows: Figure 5 As shown, the load curve of the integrated energy system (source, grid, load, and storage) in the industrial and commercial park during winter is as follows: Figure 6 As shown, the park's load curve on the 1st of each month throughout the year is as follows: Figure 7 As shown, the annual 8760-hour park load curve is as follows: Figure 8 As shown, the curve of the modular solar power generation components in the park for 8760 hours throughout the year is as follows: Figure 9 As shown, the overall power load curve exhibits certain seasonality and intraday peak-valley characteristics based on the park's production and office operations. In recent historical years, the park's annual electricity consumption was 12707.637 MWh, the annual solar power consumption rate was 84.1%, and the annual clean and green electricity ratio was 8.62%.

[0335] The park's power grid purchases electricity according to the industrial and commercial time-of-use pricing standard. The time-of-use pricing period and price standard are as follows: Figure 10 As shown in Table 1, considering the constraints of the park's construction land planning, the planned installed capacity of modular solar power generation modules is capped at 5.1MW, and the planned capacity of modular energy storage modules is capped at 3.5MWh. Energy storage will uniformly use 1C charge / discharge rate models. The core planning constraints are shown in Table 1.

[0336] Table 1: Park Planning Configuration Parameters

[0337] Parameter categories Parameter data Parameter Units Reference solar power modular component scale 0.18 MW Energy storage modular component charging efficiency 93 % Energy storage modular component discharge efficiency 92 % Unit investment of modular solar power generation components 2500 Yuan / kW Energy storage modular component unit investment 1500 Yuan / kWh personnel wages 15 10,000 yuan / person / year Staffing 3 people Discount rate 6 % Evaluation cycle 25 Year Carbon emission coefficient 0.704 kg / kWh

[0338] The optimized planning and configuration results of the integrated energy system (source, grid, load, and storage) for the industrial and commercial park are as follows: the optimal planned configuration for the installed capacity of modular solar power generation modules is 3.67MW, the optimal planned configuration for the capacity of 1C-type modular energy storage modules is 3.5MWh, the grid-connected electricity ratio is 72.83%, the green electricity ratio is 27.17%, and the renewable energy consumption rate is 80.3%. Specific results of the park's planning and configuration optimization are shown in Table 2.

[0339] Table 2: Results of Park Planning and Configuration Optimization

[0340] category data unit Maximum annual solar power generation 4653.37 MWh Annual off-grid electricity volume 9258.35 MWh Annual load electricity consumption 12712.81 MWh Annual carbon emissions 3888.00 t Investment in modular solar power generation components 917.69 Ten thousand yuan Investment in modular energy storage components 525.00 Ten thousand yuan Electricity purchase cost 351.50 Ten thousand yuan Annual operating costs 439.78 Ten thousand yuan

[0341] The time-series balance results of the park's annual 8760-hour electricity load, energy storage charging power, energy storage discharging power, photovoltaic output, and grid power purchase are as follows: Figure 11 As shown in the figure, the time-series balance results indicate that the system's power operation pattern closely matches the seasonal energy consumption characteristics of the park: except for the spring holidays, the park's electricity load is high in winter and early to mid-March, resulting in insufficient photovoltaic output and a large amount of electricity purchased by the grid; from late March to May, the park's load is low, photovoltaic output is abundant, and the amount of electricity purchased by the grid decreases significantly; in summer, the park's load is low, but photovoltaic output fluctuates greatly, coupled with the park's ice storage and air conditioning system's nighttime cold storage and daytime cold release operation characteristics, resulting in a high level of grid electricity purchases and nighttime charging power of energy storage; from late September to November, the park's load is low, photovoltaic output is stable, grid electricity purchases are small, and the energy storage charging and discharging rhythm is stable. Overall, the region's electricity load is highest in winter, while grid electricity purchase demand is prominent in summer and winter. Photovoltaic output is concentrated in spring and summer, and stable in autumn. Energy storage exhibits obvious characteristics of continuous charging and discharging regulation throughout the year. After optimization, the system operation conforms to the actual energy consumption patterns of the park, and the operation effect meets the standards.

[0342] This embodiment extracts typical daily operation data for the park in spring, summer, autumn, and winter, and analyzes the system planning and configuration effects and the characteristics of photovoltaic-storage collaborative operation. The typical daily operation effect of the park's planning and configuration in spring is as follows: Figure 12 As shown, the operational effect of the park's planned configuration on a typical summer day is as follows: Figure 13 As shown, the solar-storage synergy effect in a typical spring day in the park is as follows: Figure 14 As shown, the solar-storage synergy effect in the park on a typical summer day is as follows: Figure 15 As shown, the operational effect of the park's planned configuration on a typical autumn day is as follows: Figure 16 As shown, the operational effect of the park's planned configuration on a typical winter day is as follows: Figure 17 As shown, the solar-storage synergy effect in the park on a typical autumn day is as follows: Figure 18 As shown, the solar-storage synergy effect in a typical winter day in the park is as follows: Figure 19As shown, the annual planned power load and curtailed solar power operation characteristics of the park are as follows: Figure 20 As shown, the annual planned configuration of source, grid, load, and storage operation characteristics of the park are as follows: Figure 21 As shown.

[0343] Combination Figure 12 and Figure 14 The typical daily operation curves in spring show that: during the 1561-1568h electricity price range, the park's electricity load is relatively high, and the system prioritizes purchasing low-priced off-peak electricity from the grid to meet energy demand; during peak electricity price periods, energy storage releases electricity, reducing or even cutting off grid power purchases and lowering peak-period electricity purchase costs; during the midday peak photovoltaic power generation period, energy storage continuously charges, fully absorbing surplus photovoltaic power, simultaneously reducing the park's carbon emissions and energy purchase costs. On typical spring days, during off-peak load periods and peak photovoltaic power generation periods, all surplus renewable energy is absorbed by energy storage, highlighting its peak-shifting and valley-filling role, exhibiting a typical peak-valley arbitrage operation characteristic of two charging and two discharging cycles.

[0344] Combination Figure 13 and Figure 15 Typical daily operation curves in summer show that: during off-peak hours at night, the park's load is relatively high, and the system purchases low-priced off-peak electricity to supply the load. Simultaneously, energy storage rapidly stores electricity during the 3776-hour off-peak period, with the stored energy used to supply power during the daytime 3777-3778-hour photovoltaic power shortage. During peak photovoltaic power generation periods in the daytime, energy storage continuously charges to absorb excess photovoltaic power. During the peak electricity price range of 3786-3790 hours, energy storage continuously discharges to meet load demand, minimizing peak-hour power purchases. Summer energy storage also maintains a two-charge, two-discharge arbitrage model, resulting in high photovoltaic utilization and significant cost reduction and greening effects through the synergy of photovoltaic and energy storage.

[0345] Combination Figure 16 and Figure 18 Typical daily operating curves in autumn show that: during the off-peak electricity price period of 5977h-5984h, the system purchases off-peak electricity to meet the high nighttime load demand; during the daytime, the industrial park's production load is relatively low, resulting in surplus photovoltaic output with significant fluctuations, and a period of curtailment occurs from 5986h to 5990h; during the peak photovoltaic power generation period of 5991h-5994h, energy storage is used for fast charging, and during the peak electricity price period of 5995h-5998h, energy storage is used for discharging and supplying energy. In autumn, the system only purchases electricity during off-peak periods and a small amount of time when the price is at parity, and the coordinated configuration of photovoltaics and energy storage effectively reduces the annual operating cost of the industrial park.

[0346] Combination Figure 17 and Figure 19Typical winter daily operation curves show that during the off-peak electricity price period from 8:161 to 8:167, the park's load is concentrated, and the system can meet the load by purchasing off-peak electricity from outside the system. From 8:168 to 8:184, the load is relatively low, and the system power can be balanced by a single charge and discharge cycle of energy storage, achieving full absorption of daytime photovoltaic power while reducing the amount of electricity purchased from the grid. In winter, energy storage adopts a one-charge-one-discharge operation mode, balancing the dual goals of renewable energy absorption and cost reduction in electricity purchases.

[0347] Based on typical daily conditions across all four seasons and year-round operating conditions, it is evident that the park's power generation, grid, load, and energy storage system is subject to real-time coordinated regulation to ensure power balance due to the coupled influence of multiple factors, including load, daily peak-valley fluctuations in photovoltaic power, and time-of-use pricing. Consequently, the system experiences periods of curtailment. Combined with... Figure 20 The annual distribution of curtailment shows that: The park's load is low in late March, April, October, and early November; photovoltaic output fluctuates wildly in spring and remains high and stable in autumn, with curtailment being a prominent issue during these periods; photovoltaic output and load fluctuate significantly in both August and September, resulting in a relatively high level of curtailment; during the Spring Festival shutdown in early February, the park's production lines, lighting, and charging piles are almost completely shut down, leading to significant curtailment in certain periods. From a seasonal perspective: During non-holiday winter and early to mid-August (summer), the load is high, with strong local photovoltaic absorption capacity and low curtailment; in late spring and late autumn, the load is low, with weak photovoltaic absorption capacity and significant curtailment.

[0348] Depend on Figure 21 The system operation characteristic curves for 8760 hours throughout the year show that the time-series trend of the park's electricity load is highly consistent with the time-series trend of the power grid's electricity purchase; the higher the load, the greater the amount of electricity purchased by the power grid. The density of fluctuations in the energy storage's state of charge is perfectly matched with the density of fluctuations in the electricity load, verifying that the optimized energy storage has the ability to adapt to seasonal changes in the park's energy consumption throughout the entire cycle.

[0349] This embodiment takes the scenario of industrial and commercial incremental parks and all photovoltaic energy storage self-consumption as the carrier, and completes the simulation verification through the proposed planning-scheduling-evaluation closed-loop optimization method to prove that the model and optimization strategy of this invention have effectiveness, engineering applicability and universality. The verification analysis of the embodiment shows that: (1) The economic and low-carbon collaborative multi-objective optimization strategy of this invention can achieve the optimal collaborative configuration of photovoltaic and energy storage in the park, taking into account both economic and low-carbon objectives. The proportion of clean green electricity in the park increases from 8.62% before optimization to 27.17% after optimization, effectively reducing the total cost of purchasing electricity from the external grid in the park and increasing the proportion of new energy self-consumption in the park. (2) The optimization model of this invention can accurately adapt to the seasonal and intraday dual time sequence operation characteristics of the park. In the intraday dimension, the photovoltaic-storage coupling and synergistic performance is excellent. The energy storage can flexibly switch between one-charge-one-discharge and two-charge-two-discharge operation modes, giving full play to the peak-valley arbitrage and new energy consumption functions. In the seasonal dimension, the system operation rhythm matches the seasonal production and energy consumption patterns of the park. The load fluctuation, power purchase scale, and energy storage charge status are highly coupled in time sequence. The amount of curtailed photovoltaic power during high load periods is significantly reduced, and the seasonal cross-cycle adjustment capability of energy storage is effectively verified. (3) For the park's self-consumption exclusive operation mode, this invention fully explores the market-driven value of time-of-use electricity pricing, couples with policy low-carbon constraint indicators, takes into account the dual needs of economic operation and low-carbon emission reduction, and can be adapted to intraday and seasonal multi-time scale scheduling optimization to ensure the operation of the park with high photovoltaic absorption rate and high green electricity penetration rate, and meet the market-oriented risk control and long-term compliant operation needs of incremental source-grid-load-storage projects in industrial and commercial parks.

[0350] Example 3

[0351] This embodiment 3 introduces an apparatus based on a multi-stage closed-loop planning, scheduling, and evaluation method for an integrated source-grid-load-storage system, comprising:

[0352] The data acquisition module is used to: acquire the operational baseline data of the integrated source-grid-load-storage system in the park;

[0353] The data processing module is used to: input the operating baseline data into a pre-built energy system optimization model for processing, and obtain planning and scheduling evaluation results;

[0354] The energy system optimization model construction module is used for: constructing a modular component model and a comprehensive power load correction model for the energy system; establishing a multi-objective optimization configuration objective function for economic and low-carbon development throughout the entire life cycle; setting mandatory binding rules for the safe domain operation space and mandatory binding rules for policy compliance to form a planning stage model; establishing an optimization scheduling objective function to prevent uncertainty risks under market-oriented operation; constructing a source-load super-rectangular uncertainty model to form a scheduling stage model; and constructing an evaluation model including single-item evaluation scores for positive and negative trend benefit indicators and comprehensive multi-attribute evaluation based on the effects of the planning stage model or scheduling stage model.

[0355] Energy system optimization model processing module: used to: input the operating baseline data into the planning stage model and the scheduling stage model, the planning stage model and the scheduling stage model respectively perform economic and low-carbon planning configuration and optimized scheduling of the integrated source-grid-load-storage system of the park, the evaluation model scores the economic and low-carbon planning configuration results and optimized scheduling results respectively, the planning stage model and the scheduling stage model decide whether to adjust the planning parameters and configuration based on their respective scores; output the economic and low-carbon planning configuration results of the planning stage model, the optimized scheduling results of the scheduling stage model and the scoring results of the evaluation model.

[0356] It is worth noting that this device embodiment corresponds to the above method embodiment. The implementation methods of the above method embodiments are all applicable to this device embodiment and can achieve the same or similar technical effects, so they will not be described in detail here.

[0357] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0358] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0359] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0360] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0361] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A multi-stage closed-loop planning, scheduling, and evaluation method for an integrated source-grid-load-storage system, characterized in that, include: Obtain operational baseline data for the integrated power generation, grid, load, and storage system of the industrial park; The operational baseline data is input into a pre-built energy system optimization model for processing to obtain planning and scheduling evaluation results; The energy system optimization model includes a planning phase model, a scheduling phase model, and an evaluation model. The construction steps of the energy system optimization model are as follows: Construct a modular component model for the energy system and a comprehensive power load correction model, establish a multi-objective optimization configuration objective function for economic and low-carbon development throughout the entire life cycle, and set mandatory binding rules for the safe domain operation space and mandatory binding rules for policy compliance to form a planning stage model; Establish an optimized scheduling objective function to prevent uncertainty risks under market-oriented operation, and construct a source-load hyperrectangular uncertainty model to form a scheduling phase model; Based on the effects of the planning stage model or scheduling stage model, an evaluation model is constructed that includes individual evaluation scores for positive and negative trend benefit indicators and comprehensive multi-attribute evaluation. The execution steps of the energy system optimization model include: The operational baseline data is input into the planning stage model and the scheduling stage model. The planning stage model and the scheduling stage model respectively perform economic and low-carbon planning configuration and optimized scheduling of the integrated source-grid-load-storage system of the park. The evaluation model scores the economic and low-carbon planning configuration results and optimized scheduling results respectively. The planning stage model and the scheduling stage model decide whether to adjust the planning parameters and configuration based on their respective scores. Output the economic low-carbon planning configuration results of the planning phase model, the optimized scheduling results of the scheduling phase model, and the scoring results of the evaluation model.

2. The multi-stage closed-loop planning, scheduling, and evaluation method for an integrated source-grid-load-storage system according to claim 1, characterized in that, The operational baseline data includes: The input dataset for the planning phase model includes: actual operating wind speed, historical operating wind speed, and operating characteristic model parameters of wind power generation components; irradiance and model parameters of basic solar power generation components during operation and historical operation; loss rate, initial energy storage capacity, charging efficiency, and discharging efficiency of energy storage components; annual average power factor of components at the public grid connection point; maximum power capacity of power supply transformers; maximum power capacity of power supplied from the grid to the system and power fed back to the grid; historical data of comprehensive power load and calibration model parameters; step size of optimized operating data points; optimized operating cycle; maximum allowable wind curtailment rate and maximum solar curtailment rate within the optimized operating cycle; installed capacity limits of solar and wind power generation components; system demand capacity control and power feedback baseline control; and other parameters to be determined. The planning includes the maximum initial investment cost of the equipment, the area occupied by the equipment installation and the usable land area of ​​the park, the maximum allowable load ratio, the binary variable of interruptible state, the set of allowable interruption periods, the upper limit of the annual interruptible electricity consumption ratio of the total electricity consumption as stipulated by policies or agreements, the minimum continuous interruption duration and unit interruption power cost, the lower limit of the green electricity direct supply self-consumption ratio stipulated by policies, the upper limit of the on-grid electricity ratio, the lower limit of the renewable energy power generation ratio of the total electricity consumption ratio, the regional grid carbon emission factor corresponding to the park's purchase of electricity from the grid, the upper limit of annual energy consumption carbon emissions stipulated by policies, the unit electricity purchase price, the unit maintenance cost, the unit capacity installation cost, the capacity, the investment recovery coefficient, the discount rate, the working years of energy storage components, the annual salary and number of employees, and the unit power equivalent carbon emission coefficient. The input dataset of the scheduling phase model includes: the step size of the optimized scheduling operation data points, the electricity price of the park purchasing electricity from the upper-level grid and selling electricity to the grid during the optimized scheduling, the operation and maintenance cost of the unit charging and discharging power of the energy storage components, the unit compensation cost of interruptible loads, the electricity market price for the park to increase and decrease reserve capacity, the time period set during which the park is allowed to sell electricity to the grid, interruptible loads, and provide upper and lower reserve capacity, and the maximum absolute value of the output deviation of the wind power generation components and solar power generation components during the optimized scheduling. The planning and scheduling evaluation results include: The economic and low-carbon planning configuration results of the planning phase model are as follows: predicted output power and corrected historical data of wind power generation components, predicted output power and corrected historical data of solar power generation components, corrected comprehensive power load data, capacity, charging power, and discharging power of energy storage components, output power of power supply transformers at public grid access points, power supply from the grid to the system, economic target result value, energy cost value, operation and maintenance cost value, equipment construction investment cost value, salary cost of full-time operation and management personnel, low-carbon target value, actual power input to the system from wind power generation and solar power generation, satisfaction with economic and low-carbon multi-objectives, actual wind curtailment rate, actual solar curtailment rate, energy storage capacity of optimized planning configuration of energy storage components, installed capacity of optimized planning configuration of solar power generation components and wind power generation components, total interruption cost of interruptible load throughout the year, and actual interrupted power of the park's energy system. The optimized scheduling results of the scheduling phase model are: the total operating cost of the park during the optimized scheduling cycle, the active power of the park purchasing electricity from and selling electricity to the grid during the optimized scheduling, the upward and downward reserve capacity provided by the park to the grid, and the actual planned utilization output of wind power generation components, solar power generation components, and the actual planned utilization demand of comprehensive power load during the optimized scheduling. The scoring results of the evaluation model are: single evaluation scores for positive trend benefit indicators, single evaluation scores for negative trend benefit indicators, and comprehensive multi-attribute evaluation scores.

3. The multi-stage closed-loop planning, scheduling, and evaluation method for an integrated source-grid-load-storage system according to claim 1, characterized in that, The modular component model of the energy system includes modular component models for wind power generation, solar power generation, energy storage, and public grid access points; Constructing the modular component model for wind power generation includes: ， The constraints are: ， in, For modular components of wind power generation Predicted output power at time of day For modular components of wind power generation The actual operating wind speed at any given time. The minimum operating wind speed for the modular wind power generation components. The wind speed limit for safe operation of modular wind power generation components. The rated installed capacity of the modular wind power generation components. The rated operating wind speed for modular wind power generation components, For wind energy Power at any moment The operating power factor of the wind turbine rotor. For wind-driven turbine operation characteristic coefficients, This refers to the air density in the environment where the wind power generation equipment is located. The effective area swept by the rotor of a wind power generation device. The upper limit of the polynomial power value for predicting the output curve of modular wind power generation components. The first predicted output curve for modular wind power generation components A number of constant fitting coefficients For modular components of wind power generation The actual operating wind speed at any given time Power value The constant term coefficients are used to fit the predicted output curve of the modular wind power generation module. , , , , , , , These are the different characteristic coefficients of the function relating the tip speed ratio and pitch angle of wind power generation equipment. For the tip speed ratio of wind power generation equipment, The pitch angle of the wind power generation equipment; Constructing the modular solar power generation component model includes: ， in, Modular solar power generation module station Predicted power generation at any given time The coefficient for adjusting the temperature variation of power output in the basic unit of a modular solar power generation module. For generating electricity from basic units of modular solar power modules The ambient temperature during constant operation. The standard test environment temperature for the basic unit of the modular solar power generation module is set to ensure optimal power generation. For generating electricity from basic units of modular solar power modules The intensity of light during constant operation. The rated operating temperature for generating electricity in the basic unit of a modular solar power generation module. This refers to the temperature reference value for the basic unit of a modular solar power generation module in its actual operating environment. This serves as a benchmark value for the actual light intensity in the working environment of the basic unit of a modular solar power generation module. This refers to the total number of basic units of the modular solar power generation modules within the solar power generation module station. The average annual comprehensive power generation efficiency of the modular solar power generation station. The rated power output of a basic unit of a modular solar power generation module under standard testing conditions. The light intensity of the power generation working environment of the basic unit of the modular solar power generation module under the standard specification test environment of the basic unit of the modular solar power generation module; Constructing the modular energy storage component model includes: ， in, Modular components for energy storage Energy stored at all times The loss rate of modular energy storage components. Modular components for energy storage Energy stored at all times and Modular energy storage components are respectively located in Time and Charging power at any time and These represent the charging efficiency and discharging efficiency of the modular energy storage components, respectively. and Modular energy storage components are respectively located in Time and Discharge power at any given time To optimize the step size of operational data points in the planning and configuration, The numerical value of the regular operating interval of the modular energy storage components. The energy stored in the modular energy storage component during its initial startup and operation is denoted by , and the discharge efficiency of the modular energy storage component is denoted by . The stored energy is the energy stored at the first moment of operation within the regular operating cycle of the modular energy storage component. Constructing the modular component model of the public network access point includes: The park's distributed solar power generation projects adopt either a fully self-consumption model or a self-consumption with surplus electricity fed into the grid model. The modular component model of the public network access point under the self-generated and self-used mode is as follows: ， in, Power supply transformer for modular components of public network access points The power supplied by the power grid to the integrated energy system of source, grid, load and storage at any given time. Modular components for public network access points The power of the input power transformer at any given time. The maximum power capacity of the power supply transformer for the modular components of the public network access point. The annual average power factor of modular components for public network access points; The modular component model of the public network access point under the self-generated and self-consumption surplus power grid connection mode is as follows: ， in, The upper limit of power supply capacity for modular components at public grid access points to the integrated energy system of source-grid-load-storage. Modular components for public network access points The power output of the integrated energy system (source, grid, load, and storage) fed back to the grid at any given time. The upper limit of the power capacity that a modular component-based integrated energy system for public grid access points can send back to the grid is defined; Constructing the comprehensive power load correction model includes: Based on seasonal anomaly detection, seasonal components are extracted and residual models are calculated: ， in, for Typical values ​​of seasonal components at any given time. for Before and after the moment Data from the same period of the day, This is the number of days offset. for Remove the seasonal residual components at all times. for Raw load data at any given time; Determine the dynamic threshold, calculate local statistics, and determine the anomaly rule model: ， in, This is the local median. for Remove the seasonal residual components at all times. for Remove the seasonal residual components at all times. for Remove the seasonal residual components at all times. for Remove the seasonal residual components at all times. for Remove the seasonal residual components at all times. The bias is determined by the local median. This is the median operation function. The function for calculating the absolute deviation of the median. This is an exception indicator variable; it returns 1 when the data is determined to be abnormal, and 0 otherwise. Construct a similarity measurement model for matching similar days: ， in, for Time and Similarity of time, It is a natural exponential function. for The date feature vector at any given time. for The date feature vector at any given time. for Vector and Euclidean distance between vectors The standard deviation of the distance; Constructing a weighted least squares model for locally weighted regression: ， in, for Corrected load data at any given time. For loss function, To minimize the parameter function, for Most similar in time A set of indexes for each time point. For weighted interpolation weights, for Raw load data at any given time. Different auxiliary operation parameters for the loss function, For about variables kernel function, For about variables kernel function, for Time and Similarity of time, For bandwidth.

4. The multi-stage closed-loop planning, scheduling, and evaluation method for an integrated source-grid-load-storage system according to claim 1, characterized in that, The objective function for multi-objective optimization of economic and low-carbon configuration throughout the entire life cycle includes economic optimization objective, low-carbon optimization objective, and multi-objective of economic and low-carbon throughout the entire life cycle. Constructing the economic optimization objective throughout the entire life cycle includes: ， The constraints are: ， in, To optimize the economic goals of an integrated energy system encompassing generation, grid, load, and storage during its planned and configured operational cycle, To optimize the energy costs of an integrated energy system (source, grid, load, and storage) during its planned and configured operation cycle, To optimize the operation and maintenance costs of integrated energy systems (source, grid, load, and storage) during their planned and configured operating cycles, To optimize the investment cost of integrated energy system construction for power generation, grid, load, and storage during the planned operation cycle, To optimize the allocation and operation cycle of an integrated energy system encompassing power generation, grid, load, and storage, the salary costs of dedicated operation and management personnel are planned. The total interruption cost of interruptible load throughout the year. for The unit price of electricity at any given time for The power purchased from the upper-level power grid by the integrated energy system of source, grid, load and storage. To optimize the step size of operational data points in the planning and configuration, For equipment The unit maintenance cost for Time device of effort, For equipment The unit capacity installation cost, For equipment capacity, This is the investment recovery factor. For the discount rate, Modular components for energy storage Years of service, For staff annual salary and wages, The number of employees is the fixed number. The construction of the low-carbon optimization target throughout the entire life cycle includes: ， in, To achieve the low-carbon optimization goals of integrated energy systems (source, grid, load, and storage) under the planned and optimized operating cycle, For the integrated energy system of source, grid, load and storage The equivalent carbon emission factor per unit power of electricity purchased from the main power grid at all times. Modular components for public network access points The power supplied by the power grid to the integrated energy system of source, grid, load and storage at any given time. For wind power generation The actual power input to the park's integrated energy system at all times. For modular solar power generation components The actual power output input into the park's integrated energy system of source, grid, load and storage at all times; The construction of the aforementioned multi-objective economic and low-carbon economy throughout the entire life cycle includes: ， The constraints are: ， in, For the economic and low-carbon multi-objective function throughout the entire life cycle, To achieve multi-objective satisfaction in economic low-carbon development, The minimum carbon emission value corresponding to the single-objective optimization for low-carbon operation goals. The maximum value of the corresponding carbon emissions when performing single-objective optimization for the goal of low-carbon operation. To quantify the level of satisfaction with carbon emission levels, To quantify the level of satisfaction with economic cost levels, The minimum economic value corresponding to the single-objective optimization of the economic operation objective. The maximum value of the corresponding economic value when optimizing a single objective for the purpose of optimizing economic operation.

5. The multi-stage closed-loop planning, scheduling, and evaluation method for an integrated source-grid-load-storage system according to claim 1, characterized in that, The mandatory binding rules for the safe domain operation space include the safe domain operation space for wind power generation, the safe domain operation space for solar power generation, power supply and demand matching, the power distribution status of modular components at public grid access points, the safe domain operation space for energy storage modular components, the investment capacity of modular component resources, the building area occupied by modular component resources, and the mandatory binding rules for the interruptible regulation of comprehensive power load. Constructing the mandatory binding rules for the wind power generation safety domain operation space includes: ， in, For wind power generation The actual power input to the park's integrated energy system at all times. For modular components of wind power generation Predicted output power at time of day This represents the maximum power generation capacity of wind power generation. To optimize the operational cycle for planning and configuration, To optimize the maximum allowable wind curtailment rate during the planned operation cycle; Constructing the mandatory binding rules for the operating space of the solar power generation safety domain includes: ， in, For modular solar power generation components The actual power output input to the park's integrated energy system (source, grid, load, and storage) at any given time. Modular solar power generation module station Predicted power generation at any given time This represents the maximum power output of the modular solar power generation module. To optimize the maximum allowable curtailment rate during the planned operation cycle; Constructing the power supply and demand matching mandatory binding rule includes: ， in, Modular components for public network access points The power supplied by the power grid to the integrated energy system of source, grid, load and storage at any given time. Modular components for energy storage Discharge power at any given time Modular components for public network access points The power output of the integrated energy system (source, grid, load, and storage) fed back to the grid at any given time. Modular components for energy storage Charging power at any time for Total electrical load at any given time In order to be in The actual electrical load power interrupted by the park's energy system at any given time; Constructing the mandatory binding rules for the power distribution state of the modular components of the public network access point includes: ， in, This refers to the demand and capacity control of an integrated energy system encompassing power generation, grid, load, and storage. This refers to the set of demand and capacity control periods for an integrated energy system encompassing power generation, grid, load, and storage. This is the baseline control quantity for the power transmission from the integrated energy source-grid-load-storage system. The set of control periods for the power return baseline of the integrated energy source-grid-load-storage system; Constructing the mandatory binding rules for the operating space of the security domain of the energy storage modular component includes: ， in, Modular components for energy storage The minimum proportion of energy stored in modular energy storage components relative to the equipment capacity. Modular components for energy storage capacity, Modular components for energy storage exist Energy stored at all times Modular components for energy storage The modular energy storage components account for the largest proportion of the equipment capacity. Modular components for energy storage exist Charging power at any time Modular components for energy storage Maximum charging, Modular components for energy storage exist Discharge power at any given time Modular components for energy storage The power of the energy release, Modular components for energy storage The stored energy at the beginning of a regular operating interval cycle. The energy matching degree at the beginning and end of the cycle of the modular energy storage component. Modular components for energy storage The stored energy at the end of a regular operating interval period. Modular components for energy storage exist The charging and discharging power value at any given time. Modular components for energy storage exist The charging / discharging state transition flag at any given time has a value of 1 indicating a state transition has occurred, and a value of 0 indicating that no state transition has been sent. Modular components for energy storage exist The charging and discharging power value at any given time. The maximum number of charge / discharge cycles is limited within the regular operating interval of modular energy storage components. The periodic value of the regular operation interval of the modular energy storage components; The mandatory binding rules for the resource investment capacity of the modular components include: ， in, The investment cost of the equipment to be planned and configured. The maximum initial investment cost for the equipment to be planned and configured; The rules for mandating the building area occupancy of the modular components are constructed as follows: ， in, The first modular component in the integrated energy system of source, grid, load and storage The installation area occupied by this type of equipment The land area available for the integrated energy system of source, grid, load and storage in the park; Constructing the mandatory binding rules for the interruptible regulation of the integrated power load includes: ， in, In order to be in The actual electrical load power interrupted by the park's energy system at any given time. In order to be in The maximum allowable load percentage for interruption at any given time. for Total electrical load at any given time In order to be in The interruptibility status is a binary variable at any given time, where 1 indicates that interruption is allowed at that time, and 0 indicates that interruption is not allowed. For a predefined set of allowed interruption periods, To optimize the operational cycle for planning and configuration, To optimize the step size of operational data points in the planning and configuration, The annual interruptible electricity consumption as a percentage of total electricity consumption is the upper limit stipulated by policy or agreement. For internal summation index, The minimum duration of continuous interruption. In order to be in The interruptibility status is a binary variable at any given time, where 1 indicates that interruption is allowed at that time, and 0 indicates that interruption is not allowed. In order to be in The interruptible state binary variable at time point. The total interruption cost of interruptible load throughout the year. In order to be in The unit interrupt power cost at any given moment.

6. The multi-stage closed-loop planning, scheduling, and evaluation method for an integrated source-grid-load-storage system according to claim 1, characterized in that, The mandatory binding rules for policy compliance include: the proportion of green electricity directly supplied for self-consumption, the proportion of electricity fed into the grid, the proportion of renewable energy generation in total electricity consumption, and mandatory binding rules for energy consumption, carbon emissions, and green electricity penetration rate. The mandatory binding rules for the proportion of green electricity directly supplied for self-consumption include: ， in, To optimize the operational cycle for planning and configuration, In order to be in The self-consumption capacity of renewable energy power generation in the park at any given time. To optimize the step size of operational data points in the planning and configuration, For modular solar power generation components The actual power output input to the park's integrated energy system (source, grid, load, and storage) at any given time. For wind power generation The actual power input to the park's integrated energy system at all times. This is the lower limit for the proportion of green electricity directly supplied for self-consumption as stipulated by policy; The mandatory binding rule for the proportion of electricity consumed during internet access is constructed as follows: ， in, Modular components for public network access points The power output of the integrated energy system (source, grid, load, and storage) fed back to the grid at any given time. This is the upper limit of the proportion of electricity used for internet access as stipulated by policy. Constructing a mandatory binding rule for the proportion of renewable energy generation to total electricity consumption includes: ， in, for Total electrical load at any given time This is the lower limit for the proportion of renewable energy power generation in total electricity consumption as stipulated by policy. The mandatory binding rules for energy consumption and carbon emissions are constructed as follows: ， in, This represents the park's annual energy consumption and carbon emissions. The regional power grid carbon emission factor corresponding to the park's purchase of electricity from the power grid. Modular components for public network access points The power supplied by the power grid to the integrated energy system of source, grid, load and storage at any given time. The annual energy consumption carbon emission cap stipulated by policy; The mandatory binding rule for green electricity penetration rate is constructed as follows: ， in, This is the lower limit for the annual green electricity penetration rate stipulated by policy.

7. The multi-stage closed-loop planning, scheduling, and evaluation method for an integrated source-grid-load-storage system according to claim 1, characterized in that, Establish an optimized scheduling objective function to mitigate uncertainty risks under the aforementioned market-based operation, including: ， The constraints are: ， in, To optimize for the maximum value within the range of source load uncertainties, To optimize the total operating cost within the scheduling cycle of the park, To optimize the scheduling operation cycle, To optimize scheduling The electricity price that the industrial park purchases from the upper-level power grid at any given time. To optimize scheduling The active power of electricity purchased by the industrial park from the upper-level power grid at any given time. To optimize scheduling and the step size of running data points, To optimize scheduling The variable represents the state of the park selling electricity to the grid at any given time. A value of 1 indicates that the park is allowed to sell electricity to the grid at that time, while a value of 0 indicates that the park is not allowed to sell electricity to the grid. To optimize scheduling The electricity price that the industrial park sells to the grid at any given time. To optimize scheduling The active power of electricity sold from the industrial park to the power grid at any given time. To optimize the operation and maintenance cost per unit charging power of modular energy storage components during scheduling, Modular components for energy storage Charging power at any time To optimize the operation and maintenance cost per unit discharge power of modular energy storage components during scheduling, Modular components for energy storage Discharge power at any given time To optimize scheduling The status of the park's interruptible load scenario at any given time is represented by a binary variable. A value of 1 indicates that the park's interruptible load is allowed at that time, while a value of 0 indicates that the park's interruptible load is not allowed. To optimize scheduling The unit compensation cost of a load that can be interrupted at any time. In order to be in The actual electrical load power interrupted by the park's energy system at any given time. To optimize scheduling The variable represents the scenario state of the reserve capacity provided by the park to the upper-level power grid at any given time. A value of 1 indicates that the park is allowed to provide reserve capacity to the upper-level power grid at that time, while a value of 0 indicates that the park is not allowed to provide reserve capacity to the upper-level power grid. To optimize scheduling The park is constantly raising the electricity market price for reserve capacity. To optimize scheduling The park is constantly increasing its reserve capacity to the higher-level power grid. To optimize scheduling The electricity market price for reserve capacity should be lowered at any time. To optimize scheduling The park is constantly providing reserve capacity to the higher-level power grid. For the set of uncertainties in the source load, To optimize scheduling The actual planned utilization of power output from the modular wind power generation components. To optimize scheduling The actual planned utilization of the power output of the modular solar power generation components at any given time. To optimize scheduling Real-time comprehensive power load actual planned utilization demand value, To optimize scheduling The lower limit of the output of modular wind power generation components at any given time. To optimize scheduling The upper limit of the output of modular wind power generation components at any given time. To optimize scheduling The lower limit of the output of modular solar power generation modules at any given time. To optimize scheduling The upper limit of the output of modular solar power generation modules at any given time. To optimize scheduling The lower limit of the overall power load demand at any given time. To optimize scheduling The upper limit of the overall power load demand at any given time. To optimize the set of time periods during which the park is allowed to sell electricity to the grid, To optimize scheduling, the set of time periods that allow interruptible loads in the park should be selected. To optimize scheduling, the set of time periods during which the park is allowed to provide upper and lower reserve capacity to the upper-level power grid.

8. The multi-stage closed-loop planning, scheduling, and evaluation method for an integrated source-grid-load-storage system according to claim 1, characterized in that, The source-load superrectangular uncertainty model includes the power output of wind power generation modular components, the power output of solar power generation modular components, and the comprehensive power load demand superrectangular uncertainty model. Constructing the superrectangular uncertainty model for the output of the modular wind power generation component includes: ， in, To optimize scheduling The actual planned utilization of power output from the modular wind power generation components. To optimize scheduling The lower limit of the output of modular wind power generation components at any given time. To optimize scheduling The upper limit of the output of modular wind power generation components at any given time. To optimize scheduling Predicted output of modular wind power generation components at any given time To optimize scheduling The absolute value of the maximum deviation in the output of the modular wind power generation components at any given moment; Constructing the superrectangular uncertainty model for the output of the aforementioned modular solar power generation module includes: ， in, To optimize scheduling The actual planned utilization of the power output of the modular solar power generation components at any given time. To optimize scheduling The lower limit of the output of modular solar power generation modules at any given time. To optimize scheduling The upper limit of the output of modular solar power generation modules at any given time. To optimize scheduling Predicted output of modular solar power generation modules at any given time. To optimize scheduling The absolute value of the maximum deviation in the output of the modular solar power generation module at any given moment; The superrectangular uncertainty model for the comprehensive power load demand is constructed as follows: ， in, To optimize scheduling Real-time comprehensive power load actual planned utilization demand value, To optimize scheduling The lower limit of the overall power load demand at any given time. To optimize scheduling The upper limit of the overall power load demand at any given time. To optimize scheduling The forecast value of comprehensive power load demand at all times. To optimize scheduling The maximum absolute value of the comprehensive power load demand at any given time.

9. The multi-stage closed-loop planning, scheduling, and evaluation method for an integrated source-grid-load-storage system according to claim 1, characterized in that, The steps for constructing an evaluation model that includes individual evaluation scores for positive and negative trend benefit indicators and comprehensive multi-attribute evaluations include: Construct a scoring model for single-item evaluation of positive trend benefit indicators, including: ， in, For the first The individual evaluation scores for each positive trend benefit indicator, after being processed to be dimensionless, range from [60, 100]. The first one obtained during the planning or scheduling phase The actual result value of a positive trending benefit indicator For the first A standard reference benchmark value for a positive trending benefit indicator, corresponding to 60 points. For the first The full score reference value for each positive trend benefit indicator is 100 points. Index of positive trend benefit indicators. A subset is formed by positively trending benefit indicators. To perform the minimum value operation, This is for calculating the maximum value. Construct a single-item evaluation scoring model for negative trend benefit indicators, including: ， in, For the first The individual evaluation scores for each negative trend benefit indicator, after being processed to be dimensionless, range from [60, 100]. For the first The standard reference value for a negative trend benefit indicator is 60 points. The first one obtained during the planning or scheduling phase The actual value of a negative-trend benefit indicator For the first The full score reference value for each negative trend benefit indicator is 100 points. A subset is formed by indicators with negative trends in efficiency. Index of negative trend benefit indicators; Construct a comprehensive multi-attribute evaluation model, including: ， in, To comprehensively evaluate the scores based on multiple attributes, the value range is [60, 100]. For the first The importance weighting coefficient of the score of each positively trending benefit indicator. For the first The importance weighting coefficient of the score of each negative trend benefit indicator.

10. An apparatus based on the multi-stage closed-loop planning, scheduling, and evaluation method for an integrated source-grid-load-storage system according to any one of claims 1-9, characterized in that, include: The data acquisition module is used to: acquire the operational baseline data of the integrated source-grid-load-storage system in the park; The data processing module is used to: input the operating baseline data into a pre-built energy system optimization model for processing, and obtain planning and scheduling evaluation results; The energy system optimization model construction module is used for: constructing a modular component model and a comprehensive power load correction model for the energy system; establishing a multi-objective optimization configuration objective function for economic and low-carbon development throughout the entire life cycle; setting mandatory binding rules for the safe domain operation space and mandatory binding rules for policy compliance to form a planning stage model; establishing an optimization scheduling objective function to prevent uncertainty risks under market-oriented operation; constructing a source-load super-rectangular uncertainty model to form a scheduling stage model; and constructing an evaluation model including single-item evaluation scores for positive and negative trend benefit indicators and comprehensive multi-attribute evaluation based on the effects of the planning stage model or scheduling stage model. Energy system optimization model processing module: used to: input the operating baseline data into the planning stage model and the scheduling stage model, the planning stage model and the scheduling stage model respectively perform economic and low-carbon planning configuration and optimized scheduling of the park's integrated source-grid-load-storage system, the evaluation model scores the economic and low-carbon planning configuration results and optimized scheduling results respectively, and the planning stage model and the scheduling stage model decide whether to adjust the planning parameters and configuration based on their respective scores; Output the economic low-carbon planning configuration results of the planning phase model, the optimized scheduling results of the scheduling phase model, and the scoring results of the evaluation model.

Images