Source and storage scale optimization configuration method and system facing multiple types of new energy and load characteristics

Abstract

The application discloses a source and storage scale optimization configuration method and system facing multiple types of new energy and load characteristics, and the method of the scheme establishes new energy resource portraits of photovoltaic power, wind power, biomass and small hydropower, identifies characteristic parameters of basic rigid load, production batch type load, transferable load, interruptible load and cold and heat supply coupled load, calculates a source and load space-time complementary relationship and generates a source and load matching matrix, and then establishes a multi-type energy storage adaptation model based on new energy fluctuation scales and load response capabilities, and carries out multi-objective optimization by taking source scale, energy storage power capacity, energy storage energy capacity and load response scale as variables, and outputs scale configuration and hierarchical control strategies through multi-scenario robustness checking, so that the application scheme can improve new energy utilization rate, energy supply reliability and configuration economy.

Description

Technical Field

[0001] This invention relates to the field of power grid management technology, and in particular to a method and system for optimizing the allocation of energy sources and storage capacity for various types of new energy sources and load characteristics. Background Technology

[0002] With the rapid development of new power systems and integrated energy systems, the integration rate of various new energy sources, such as photovoltaic, wind power, biomass power generation, and small hydropower, in industrial parks, factories, regional microgrids, and multi-energy collaborative power supply scenarios is continuously increasing. Simultaneously, user-side loads are evolving from traditional single-type electrical loads to composite load structures that include continuous industrial loads, batch-type production loads, transferable loads, interruptible loads, and combined cooling, heating, and power (CCHP) coupled loads. In these scenarios, the uncertainty of new energy output, the differences in load structures, and the limitations of energy storage regulation capabilities collectively influence the allocation of energy source and storage scale.

[0003] Existing energy source and energy storage configuration methods typically establish the configuration relationship between new energy sources and energy storage capacity based on historical maximum load, annual electricity consumption, typical daily load curves, or empirical installed capacity ratios. While such methods can meet preliminary planning requirements to some extent, they often treat different new energy sources uniformly as renewable output models, failing to adequately distinguish the differences such as the significant impact of irradiance and shading on photovoltaics, the significant impact of wind power on wind speed distribution and turbulence, the certain dispatchability of biomass power generation, and the obvious seasonal constraints of small hydropower. This results in the inaccurate representation of the resource endowment of new energy sources.

[0004] Meanwhile, existing methods typically treat user-side load as a single total load curve, lacking classification modeling for rigid loads, production batch loads, transferable loads, interruptible loads, and combined cooling, heating, and power (CCHP) coupled loads. For complex loads with production process constraints, shift window constraints, interruption penalty constraints, and CHP-electricity conversion relationships, using only the total load curve for source and storage configuration can easily overlook the load-side adjustability potential, leading to a timing mismatch between renewable energy output and load demand.

[0005] Furthermore, existing energy storage configuration methods often focus on single electrochemical energy storage, frequently optimizing storage capacity as a uniform variable without fully considering the differences in response speed, duration, cycle life, energy conversion efficiency, and investment cost among high-response power storage, electrical energy storage, cold storage devices, and thermal storage devices. When the fluctuation scale of new energy sources and the load response scale are mismatched, single-energy storage models are prone to problems such as over-allocation of energy storage, low energy storage utilization, excessive energy storage cycle pressure, and unutilized cooling and heating load regulation capabilities.

[0006] Furthermore, some existing methods only optimize source and storage capacity based on typical days or single operating scenarios, without robust verification for scenarios such as rainy days, low wind speeds, peak loads, off-peak loads, equipment maintenance, and sudden fluctuations. This may lead to problems such as insufficient renewable energy consumption, increased risk of load outages, frequent deep cycling of energy storage, and economic returns deviating from expectations in actual operation of the configuration scheme formed during the planning stage.

[0007] Therefore, there is an urgent need to propose a source-storage scale optimization configuration method and system that can simultaneously consider the differences in multiple types of new energy resources, the differences in multiple types of load response, and / or the differences in multiple types of energy storage adaptation, so as to solve the problem that the existing source-storage configuration methods do not adequately consider the differences in new energy types, load structure differences, and / or energy storage regulation boundaries, and improve the economy, reliability, renewable energy utilization rate, and engineering adaptability of the configuration results. Summary of the Invention

[0008] In view of this, the purpose of this invention is to propose a method and system for optimizing the allocation of source and storage scale for multiple types of new energy sources and load characteristics.

[0009] To achieve the above-mentioned technical objectives, the technical solution adopted by this invention is as follows: A method for optimizing the allocation of energy sources and storage capacity to accommodate various types of new energy sources and load characteristics, comprising: S1. Obtain resource data of multiple types of new energy sources within the planning area, establish a power generation model for multiple types of new energy sources based on the resource data, and extract fluctuation characteristics, predictability indicators and / or equivalent dispatchable capacity indicators according to the power generation model to generate a new energy resource profile library. S2. Obtain energy consumption data of multiple types of loads within the planning area and generate a set of load-side characteristic parameters according to preset conditions; S3. Based on the new energy resource profile library and the load characteristic parameter set, calculate the spatiotemporal complementary characteristics between different new energy sources and between new energy sources and loads according to different time scales, identify the matching relationship between new energy output and load demand and the load adjustment potential's ability to offset new energy fluctuations, and generate a source-load matching matrix. S4. Determine the source-load imbalance power sequence within the planning area based on the source-load matching matrix, identify the fluctuation scale of the source-load imbalance power sequence, determine the demand conditions, and establish a multi-type energy storage adaptation model. S5. Based on the multi-type energy storage adaptation model, a multi-objective optimization model for source and storage scale is established with preset decision variables. Based on the preset optimization objectives and constraints, multiple candidate source and storage configuration schemes are obtained. S6. Perform multi-scenario robust simulation on the multiple candidate source-storage configuration schemes, evaluate the new energy utilization rate, load failure probability, energy storage cycle pressure and economic return level of each candidate source-storage configuration scheme, and select the target source-storage configuration scheme based on the evaluation results. S7. Based on the target source and storage configuration scheme, output the recommended installed capacity of various new energy sources, the recommended configuration scale of various energy storage systems, the response capacity construction scheme for different load categories, and / or the hierarchical operation control strategy.

[0010] As one possible implementation, further, in step S1 of this solution, the multiple types of new energy sources include photovoltaic, wind power, biomass power generation / or small hydropower.

[0011] As a possible implementation method, this solution further establishes a power generation model for the following types of new energy sources when establishing the power generation model: A power generation model for photovoltaics was established, incorporating irradiance, temperature, orientation, and shading characteristics. A model for the generating capacity of wind power is established, which includes wind speed distribution, turbulence intensity, and intraday fluctuation characteristics. A power generation model for biomass power generation, including fuel supply stability and adjustable output range, is established. A model for the generating capacity of small hydropower stations is established, which includes the seasonality of water inflow and the upper limit of power output.

[0012] As a possible implementation, step S1 of this scheme further includes: constructing a resource profile vector for any new energy type, wherein the resource profile vector includes average available output, output fluctuation intensity, maximum ramp rate per unit time, prediction error, equivalent dispatchable capacity, annualized cost per unit capacity and / or carbon emission reduction coefficient per unit electricity, and storing the resource profile vectors of each new energy type in the new energy side resource profile library according to a unified time scale.

[0013] As a possible implementation, step S2 of this scheme further includes: acquiring energy consumption data of multiple types of loads within the planning area, wherein the multiple types of loads include basic rigid loads, production batch loads, transferable loads, interruptible loads / or combined cooling and heating loads, extracting the peak-to-valley ratio, duration, fluctuation rate, allowable shift window, energy supply reliability requirements / or interruption penalty cost of each type of load, and establishing the conversion relationship between electrical load, heat load and cold load for combined cooling and heating loads, and generating a set of load-side characteristic parameters; As a preferred implementation option, step S2 of this solution preferably includes one of the following: (1) Establish an energy conservation constraint that the total energy consumption before and after the relocation is equal for transferable loads; (2) Establish an interruption power upper limit constraint and an interruption penalty cost model for interruptible loads; Establish constraints on batch start time, batch duration, and batch end time for production batch-type workloads; (3) Establish a conversion model for cooling load and heating load to equivalent electrical load for combined cooling and heating load.

[0014] In step S3, the spatiotemporal complementarity characteristics between different new energy sources and between new energy sources and loads are calculated according to intraday, intraweekly, and seasonal scales. In step S3, the elements in the source-load matching matrix are obtained by weighting time-series matching index, fluctuation offset index, seasonal matching index and / or load response adaptation index.

[0015] The time-series matching index is used to characterize the degree of similarity between the renewable energy output curve and the load demand curve in terms of time distribution; the fluctuation offset index is used to characterize the ability of renewable energy output to reduce load fluctuations; the seasonal matching index is used to characterize the degree of matching between the seasonal renewable energy output and the seasonal load demand; and the load response adaptation index is used to characterize the ability of load regulation capacity to absorb fluctuations in renewable energy output.

[0016] As one possible implementation, step S4 of this scheme further includes: determining the source-load imbalance power sequence within the planning area based on the source-load matching matrix, identifying the fluctuation scale of the source-load imbalance power sequence, determining the fluctuation suppression demand at the second to minute level, the peak shifting and valley filling demand at the hour level, and the cold and hot energy transfer demand, and establishing a multi-type energy storage adaptation model including high-response power storage, electric energy storage, cold storage devices, and hot storage devices.

[0017] As a preferred implementation option, in step S4 of this scheme, the multi-type energy storage adaptation model calculates the energy storage adaptation coefficient based on the energy storage response time, energy storage continuous discharge duration, energy storage efficiency, energy storage cycle life and / or unit capacity cost, and determines the high-response power type energy storage for second- to minute-level fluctuation suppression, the electrical energy type energy storage for hour-level peak shifting and valley filling, the cold storage device for cold load time shift regulation, and the heat storage device for heat load time shift regulation based on the energy storage adaptation coefficient.

[0018] As a preferred implementation option, in step S5 of this scheme, a multi-objective optimization model for source and storage scale is established using the installed capacity of various new energy sources, the rated power of various energy storage, the rated energy capacity of various energy storage, and the load response scale of various loads as decision variables.

[0019] As a preferred implementation option, preferably, in step S5 of this scheme, the multi-objective optimization model for source and storage scale takes the minimum comprehensive energy supply cost, the maximum renewable energy utilization rate, the maximum power supply reliability, the minimum grid-connected power fluctuation and / or the maximum carbon emission reduction benefit as the optimization objectives, and sets energy output constraints, load energy supply satisfaction constraints, energy storage status constraints, reserve capacity constraints, response time constraints, land constraints, roof constraints and / or installation capacity constraints.

[0020] The multi-objective optimization model for source-storage scale also includes source-load-storage power balance constraints, recursive constraints on energy storage state of charge, upper and lower limits constraints on energy storage state of charge, load response time constraints, load response capacity constraints, upper and lower limits constraints on new energy output, reserve capacity constraints, and / or grid-connected power constraints.

[0021] As a preferred implementation option, in step S6 of this scheme, multi-scenario robust simulations are performed on the multiple candidate source-storage configuration schemes for sunny days, rainy days, low wind speeds, peak loads, low loads, equipment maintenance, and / or sudden fluctuations. The new energy utilization rate, load failure probability, energy storage cycle pressure, and economic return level of each candidate source-storage configuration scheme are evaluated respectively, and the target source-storage configuration scheme is selected based on the evaluation results.

[0022] As a preferred implementation option, in step S6 of this scheme, the multi-scenario robust simulation includes simulating meteorological change scenarios, load change scenarios, equipment availability change scenarios and / or sudden disturbance scenarios, and calculating robust evaluation values ​​based on the comprehensive index expectation values ​​and conditional risk values ​​of each candidate source-storage configuration scheme under different scenarios. Candidate source-storage configuration schemes whose robust evaluation values ​​meet the preset screening conditions are determined as target source-storage configuration schemes.

[0023] As a preferred implementation option, preferably, in step S7 of this scheme, the hierarchical operation control strategy includes a day-ahead planning layer, an intraday adjustment layer, and a real-time correction layer.

[0024] In step S7 of this scheme, the day-ahead planning layer generates a new energy output plan, an energy storage charging and discharging plan, and a load response plan based on the day-ahead new energy forecast results and the day-ahead load forecast results. The intraday adjustment layer corrects the energy storage charging and discharging plan and the load response plan based on the rolling forecast error. The real-time correction layer controls the energy storage output power and triggers interruptible load response based on the real-time source-load power deviation.

[0025] Based on the above, this solution also proposes a source-storage scale optimization configuration system for multiple types of new energy sources and load characteristics, which includes: a new energy profile module, a load characteristic identification module, a complementarity analysis module, an energy storage adaptation module, a scale optimization solution module, a robust simulation module, and a control strategy generation module. The new energy profiling module is used to acquire resource data of photovoltaic, wind power, biomass power generation and small hydropower within the planning area, and to construct power generation models, fluctuation characteristic models, predictability indicators and equivalent dispatchable capacity indicators for various new energy sources, thereby generating a new energy resource profiling library. The load characteristic identification module is used to acquire energy consumption data of basic rigid loads, production batch loads, transferable loads, interruptible loads and combined cooling and heating loads within the planning area, extract characteristic parameters of various types of loads, establish the conversion relationship between electrical load, heat load and cold load in combined cooling and heating loads, and generate a set of load-side characteristic parameters. The complementarity analysis module is used to calculate the spatiotemporal complementarity characteristics between different new energy sources and between new energy sources and loads at intraday, intraweek, and seasonal scales based on the new energy resource profile library and the load characteristic parameter set, and generate a source-load matching matrix. The energy storage adaptation module is used to determine the source-load imbalance power sequence according to the source-load matching matrix, identify the fluctuation scale of the source-load imbalance power sequence, and establish the adaptation relationship between high-response power storage, electric energy storage, cold storage devices and heat storage devices and different regulation requirements. The scale optimization solution module is used to construct and solve a multi-objective optimization model of source and storage scale using various new energy installed capacity, various energy storage rated power, various energy storage rated energy capacity and various load response scale as decision variables, and obtain multiple candidate source and storage configuration schemes. The robust simulation module is used to perform multi-scenario robust simulations on the multiple candidate source-storage configuration schemes, and to select the target source-storage configuration scheme based on the utilization rate of new energy sources, the probability of load failure, the energy storage cycle pressure and the level of economic return. The control strategy generation module is used to generate recommended installed capacity for various new energy sources, recommended configuration scale for various energy storage sources, response capacity construction plans for different load categories, and hierarchical operation control strategies based on the target source and storage configuration scheme.

[0026] As a preferred implementation option, the system described in this solution preferably also includes a data update module and a model correction module. The data update module is used to periodically update new energy resource data, load energy consumption data, energy storage operation data, and equipment status data. The model correction module is used to correct the new energy side resource profile library, load side characteristic parameter set, source-load matching matrix, and multi-type energy storage adaptation model based on the updated data.

[0027] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art: This invention establishes separate resource profiles for photovoltaic, wind, biomass, and small hydropower, incorporating the power generation capacity, fluctuation characteristics, predictability, and equivalent dispatchability of different new energy sources into a unified configuration model, avoiding the simplistic equalization of various types of new energy. This more accurately reflects the differences in daytime photovoltaic output, random fluctuations in wind power, adjustable biomass output, and seasonal water inflow for small hydropower, making the configuration of new energy installed capacity more closely aligned with actual resource endowments.

[0028] Based on the establishment of resource profiles, the present invention establishes load characteristic models for basic rigid loads, production batch loads, transferable loads, interruptible loads, and combined cooling and heating loads. It incorporates peak-to-valley ratio, duration, fluctuation rate, shift window, energy supply reliability requirements, and interruption penalty costs into the source-storage configuration process. This can fully identify the load-side adjustment potential and avoid source-load mismatch problems caused by configuring solely based on the total load curve.

[0029] Furthermore, this invention also generates a source-load matching matrix by constructing a daily, weekly, and seasonal complementarity analysis mechanism between new energy sources and loads. This allows for scale optimization that no longer relies solely on empirical installed capacity ratios but is instead based on the matching relationship between new energy output curves and load demand curves. This improves the local absorption capacity of new energy sources, reduces passive dependence on energy storage capacity, and minimizes grid-connected power fluctuations.

[0030] Furthermore, this invention establishes multi-type energy storage adaptation models, matching high-response power energy storage, electrical energy storage, cold storage devices, and thermal storage devices with different fluctuation scales and load regulation requirements, respectively. This transforms energy storage configuration from single capacity optimization to synergistic optimization of type, power, capacity, and response time. This reduces the risk of over-configuration, improves energy storage utilization efficiency, and delays the lifespan degradation caused by frequent deep cycling.

[0031] This invention also establishes a multi-objective optimization model targeting comprehensive energy supply cost, renewable energy utilization rate, power supply reliability, power fluctuation, and carbon emission reduction benefits. Combined with robust verification across multiple scenarios, the model screens and verifies operational performance under various conditions, including sunny days, rainy days, low wind speeds, peak loads, off-peak loads, equipment maintenance, and sudden fluctuations. This improves the adaptability of the configuration scheme to weather changes, load disturbances, and equipment status changes, ensuring the final configuration scheme balances economy, reliability, and scalability.

[0032] In terms of output, the present invention not only outputs the scale configuration results of new energy and energy storage, but also simultaneously outputs a hierarchical operation control strategy consisting of a day-ahead planning layer, an intraday adjustment layer, and a real-time correction layer. This enables the source and storage scale determined in the planning stage to be connected with the actual operation and scheduling, avoids the configuration scheme from remaining at the static capacity calculation level, and improves the operational stability and actual benefits after the project is implemented. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a simplified implementation flowchart of the source-storage scale optimization configuration method for various types of new energy sources and load characteristics in this scheme; Figure 2 This is a schematic diagram of the unit module connections of the source-storage scale optimization configuration system for various types of new energy sources and load characteristics in this solution. Detailed Implementation

[0035] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are for illustrative purposes only and do not limit the scope of the invention. Similarly, the following embodiments are only some, not all, embodiments of the present invention, and all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] like Figure 1 As shown in the figure, this embodiment presents a method for optimizing the allocation of source and storage scale for multiple types of new energy sources and load characteristics, which includes: S1. Obtain resource data of multiple types of new energy sources within the planning area, establish a power generation model for multiple types of new energy sources based on the resource data, and extract fluctuation characteristics, predictability indicators and / or equivalent dispatchable capacity indicators according to the power generation model to generate a new energy resource profile library. S2. Obtain energy consumption data of multiple types of loads within the planning area and generate a set of load-side characteristic parameters according to preset conditions; S3. Based on the new energy resource profile library and the load characteristic parameter set, calculate the spatiotemporal complementary characteristics between different new energy sources and between new energy sources and loads according to different time scales, identify the matching relationship between new energy output and load demand and the load adjustment potential's ability to offset new energy fluctuations, and generate a source-load matching matrix. S4. Determine the source-load imbalance power sequence within the planning area based on the source-load matching matrix, identify the fluctuation scale of the source-load imbalance power sequence, determine the demand conditions, and establish a multi-type energy storage adaptation model. S5. Based on the multi-type energy storage adaptation model, a multi-objective optimization model for source and storage scale is established with preset decision variables. Based on the preset optimization objectives and constraints, multiple candidate source and storage configuration schemes are obtained. S6. Perform multi-scenario robust simulation on the multiple candidate source-storage configuration schemes, evaluate the new energy utilization rate, load failure probability, energy storage cycle pressure and economic return level of each candidate source-storage configuration scheme, and select the target source-storage configuration scheme based on the evaluation results. S7. Based on the target source and storage configuration scheme, output the recommended installed capacity of various new energy sources, the recommended configuration scale of various energy storage systems, the response capacity construction scheme for different load categories, and / or the hierarchical operation control strategy.

[0037] Regarding the types of new energy sources, as a possible implementation method, further, in step S1 of this scheme, the multiple types of new energy sources include photovoltaic, wind power, biomass power generation / or small hydropower.

[0038] As a possible implementation method, this solution further establishes a power generation model for the following types of new energy sources when establishing the power generation model: A power generation model for photovoltaics was established, incorporating irradiance, temperature, orientation, and shading characteristics. A model for the generating capacity of wind power is established, which includes wind speed distribution, turbulence intensity, and intraday fluctuation characteristics. A power generation model for biomass power generation, including fuel supply stability and adjustable output range, is established. A model for the generating capacity of small hydropower stations is established, which includes the seasonality of water inflow and the upper limit of power output.

[0039] As another possible implementation, step S1 of this scheme further includes: constructing a resource profile vector for any new energy type, wherein the resource profile vector includes average available output, output fluctuation intensity, maximum ramp rate per unit time, prediction error, equivalent dispatchable capacity, annualized cost per unit capacity and / or carbon emission reduction coefficient per unit electricity, and storing the resource profile vectors of each new energy type in the new energy side resource profile library according to a unified time scale.

[0040] The existing source-storage scale allocation methods simplify different new energy sources such as photovoltaic, wind power, biomass power generation, and small hydropower into a unified renewable energy output curve. However, different new energy sources have different output mechanisms, fluctuation scales, predictability, and dispatchability. If their resource endowments are not classified and modeled, subsequent source-load matching, energy storage adaptation, and scale optimization will all lack accurate basic input.

[0041] Therefore, in step S1 of this scheme, a power generation model, a fluctuation characteristic model, a predictability index, and an equivalent dispatchable capability index are established for different new energy sources, forming a new energy resource profile library that can be called upon in subsequent steps.

[0042] As an example of implementation, step S1 of this solution includes the following sub-steps: Within the planning area, the planning period and time discrete scale are first uniformly determined. Let the set of discrete times within the planning period be:

[0043] in, This represents the set of all time points covered by the planning calculations. Indicates the number of time points, with a time interval of 1. Subsequent calculations of renewable energy output, load demand, energy storage status, and control commands are all performed on this unified time scale to ensure direct data flow between preceding and subsequent steps.

[0044] Set up a collection of multiple types of new energy sources for:

[0045] in, Indicates photovoltaic power generation, Indicates wind power, Indicates biomass power generation, This refers to small hydropower.

[0046] 1. Construction of a photovoltaic resource profile The output of photovoltaic (PV) power generation is mainly affected by irradiance, ambient temperature, module temperature, module orientation, tilt angle, and shading conditions. PV power generation output can be derived from "installed capacity × effective output coefficient per unit capacity".

[0047] First, the photovoltaic output per unit capacity is approximately proportional to the effective irradiance. Meanwhile, increased temperature leads to a decrease in module efficiency. Therefore, the following model is established:

[0048] in, Indicates time period The photovoltaic power generation capacity; Indicates photovoltaic installed capacity; Indicates time period Effective irradiance incident on the plane of the photovoltaic module; Indicates the reference irradiance under standard test conditions; Indicates the overall efficiency of the photovoltaic system; This represents the power degradation coefficient of a photovoltaic module due to temperature. Indicates time period The component temperature; Indicates reference temperature; This represents the occlusion correction factor.

[0049] Among them, component temperature It can be further estimated from ambient temperature and radiation intensity:

[0050] in, Indicates ambient temperature; This represents the correction factor for the temperature rise of the module due to irradiation intensity.

[0051] Therefore, the photovoltaic resource profile not only obtains the power generation capacity of photovoltaics at different times. Furthermore, the sensitivity parameters of photovoltaics to sunlight, temperature, and shading conditions were obtained, providing a basis for subsequent judgment on its matching relationship with daytime production load.

[0052] 2. Wind Power Resource Profile Construction For wind power, its output is mainly determined by wind speed distribution, turbulence intensity, and the wind turbine power curve. Wind power output It is generally derived from the relationship of aerodynamic kinetic energy conversion:

[0053] in, Indicates air density, Indicates the area swept by the wind turbine. Indicates wind energy utilization coefficient, This represents wind speed. Considering that the wind turbine has a cut-in wind speed, rated wind speed, and cut-out wind speed, in practical applications, the above physical relationship can be transformed into a piecewise power curve model:

[0054] in, Indicates time period The wind power generation capacity; Indicates wind power installed capacity; Indicates time period wind speed; Indicates time period turbulence intensity; This represents the unit capacity wind power output function obtained from the wind turbine power curve and the turbulence correction relationship.

[0055] Turbulence intensity can be expressed as:

[0056] in, Indicates time period Standard deviation of wind speed within the relevant statistical window; This indicates the average wind speed within the statistical window.

[0057] Therefore, the wind power resource profile includes not only the power generation capacity of wind power. It also includes wind speed volatility, intraday volatility characteristics, and forecast uncertainty, which are used to subsequently identify its adaptability to nighttime base load or continuous load.

[0058] 3. Construction of a biomass power generation resource profile The key difference between biomass power generation and photovoltaic and wind power lies in its certain dispatchability, but it is limited by fuel supply, fuel calorific value, inventory level, unit operating limits and ramp-up capability.

[0059] Set time period The available fuel mass is The lower heating value of fuel is The unit's power generation efficiency is Then, the theoretical power generation of biomass power generation can be obtained from the law of conservation of fuel energy. :

[0060] Convert energy into time intervals (time interval is...) Average power ,get:

[0061] Taking into account both unit capacity limitations and stable operation limitations, the adjustable output range of biomass power generation is obtained as follows:

[0062] in, Indicates the time period of biomass units The minimum stable output; Indicates the time period of biomass units The maximum available output, and:

[0063] in, This indicates the installed capacity of the biomass machine.

[0064] To reflect its adjustment capability, a climbing constraint is also established:

[0065] in, This indicates the maximum ramp rate of the biomass unit.

[0066] Therefore, the biomass resource profile includes fuel supply stability, maximum available output, minimum stable output, ramp-up capability, and dispatchability, providing a basis for its subsequent use as a balanced power source in optimization.

[0067] 4. Construction of Small Hydropower Resource Profile For small hydropower, its power generation capacity is mainly determined by the inflow rate, effective head, and turbine efficiency. Based on the fundamental relationships in hydropower generation, the following model is established:

[0068] in, Indicates time period The power generation capacity of small hydropower stations; Indicates the efficiency of a hydroelectric power generation system; Indicates the density of water; Represents gravitational acceleration; Indicates time period Available water flow rate; Indicates time period Effective head.

[0069] Considering ecological outflow and unit capacity limitations, the actual available flow is:

[0070] in, Indicates the natural inflow rate; Indicates the ecological outflow; This indicates the flow rate that can be used for power generation.

[0071] Therefore, the actual power generation capacity of small hydropower is for:

[0072] in, This indicates the installed capacity of small hydropower plants.

[0073] Therefore, the small hydropower resource profile reflects its seasonal water inflow constraints, power output ceiling, and differences between dry and wet seasons.

[0074] 5. Create a vector profile of new energy resources. After obtaining the time-series output of various new energy sources, for any new energy type Construct resource profile vectors:

[0075] in, Indicates new energy type Average available output during the planning period; This indicates the intensity of its output fluctuation; Indicates the maximum gradient rate per unit time; Indicates prediction error; Indicates equivalent schedulable capacity; This represents the annualized cost per unit capacity. This represents the carbon emission reduction factor per unit of electricity.

[0076] in:

[0077]

[0078]

[0079]

[0080] in, Indicates new energy type During the period The power generation capacity; Indicates actual historical contributions; Indicates predicted output; Indicates new energy type The installed capacity; To prevent extremely small positive numbers with a denominator of zero.

[0081] After the above processing, the data output in step S1 of this solution includes: , , , , and The data obtained in this step will be used as the new energy side input for source-load complementarity analysis in step S3 and scale optimization in step S5.

[0082] As a possible implementation, step S2 of this scheme further includes: acquiring energy consumption data of multiple types of loads within the planning area, wherein the multiple types of loads include basic rigid loads, production batch loads, transferable loads, interruptible loads / or combined cooling and heating loads, extracting the peak-to-valley ratio, duration, fluctuation rate, allowable shift window, energy supply reliability requirements / or interruption penalty cost of each type of load, and establishing the conversion relationship between electrical load, heat load and cold load for combined cooling and heating loads, and generating a set of load-side characteristic parameters; As a preferred implementation option, step S2 of this solution preferably includes one of the following: (1) Establish an energy conservation constraint that the total energy consumption before and after the relocation is equal for transferable loads; (2) Establish an interruption power upper limit constraint and an interruption penalty cost model for interruptible loads; Establish constraints on batch start time, batch duration, and batch end time for production batch-type workloads; (3) Establish a conversion model for cooling load and heating load to equivalent electrical load for combined cooling and heating load.

[0083] Existing energy source and storage configuration methods simply treat user-side load as a total load curve, which is problematic because different loads have different rigidities, displacement capabilities, interruption capabilities, durations, and thermal-electrical coupling relationships. Without classifying and modeling the loads, it is impossible to accurately identify the user-side regulation potential or determine the matching relationship between different renewable energy outputs and different load demands.

[0084] Therefore, step S2 of this scheme establishes a set of load-side characteristic parameters, providing load-side input for source-load matching analysis in step S3, energy storage adaptation in step S4, and optimization modeling in step S5.

[0085] As an example of implementation, step S2 of this solution includes the following sub-steps: Let the set of multiple load types be:

[0086] in, Indicates rigid load on the foundation; Indicates production batch load; Indicates transferable load; Indicates interruptible load; This indicates a combined cooling and heating load.

[0087] The planned area during the time period The total equivalent load can be expressed as:

[0088] in, Indicates time period The total equivalent load; Indicates rigid load on the foundation; Indicates production batch load; Indicates transferable load; Indicates interruptible load; This represents the equivalent electrical load after conversion of combined cooling and heating load.

[0089] 1. Modeling of rigid loads on foundations Rigid loads refer to loads that require high reliability of power supply, cannot be interrupted, and cannot be moved, such as safety lighting, basic process equipment, and continuously operating equipment. Their constraints are:

[0090] in, This represents the actual power supplied to the rigid load of the foundation. This constraint means that the rigid load of the foundation must be fully satisfied and will not participate in displacement or interruption.

[0091] 2. Production batch load modeling Production batch loads have clearly defined start time, duration, and process end time constraints. Let's assume a specific production batch... The operating power is The duration is Allow the launch window to be Then it is in the time period The load can be expressed as:

[0092] in, For batch During the period A status variable indicating whether the batch is running. During the period During runtime, ;otherwise .

[0093] The batch run duration constraint is:

[0094] in, Indicates batch The actual start time, and satisfying:

[0095] This model is used to describe how production batch loads can be adjusted within a certain time window without disrupting the continuity of the process.

[0096] 3. Transferable load modeling Transferable load refers to a load whose energy consumption period can be changed within an allowable window, provided that the total energy consumption remains constant. Let's assume a transferable load... The allowed shift window is The load before the shift was The load after displacement is Then the law of conservation of energy should be satisfied:

[0097] in, Indicates the period before the shift The load power; Indicates the time period after the shift The load power; Indicates a time interval.

[0098] Meanwhile, the relocated load should meet the upper and lower power limits:

[0099] in, and These represent the transferable loads in different time periods. The lower and upper limits.

[0100] 4. Interruptible load modeling Interruptible loads are loads whose power supply can be reduced or suspended during specific periods, but which incur interruption penalty costs. Let the time period be... The interrupt power is Then we have:

[0101] in, Indicates time period Maximum power that can be interrupted.

[0102] The cost of interruption can be expressed as:

[0103] in, This indicates the cost of interruption penalties; Indicates time period The penalty cost per unit of power interruption.

[0104] 5. Modeling of Coupled Cooling and Heating Loads For combined cooling and heating loads, the cooling and heating loads need to be converted into equivalent electrical loads to reflect their impact on energy source and storage configuration. (Time period is specified.) The electrical load, cooling load, and heating load are respectively , and The equivalent electrical load is:

[0105] in, This represents the equivalent electrical load after conversion of combined cooling and heating load; Indicates the coefficient of performance (COP) of refrigeration equipment; This indicates the electrothermal conversion efficiency.

[0106] The above formula is derived based on the principle of energy equivalence, where cooling load... It needs to consume electrical energy through refrigeration equipment to generate its equivalent electrical power. Heat load When supplied through an electrothermal conversion device, its equivalent electrical power is .

[0107] 6. Form a load characteristic parameter set d For any load type ,in, For a collection of multiple types of loads, For each load type, construct a load characteristic vector:

[0108] in, Indicates load type Maximum power; Indicates minimum power; Indicates the peak-to-valley ratio; Indicates duration; Indicates the rate of fluctuation; Indicates that the shift window is allowed; Indicates the power supply reliability requirements; This indicates the cost of interruption penalties.

[0109] The peak-to-valley ratio is:

[0110] The fluctuation rate is:

[0111] in, In time Load demand at that time This represents the entire set of time points covered by the planning calculations; The time difference between load demands at adjacent time points; It is a very small constant to avoid the denominator being 0.

[0112] The output time is achieved through step S2. Load demand at time Planning area during time period Total equivalent load Load characteristic vector The load shifting constraints (i.e., load transfer constraints), interruption constraints (i.e., load interruption constraints), and cooling, heating, and electricity conversion relationships will serve as inputs for calculating the source-load matching matrix in step S3 and establishing load power supply constraints in step S5.

[0113] Step S3 of this plan mainly addresses the lack of quantitative matching analysis between renewable energy output and load demand. Existing energy source and storage configuration methods are mostly based on installed capacity experience or total balance. However, even if the annual power generation and annual power consumption are close, there may still be intraday mismatch, intraweekly mismatch, and seasonal mismatch, which will lead to increased curtailment, passive expansion of energy storage capacity, or increased dependence on the external power grid.

[0114] Therefore, the purpose of step S3 in this scheme is to calculate the complementary relationship between different new energy sources and different loads based on the time-series output of new energy sources output in step S1 and the time-series demand of loads output in step S2, and to form a source-load matching matrix, so as to provide a structured basis for energy storage adaptation and scale optimization.

[0115] In step S3 of this scheme, the spatiotemporal complementarity characteristics between different new energy sources and between new energy sources and loads are calculated according to intraday, intraweekly and seasonal scales.

[0116] In step S3 of this scheme, the elements in the source-load matching matrix are obtained by weighting time-series matching index, fluctuation offset index, seasonal matching index and / or load response adaptation index.

[0117] The time-series matching index is used to characterize the degree of similarity between the renewable energy output curve and the load demand curve in terms of time distribution; the fluctuation offset index is used to characterize the ability of renewable energy output to reduce load fluctuations; the seasonal matching index is used to characterize the degree of matching between the seasonal renewable energy output and the seasonal load demand; and the load response adaptation index is used to characterize the ability of load regulation capacity to absorb fluctuations in renewable energy output.

[0118] As an example, step S3 of this solution includes the following sub-steps: First, the new energy output obtained in step S1 and the load demand obtained in step S2 Normalization is performed to eliminate the influence between different renewable energy capacity levels and different load power levels.

[0119] The output of new energy sources is normalized as follows:

[0120] Load demand normalization is as follows:

[0121] in, This represents the normalized output of new energy sources; This represents the normalized load demand. and These represent the types of new energy sources. Maximum and minimum output during the planning period; and Representing load type Maximum and minimum demand during the planning period. It is a very small constant to avoid the denominator being 0.

[0122] 1. Time-series matching metrics The closer the time distribution of renewable energy output and load demand, the smaller the difference between them. Based on this, a time-series matching index is constructed:

[0123] in, Indicates new energy type With load type The degree of time-series matching. The closer this indicator is to 1, the closer the new energy output curve is to the load demand curve in terms of time distribution; the closer this indicator is to 0, the higher the degree of mismatch between the two. It is a very small constant to avoid the denominator being 0.

[0124] Under this formula logic, if the new energy output curve is completely consistent with the load curve, the numerator is 0 and the matching index is 1; if the difference between the two is large, the numerator increases and the matching index decreases.

[0125] 2. Volatility Offset Indicator When changes in renewable energy output can offset load fluctuations, net load fluctuations can be reduced, thus decreasing the need for energy storage regulation. Therefore, a fluctuation offsetting index is defined as follows:

[0126] in, Indicates new energy type For load type The ability to offset fluctuations; This represents the standard deviation of the net load after the source loads are superimposed; This represents the standard deviation of the original load.

[0127] Under this formula, if the net load standard deviation is less than the original load standard deviation after the introduction of new energy sources, it indicates that the output of new energy sources has a reducing effect on load fluctuations; the greater the reduction in the net load standard deviation, the greater the fluctuation offset index.

[0128] 3. Seasonal Matching Indicators For photovoltaic, small hydropower, and some biomass resources, seasonality is significant. To avoid judging source-load matching solely based on intraday curves, further calculation of seasonal matching indices is needed. Assume the planning period is divided into several seasons or monthly sets. ,but:

[0129] in, Indicates the seasonal-scale matching index; Indicates new energy type In the season The average normalized output; Indicates load type In the season Average normalized demand, It is a very small constant to avoid the denominator being 0.

[0130] This indicator is used to reflect, for example, whether the high output of photovoltaic power in summer matches the high demand for cooling load, and whether the high output of small hydropower in the high-water season matches the regional base load demand.

[0131] 4. Load response adaptation indicators The load's own transferability or interruptibility can absorb fluctuations in renewable energy sources. Let the load type be... The responsiveness is New energy types The forecast error or fluctuation requirement is The load response adaptation index is:

[0132] in, Indicates load type For new energy types Fluctuation response adaptability; This indicates the deviation between the actual or simulated output of new energy sources and the predicted output. Indicates load type During the period Available response power This represents the set of all time points covered by the planning calculations. It is a very small constant to avoid the denominator being 0.

[0133] Under this formula, if the load response capability can cover the fluctuations in new energy sources, the unabsorbed fluctuations will be smaller and the adaptation index will be higher; if the load response capability is insufficient, the adaptation index will be lower.

[0134] 5. Generate the source-load matching matrix Based on the above indicators, the types of new energy sources are obtained. With load type Overall matching value:

[0135] in, This represents an element in the source-load matching matrix; Let represent the weights for time-series matching, fluctuation offsetting, seasonal matching, and load response adaptation, respectively, and satisfy the following: .

[0136] The final source-load matching matrix is ​​formed as follows:

[0137] in, Indicates the number of new energy types. Indicates the number of load types.

[0138] After step S3, the output source-load matching matrix It will be directly passed to step S4 to determine which type of energy storage or load response will absorb the fluctuations of different new energy sources; at the same time, it will be passed to step S5 to constrain or weight the optimal configuration of different new energy installed capacity and load response scale.

[0139] Step S4 of this solution primarily addresses the problem of treating energy storage as a single capacity variable in existing energy source-storage configuration methods. New energy fluctuations have different time scales; for example, photovoltaic cloud shading fluctuations may be on the order of minutes, wind power fluctuations may span from minutes to hours, load peak-valley differences may be on the order of hours, and heating and cooling loads exhibit thermal inertia and time-shifting characteristics. If a single energy storage model is adopted, problems such as insufficient response speed, over-allocation of capacity, or underutilization of the value of heating and cooling energy storage may easily occur.

[0140] Therefore, the purpose of step S4 in this scheme is to identify the time scale of regulation demand based on the source-load matching matrix and source-load imbalance power sequence obtained in step S3, and to adapt it to different energy storage types.

[0141] As one possible implementation, step S4 of this scheme further includes: determining the source-load imbalance power sequence within the planning area based on the source-load matching matrix, identifying the fluctuation scale of the source-load imbalance power sequence, determining the fluctuation suppression demand at the second to minute level, the peak shifting and valley filling demand at the hour level, and the cold and hot energy transfer demand, and establishing a multi-type energy storage adaptation model including high-response power storage, electric energy storage, cold storage devices, and hot storage devices.

[0142] As a preferred implementation option, in step S4 of this scheme, the multi-type energy storage adaptation model calculates the energy storage adaptation coefficient based on the energy storage response time, energy storage continuous discharge duration, energy storage efficiency, energy storage cycle life and / or unit capacity cost, and determines the high-response power type energy storage for second- to minute-level fluctuation suppression, the electrical energy type energy storage for hour-level peak shifting and valley filling, the cold storage device for cold load time shift regulation, and the heat storage device for heat load time shift regulation based on the energy storage adaptation coefficient.

[0143] As an example, step S4 of this solution includes the following sub-steps: First, based on the new energy output obtained in step S1 The load demand obtained in step S2 and the source-load matching matrix obtained in step S3 Calculate the source-load imbalance power sequence after considering source-load matching.

[0144] Assume the installed capacity of various new energy sources is The unit capacity output curve is ,but:

[0145] in, Indicates new energy type During the period Output per unit capacity.

[0146] The net load after considering load response is:

[0147] in, Indicates time period Net load; Indicates load type During the period Provided response power It is a collection of multiple types of loads.

[0148] Therefore, the unbalanced power of the source and load is:

[0149] in, It is a collection of multiple types of new energy sources. It is a new energy type. Indicates time period The source load is unbalanced power. When When this occurs, it indicates that the output of new energy sources is insufficient, requiring supplementation through energy storage discharge, grid power purchase, or other power sources; when When this occurs, it indicates that there is an oversupply of new energy power, requiring energy storage for charging, load transfer, or external transmission for consumption.

[0150] 1. Wave Scale Decomposition To differentiate between different energy storage adaptation needs, Perform scale decomposition. Hourly low-frequency components can be extracted using a moving average method.

[0151] in, Indicates the low-frequency unbalanced power component; This indicates the length of the sliding window, corresponding to an hourly adjustment scale. The first after scale decomposition Individual source load unbalanced power.

[0152] The high-frequency components are:

[0153] in, This indicates a rapid fluctuation component ranging from seconds to minutes.

[0154] For combined cooling and heating (CCHP) scenarios, the time-shifted demand for cooling and heating energy is also extracted based on the cooling and heating load conversion relationship in step S2:

[0155] in, This represents the equivalent electrical adjustment demand generated by converting heating and cooling loads.

[0156] 2. Power and capacity calculations for different regulation requirements For the need to suppress high-frequency fluctuations, the main focus is on the energy storage power response capability, and the required power can be calculated as follows:

[0157] in, This indicates the energy storage power required to suppress fluctuations at the second to minute level.

[0158] For hourly peak shaving and valley filling needs, both power and energy capacity are considered. The cumulative energy deviation of the low-frequency component is:

[0159] The required energy capacity can then be expressed as:

[0160] in, This indicates the energy storage capacity required for peak shaving and valley filling on an hourly basis.

[0161] For the demand for cold and hot energy storage, the cold storage capacity and the hot storage capacity can be expressed as follows:

[0162]

[0163] in, This indicates the required cold storage capacity. Indicates the required heat storage capacity; Indicates time period Real-time supply of cooling capacity; Indicates time period Real-time supply of heat; For time period Cooling load; For time period The heat load.

[0164] 3. Energy Storage Adaptability Coefficient Model Let the set of energy storage types be:

[0165] in, This indicates high-response power energy storage; Indicates electrical energy storage; Indicates a cold storage device; This indicates a heat storage device.

[0166] For energy storage types and regulating demand types Establish an energy storage adaptation factor:

[0167] in, Indicates energy storage type To regulate demand The adaptation coefficient; Indicates energy storage type Response time; Indicates demand adjustment Required response time; Indicates energy storage type The duration of continuous discharge; Indicates demand adjustment The duration; Indicates energy storage efficiency; Indicates cycle life; Indicates the maximum cycle life among various types of energy storage; Indicates cost per unit capacity; This represents the maximum unit capacity cost among various types of energy storage; - This represents the weighting coefficient.

[0168] Under this formula, the closer the response time, duration, efficiency, lifespan, and cost of this solution are to the demand, the better the fit between the energy storage type and the demand for regulation.

[0169] After step S4, the output data includes: , , , Energy storage compatibility factor This will serve as the input for the boundary of the energy storage power and capacity decision variables and the correction of the objective function weights in step S5.

[0170] Step S5 of this plan primarily addresses the problem that existing energy source and storage configuration methods rely solely on economic efficiency or empirical capacity, lacking consideration of factors such as renewable energy utilization, power supply reliability, grid connection fluctuations, carbon emission reduction benefits, and load response coordination. Optimizing for a single objective can easily lead to configuration schemes with lower investment but insufficient renewable energy utilization or reliability; conversely, it may result in high reliability but over-allocation of energy storage.

[0171] Therefore, the purpose of step S5 in this scheme is to establish a multi-objective, multi-constraint source-storage scale optimization model based on the new energy profile, load characteristic parameters, source-load matching matrix and energy storage adaptation results formed in the aforementioned steps.

[0172] As a preferred implementation option, in step S5 of this scheme, a multi-objective optimization model for source and storage scale is established using the installed capacity of various new energy sources, the rated power of various energy storage, the rated energy capacity of various energy storage, and the load response scale of various loads as decision variables.

[0173] As a preferred implementation option, preferably, in step S5 of this scheme, the multi-objective optimization model for source and storage scale takes the minimum comprehensive energy supply cost, the maximum renewable energy utilization rate, the maximum power supply reliability, the minimum grid-connected power fluctuation and / or the maximum carbon emission reduction benefit as the optimization objectives, and sets energy output constraints, load energy supply satisfaction constraints, energy storage status constraints, reserve capacity constraints, response time constraints, land constraints, roof constraints and / or installation capacity constraints.

[0174] The multi-objective optimization model for source-storage scale also includes source-load-storage power balance constraints, recursive constraints on energy storage state of charge, upper and lower limits constraints on energy storage state of charge, load response time constraints, load response capacity constraints, upper and lower limits constraints on new energy output, reserve capacity constraints, and / or grid-connected power constraints.

[0175] As an example, step S5 of this solution includes the following sub-steps: Let the optimization decision variables be:

[0176] in, Indicates new energy type The installed capacity; Indicates energy storage type Rated power; Indicates energy storage type Rated energy capacity; Indicates load type The scale of response capacity construction.

[0177] 1. Overall Energy Supply Cost Target The overall cost of energy supply consists of the investment cost of new energy sources, the investment cost of energy storage, the operation and maintenance cost, the cost of purchasing electricity, and the load response cost.

[0178] in, This represents the annualized comprehensive energy supply cost; Indicates new energy type Annualized cost per unit capacity; Indicates energy storage type Annualized cost per unit power; Indicates energy storage type Annualized cost per unit energy capacity; Indicates operating and maintenance costs; Indicates the cost of purchasing electricity; This represents the load response cost.

[0179] 2. Renewable energy utilization rate target Renewable energy utilization rate represents the proportion of electricity actually generated from renewable energy sources relative to the total amount of electricity that can be generated.

[0180] in, Indicates the utilization rate of renewable energy; Indicates new energy type During the period The power actually utilized; Indicates new energy type During the period The power generation capacity, This represents the entire set of time points covered by the planning calculations; It is a collection of multiple types of new energy sources. It is a very small constant to avoid the denominator being 0.

[0181] 3. Power supply reliability objectives Power supply reliability can be represented by the expected amount of power not supplied:

[0182] in, Indicates the expected amount of electricity not supplied; Indicates time period The load is short of power supply.

[0183] When the output of power generation and storage, external power purchase, and load response are still unable to meet load demand, This indicator corresponds to the load reliability requirement in step S2. Directly related.

[0184] 4. Target for grid-connected power fluctuation To reduce the impact on the power grid, a grid-connected power fluctuation index is defined:

[0185] in, Indicates the intensity of grid-connected power fluctuation; Indicates time period Grid-connected switching power; It represents the standard deviation.

[0186] When the source-load matching degree is high and the energy storage configuration is reasonable, The fluctuations should be reduced.

[0187] 5. Carbon emission reduction benefit targets The benefits of carbon emission reduction can be expressed as:

[0188] in, Indicates the benefits of carbon emission reduction; This indicates the price or conversion factor for the unit carbon emission reduction benefit. Indicates new energy type The carbon emission reduction coefficient per unit of electricity; Indicates new energy type During the period The power actually utilized.

[0189] 6. Multi-objective integrated optimization function The above objectives are unified into a minimization model:

[0190] in, - These represent the weighting coefficients for overall energy supply cost, renewable energy utilization rate, power supply reliability, grid-connected power fluctuation, and carbon emission reduction benefits, respectively.

[0191] Under the logic of the above objective function, the lower the cost, underutilization rate, unused electricity, and grid connection fluctuations, the better; therefore, we take the positive minimization. The higher the carbon emission reduction benefits, the better; therefore, we take the negative term in the minimization objective.

[0192] 7. Main Constraints The source-load-storage power balance constraint is:

[0193] in, Indicates the energy storage discharge power; Indicates the energy storage charging power; This indicates the power purchased from the power grid; This indicates the power supplied to the power grid; Indicates time period The load is short of power supply; Indicates time period Net load; Indicates new energy type During the period The power actually utilized.

[0194] The recursive constraint for energy storage state is:

[0195] in, Indicates energy storage type During the period The state of charge; Indicates charging efficiency; Indicates discharge efficiency; Indicates energy storage type Rated energy capacity.

[0196] Energy storage power constraints are:

[0197]

[0198] The energy storage capacity state constraint is:

[0199] The constraints on new energy installation capacity are:

[0200] in, It is determined by the land, roof, resource endowment and installation capacity limitations in step S1.

[0201] The load response constraints are:

[0202] in, The shift window, interruption limit, and production process constraints for each type of load in step S2 are determined.

[0203] This step outputs multiple candidate source storage configuration schemes. Each scheme includes the scale of new energy installed capacity, energy storage power capacity, energy storage capacity and load response scale, and is passed to step S6 for robust verification.

[0204] Step S6 of this solution primarily addresses the issue of insufficient adaptability of configuration schemes obtained under a single typical day or a single forecast scenario in actual operation. New energy output is significantly affected by weather, load is significantly affected by production plans and ambient temperature, and equipment maintenance and sudden disturbances can also alter the system's operating boundaries. Without multi-scenario verification, the optimization results may only perform well under ideal scenarios, while in actual operating conditions, power outages, power curtailment, or excessive energy storage cycles may occur.

[0205] Therefore, the purpose of step S6 in this scheme is to conduct a multi-scenario robustness evaluation of the candidate configuration schemes obtained in step S5, and select the target scheme with better overall economy, reliability and adaptability.

[0206] As a preferred implementation option, in step S6 of this scheme, multi-scenario robust simulations are performed on the multiple candidate source-storage configuration schemes for sunny days, rainy days, low wind speeds, peak loads, low loads, equipment maintenance, and / or sudden fluctuations. The new energy utilization rate, load failure probability, energy storage cycle pressure, and economic return level of each candidate source-storage configuration scheme are evaluated respectively, and the target source-storage configuration scheme is selected based on the evaluation results.

[0207] As a preferred implementation option, in step S6 of this scheme, the multi-scenario robust simulation includes simulating meteorological change scenarios, load change scenarios, equipment availability change scenarios and / or sudden disturbance scenarios, and calculating robust evaluation values ​​based on the comprehensive index expectation values ​​and conditional risk values ​​of each candidate source-storage configuration scheme under different scenarios. Candidate source-storage configuration schemes whose robust evaluation values ​​meet the preset screening conditions are determined as target source-storage configuration schemes.

[0208] As an example, step S6 of this solution includes the following sub-steps: Building a collection of scenes:

[0209] in, Represents a collection of multiple scenes; defines Indicates the first The scenarios can include sunny days, cloudy / rainy days, low wind speeds, high wind speed fluctuations, peak loads, off-peak loads, equipment maintenance, reduced fuel supply, insufficient water supply, and sudden load shocks.

[0210] For each scenario The output of new energy sources, load demand, and equipment availability are respectively represented as follows: .

[0211] in, Representing a scene New energy types During the period contribution; Representing a scene Download type The demand; Indicates the available status parameters of the device.

[0212] 1. Comprehensive evaluation indicators in the context of the scenario For candidate solutions Calculate its value in the scene The overall evaluation value below :

[0213] in, - They represent the scenes respectively. The weighting coefficients for the recalculated overall energy supply cost, renewable energy utilization rate, power supply reliability, grid-connected power fluctuation, and carbon emission reduction benefits are as follows: 2. Energy storage cycle pressure assessment Energy storage cycle pressure is used to measure whether energy storage is overcharged and over-discharged under various operating scenarios. This applies to different energy storage types. Its cyclic pressure can be expressed as:

[0214] in, Indicate candidate solutions In the scene Lower energy storage type The equivalent number of loops; Indicates the rated energy capacity of the energy storage; Indicates in the scene The energy storage discharge power is as follows; Indicates in the scene The energy storage charging power is below.

[0215] In this scheme, the higher the equivalent cycle count index, the more frequent the energy storage charging and discharging, and the higher the lifespan degradation pressure.

[0216] 3. Robust evaluation function To simultaneously consider average performance and extreme scenario risks, a robustness evaluation function is established:

[0217] in, Indicate candidate solutions Robustness rating; This represents the expected value of the comprehensive evaluation across multiple scenarios. Indicates the confidence level as Conditional risk value; This represents the risk preference coefficient.

[0218] Under the logic of this formula, considering only the expected value may overlook extreme adverse scenarios; therefore, a tail risk term is added to the expected value evaluation. If a certain scheme performs poorly under extreme weather or peak load conditions, then its... The larger the value, the worse the robustness rating.

[0219] 4. Solution Selection Rules The target source-storage configuration scheme can be represented as:

[0220] At the same time, the solution needs to meet the following constraints:

[0221]

[0222]

[0223] in, Indicates the minimum renewable energy utilization rate requirement; Indicates the maximum amount of power not supplied; This indicates the maximum allowable cycle pressure for energy storage.

[0224] This step produces the target source and storage configuration scheme. This includes the final installed capacity of new energy sources, the scale of energy storage configuration, and the load response scale, and is then passed to step S7 to generate an executable operation control strategy.

[0225] Step S7 of this plan mainly addresses the disconnect between the optimized source-storage scale results and actual operation control. If only the installed capacity and energy storage capacity are output without the formation of day-ahead, intraday, and real-time control strategies, the configuration plan may not achieve the renewable energy absorption rate, reliability, and economic indicators of the optimized design in actual operation.

[0226] Therefore, the purpose of step S7 in this scheme is to transform the target configuration scheme obtained in step S6 into an executable scale configuration result and a hierarchical operation control strategy.

[0227] As a preferred implementation option, preferably, in step S7 of this scheme, the hierarchical operation control strategy includes a day-ahead planning layer, an intraday adjustment layer, and a real-time correction layer.

[0228] In step S7 of this scheme, the day-ahead planning layer generates a new energy output plan, an energy storage charging and discharging plan, and a load response plan based on the day-ahead new energy forecast results and the day-ahead load forecast results. The intraday adjustment layer corrects the energy storage charging and discharging plan and the load response plan based on the rolling forecast error. The real-time correction layer controls the energy storage output power and triggers interruptible load response based on the real-time source-load power deviation.

[0229] According to the target source and storage configuration plan Output recommended installed capacity for various new energy sources:

[0230] in, Indicates new energy type Recommended installation capacity.

[0231] Output recommended configuration scales for various energy storage types:

[0232] in, Indicates energy storage type Recommended rated power; Indicates energy storage type Recommended rated energy capacity.

[0233] Output the construction scale of various load response capabilities:

[0234] in, Indicates load type The scale of the recommendation response capability.

[0235] 1. Current planning level The day-ahead planning layer formulates the next day's operation plan based on day-ahead renewable energy forecasts and day-ahead load forecasts. Let the day-ahead forecasted renewable energy output be... The current forecast load is The current day's net load is:

[0236] in, This indicates the predicted source-load imbalance power at the current planning level.

[0237] The planning level recently based on Generate energy storage charging and discharging plan and load response plan Make the following expression as close to zero as possible:

[0238] in, A positive value indicates energy storage discharge, while a negative value indicates energy storage charging.

[0239] 2. Intraday Adjustment Layer The intraday adjustment layer revises the daily plan based on rolling forecast results. Let the updated intraday new energy forecast be... The updated load forecast for the day is The prediction bias is:

[0240] in, This indicates the deviation of the net load forecast from the previous day's plan.

[0241] The intraday adjustment layer adjusts the energy storage plan and load response plan based on this deviation:

[0242]

[0243] in, This indicates the revised energy storage capacity plan for the day; Indicates the energy storage power correction amount; This indicates the revised load response plan for the day; This indicates the load response correction amount.

[0244] This layer is mainly used to handle weather forecast errors, production plan changes, and deviations in heating and cooling loads.

[0245] 3. Real-time correction layer The real-time correction layer performs rapid closed-loop control based on actual operating data. Let the real-time renewable energy output be... Real-time load is Then the real-time source-load deviation is:

[0246] in, This indicates the real-time power deviation.

[0247] Real-time energy storage power command can be expressed as:

[0248] in, Indicates real-time power command for energy storage; Indicates energy storage type Allocation coefficients for real-time deviations; This represents the limiting function, used to ensure that the energy storage power does not exceed its rated power range.

[0249] When the real-time deviation exceeds the adjustable range of the energy storage, an interruptible load response is triggered:

[0250] in, This indicates the power of interruptible load response triggered in real time. Indicates time period The maximum interruptible load.

[0251] Through the aforementioned day-ahead planning layer, intraday adjustment layer, and real-time correction layer, step S7 transforms the static configuration results obtained in step S6 into a dynamic operation strategy, enabling various new energy sources, energy storage, and load response resources to work synergistically in actual operation.

[0252] Combination Figure 2 As shown above, this scheme also proposes a source-storage scale optimization configuration system for multiple types of new energy and load characteristics, which includes: a new energy profiling module, a load characteristic identification module, a complementarity analysis module, an energy storage adaptation module, a scale optimization solution module, a robust simulation module, and a control strategy generation module. The new energy profiling module is used to acquire resource data of photovoltaic, wind power, biomass power generation and small hydropower within the planning area, and to construct power generation models, fluctuation characteristic models, predictability indicators and equivalent dispatchable capacity indicators for various new energy sources, thereby generating a new energy resource profiling library. The load characteristic identification module is used to acquire energy consumption data of basic rigid loads, production batch loads, transferable loads, interruptible loads and combined cooling and heating loads within the planning area, extract characteristic parameters of various types of loads, establish the conversion relationship between electrical load, heat load and cold load in combined cooling and heating loads, and generate a set of load-side characteristic parameters. The complementarity analysis module is used to calculate the spatiotemporal complementarity characteristics between different new energy sources and between new energy sources and loads at intraday, intraweek, and seasonal scales based on the new energy resource profile library and the load characteristic parameter set, and generate a source-load matching matrix. The energy storage adaptation module is used to determine the source-load imbalance power sequence according to the source-load matching matrix, identify the fluctuation scale of the source-load imbalance power sequence, and establish the adaptation relationship between high-response power storage, electric energy storage, cold storage devices and heat storage devices and different regulation requirements. The scale optimization solution module is used to construct and solve a multi-objective optimization model of source and storage scale using various new energy installed capacity, various energy storage rated power, various energy storage rated energy capacity and various load response scale as decision variables, and obtain multiple candidate source and storage configuration schemes. The robust simulation module is used to perform multi-scenario robust simulations on the multiple candidate source-storage configuration schemes, and to select the target source-storage configuration scheme based on the utilization rate of new energy sources, the probability of load failure, the energy storage cycle pressure and the level of economic return. The control strategy generation module is used to generate recommended installed capacity for various new energy sources, recommended configuration scale for various energy storage sources, response capacity construction plans for different load categories, and hierarchical operation control strategies based on the target source and storage configuration scheme.

[0253] As a preferred implementation option, the system described in this solution preferably also includes a data update module and a model correction module. The data update module is used to periodically update new energy resource data, load energy consumption data, energy storage operation data, and equipment status data. The model correction module is used to correct the new energy side resource profile library, load side characteristic parameter set, source-load matching matrix, and multi-type energy storage adaptation model based on the updated data.

[0254] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0255] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0256] The above description is only a part of the embodiments of the present invention and does not limit the scope of protection of the present invention. Any equivalent device or equivalent process transformation made based on the content of the present invention specification and drawings, or direct or indirect application in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for optimizing the allocation of energy source and storage scale based on the characteristics of multiple types of new energy sources and loads, characterized in that, It includes: S1. Obtain resource data of multiple types of new energy sources within the planning area, establish a power generation model for multiple types of new energy sources based on the resource data, and extract fluctuation characteristics, predictability indicators and / or equivalent dispatchable capacity indicators according to the power generation model to generate a new energy resource profile library. S2. Obtain energy consumption data of multiple types of loads within the planning area and generate a set of load-side characteristic parameters according to preset conditions; S3. Based on the new energy resource profile library and the load characteristic parameter set, calculate the spatiotemporal complementary characteristics between different new energy sources and between new energy sources and loads according to different time scales, identify the matching relationship between new energy output and load demand and the load adjustment potential's ability to offset new energy fluctuations, and generate a source-load matching matrix. S4. Determine the source-load imbalance power sequence within the planning area based on the source-load matching matrix, identify the fluctuation scale of the source-load imbalance power sequence, determine the demand conditions, and establish a multi-type energy storage adaptation model. S5. Based on the multi-type energy storage adaptation model, a multi-objective optimization model for source and storage scale is established with preset decision variables. Based on the preset optimization objectives and constraints, multiple candidate source and storage configuration schemes are obtained. S6. Perform multi-scenario robust simulation on the multiple candidate source-storage configuration schemes, evaluate the new energy utilization rate, load failure probability, energy storage cycle pressure and economic return level of each candidate source-storage configuration scheme, and select the target source-storage configuration scheme based on the evaluation results. S7. Based on the target source and storage configuration scheme, output the recommended installed capacity of various new energy sources, the recommended configuration scale of various energy storage systems, the response capacity construction scheme for different load categories, and / or the hierarchical operation control strategy.

2. The source-storage scale optimization allocation method for multiple types of new energy sources and load characteristics as described in claim 1, characterized in that, In step S1, the various types of new energy sources include photovoltaic, wind power, biomass power generation / or small hydropower; When establishing a model for renewable energy generation A power generation model for photovoltaics was established, incorporating irradiance, temperature, orientation, and shading characteristics. A model for the generating capacity of wind power is established, which includes wind speed distribution, turbulence intensity, and intraday fluctuation characteristics. A power generation model for biomass power generation, including fuel supply stability and adjustable output range, is established. A model for the generating capacity of small hydropower stations is established, which includes the seasonality of water inflow and the upper limit of power output.

3. The source-storage scale optimization configuration method for multiple types of new energy sources and load characteristics as described in claim 1 or 2, characterized in that, Step S1 further includes: constructing a resource profile vector for any new energy type, wherein the resource profile vector includes average available output, output fluctuation intensity, maximum ramp rate per unit time, prediction error, equivalent dispatchable capacity, annualized cost per unit capacity and / or carbon emission reduction coefficient per unit electricity, and storing the resource profile vectors of each new energy type in the new energy side resource profile library according to a unified time scale.

4. The source-storage scale optimization configuration method for multiple types of new energy sources and load characteristics as described in claim 1, characterized in that, Step S2 includes: acquiring energy consumption data of multiple types of loads within the planning area, including basic rigid loads, production batch loads, transferable loads, interruptible loads / or combined cooling and heating loads; extracting the peak-to-valley ratio, duration, fluctuation rate, allowable shift window, energy supply reliability requirements / or interruption penalty cost for each type of load; and establishing the conversion relationship between electrical load, heat load, and cold load for combined cooling and heating loads to generate a set of load-side characteristic parameters.

5. The source-storage scale optimization allocation method for multiple types of new energy sources and load characteristics as described in claim 4, characterized in that, Step S2 also includes one of the following: (1) Establish an energy conservation constraint that the total energy consumption before and after the relocation is equal for transferable loads; (2) Establish an interruption power upper limit constraint and an interruption penalty cost model for interruptible loads; Establish constraints on batch start time, batch duration, and batch end time for production batch-type workloads; (3) Establish a conversion model for cooling load and heating load to equivalent electrical load for combined cooling and heating load.

6. In step S3, the spatiotemporal complementarity characteristics between different new energy sources and between new energy sources and loads are calculated according to intraday, intraweekly and seasonal scales; In step S3, the elements in the source-load matching matrix are obtained by weighting time-series matching index, fluctuation offset index, seasonal matching index and / or load response adaptation index. in, The time-series matching index is used to characterize the degree of similarity between the new energy output curve and the load demand curve in terms of time distribution. The fluctuation offset index is used to characterize the ability of new energy output to reduce load fluctuations. The seasonal matching index is used to characterize the degree of matching between the seasonal output of new energy and the seasonal demand of the load. The load response adaptation index is used to characterize the ability of the load regulation capacity to absorb fluctuations in new energy output.

7. The source-storage scale optimization allocation method for multiple types of new energy sources and load characteristics as described in claim 1, characterized in that, Step S4 includes: determining the source-load imbalance power sequence within the planning area based on the source-load matching matrix, identifying the fluctuation scale of the source-load imbalance power sequence, determining the fluctuation suppression requirements at the second to minute level, the peak shifting and valley filling requirements at the hour level, and the cold and hot energy transfer requirements, and establishing a multi-type energy storage adaptation model that includes high-response power storage, electric energy storage, cold storage devices, and hot storage devices. In step S4, the multi-type energy storage adaptation model calculates the energy storage adaptation coefficient based on the energy storage response time, energy storage continuous discharge duration, energy storage efficiency, energy storage cycle life and / or unit capacity cost, and determines the high-response power type energy storage for second- to minute-level fluctuation suppression, the electrical energy type energy storage for hour-level peak shifting and valley filling, the cold storage device for cold load time shift regulation, and the heat storage device for heat load time shift regulation based on the energy storage adaptation coefficient.

8. The source-storage scale optimization configuration method for multiple types of new energy sources and load characteristics as described in claim 1, characterized in that, In step S5, a multi-objective optimization model for source and storage scale is established using the installed capacity of various new energy sources, the rated power of various energy storage, the rated energy capacity of various energy storage, and the load response scale of various energy storage as decision variables. In step S5, the multi-objective optimization model for energy source and storage scale takes the minimum comprehensive energy supply cost, the maximum renewable energy utilization rate, the maximum power supply reliability, the minimum grid-connected power fluctuation and / or the maximum carbon emission reduction benefit as optimization objectives, and sets energy output constraints, load energy supply satisfaction constraints, energy storage status constraints, reserve capacity constraints, response time constraints, land constraints, roof constraints and / or installation capacity constraints. The multi-objective optimization model for source-storage scale also includes source-load-storage power balance constraints, recursive constraints on energy storage state of charge, upper and lower limits constraints on energy storage state of charge, load response time constraints, load response capacity constraints, upper and lower limits constraints on new energy output, reserve capacity constraints, and / or grid-connected power constraints.

9. The source-storage scale optimization allocation method for multiple types of new energy sources and load characteristics as described in claim 1, characterized in that, In step S6, multi-scenario robust simulations are performed on the multiple candidate source-storage configuration schemes for sunny days, rainy days, low wind speeds, peak loads, low loads, equipment maintenance and / or sudden fluctuations. The new energy utilization rate, load failure probability, energy storage cycle pressure and economic return level of each candidate source-storage configuration scheme are evaluated respectively, and the target source-storage configuration scheme is selected based on the evaluation results. In step S6, the multi-scenario robust simulation includes simulating meteorological change scenarios, load change scenarios, equipment availability change scenarios and / or sudden disturbance scenarios, and calculating robust evaluation values ​​based on the comprehensive index expectation values ​​and conditional risk values ​​of each candidate source-storage configuration scheme under different scenarios. Candidate source-storage configuration schemes whose robust evaluation values ​​meet the preset screening conditions are determined as target source-storage configuration schemes.

10. The source-storage scale optimization configuration method for multiple types of new energy sources and load characteristics as described in claim 1, characterized in that, In step S7, the hierarchical operation control strategy includes a day-ahead planning layer, an intraday adjustment layer, and a real-time correction layer; In step S7, the day-ahead planning layer generates a new energy output plan, an energy storage charging and discharging plan, and a load response plan based on the day-ahead new energy forecast results and the day-ahead load forecast results. The day-ahead adjustment layer corrects the energy storage charging and discharging plan and the load response plan based on the rolling forecast error. The real-time correction layer controls the energy storage output power and triggers interruptible load response based on the real-time source-load power deviation.

11. A source-storage scale optimization configuration system for multiple types of new energy sources and load characteristics, characterized in that, It includes: The module includes a new energy profiling module, a load characteristic identification module, a complementarity analysis module, an energy storage adaptation module, a scale optimization solution module, a robust simulation module, and a control strategy generation module. The new energy profiling module is used to acquire resource data of photovoltaic, wind power, biomass power generation and small hydropower within the planning area, and to construct power generation models, fluctuation characteristic models, predictability indicators and equivalent dispatchable capacity indicators for various new energy sources, thereby generating a new energy resource profiling library. The load characteristic identification module is used to acquire energy consumption data of basic rigid loads, production batch loads, transferable loads, interruptible loads and combined cooling and heating loads within the planning area, extract characteristic parameters of various types of loads, establish the conversion relationship between electrical load, heat load and cold load in combined cooling and heating loads, and generate a set of load-side characteristic parameters. The complementarity analysis module is used to calculate the spatiotemporal complementarity characteristics between different new energy sources and between new energy sources and loads at intraday, intraweek, and seasonal scales based on the new energy resource profile library and the load characteristic parameter set, and generate a source-load matching matrix. The energy storage adaptation module is used to determine the source-load imbalance power sequence according to the source-load matching matrix, identify the fluctuation scale of the source-load imbalance power sequence, and establish the adaptation relationship between high-response power storage, electric energy storage, cold storage devices and heat storage devices and different regulation requirements. The scale optimization solution module is used to construct and solve a multi-objective optimization model of source and storage scale using various new energy installed capacity, various energy storage rated power, various energy storage rated energy capacity and various load response scale as decision variables, and obtain multiple candidate source and storage configuration schemes. The robust simulation module is used to perform multi-scenario robust simulations on the multiple candidate source-storage configuration schemes, and to select the target source-storage configuration scheme based on the utilization rate of new energy sources, the probability of load failure, the energy storage cycle pressure and the level of economic return. The control strategy generation module is used to generate recommended installed capacity for various new energy sources, recommended configuration scale for various energy storage sources, response capacity construction plans for different load categories, and hierarchical operation control strategies based on the target source and storage configuration scheme.

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