Zero-carbon park source network load storage coordination control method, system and device based on grey wolf optimization algorithm and medium

Abstract

The invention discloses a zero-carbon park source network load storage coordination control method, system and device based on a grey wolf optimization algorithm and a medium, and belongs to the technical field of zero-carbon park energy management and intelligent control, and the method comprises the steps: obtaining the operation data of a zero-carbon park, and building a mathematical model of each energy unit; constructing an optimization function of the zero-carbon park; defining constraint conditions of the optimization problem in a scheduling period; solving an optimization model formed by the optimization function and the constraint condition by applying an intelligent optimization algorithm so as to obtain an optimal operation strategy of each controllable unit in the scheduling period; and analyzing the optimal operation strategy, and converting the optimal operation strategy into a specific control instruction sequence. According to the method, a multi-objective optimization function including economic cost and carbon emission is constructed, and the complex nonlinear and high-dimensional optimization problem of the park is effectively dealt with by means of the global optimization ability of the GWO algorithm, so that comprehensive optimization of economic benefits and environmental benefits is realized, a high-quality coordination control strategy can be found, and local optimization is effectively avoided.

Description

Technical Field

[0001] This invention relates to the field of energy management and intelligent control technology for zero-carbon industrial parks, specifically to a method, system, equipment, and medium for coordinated control of energy sources, grids, loads, and storage in zero-carbon industrial parks based on the gray wolf optimization algorithm. Background Technology

[0002] With the increasing severity of global climate change and the proposed goals of "carbon peaking and carbon neutrality," building a new power system based on new energy sources and promoting the green and low-carbon transformation of energy consumption has become an inevitable trend. Zero-carbon (or near-zero-carbon) smart parks, as important carriers of energy transformation, integrate a large number of distributed renewable energy sources (such as photovoltaics and wind power), energy storage systems, diverse loads, and interfaces with the main power grid, forming a complex integrated micro-energy system encompassing source, grid, load, and storage.

[0003] Achieving coordinated and optimized operation of various energy units within the industrial park to maximize the absorption of renewable energy, reduce overall operating costs, minimize carbon emissions, and ensure the reliability and flexibility of energy supply are the core challenges facing the construction and operation of zero-carbon industrial parks. Traditional control methods, such as rule-based control or simple time-of-use pricing strategies, often struggle to adapt to the volatility of renewable energy, the diversity of loads, and the dynamic nature of the market environment, resulting in low system efficiency, poor coordination, and difficulty in achieving global optimization.

[0004] In recent years, some advanced optimization algorithms have been introduced into energy management in microgrids or industrial parks. For example, linear programming (LP), mixed-integer linear programming (MILP), and dynamic programming (DP) can achieve good results in specific scenarios, but these methods often require high linearity of the model and may face high computational complexity and long solution times when solving large-scale, nonlinear, and multi-constraint problems. Heuristic optimization algorithms, such as genetic algorithms (GA) and particle swarm optimization (PSO), are widely studied due to their lower model requirements and strong global search capabilities. However, these algorithms may also suffer from complex parameter settings, susceptibility to local optima, and slow convergence speed.

[0005] The Grey Wolf Optimizer (GWO) algorithm is a relatively new metaheuristic optimization algorithm that simulates the hunting behavior of grey wolf packs in nature. The GWO algorithm has advantages such as simple structure, few parameters requiring adjustment, fast convergence speed, ease of implementation, and good global search ability and convergence performance when solving complex optimization problems. Applying it to the complex source-grid-load-storage coordination control problem in zero-carbon parks is expected to overcome the shortcomings of existing methods and achieve a more efficient, economical, and low-carbon operation strategy. However, current research on directly applying the GWO algorithm to the source-grid-load-storage coordination control of zero-carbon parks considering multiple objectives such as economy and low carbon emissions, and forming a complete and practical control method, is still insufficient. Existing research often focuses on single objectives or simplified system models, failing to fully consider the complex coupling relationships, operational constraints, and dynamic characteristics across multiple time scales within the park.

[0006] Therefore, it is necessary to develop an advanced coordinated control method that can effectively address the complexity and uncertainty of zero-carbon industrial parks while taking into account both economic and environmental benefits. Summary of the Invention

[0007] In view of the above-mentioned problems, the present invention provides a method, system, equipment and medium for coordinated control of source, grid, load and storage in zero-carbon industrial parks based on the gray wolf optimization algorithm.

[0008] Therefore, the technical problem solved by this invention is that it aims to address the problems existing in the energy management methods of zero-carbon parks, such as poor coordination, single optimization objectives, difficulty in balancing economic efficiency and low carbon emissions, and insufficient adaptability to fluctuations in renewable energy and load uncertainties.

[0009] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a coordinated control method for source-grid-load-storage systems in zero-carbon industrial parks based on the gray wolf optimization algorithm, comprising, Obtain operational data of the zero-carbon park within a preset scheduling cycle and perform preprocessing.

[0010] Based on the collected and preprocessed operational data, mathematical models of each energy unit within the zero-carbon park are established.

[0011] An optimization function is constructed with the objective of optimizing the economic operation indicators and environmental impact indicators of the zero-carbon park within the scheduling cycle.

[0012] Define the constraints that the optimization problem must satisfy at each time step within the scheduling period.

[0013] The optimization model, which is composed of the optimization function and the constraints, is solved using an intelligent optimization algorithm to obtain the optimal operating strategy for each controllable unit within the scheduling cycle.

[0014] The optimal operating strategy is analyzed and transformed into a specific sequence of control instructions for each time step within the scheduling period.

[0015] As a preferred embodiment of the source-grid-load-storage coordinated control method for zero-carbon industrial parks based on the gray wolf optimization algorithm described in this invention, the step of acquiring and preprocessing the operational data of the zero-carbon industrial park within a preset scheduling period includes: The operational data is then standardized.

[0016] The running data is normalized to a time scale to form a unified time sequence of input data.

[0017] As a preferred embodiment of the zero-carbon park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm described in this invention, the step of establishing the mathematical model of each energy unit within the zero-carbon park includes, Based on renewable energy power generation forecast data, a mathematical model is constructed to predict the power output and the actual dispatchable power output.

[0018] Based on the state information of the energy storage system, a mathematical model is constructed to illustrate the dynamic change process of the state of charge and the constraints of charging and discharging operation.

[0019] Based on the park's load forecast data, a load model is constructed according to load characteristics, including uncontrollable loads, interruptible loads, and transferable loads.

[0020] Based on grid interaction information and carbon emission factors, an interaction model and an emission model describing the economics of electricity purchase and sale and the carbon emissions of electricity consumption are constructed respectively.

[0021] As a preferred embodiment of the source-grid-load-storage coordinated control method for zero-carbon industrial parks based on the gray wolf optimization algorithm described in this invention, the optimization function constructed with the objective of optimizing the economic operation indicators and environmental impact indicators of the zero-carbon industrial park within the scheduling cycle includes, A comprehensive operating cost calculation function is constructed by integrating electricity purchase and sale costs, demand response compensation costs, energy storage loss costs, and renewable energy curtailment penalty costs.

[0022] A function for calculating total carbon emissions is constructed based on the carbon emission factors of the power grid and local power generation.

[0023] The comprehensive operating cost calculation function and the total carbon emission calculation function are integrated into a unified optimization objective function through a multi-objective decision-making method.

[0024] As a preferred embodiment of the zero-carbon park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm described in this invention, the constraints that the optimization problem must satisfy at each time step within the scheduling period include: Based on the mathematical model of the energy unit, the operating constraints of each unit are extracted, including the upper and lower limits of the state of charge of the energy storage system and the limits of charging and discharging power.

[0025] Establish system-level real-time power balance constraints to ensure that the total power generation is equal to the total power consumption at any given time.

[0026] Based on the power grid interaction information, upper and lower limits of power transmission between the park and the main power grid are set.

[0027] Based on the load model, set an upper limit on the number of interruptible loads that can be called and a total constraint on the total number of loads that can be moved.

[0028] As a preferred embodiment of the zero-carbon park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm described in this invention, the step of applying an intelligent optimization algorithm to solve for the optimal operating strategy of each controllable unit within the scheduling cycle includes... The energy storage charging and discharging power, grid interaction power, and controllable load status at each moment within the scheduling period are used as decision variables to initialize the population position of the Grey Wolf optimization algorithm.

[0029] The value of the optimization function is used as the fitness to evaluate the quality of each individual in the population.

[0030] By simulating the social hierarchy and hunting behavior of gray wolves, and iteratively updating the position of each individual in the population, the optimal solution is gradually approached.

[0031] During the iteration process, the updated position is handled for out-of-bounds errors and the constraints are corrected to ensure that the solution satisfies all constraints.

[0032] As a preferred embodiment of the zero-carbon park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm described in this invention, the step of analyzing the optimal operating strategy and converting it into a specific control instruction sequence for each time step within the scheduling period includes: The decision variable values ​​corresponding to each time step in the optimal operating strategy are parsed into charging and discharging power commands for the energy storage system.

[0033] The decision variable values ​​corresponding to each time step in the optimal operating strategy are parsed into interactive power commands with the main power grid.

[0034] The decision variable values ​​corresponding to each time step in the optimal operation strategy are parsed into commands for calling or shifting interruptible and shiftable loads within the park.

[0035] The specific control instructions for each time step are arranged into an instruction sequence in chronological order and sent to the park's energy management system for execution.

[0036] This invention provides a zero-carbon industrial park source-grid-load-storage coordinated control system based on the gray wolf optimization algorithm.

[0037] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a zero-carbon park source-grid-load-storage coordinated control system based on the gray wolf optimization algorithm, comprising: a data acquisition and processing module, a model construction and optimization calculation module, a control command generation and scheduling module, an energy management system execution and monitoring module, and a rolling optimization and update module.

[0038] The data acquisition and processing module obtains the operational data of the zero-carbon park within a preset scheduling cycle and performs preprocessing.

[0039] The model building and optimization calculation module establishes mathematical models for each energy unit within the zero-carbon park based on the collected and preprocessed operational data.

[0040] An optimization function is constructed with the objective of minimizing the overall operating cost and carbon emissions of the zero-carbon park within the scheduling cycle.

[0041] Define the constraints that the optimization problem must satisfy at each time step within the scheduling period.

[0042] The control command generation and scheduling module applies the Grey Wolf optimization algorithm to solve the optimization model composed of the optimization function and the constraints to obtain the optimal operating strategy of each controllable unit within the scheduling cycle.

[0043] The rolling optimization and update module parses the optimal operating strategy and transforms it into a specific sequence of control instructions for each time step within the scheduling period.

[0044] The present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the zero-carbon park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm.

[0045] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the steps of the aforementioned zero-carbon park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm.

[0046] The beneficial effects of this invention are as follows: By constructing a multi-objective optimization function that includes economic costs and carbon emissions, and leveraging the powerful global optimization capability of the GWO algorithm, this invention effectively addresses the complex nonlinear and high-dimensional optimization problems in industrial parks, thereby achieving comprehensive optimization of economic and environmental benefits. It can find high-quality coordinated control strategies and effectively avoid local optima. The optimized strategy can improve the renewable energy absorption rate in industrial parks, and through intelligent scheduling of energy storage and optimized power purchase and sale, it can reduce operating costs and carbon emissions. Simultaneously, the coordinated control capability effectively smooths out renewable energy fluctuations, improving the operational flexibility and power supply reliability of the power system. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0048] Figure 1 The above is a flowchart of a zero-carbon industrial park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm, provided as an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of a scheme for a zero-carbon industrial park source-grid-load-storage coordinated control system based on the gray wolf optimization algorithm, provided as an embodiment of the present invention. Detailed Implementation

[0050] To make the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0051] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a coordinated control method for source-grid-load-storage systems in zero-carbon industrial parks based on the gray wolf optimization algorithm, comprising: S1. Obtain the operation data of the zero-carbon park within the preset scheduling cycle and perform preprocessing.

[0052] S2. Based on the collected and preprocessed operational data, establish mathematical models for each energy unit within the zero-carbon park.

[0053] S3. Construct an optimization function with the objective of optimizing the economic operation indicators and environmental impact indicators of the zero-carbon park within the scheduling cycle.

[0054] S4. Define the constraints that the optimization problem must satisfy at each time step within the scheduling period.

[0055] S5. Solve the optimization model formed by the optimization function and the constraints using an intelligent optimization algorithm to obtain the optimal operating strategy for each controllable unit within the scheduling cycle.

[0056] S6. Analyze the optimal operating strategy and transform it into a specific control instruction sequence for each time step within the scheduling period.

[0057] This invention unifies the processing of multi-source operational data from zero-carbon industrial parks and establishes a refined mathematical model. It constructs a multi-objective function focused on simultaneously optimizing economic operation and environmental impact, and then utilizes intelligent optimization algorithms to efficiently solve the problem under strict system constraints. This invention can automatically generate an optimal scheduling instruction sequence that balances economic efficiency and low carbon emissions, thereby reducing the overall energy cost and carbon emissions of the industrial park, and improving the local absorption rate of renewable energy and energy utilization efficiency.

[0058] Example 2, an embodiment of the present invention, provides a zero-carbon industrial park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm, based on the previous embodiment, including: In S1, obtain the operational data of the zero-carbon park within the preset scheduling cycle and perform preprocessing including steps A1-A2: A1. Standardize the running data.

[0059] Collect relevant operational data of the zero-carbon park for one or more future control cycles (e.g., the next 24 hours) and preprocess it. The data includes at least: future Hours (time resolution) Photovoltaic output forecast (minutes) Wind power output forecast ;future Total load forecast for hours and interruptible load Information and ;future Time-of-use electricity price of the main power grid and carbon emission factors from grid power supply. Current energy storage systems and its rated capacity Maximum charge and discharge power and Charge and discharge efficiency and SoC upper and lower limits and The maximum power that can be purchased or sold at the grid interface. and Energy storage cycle life model parameters are used to calculate equivalent loss costs. .

[0060] A2. Perform time-scale normalization on the running data to form a unified time-series input data sequence.

[0061] Time scale normalization is the process of unifying data with different time resolutions.

[0062] In the embodiments of this application, the standardization process in A1 involves cleaning abnormal data and imputing missing data to eliminate data defects and ensure continuity. Specifically, it includes setting a threshold range based on historical statistical data to identify and mark abnormal data points that exceed the allowable fluctuation, and then removing these abnormal points to form data gaps; secondly, analyzing various missing patterns in the data sequence, filling isolated missing points with linear interpolation of the preceding and following valid data, filling short-term continuous missing points with spline interpolation that maintains the smoothness of the curve, and filling long-term or complex missing points with intelligent data by matching historical similar day data patterns and calibrating; finally, verifying the physical rationality of the processed complete data sequence and outputting a standardized time-series dataset that meets the quality requirements.

[0063] In one alternative implementation, the standardization process can be achieved by automatically identifying outliers by setting dynamic thresholds based on data distribution characteristics: First, the mean and standard deviation are calculated in real time based on historical data within a sliding time window, and upper and lower limit thresholds are dynamically generated using the 3σ principle; second, the isolated forest algorithm is used to perform unsupervised clustering analysis on the data points, and sample points that deviate significantly from the main distribution are marked as outliers; finally, the marked outliers are replaced with the weighted average of the nearest valid data, and data smoothing filtering is performed after the replacement to eliminate abrupt changes.

[0064] In another alternative implementation, the standardization process can also involve using a time series prediction model to generatively impute missing data: first, an LSTM or Transformer prediction model is trained using complete historical data to learn the time dependency patterns of various types of running data; second, the data sequence containing the missing segments is input into the prediction model, and the numerical values ​​of the missing periods are reconstructed through the model's sequence generation capability; finally, a residual correction module based on an attention mechanism is used to make reasonable corrections to the generated results.

[0065] Furthermore, the establishment of mathematical models for each energy unit within the zero-carbon park in S2 includes steps B1-B4: B1. Based on renewable energy power generation forecast data, construct mathematical models for predicted power output and actual dispatchable power output.

[0066] Renewable energy models include: Photovoltaic power output model: in, express The actual output power of the photovoltaic power generation system at any given time; The photoelectric conversion efficiency of a photovoltaic module; This refers to the total area of ​​the photovoltaic array. for Solar irradiance on the surface of the photovoltaic panel at any given time; This is the power temperature coefficient of the photovoltaic module (usually a negative value). for The actual operating temperature of the photovoltaic cells at all times; The standard reference temperature for photovoltaic modules (usually taken as...) ).

[0067] Wind turbine output model: in, This refers to the rated power of the wind turbine. , and These are the cut-in wind speed, cut-out wind speed, and rated wind speed, respectively.

[0068] Actual dispatchable output is limited by predicted values: in, , They represent The actual dispatch output of photovoltaic and wind power at any given time (i.e., the power ultimately adopted by the system). , They represent Predicted output of solar and wind power at all times.

[0069] B2. Based on the state information of the energy storage system, construct a mathematical model of the dynamic change process of the state of charge and the constraints of charging and discharging operation.

[0070] Energy storage system model: (Charging and discharging states are mutually exclusive) in, , They are respectively It can store and discharge energy at any time. and These are the charge and discharge efficiencies, respectively. Rated capacity; and For SoC upper and lower limits; and This is the maximum charge / discharge power; and for State variables, express The state of charge of the energy storage system at all times; Indicates the elapsed time step The state of charge of the energy storage system at the next moment; This indicates the preset scheduling time interval.

[0071] B3. Based on the park load forecast data, construct a load model that includes uncontrollable loads, interruptible loads, and transferable loads according to load characteristics.

[0072] Load model: (Interruptible load) (Total load that can be shifted) in, express The predicted total load power within the park at any given time; express Power of the rigid load (uncontrollable load) on the foundation at any given time; express The upper limit of the predicted load power that can participate in the interruption response at any time; express The load forecast power that can participate in the translation response at any time; Indicates after demand response The actual total load power of the park at any given time; express The actual amount of load interruption power executed at any given time; express The actual load power after the shift is executed at any given moment; This represents the total energy that can be shifted within a scheduling cycle (i.e., the total electricity consumption before and after the shift should remain constant). This is the scheduling time step.

[0073] B4. Based on grid interaction information and carbon emission factors, construct interaction models and emission models to describe the economics of electricity purchase and sale and the carbon emissions of electricity consumption, respectively.

[0074] Power grid interaction model: This indicates that electricity is purchased from the power grid. This indicates the sale of electricity to the power grid.

[0075] Electricity purchase cost Electricity sales revenue in, express The interaction power between the park and the main power grid at any given time (positive values ​​represent electricity purchases, and negative values ​​represent electricity sales). and These are the minimum and maximum allowable transmission power limits for the interconnection lines between the park and the power grid, respectively. express The amount of electricity that the park purchases from the main power grid at any given time; express The amount of electricity that the industrial park sells to the main power grid at any given time; express Time-of-use electricity pricing at specific times; express The on-grid electricity price (or protective purchase price) at any given time. express The cost of purchasing electricity at any given moment; express Revenue from electricity sales at any given time.

[0076] Carbon emission model: in, express The total carbon emissions generated by electricity consumption in the industrial park at any given moment; express The amount of electricity that the park purchases from the main power grid at any given time; Represents the time interval $t$ within the park. The actual power generation capacity of each non-renewable energy generation unit (if any); The carbon emission factor of grid electricity (may vary over time). For other non-renewable energy power generation units within the park (If applicable) carbon emission factors.

[0077] In this embodiment of the application, the optimization function with economic operation indicators and environmental impact indicators as objectives in S3, namely the optimization function with comprehensive operating cost and carbon emissions as objectives, specifically includes calculating the total operating cost within a scheduling cycle T (e.g., 24 hours): In this embodiment, to simplify calculations, it is assumed that the park prioritizes the full utilization of renewable energy and has no other fossil fuel power generation equipment; therefore, a penalty cost for curtailment is set. Other power generation equipment operating costs Demand response compensation costs Specifically, it is expressed as follows: For total carbon emissions, in a scheduling cycle Internal calculation: Furthermore, since this embodiment assumes no other fossil fuel power generation, the carbon emissions from self-generated electricity are omitted.

[0078] In one optional implementation, the economic operation indicators and environmental impact indicators are used as the objective optimization function, specifically, aiming to minimize the total electricity cost within the scheduling cycle and maximize the renewable energy consumption ratio. Here, the total electricity cost is the sum of expenditures on purchasing electricity from the grid, subsidies for implementing demand response, and operation and maintenance costs of energy storage equipment; the renewable energy consumption ratio is the ratio of actual renewable energy generation within the park to the predicted total generation. By adjusting the weighting coefficients between these two objectives, a balance is achieved between controlling energy expenditures and increasing green energy self-sufficiency in the park.

[0079] In another optional implementation, the economic operation indicators and environmental impact indicators are used as the target optimization function. Specifically, the objective is to minimize net operating expenses and minimize equivalent carbon emission intensity within the scheduling cycle. Here, net operating expenses are the sum of electricity purchase costs from the grid, power generation fuel costs, and demand response compensation costs, minus the revenue from selling electricity to the grid; the equivalent carbon emission intensity is the ratio of the park's total carbon emissions to its total electricity consumption, used to measure the carbon emission level per unit of electricity consumption. This method, through optimized scheduling, systematically reduces the carbon footprint density of the park's overall energy consumption while ensuring the park's energy economy.

[0080] Furthermore, constructing an optimization function aimed at optimizing the economic operation indicators and environmental impact indicators of the zero-carbon park within the scheduling cycle includes steps C1-C3: C1. Integrate the costs of electricity purchase and sale, demand response compensation costs, energy storage loss costs, and renewable energy curtailment penalties to construct a comprehensive operating cost calculation function.

[0081] C2. Construct a function for calculating total carbon emissions based on the carbon emission factors of the power grid and the carbon emission factors of local power generation.

[0082] C3. The comprehensive operating cost calculation function and the total carbon emission calculation function are integrated into a unified optimization objective function through a multi-objective decision-making method (multi-objective optimization processing method).

[0083] In the embodiments of this application, the multi-objective decision-making method in C3, namely the multi-objective optimization processing method, specifically includes: defining an optimization function with the objectives of minimizing the overall operating cost of the park and minimizing carbon emissions; and using a weighted sum method to combine the above two objectives into a single objective function. Specifically, this is achieved by introducing adjustable weight factors before the two objective functions and performing a weighted summation, wherein the sum of the weight factors is 1, and the specific values ​​of each weight factor are dynamically set and adjusted according to the different emphase strategies of economic efficiency or low carbon emissions in the actual operation of the park.

[0084] The weighted sum method is expressed as: in, and As a weighting factor, Its value can be adjusted according to the park's operation strategy (emphasizing economic efficiency or low carbon emissions), and a weight is set in this invention. , .

[0085] In one alternative implementation, the multi-objective decision-making method can be the ε-constraint method, which specifically includes: first, setting carbon emissions as the main objective function for separate optimization to obtain its theoretical optimal value; then, setting the comprehensive operating cost as one of the constraints, and constructing the allowable deviation range of carbon emissions by introducing an adjustable ε parameter; finally, under the premise of satisfying the carbon emission constraint, optimizing the comprehensive operating cost, and obtaining a set of economically optimal solutions reflecting different environmental protection levels by gradually adjusting the ε parameter.

[0086] In another alternative implementation, the multi-objective decision-making method can also be the fuzzy satisfaction method, which specifically includes: first, defining membership functions for two objectives, operating cost and carbon emissions, to represent the decision-maker's satisfaction with different cost and emission levels, respectively; then, aggregating the membership degrees of the two objectives through fuzzy logic rules to construct a comprehensive satisfaction function; finally, optimizing the comprehensive satisfaction function to obtain a compromise solution that maximizes the decision-maker's overall satisfaction, and reflecting different preferences for economic efficiency or environmental friendliness by setting weighting factors.

[0087] Furthermore, the constraints that the optimization problem defined in S4 must satisfy at each time step within the scheduling period include steps D1-D4: D1. Based on the mathematical model of the energy unit, extract the operating constraints of each unit, including the upper and lower limits of the state of charge of the energy storage system and the limits of charging and discharging power.

[0088] D2. Establish system-level real-time power balance constraints to ensure that the total power generation is equal to the total power consumption at any given time.

[0089] Specifically, in addition to the constraints inherent in each unit model in step S2, system-level constraints also need to be defined: Power balance constraint: at each time step The total power generation in the park (including renewable energy dispatch output, energy storage discharge, purchased power, and other generator output) must be equal to the total power consumption (including load, energy storage charging, and power sold to the grid).

[0090] Energy storage operation constraints: such as SoC range constraints, charge / discharge power constraints, charge / discharge state constraints as described in step S2, as well as possible daily charge / discharge frequency constraints and initial / final SoC constraints (e.g., requiring the SoC to recover to a certain value at the end of the scheduling cycle). Energy storage constraints: D3. Based on the power grid interaction information, set upper and lower limit constraints for the transmission of interactive power between the park and the main power grid.

[0091] Specifically, grid interaction constraints include line transmission power constraints as described in step S2.

[0092] Renewable energy consumption constraints: as described in step S2: D4. Based on the load model, set the upper limit for the number of interruptible loads to be called and the total scheduling limit for the number of loads that can be moved.

[0093] Load supply constraints: As described in step S2, ensure basic load supply and manage interruptible and shiftable loads.

[0094] Backup constraints: To cope with forecast errors and emergencies, certain requirements for spinning reserve or interruptible load backup can be set.

[0095] In this embodiment of the application, the intelligent optimization algorithm in S5, namely the Grey Wolf optimization algorithm, specifically includes: Initialize the gray wolf population: (1) Determine the population size (Number of gray wolves) and maximum number of iterations .

[0096] Each gray wolf's position represents a set of decision variables to be optimized. These decision variables are determined during the scheduling cycle. Each time step The output or state of each controllable unit, for example: , , , , , , Notice , The decision-making process essentially involves deciding whether to curtail wind and solar power. Typically, full grid connection is prioritized, but adjustments may be necessary under specific constraints (such as grid backfeed power limits, full energy storage, and low load). Alternatively, the amount of wind and solar power curtailed can be used as a decision variable. For simplicity, we assume here that full grid connection is prioritized.

[0097] Randomly generate within the feasible region of the decision variables. The initial position of the gray wolf. For initial positions that do not meet the constraints, they need to be corrected or regenerated until all constraints are met.

[0098] initialization The location of the gray wolf. Each location is a string of length [missing information]. A vector (corresponding to the four main decision variables across 96 time steps). Ensure the initial position satisfies the boundary constraints.

[0099] (2) Calculate fitness: For each gray wolf Substitute its position vector into the objective function defined in step S3. Calculate its fitness value The goal is to minimize Therefore, the smaller the fitness value, the better.

[0100] (3) Identify the leader ( , , Wolf): Based on the fitness values ​​of all gray wolves in the current population, select the three gray wolves with the lowest (optimal) fitness values ​​and mark them as follows: Wolf, Wolf, Wolves. Record their locations. , , .

[0101] For others Wolf, calculated according to the formula , and update its location: in, Indicates the first The updated position vector of the individual gray wolf in the next iteration; These represent the top three gray wolves with the best fitness values ​​in the current population. Wolf, Wolf, The position vector of the wolf; This is the convergence coefficient vector of the algorithm, used to control the exploration and development process; Representing the current individual and Wolf, Wolf, Distance vector between wolves; These represent the current individual's experience. Wolf, Wolf, The wolf influences the calculated potential movement positions.

[0102] Perform boundary and constraint checks on the updated location (repairing or penalizing if necessary). Update parameters. Determine whether it has been achieved If the target is not reached, continue iterating; if the target is reached, end the iteration. Output the current value. Wolf's position As the optimal solution.

[0103] (4) Update the position of the gray wolf: For populations except , , Other than Wolves, update their positions according to the following formula: Calculation and , , Distance vector of the wolf: in, It is the current number During iteration, a certain The wolf's position They represent respectively targeting Wolf, Wolf, The random coefficient vector of the wolf.

[0104] Calculate the coefficient vector and : in, , yes A random vector between [variables]. Control parameters. from linearly decreasing to The calculation formula is: , This represents the current iteration number.

[0105] Calculate orientation , , The wolf's step size vector: renew Wolf positions (average): After the position is updated, it is necessary to check again whether the new position exceeds the boundary of the decision variable or violates the constraints. If it exceeds the boundary, it can be placed at the boundary value; if it violates the constraints, a repair operator can be applied or the original position can be simply retained (or penalized).

[0106] (5) Update Values, iterations, and termination: Update control parameters .

[0107] Repeat steps (2) through (4) until the maximum number of iterations is reached. Or it may satisfy other convergence criteria (e.g., the change in the optimal solution over multiple consecutive generations is less than a threshold).

[0108] In one alternative implementation, the intelligent optimization algorithm can be Particle Swarm Optimization (PSO), which specifically includes: first, randomly initializing a group of particles within the feasible region of the decision variables, where the position of each particle represents a potential running strategy and its velocity represents its search direction; dynamically updating the velocity and position of each particle based on its own historical best position and the global best position of the population; calculating the fitness value of each particle in each iteration and updating the individual and global best records; and finally converging the particle swarm to the optimal solution region through the global exploration and local development capabilities of the inertia weight and learning factor balancing algorithm.

[0109] In another alternative implementation, the intelligent optimization algorithm can also be a genetic algorithm (GA), which specifically includes: first, encoding the decision variables as chromosomes and randomly generating an initial population; evaluating the merits of each chromosome by calculating fitness; using roulette wheel selection to select dominant individuals and simulating gene recombination through crossover to generate new individuals; performing mutation operations on individuals with a certain probability to maintain population diversity; and gradually approaching the optimal solution set through iterative evolution of selection, crossover, and mutation.

[0110] Furthermore, the application of intelligent optimization algorithms in S5 to solve for the optimal operating strategy of each controllable unit within the scheduling period includes steps E1-E4: E1. Use the energy storage charging and discharging power, grid interaction power and controllable load status at each moment in the scheduling period as decision variables to initialize the population position of the gray wolf optimization algorithm. E2. Use the value of the optimization function as the fitness to evaluate the quality of each individual in the population; E3. Simulate the social hierarchy and hunting behavior of gray wolves, and gradually approach the optimal solution by iteratively updating the position of each individual in the population; E4. During the iteration process, the updated position is handled for out-of-bounds errors and the constraints are corrected to ensure that the solution satisfies all constraints.

[0111] Furthermore, the analysis of the optimal operating strategy in S6, which transforms it into a specific control instruction sequence for each time step within the scheduling period, includes steps F1-F4. F1. The decision variable values ​​corresponding to each time step in the optimal operation strategy are parsed into the charging and discharging power commands of the energy storage system. Specifically, this includes, from the optimal solution Extracting the future Each hour Minutes of energy storage charging and discharging power command and Power command interaction with the power grid and the amount of load that needs to be interrupted. .

[0112] F2. Parse the decision variable values ​​corresponding to each time step in the optimal operating strategy into interactive power commands with the main power grid; F3. The decision variable values ​​corresponding to each time step in the optimal operation strategy are parsed into call or transfer instructions for interruptible loads and transferable loads within the park. F4. Arrange the specific control instructions for each time step into an instruction sequence in chronological order and send it to the park's energy management system for execution.

[0113] Specifically, the optimal solution output by the GWO algorithm (i.e. The wolf's position vector is parsed into a specific sequence of control commands. For example, extracting the position vector at each moment... of , , , , Once the optimal value is reached, these control commands are sent to the park's energy management system. Based on the received commands, the controller controls the energy storage system in the park to charge and discharge, adjusts the power interaction with the grid, controls the operation of interruptible / transferable loads, and adjusts the output of renewable energy (wind and solar curtailment) when necessary.

[0114] Furthermore, during actual system operation, every [time period] Repeat steps S1-S6 for one hour (or a shorter or longer period as needed). In S1, replace the initial value with the actual energy storage SoC at the current moment and obtain the latest future value. Hourly forecast data. After re-optimization, only future forecasts are executed. Hourly control commands. This rolling method can effectively address prediction errors.

[0115] Example 3, referring to Figure 2 This is one embodiment of the present invention, which provides a zero-carbon industrial park source-grid-load-storage coordinated control system based on the gray wolf optimization algorithm, including: a data acquisition and processing module, a model building and optimization calculation module, a control command generation and scheduling module, an energy management system execution and monitoring module, and a rolling optimization and update module.

[0116] The data acquisition and processing module obtains the operational data of the zero-carbon park within a preset scheduling cycle and performs preprocessing.

[0117] The model building and optimization calculation module establishes mathematical models for each energy unit within the zero-carbon park based on the collected and preprocessed operational data.

[0118] An optimization function is constructed with the objective of minimizing the overall operating cost and carbon emissions of the zero-carbon park within the scheduling cycle.

[0119] Define the constraints that the optimization problem must satisfy at each time step within the scheduling period.

[0120] The control instruction generation and scheduling module applies the Grey Wolf optimization algorithm to solve the optimization model composed of the optimization function and the constraints to obtain the optimal operating strategy of each controllable unit within the scheduling cycle.

[0121] The rolling optimization and update module parses the optimal operating strategy and transforms it into a specific sequence of control instructions for each time step within the scheduling period.

[0122] This embodiment also provides an electronic device applicable to a zero-carbon park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the zero-carbon park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm proposed in the above embodiment.

[0123] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a zero-carbon park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm proposed in the above embodiment.

[0124] The storage medium proposed in this embodiment and the method for coordinated control of source, grid, load and storage in a zero-carbon park based on the gray wolf optimization algorithm proposed in the above embodiment belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0125] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A coordinated control method for source-grid-load-storage systems in zero-carbon industrial parks based on the gray wolf optimization algorithm, characterized in that: include, Acquire operational data of the zero-carbon industrial park within a preset scheduling cycle and perform preprocessing; Based on the collected and preprocessed operational data, a mathematical model of each energy unit in the zero-carbon park is established. Construct an optimization function with the objective of optimizing the economic operation indicators and environmental impact indicators of the zero-carbon park within the scheduling cycle; Define the constraints that the optimization problem must satisfy at each time step within the scheduling period; The optimization model formed by the optimization function and the constraints is solved by applying an intelligent optimization algorithm to obtain the optimal operating strategy of each controllable unit within the scheduling cycle. The optimal operating strategy is analyzed and transformed into a specific sequence of control instructions for each time step within the scheduling period.

2. The zero-carbon industrial park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm as described in claim 1, characterized in that: The process of acquiring and preprocessing the operational data of the zero-carbon park within a preset scheduling period includes: The operational data is standardized. The running data is normalized to a time scale to form a unified time sequence of input data.

3. The zero-carbon industrial park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm as described in claim 2, characterized in that: The establishment of mathematical models for each energy unit within the zero-carbon park includes, Based on renewable energy power generation forecast data, a mathematical model is constructed to predict the power output and the actual dispatchable power output. Based on the state information of the energy storage system, a mathematical model is constructed to illustrate the dynamic change process of the state of charge and the constraints of charging and discharging operation. Based on the park's load forecast data, a load model is constructed according to load characteristics, including uncontrollable loads, interruptible loads, and transferable loads. Based on grid interaction information and carbon emission factors, an interaction model and an emission model describing the economics of electricity purchase and sale and the carbon emissions of electricity consumption are constructed respectively.

4. The zero-carbon industrial park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm as described in claim 3, characterized in that: The optimization function constructed with the objective of optimizing the economic operation indicators and environmental impact indicators of the zero-carbon park within the scheduling cycle includes, A comprehensive operating cost calculation function is constructed by integrating electricity purchase and sale costs, demand response compensation costs, energy storage loss costs, and renewable energy curtailment penalty costs. A function for calculating total carbon emissions is constructed based on the carbon emission factors of the power grid and local power generation. The comprehensive operating cost calculation function and the total carbon emission calculation function are integrated into a unified optimization objective function through a multi-objective decision-making method.

5. The zero-carbon industrial park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm as described in claim 4, characterized in that: The constraints that the optimization problem must satisfy at each time step within the scheduling period include: Based on the mathematical model of the energy unit, the operating constraints of each unit are extracted, including the upper and lower limits of the state of charge of the energy storage system and the limits of charging and discharging power. Establish system-level real-time power balance constraints to ensure that the total power generation is equal to the total power consumption at any given time. Based on the power grid interaction information, set upper and lower limit constraints for the transmission of interactive power between the park and the main power grid; Based on the load model, set an upper limit on the number of interruptible loads that can be called and a total constraint on the total number of loads that can be moved.

6. The zero-carbon industrial park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm as described in claim 4, characterized in that: The application of intelligent optimization algorithms to solve for the optimal operating strategy of each controllable unit within the scheduling period includes: The energy storage charging and discharging power, grid interaction power, and controllable load status at each moment within the scheduling period are used as decision variables to initialize the population position of the Grey Wolf optimization algorithm. The value of the optimization function is used as the fitness to evaluate the quality of each individual in the population; The social hierarchy and hunting behavior of gray wolves are simulated, and the optimal solution is gradually approached by iteratively updating the position of each individual in the population. During the iteration process, the updated position is handled for out-of-bounds errors and the constraints are corrected to ensure that the solution satisfies all constraints.

7. The zero-carbon industrial park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm as described in claim 4, characterized in that: The process of parsing the optimal operating strategy and converting it into a specific sequence of control instructions for each time step within the scheduling period includes... The decision variable values ​​corresponding to each time step in the optimal operation strategy are parsed into the charging and discharging power commands of the energy storage system. The decision variable values ​​corresponding to each time step in the optimal operation strategy are parsed into interactive power commands with the main power grid. The decision variable values ​​corresponding to each time step in the optimal operation strategy are parsed into call or transfer instructions for interruptible and transferable loads within the park. The specific control instructions for each time step are arranged into an instruction sequence in chronological order and sent to the park's energy management system for execution.

8. A zero-carbon industrial park source-grid-load-storage coordinated control system based on the gray wolf optimization algorithm, employing the zero-carbon industrial park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm as described in any one of claims 1 to 7, characterized in that, include: The system includes a data acquisition and processing module, a model building and optimization calculation module, a control command generation and scheduling module, an energy management system execution and monitoring module, and a rolling optimization and update module. The data acquisition and processing module acquires the operational data of the zero-carbon park within a preset scheduling cycle and performs preprocessing. The model building and optimization calculation module establishes mathematical models of each energy unit in the zero-carbon park based on the collected and preprocessed operational data. Construct an optimization function with the objective of minimizing the overall operating cost and carbon emissions of the zero-carbon park within the scheduling cycle; Define the constraints that the optimization problem must satisfy at each time step within the scheduling period; The control command generation and scheduling module applies the Grey Wolf optimization algorithm to solve the optimization model composed of the optimization function and the constraints to obtain the optimal operating strategy of each controllable unit within the scheduling cycle. The rolling optimization and update module parses the optimal operating strategy and transforms it into a specific sequence of control instructions for each time step within the scheduling period.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the zero-carbon park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the zero-carbon park source-grid-load-storage coordinated control method based on the gray wolf optimization algorithm as described in any one of claims 1 to 7.

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