A renewable energy power system supply-delivery-demand-storage collaborative optimization configuration method

Abstract

The application provides a renewable energy power system supply-distribution-demand-storage collaborative optimization configuration method, relates to the technical field of wisdom energy, and comprises the following steps: acquiring basic data of multiple provinces and cities, including renewable energy resource data and auxiliary data; establishing a collaboration degree calculation model, calculating the collaboration degree of each subsystem respectively, calculating the overall collaboration degree based on the collaboration degree of each subsystem, and constructing a subsystem collaboration strength matrix to quantify the coupling relationship between each subsystem; establishing a multi-subpopulation collaborative optimization architecture, using a collaboration degree-oriented initialization strategy to generate an initial solution set for each subpopulation; using an improved sparrow optimization algorithm to perform parallel optimization on each subpopulation, and obtaining an optimal configuration result when the convergence condition is reached; and outputting a renewable energy power system supply-distribution-demand-storage collaborative optimization configuration scheme according to the optimal configuration result. The application can improve the overall operation efficiency and economy of the system.

Description

Technical Field

[0001] This invention relates to the field of smart energy technology, and in particular to a method for coordinated optimization of supply, transmission, demand and storage in a renewable energy power system. Background Technology

[0002] With the accelerated pace of global energy transition and carbon emission reduction, renewable energy has become the dominant direction for future energy development. However, the distribution of renewable energy resources exhibits significant regional characteristics. For example, wind energy resources are mainly concentrated in the "Three Norths" region (Northeast, North, and Northwest China), and there is a spatial mismatch between areas rich in solar energy resources and electricity load centers. Furthermore, renewable energy generation is characterized by intermittency, volatility, and uncertainty, and the capacity for renewable energy absorption within a single region is limited. This necessitates cross-regional optimized dispatching to achieve large-scale optimal allocation of renewable energy. Cross-regional dispatching can not only smooth out fluctuations in renewable energy output and improve the overall power supply reliability of the system, but also fully leverage the complementary advantages of different regions' resources, maximizing the utilization efficiency of renewable energy. It has significant technical and practical value for building a clean, low-carbon, safe, and efficient modern energy system.

[0003] In the field of renewable energy optimization and scheduling technology, intelligent optimization algorithms have become the main means to solve large-scale complex optimization problems. Traditional heuristic algorithms, such as genetic algorithms and particle swarm optimization algorithms, have been widely used in the optimization of microgrids and distributed energy systems. In recent years, deep reinforcement learning algorithms have shown significant advantages in real-time optimization and scheduling of integrated energy systems due to their ability to solve continuous sequence decision problems. However, existing algorithms generally suffer from technical limitations when dealing with large-scale, multi-constraint complex power system collaborative optimization problems, such as low search efficiency, susceptibility to local optima, and poor adaptability to high-dimensional problems, making it difficult to meet the technical requirements of efficient collaborative optimization for new power systems.

[0004] Chinese invention patent CN117151394A discloses an optimal scheduling method for cross-regional hydrogen-electricity coupling systems based on supply-transmission-demand coordination. This technology constructs dedicated hydrogen production zones through electrolysis in renewable energy-rich areas and achieves hydrogen-electricity demand matching in remote energy-consuming regions through cross-regional hydrogen or electricity transportation. The method establishes a multi-objective optimal scheduling model with the goals of minimizing total cost and carbon emissions, and uses the DDPG deep reinforcement learning algorithm to achieve on-demand allocation of hydrogen and electricity. However, this technical solution has the following problems: First, although the DDPG algorithm used in this method is suitable for decision-making problems in continuous action spaces, it suffers from poor convergence stability and sensitivity to initialization when dealing with multi-subsystem coupling constraints and large-scale combinatorial optimization problems. Second, the coordination mechanism of this method is mainly reflected at the hydrogen-electricity conversion level, lacking a quantitative evaluation and dynamic adjustment mechanism for the coordination degree of each subsystem within the power system, making it difficult to achieve truly deep collaborative optimization. Summary of the Invention

[0005] In view of this, the present invention provides a method for coordinated optimization configuration of supply, transmission, demand and storage in a renewable energy power system. By establishing a coordination degree calculation model to quantify the coupling relationship between each subsystem, and using an improved multi-subgroup sparrow optimization algorithm to achieve parallel coordinated optimization of each subsystem, with the dual objectives of maximizing coordination degree and minimizing total cost, a coordinated optimization configuration scheme for the entire system that includes the installed capacity of renewable energy in each province and city, the configuration of energy storage equipment, the power transmission scheme and the demand response strategy is finally obtained, thereby improving the overall operating efficiency and economy of the system.

[0006] The technical solution of this invention is implemented as follows:

[0007] This invention provides a method for coordinated optimization of supply, transmission, demand, and storage in a renewable energy power system, comprising:

[0008] S1. Obtain basic data from multiple provinces and cities, including renewable energy resource data and auxiliary data. Standardize the renewable energy resource data and define decision variables for four subsystems: supply, transmission, demand, and energy storage, to obtain a unified resource evaluation basis and decision variable system.

[0009] S2. Establish a synergy calculation model, calculate the synergy of each subsystem, calculate the overall synergy based on the synergy of each subsystem, and construct a subsystem synergy strength matrix to quantify the coupling relationship between each subsystem.

[0010] S3. Establish a multi-subpopulation collaborative optimization architecture, decompose the population into four subpopulations according to the four subsystems of supply, transportation, demand and energy storage, and generate the initial solution set of each subpopulation by adopting a collaboration degree-oriented initialization strategy.

[0011] S4. An improved sparrow optimization algorithm is used to optimize each subpopulation in parallel. The fitness is evaluated with synergy and total cost as dual objectives. Synergy optimization among subpopulations is carried out through adaptive parameter adjustment and information exchange mechanism. When the convergence condition is met, the optimal configuration result is obtained.

[0012] S5. Based on the optimal configuration results, determine the installed capacity of renewable energy, energy storage equipment configuration, power transmission scheme and demand response strategy of each province and city, and output the supply-transmission-demand-storage coordinated optimization configuration scheme of renewable energy power system.

[0013] Preferably, step S2 includes:

[0014] S21. Establish the synergy calculation formulas for the supply subsystem, transmission subsystem, demand subsystem and energy storage subsystem respectively. The synergy of the transmission subsystem, demand subsystem and energy storage subsystem is obtained by weighted summation based on the influence indicators of each subsystem and their weights. The supply subsystem needs to deduct the uncertainty coefficient of renewable energy output. The uncertainty coefficient is quantified by calculating the variance of renewable energy power generation in each province and city.

[0015] S22. The overall synergy is obtained by calculating the synergy of each subsystem using the geometric mean method.

[0016] S23. The degree of internal uncertainty is assessed by calculating the entropy value of each subsystem. The weight coefficient of each subsystem is calculated based on the entropy value. The Pearson correlation coefficient and weight coefficient between subsystems are combined to construct a 4×4 symmetric cooperative strength matrix to quantify the degree of mutual influence between subsystems.

[0017] Preferably, step S3 includes:

[0018] S31. Establish a multi-subpopulation collaborative optimization architecture, in which the supply subpopulation is responsible for optimizing the installed capacity of renewable energy in each province and city, including the decision variables of the installed capacity of various types of renewable energy in each province and city; the transmission subpopulation is responsible for optimizing the transmission capacity of transmission lines; the demand subpopulation is responsible for optimizing the power demand guarantee level in each province and city; and the energy storage subpopulation is responsible for optimizing the energy storage installed capacity in each province and city.

[0019] S32. Calculate the system characteristic factors of each subsystem based on the auxiliary data of each province and city. The system characteristic factor of the supply subgroup is the resource endowment factor, the system characteristic factor of the transmission subgroup is the transmission demand coefficient, the system characteristic factor of the demand subgroup is the load characteristic coefficient, and the system characteristic factor of the energy storage subgroup is the configuration strategy coefficient.

[0020] S33. Based on the cooperative strength matrix and the system characteristic factors of each subsystem, generate the initial solution set for each subpopulation.

[0021] Preferably, step S4 includes:

[0022] S41. Construct a dual-objective fitness function that simultaneously considers both the maximization of synergy and the minimization of cost.

[0023] S42. Calculate the initial fitness value of each individual in each subpopulation, assign roles based on fitness ranking, and determine the number of discoverers, followers, and vigilants.

[0024] S43, Based on adaptive step size parameter Calculate dynamic early warning values:

[0025] ,

[0026] In the formula, Indicates the first Warning value in the next iteration Indicates the initial warning value. Indicates the first Adjustment coefficient of individual subpopulations Indicates the first The subpopulation in the first The adaptive step size parameter for the next iteration;

[0027] S44. The discoverer uses different search modes based on the warning value, when... When a global search strategy is used, A local search strategy is employed, in which, This indicates a safety threshold; followers adopt different following methods based on individual rankings, with the ranking at... And previous individual follower discoverers, ranked in Subsequent individuals are randomly searched. Indicates the first The size of the subpopulation; the vigilant adopts a conservative or aggressive position update strategy based on the relative fitness level;

[0028] S45. A hierarchical constraint processing mechanism is adopted to constrain individuals after location update, including boundary layer constraint processing to ensure that decision variables are within the allowable range, balance layer constraint processing to handle power system supply and demand balance constraints, and coordination layer constraint processing to handle energy storage configuration ratio constraints and transmission capacity constraints.

[0029] S46. Based on the differences in the convergence states of each subpopulation, dynamically calculate the information exchange frequency. When the exchange conditions are met, combine the current best individuals of each subpopulation to form a candidate global solution, calculate its overall fitness, and if it is better than the current global best solution, update the global best solution and feed the update information back to each subpopulation.

[0030] S47. Check if the convergence condition is met. If the maximum number of iterations is reached or the fitness improvement is less than the set threshold, output the optimal configuration result; otherwise, let t=t+1 and return to S43 to continue iterative optimization.

[0031] Preferred adaptive step size parameter Taking into account synergy feedback, convergence state, and iteration process, the calculation formula is as follows:

[0032] ,

[0033] in, This represents the initial step size parameter. Indicates the first The subpopulation in the first The degree of synergy in the next iteration. Indicates the first The maximum degree of coordination among individual subpopulations Indicates the maximum number of iterations. Indicates the first The subpopulation in the first The convergence state evaluation value of the next iteration.

[0034] Preferably, step S46 includes:

[0035] The information exchange frequency is dynamically calculated based on the differences in the convergence states of each subpopulation. When the convergence levels of the subpopulations differ significantly, the exchange frequency is increased to promote cooperation; when the convergence levels are similar, the exchange frequency is decreased to maintain diversity. The formula for calculating the information exchange frequency is:

[0036] ,

[0037] in, Indicates the initial switching frequency. Indicates the first The subpopulation in the first The convergence state evaluation value of the next iteration. This indicates a small constant that avoids a denominator of zero;

[0038] When the information exchange conditions are met, the best individuals of each subpopulation are combined to form a candidate solution and their overall fitness is calculated. If the candidate solution is better than the current global optimal solution, the global optimal solution is updated and fed back to each subpopulation.

[0039] Preferably, the hierarchical constraint processing mechanism includes: boundary layer constraint processing to truncate decision variables that exceed the variable boundaries, ensuring that all decision variables are within the allowable range; balance layer constraint processing to handle the supply and demand balance constraints of the power system, based on power demand data in auxiliary data, calculating the degree of violation when a violation of power balance constraints is detected, and repairing it through energy storage charging and discharging power adjustment and inter-provincial power exchange; and coordination layer constraint processing to handle energy storage configuration ratio constraints and transmission capacity constraints, ensuring that the ratio of energy storage capacity to renewable energy installed capacity is within a reasonable range, and adjusting the fitness value of individuals that violate the constraints through a penalty function.

[0040] Preferably, the formula for calculating the bi-objective fitness function is as follows:

[0041] ,

[0042] ,

[0043] In the formula, Indicates the first The first in the subpopulation Individual; Represents an individual fitness value; Represents an individual The overall synergy of the corresponding configuration scheme; This represents the theoretical maximum value of the degree of synergy, used for normalization; Represents an individual The total cost of the corresponding configuration scheme; This represents the maximum cost, used for normalization. and These represent the weighting coefficients for synergy and cost, respectively, satisfying... ; Indicates from an individual The decision variable vector corresponding to the a-th subsystem is extracted from the position vector; Let represent the cost function of the a-th subsystem.

[0044] Preferably, the auxiliary data in step S1 includes:

[0045] Electricity demand data includes historical electricity load data, load growth trend data, peak-valley load difference coefficient, load forecast data, and demand response potential data for each province and city, with data granularity covering annual, monthly, daily, and hourly levels; transmission network data includes the rated transmission capacity, line impedance parameters, line length, and transmission loss coefficient of existing transmission lines between provinces and cities, as well as construction planning data and transmission capacity expansion potential data for inter-regional transmission channels; energy storage-related data includes the installed capacity, technology type distribution, charging and discharging efficiency parameters, and investment and operation and maintenance costs of existing energy storage facilities in each province and city; and economic development data, energy consumption structure data, and policy support intensity data for each province and city.

[0046] Preferably, step S5 includes:

[0047] Extract specific configuration parameters for each subsystem from the global optimal solution, including the installed capacity of various renewable energy sources in each province and city of the supply subsystem, the transmission capacity configuration of each transmission line in the transmission subsystem, the power demand guarantee level in each province and city of the demand subsystem, and the installed capacity of energy storage in each province and city of the energy storage subsystem; calculate the key performance indicators of the configuration scheme, including the overall system coordination, total investment cost, annualized operating cost, renewable energy utilization rate, and power supply reliability; and generate a complete supply-transmission-demand-storage coordinated optimization configuration report.

[0048] The present invention has the following advantages over the prior art:

[0049] (1) This invention effectively solves the problem of traditional single-population optimization algorithms being prone to local optima and low search efficiency when dealing with high-dimensional complex search spaces by constructing a multi-subpopulation co-evolutionary optimization configuration method for the supply-transmission-demand-storage of renewable energy power systems. This method decomposes a single population into four specialized subpopulations responsible for the independent optimization of the four subsystems of supply, transmission, demand, and energy storage, and achieves global coordination through a coordination degree-guided information exchange mechanism. While maintaining search diversity, it improves search efficiency and achieves dual-objective optimization of maximizing coordination degree and minimizing total cost, providing a technically feasible and economically reasonable configuration scheme for large-scale renewable energy power systems.

[0050] (2) By constructing a subsystem coordination strength matrix to quantify the coupling relationship between subsystems, and combining the resource endowment characteristics of each province and city to design an intelligent initialization strategy, the problem of poor initial population quality and slow convergence speed caused by completely random initialization in the standard sparrow optimization algorithm is solved. This strategy gives the initial population a better search starting point and directionality, improving the convergence efficiency and solution quality of the algorithm;

[0051] (3) By decomposing a traditional single population into four specialized subpopulations, each subpopulation evolves independently and achieves global collaboration through information exchange, effectively solving the curse of dimensionality problem in high-dimensional complex search spaces. This mechanism maintains the specialized characteristics of each subsystem optimization and achieves global collaboration through dynamic information exchange, avoiding the algorithm from getting trapped in local optima and improving global search capabilities;

[0052] (4) The search step size and warning value are dynamically adjusted based on the synergy feedback and the convergence status of each subpopulation, which solves the problem that existing algorithms based on fixed parameter settings cannot adapt to different optimization stages and subsystem characteristics. This mechanism enables the algorithm to adaptively adjust the search strategy according to the real-time optimization status, maintain a large search step size in the early stage of optimization to enhance the global search capability, and reduce the search step size in the later stage of optimization to improve the local search accuracy, thereby improving the adaptability and convergence accuracy of the algorithm;

[0053] (5) By establishing a three-layer constraint processing architecture consisting of a boundary layer, a balance layer, and a coordination layer, the complex coupled constraints such as power balance constraints and energy storage charging and discharging constraints in the supply-transmission-demand-storage system are effectively handled. This mechanism combines layered repair and penalty functions to ensure the feasibility of the solution while maintaining the search efficiency of the algorithm, thus avoiding the performance degradation problem caused by constraint violations in traditional constraint processing methods. Attached Figure Description

[0054] 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.

[0055] Figure 1 This is a flowchart of the method of the present invention;

[0056] Figure 2 This is a flowchart of the algorithm of the present invention. Detailed Implementation

[0057] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0058] like Figure 1 As shown, the present invention provides a method for coordinated optimization of supply-transmission-demand-storage configuration in a renewable energy power system, comprising:

[0059] S1. Obtain basic data from multiple provinces and cities, including renewable energy resource data and auxiliary data. Standardize the renewable energy resource data and define decision variables for four subsystems: supply, transmission, demand, and energy storage, to obtain a unified resource evaluation basis and decision variable system.

[0060] S2. Establish a synergy calculation model, calculate the synergy of each subsystem, calculate the overall synergy based on the synergy of each subsystem, and construct a subsystem synergy strength matrix to quantify the coupling relationship between each subsystem.

[0061] S3. Establish a multi-subpopulation collaborative optimization architecture, decompose the population into four subpopulations according to the four subsystems of supply, transportation, demand and energy storage, and generate the initial solution set of each subpopulation by adopting a collaboration degree-oriented initialization strategy.

[0062] S4. An improved sparrow optimization algorithm is used to optimize each subpopulation in parallel. The fitness is evaluated with synergy and total cost as dual objectives. Synergy optimization among subpopulations is carried out through adaptive parameter adjustment and information exchange mechanism. When the convergence condition is met, the optimal configuration result is obtained.

[0063] S5. Based on the optimal configuration results, determine the installed capacity of renewable energy, energy storage equipment configuration, power transmission scheme and demand response strategy of each province and city, and output the supply-transmission-demand-storage coordinated optimization configuration scheme of renewable energy power system.

[0064] Specifically, in one embodiment of the present invention, step S1 includes:

[0065] First, renewable energy resource data from multiple provinces and cities in the economic region were obtained, specifically including basic resource endowment information for six major renewable energy types.

[0066] The wind energy resource data collection includes wind speed, wind direction distribution, and wind energy density distribution data at different altitudes in various provinces and cities. Data sources include meteorological observation station data from the National Meteorological Administration and satellite remote sensing data. The solar energy resource data collection includes solar radiation intensity, sunshine duration, and ambient temperature data in various provinces and cities, with the ambient temperature data used for temperature correction calculations of photovoltaic power generation efficiency. The hydropower resource data collection includes river runoff, water level changes, and theoretical hydropower reserves in various provinces and cities. The biomass resource data collection includes agricultural and forestry waste production, urban organic waste production, and energy crop planting area data in various provinces and cities. The geothermal resource data collection includes geothermal gradient distribution, geothermal fluid temperature, and geothermal reservoir depth information in various provinces and cities. The ocean energy resource data collection focuses on coastal provinces and cities, collecting resource distribution data on tidal energy, wave energy, and ocean current energy.

[0067] Simultaneously, this invention acquires auxiliary data required for power system operation. Electricity demand data includes historical power load data, load growth trend data, peak-valley load difference coefficients, load forecast data, and demand response potential data for each province and city. The data time granularity covers annual, monthly, daily, and hourly levels to accurately reflect the electricity consumption characteristics and demand change patterns of each province and city. Transmission network data includes line parameter information for existing transmission lines between provinces and cities, including rated transmission capacity, line impedance parameters, line length, and transmission loss coefficients. It also acquires construction planning data for inter-regional transmission channels and data on transmission capacity expansion potential. Energy storage-related data includes the installed capacity, technology type distribution, charge / discharge efficiency parameters, investment costs, and operation and maintenance costs of existing energy storage facilities in each province and city. Furthermore, it is necessary to acquire economic development data, energy consumption structure data, and policy support intensity data for each province and city as constraints and evaluation indicators for collaborative optimization.

[0068] After obtaining the basic data, the renewable energy resource data needs to be standardized to eliminate the dimensional differences between different resource data:

[0069] ,

[0070] in, Indicates province and city index, N represents the number of provinces and cities selected. Represents an index of renewable energy resource types. These correspond to wind energy, solar energy, hydropower, biomass energy, geothermal energy, and ocean energy, respectively. Indicates the first The first province / municipality The original values ​​of the resources, This is the corresponding standardized value, with a range of [0,1].

[0071] For auxiliary data processing, electricity demand data is also processed using standardization methods, with per capita electricity consumption and electricity consumption per unit of GDP in each province and city serving as standardization benchmarks to ensure the comparability of demand data. Transmission network data is categorized and standardized according to transmission distance and voltage level, with different voltage levels using corresponding standardization benchmarks. Energy storage-related data is categorized and processed according to energy storage technology type and application scenario, with different technologies such as electrochemical energy storage, pumped hydro storage, and compressed air energy storage standardized using corresponding technical and economic parameters.

[0072] Based on data acquisition and standardization, a decision variable system is defined for four subsystems: supply, transmission, demand, and energy storage. A total decision variable vector is defined to uniformly represent the decision variables of the four subsystems. Supply subsystem variables It includes the installed capacity of six types of renewable energy in multiple provinces and cities, with a total dimension of 186; transmission subsystem variables. Includes the transmission capacity of each transmission line; demand subsystem variables. Includes the power demand guarantee level of each province and city; energy storage subsystem variables. It includes the energy storage installed capacity of various provinces and cities.

[0073] Specifically, in one embodiment of the present invention, step S2 includes:

[0074] S21. Establish the synergy calculation formulas for the supply subsystem, transmission subsystem, demand subsystem and energy storage subsystem respectively. The synergy of the transmission subsystem, demand subsystem and energy storage subsystem is obtained by weighted summation based on the influence indicators of each subsystem and their weights. The supply subsystem needs to deduct the uncertainty coefficient of renewable energy output. The uncertainty coefficient is quantified by calculating the variance of renewable energy power generation in each province and city.

[0075] S22. The overall synergy is obtained by calculating the synergy of each subsystem using the geometric mean method.

[0076] S23. The degree of internal uncertainty is assessed by calculating the entropy value of each subsystem. The weight coefficient of each subsystem is calculated based on the entropy value. The Pearson correlation coefficient and weight coefficient between subsystems are combined to construct a 4×4 symmetric cooperative strength matrix to quantify the degree of mutual influence between subsystems.

[0077] In a specific example, step S2 is implemented as follows:

[0078] The calculation of the coordination degree of each subsystem reflects the degree of coordination among the elements within the subsystem and the contribution of the subsystem to the overall coordination.

[0079] The formula for calculating the degree of synergy of each subsystem is uniformly expressed as follows:

[0080] ,

[0081] in, Indicates subsystem index, , Indicates the first The number of indicators for each subsystem Indicates the first Subsystem The weight of each indicator, This represents the standardized indicator values. Specifically, the impact indicators for each subsystem include: for the supply subsystem, indicators such as installed capacity matching, output stability, and resource utilization; for the transmission subsystem, indicators such as transmission capacity utilization, network connectivity, and power flow distribution uniformity; for the demand subsystem, indicators such as load forecast accuracy, peak-valley difference coefficient, and demand response capability; and for the energy storage subsystem, indicators such as capacity configuration rationality, charging and discharging efficiency, and frequency regulation and peak shaving capability. Determined using the analytic hierarchy process and expert scoring method, while simultaneously satisfying the normalization constraint. .

[0082] Specifically, the calculation of the coordination degree of the supply subsystem has its own unique characteristics. In addition to considering conventional influencing indicators such as installed capacity matching, output stability, and resource utilization, it is also necessary to deduct the impact of the uncertainty coefficient of renewable energy output. This is because renewable energy has intermittent and fluctuating characteristics, and its output uncertainty can adversely affect the coordinated operation of the system. Therefore, it must be considered when calculating the coordination degree of the supply subsystem. The formula for calculating the coordination degree of the supply subsystem is:

[0083] ,

[0084] in, The uncertainty coefficient of renewable energy output is expressed by the following formula:

[0085] ,

[0086] in, Indicates the first The first province / municipality The power generation capacity of various renewable energy sources This represents the variance function. Variance calculations reflect the fluctuations in renewable energy output across provinces and cities. A larger variance indicates higher uncertainty and a more significant negative impact on coordination.

[0087] The overall synergy is calculated using the geometric mean method, and the formula is as follows:

[0088] ,

[0089] in, Indicates based on decision variables Overall coordination These represent the coordination degree of the four subsystems: supply, transmission, demand, and energy storage. The geometric mean method was chosen based on two considerations: first, to ensure that the importance of the coordination degree of each subsystem is equal, avoiding the overcompensation of the disadvantages of other subsystems by the advantages of one subsystem; and second, to promote a balanced improvement in the coordination degree of each subsystem, guiding the optimization algorithm to take into account the coordinated development of each subsystem while improving the overall coordination degree.

[0090] This invention also constructs a subsystem coordination strength matrix to quantify the coupling relationship between subsystems.

[0091] First, calculate the entropy value of each subsystem to assess the degree of uncertainty within the subsystem. The entropy value reflects the degree of orderliness of information within the subsystem; the smaller the entropy value, the more ordered the system is and the lower the uncertainty.

[0092] ,

[0093] in, Indicates subsystem index, , Indicates the first Subsystem The values ​​of each indicator. In the entropy calculation process, the values ​​of each indicator are first normalized, and then calculated according to the definition formula of information entropy, with the normalization coefficient... Ensure that the entropy value is within the range [0,1].

[0094] Calculate the weight coefficients of each subsystem based on entropy values:

[0095] ,

[0096] in, This represents the subsystem index. The calculation method is based on the fundamental principle of information entropy; subsystems with smaller entropy values ​​have higher information content and lower uncertainty, and therefore should be given greater weight in overall coordination.

[0097] The construction of the synergy strength matrix combines the Pearson correlation coefficient and weighting coefficients between subsystems. The formula for calculating the elements of the synergy strength matrix is ​​as follows:

[0098] ,

[0099] in, Indicates the first Subsystem and the first The Pearson correlation coefficient is used to calculate the correlation strength between the subsystems. The Pearson correlation coefficient is calculated based on historical statistical information of the subsystem indicators, and the strength of the correlation is determined by analyzing the covariance and standard deviation between the indicators of different subsystems.

[0100] The final constructed synergy strength matrix It is a 4×4 symmetric matrix, matrix elements The value range is [0,1]. The larger the value, the stronger the influence of the a-th subsystem on the b-th subsystem. The symmetry of the matrix reflects the equivalence of the mutual influence between subsystems, that is, the influence strength of the a-th subsystem on the b-th subsystem is equal to the influence strength of the b-th subsystem on the a-th subsystem.

[0101] Traditional sparrow optimization algorithms generate the initial population using completely random initialization, neglecting the coupling relationships and constraints between subsystems of the power system. This results in poor initial population quality and low search efficiency. Furthermore, single-population optimization mechanisms are prone to getting trapped in local optima when facing high-dimensional and complex search spaces, making it difficult to fully explore different regions of the solution space. To address these issues, this invention proposes a multi-subpopulation collaborative optimization architecture and a collaboration-oriented intelligent initialization strategy. By decomposing the optimization problem and employing specialized search strategies, the algorithm's global search capability and convergence efficiency are improved.

[0102] Specifically, in one embodiment of the present invention, step S3 includes:

[0103] S31. Establish a multi-subpopulation collaborative optimization architecture, in which the supply subpopulation is responsible for optimizing the installed capacity of renewable energy in each province and city, including the decision variables of the installed capacity of various types of renewable energy in each province and city; the transmission subpopulation is responsible for optimizing the transmission capacity of transmission lines; the demand subpopulation is responsible for optimizing the power demand guarantee level in each province and city; and the energy storage subpopulation is responsible for optimizing the energy storage installed capacity in each province and city.

[0104] S32. Calculate the system characteristic factors of each subsystem based on the auxiliary data of each province and city. The system characteristic factor of the supply subgroup is the resource endowment factor, the system characteristic factor of the transmission subgroup is the transmission demand coefficient, the system characteristic factor of the demand subgroup is the load characteristic coefficient, and the system characteristic factor of the energy storage subgroup is the configuration strategy coefficient.

[0105] S33. Based on the cooperative strength matrix and the system characteristic factors of each subsystem, generate the initial solution set for each subpopulation.

[0106] In this embodiment, a single population is decomposed into four specialized subpopulations, each responsible for optimizing its corresponding subsystem. Each subpopulation has an independent evolutionary mechanism, while achieving global coordination through information exchange. The subpopulation structure is designed as follows: ,in Indicates the first The first in the subpopulation Individual, , Indicates the first The size of individual populations.

[0107] The synergy-oriented initialization strategy is based on the constructed synergy strength matrix, making full use of the coupling relationships between subsystems and the system characteristic factors of each province and city. Specifically, taking the supply subpopulation as an example, the system characteristic factor of the supply subpopulation is the supply resource endowment factor:

[0108] ,

[0109] in, Indicates the first The weighting coefficients for various renewable energy sources satisfy the following conditions: , Indicates the first The first province / municipality The theoretical power generation capacity of this renewable energy source Indicates the first The maximum theoretical power generation capacity of a renewable energy source.

[0110] The initialization strategy for the supply subpopulation combines historical data, cooperation strength, and resource endowment characteristics:

[0111] ,

[0112] in, Indicates the first Historical mean of decision variables for each supply subsystem This represents the disturbance factor. Indicates the supply subsystem and the first The coordination strength of each subsystem To show obedience Uniformly distributed random numbers, Indicates the index of the decision variable.

[0113] Other subpopulations employ a similar initialization strategy, with the transport subpopulation initialized based on transport demand characteristics: , Represents the transmission demand coefficient; the demand subpopulation is initialized based on load characteristics: , Represents the load characteristic coefficient; the energy storage subpopulation is initialized based on the configuration strategy: , This represents the energy storage configuration strategy coefficient.

[0114] Traditional sparrow optimization algorithms have significant shortcomings when dealing with multi-objective collaborative optimization problems in renewable energy power systems. First, fixed parameter settings cannot adapt to different optimization stages and subsystem characteristics, leading to low search efficiency. Second, they lack effective constraint handling mechanisms, making it difficult to handle complex coupled constraints in power systems. Third, a single fitness evaluation system cannot balance the multi-objective requirements of collaborative performance and economy. To address these issues, this invention proposes an adaptive parameter adjustment mechanism, a hierarchical constraint handling architecture, and a dual-objective fitness function. By dynamically adjusting algorithm behavior, hierarchically handling complex constraints, and balancing multi-objective optimization, the algorithm's performance in complex power system collaborative optimization is significantly improved.

[0115] Specifically, such as Figure 2 As shown, in one embodiment of the present invention, step S4 includes:

[0116] S41. Construct a dual-objective fitness function that simultaneously considers both the goal of maximizing synergy and minimizing cost.

[0117] Specifically, the fitness function is designed as a weighted summation, using weight coefficients to balance the system's collaborative performance and economic requirements, providing a clear optimization direction for the algorithm. The specific calculation formula is as follows:

[0118] ,

[0119] ,

[0120] In the formula, Indicates the first The first in the subpopulation Individual; Represents an individual fitness value; Represents an individual The overall synergy of the corresponding configuration scheme; This represents the theoretical maximum value of the degree of synergy, used for normalization; Represents an individual The total cost of the corresponding configuration scheme; This represents the maximum cost, used for normalization. and These represent the weighting coefficients for synergy and cost, respectively, satisfying... ; Indicates from an individual The decision variable vector corresponding to the a-th subsystem is extracted from the position vector; Let represent the cost function of the 'a'-th subsystem. The supply subsystem cost mainly includes the investment cost and operation and maintenance cost of renewable energy equipment; the transmission subsystem cost includes the construction and expansion cost of transmission lines; the demand subsystem cost includes the investment cost of demand response equipment and the cost of purchasing electricity; and the energy storage subsystem cost includes the investment cost of energy storage equipment and the cost of charging and discharging losses.

[0121] S42. Calculate the initial fitness value of each individual in each subpopulation, assign roles based on fitness ranking, and determine the number of discoverers, followers, and vigilants.

[0122] Specifically, the initial fitness value of each individual in each subpopulation is first calculated using a bi-objective fitness function, and then roles are assigned based on the fitness ranking. Discoverers are responsible for global search, followers for local search, and vigilants for boundary exploration. Role assignment is based on fitness ranking: individuals with higher fitness become discoverers, individuals with medium fitness become followers, and individuals with lower fitness become vigilants. Number of discoverers: Number of followers: Number of vigilant personnel: . This represents the proportion of discoverers in the s-th subpopulation, with a value range of [0.1, 0.3].

[0123] S43, Based on adaptive step size parameter Calculate dynamic early warning values:

[0124] ,

[0125] In the formula, Indicates the first Warning value in the next iteration Indicates the initial warning value. Indicates the first Adjustment coefficient of individual subpopulations Indicates the first The subpopulation in the first The adaptive step size parameter for each iteration.

[0126] Specifically, adaptive step size parameters Taking into account synergy feedback, convergence state, and iteration process, the calculation formula is as follows:

[0127] ,

[0128] in, This represents the initial step size parameter. Indicates the first The subpopulation in the first The degree of synergy in the next iteration. Indicates the first The maximum degree of coordination among individual subpopulations Indicates the maximum number of iterations. Indicates the first The subpopulation in the first The convergence state evaluation value of the next iteration.

[0129] The convergence state assessment is quantified by calculating the change in the standard deviation of the fitness values ​​of each subpopulation:

[0130] ,

[0131] in, Indicates the first The subpopulation in the first The fitness standard deviation at the next iteration Indicates the number of iterations:

[0132] ,

[0133] in, Indicates the first Subpopulation number The individual in the first The fitness value of the next iteration. Indicates the first The subpopulation in the first The average fitness value of the iterations.

[0134] S44. The discoverer uses different search modes based on the warning value, when... When a global search strategy is used, A local search strategy is employed, in which, This indicates a safety threshold; followers adopt different following methods based on individual rankings, with the ranking at... And previous individual follower discoverers, ranked in Subsequent individuals are randomly searched. Indicates the first The size of individual populations; vigilant individuals adopt conservative or aggressive positional update strategies based on their relative fitness levels.

[0135] In this embodiment, the location update strategy for the discoverer employs different search modes based on the warning value. When the warning value is low ( This indicates a relatively safe environment, and a global search strategy is employed.

[0136] ,

[0137] in, Indicates the safety threshold. This represents a uniformly random number in (0,1]. In this embodiment, ST is set to 0.8. When the warning value is large ( This indicates a threat to the environment, and a local search strategy is employed.

[0138] ,

[0139] in, This represents a random number that follows a standard normal distribution. Represents the identity matrix.

[0140] The follower position update strategy employs different following methods based on the individual's position in the population. For individuals ranked lower ( ), adopt a strategy of staying away from the worst individuals:

[0141] ,

[0142] For the top-ranked individuals ( The strategy of learning from the optimal individual is adopted:

[0143] ,

[0144] in, and They represent the first The best and worst individuals in a subpopulation The symbol indicates random selection. Here, 'c' represents the rank of an individual's fitness.

[0145] The location-updating strategy of vigilants employs different behavioral patterns based on their fitness values ​​relative to the average level. Vigilants with fitness above the average level adopt a conservative strategy. ):

[0146] ,

[0147] Indicates the first The subpopulation in the first The average fitness value of the iterations.

[0148] Vigilant individuals with below-average fitness employ aggressive strategies. ):

[0149] ,

[0150] in, and This represents a random number that follows a standard normal distribution. This represents a small constant that avoids a denominator of zero.

[0151] S45. A hierarchical constraint processing mechanism is adopted to constrain individuals after location update, including boundary layer constraint processing to ensure that decision variables are within the allowable range, balance layer constraint processing to handle power system supply and demand balance constraints, and coordination layer constraint processing to handle energy storage configuration ratio constraints and transmission capacity constraints.

[0152] In this embodiment, boundary layer constraint processing ensures that all decision variables are within the allowable range, and truncates variables that exceed the boundary:

[0153] ,

[0154] in, and They represent the first The lower and upper bounds of each decision variable.

[0155] The balance layer constraint handles the supply and demand balance constraints of the power system, ensuring the balance of power supply and demand in various provinces and cities:

[0156] ,

[0157] in, and They represent the first Power received and power transmitted to each province and city and These represent the energy storage discharge and charging power, respectively. Indicates network loss power. Indicates the first Electricity demand in each province and city.

[0158] When power balance constraints are violated, the degree of violation is calculated and repaired through energy storage adjustments:

[0159] ,

[0160] When a violation of power balance constraints is detected, the system first calculates the degree of violation. This value represents the electricity supply and demand imbalance in the i-th province / city. If This indicates an oversupply of power, in which case priority should be given to increasing the charging power of energy storage. To absorb excess power, and at the same time, the power transmitted to external sources can be appropriately increased. ;like This indicates insufficient power supply; in this case, priority should be given to increasing the energy storage discharge power. To supplement the power shortage, and at the same time, the external power received can be appropriately increased. The repair process must simultaneously satisfy the charging and discharging power constraints of the energy storage devices and the transmission capacity constraints of the transmission lines. This combined repair strategy of energy storage regulation and power exchange ensures that power balance constraints are effectively met while maintaining the economic and technical feasibility of the solution.

[0161] The coordination layer handles constraints on the energy storage configuration ratio to ensure that the ratio of energy storage capacity to renewable energy installed capacity is within a reasonable range.

[0162] ,

[0163] in, and These represent the lower and upper limits of the energy storage configuration ratio, respectively.

[0164] For individuals that violate the constraints, their fitness values ​​are adjusted using a penalty function:

[0165] ,

[0166] in, Indicates the penalty coefficient. Indicates the degree of constraint violation.

[0167] S46. Combine the current best individuals of each subpopulation to form a candidate global solution, calculate its overall fitness, and if it is better than the current global best solution, update the global best solution and feed the update information back to each subpopulation.

[0168] In this embodiment, the information exchange frequency is dynamically calculated based on the convergence state differences of each subpopulation. When the information exchange conditions are met, the current best individuals of each subpopulation are combined to form a candidate global solution, and its overall fitness is calculated. If it is better than the current global best solution, the global best solution is updated and the updated information is fed back to each subpopulation.

[0169] The dynamic adjustment mechanism for information exchange frequency determines the timing of exchanges based on the degree of difference in the convergence states of each subpopulation. When the convergence levels of the subpopulations differ significantly, the exchange frequency is increased to promote cooperation; when the convergence levels are similar, the exchange frequency is decreased to maintain diversity. The formula for calculating the information exchange frequency is:

[0170] ,

[0171] in, Indicates the initial switching frequency. Indicates the first The subpopulation in the first The convergence state evaluation value of the next iteration. This represents a small constant that avoids a denominator of zero.

[0172] The dynamic adjustment mechanism for information exchange frequency among subpopulations determines the timing of exchanges based on the degree of difference in the convergence states of each subpopulation. Specifically, when the convergence degrees of each subpopulation differ significantly, it indicates that the optimization progress of different subsystems is uneven. In this case, it is necessary to increase information exchange to promote the learning of lagging subpopulations from advanced subpopulations, thereby achieving collaborative optimization. Conversely, when the convergence degrees of each subpopulation are similar, it indicates that the optimization of each subsystem is relatively balanced. In this case, the information exchange frequency should be reduced to maintain the search diversity of each subpopulation and avoid premature convergence to a local optimum. The numerator in the formula for calculating the exchange frequency reflects the degree of unevenness in the convergence states. The greater the difference between the minimum and maximum convergence degrees, the closer this value is to 1, and the higher the exchange frequency. When the difference is small, this value is close to 0, and the exchange frequency decreases.

[0173] When the conditions for information exchange are met ( (That is, the difference in convergence states among the subpopulations exceeds a threshold), combining the optimal solutions of each subpopulation to form candidate solutions: And calculate its overall fitness. If the fitness of a candidate solution is better than that of the current global optimum ( If the global optimal solution is updated, then: And feed back the corresponding part of the global optimal solution to each subpopulation: .

[0174] The implementation of the global optimal solution feedback mechanism involves three key steps: First, a candidate global solution is formed by combining the current optimal solutions of each subpopulation. This candidate solution incorporates the latest optimization results of the four subsystems. Second, the overall fitness of this candidate solution is calculated to evaluate its performance as a global solution. Finally, if the fitness of the candidate solution is better than the current global optimal solution, the global optimal solution is updated, and the portions of the updated global optimal solution corresponding to each subsystem are fed back to the respective subpopulations. This feedback mechanism ensures that each subpopulation maintains a relatively independent evolutionary process while receiving timely guidance from global optimal information, thereby accelerating convergence to the global optimal solution while maintaining search diversity. During the feedback process, each subpopulation only receives decision variable information related to its own subsystem, avoiding confusion of decision variables between different subsystems.

[0175] S47. Check if the convergence condition is met. If the maximum number of iterations is reached or the fitness improvement is less than the set threshold, output the optimal configuration result; otherwise, let t=t+1 and return to S43 to continue iterative optimization.

[0176] Convergence conditions include reaching the maximum number of iterations. And the improvement in fitness is less than the set threshold. ;in, Indicates the first The global optimal fitness value in the next iteration. (This represents the convergence threshold). The algorithm terminates and outputs the global optimal solution when any of the conditions are met. Otherwise, t=t+1, return to step S43 to continue iterating.

[0177] Specifically, in one embodiment of the present invention, step S5 includes:

[0178] From the global optimal solution Extract the specific configuration parameters of each subsystem.

[0179] The extraction process of the supply subsystem configuration parameters involves reconstructing the first 186 elements in the global optimal solution according to province / city and energy type. The specific extraction formula is as follows: ,in, This represents the optimal installed capacity of the k-th type of renewable energy in the i-th province / city. This represents the element value at the corresponding position in the global optimal solution.

[0180] The extraction of configuration parameters for the transmission subsystem is based on the topological characteristics of the power transmission network. The transmission capacity configuration of each transmission line is determined in the following way: , Indicates the first The optimal transmission capacity of the transmission line. This represents the transmission line index, covering all inter-provincial transmission channels and inter-regional transmission lines.

[0181] The extraction of configuration parameters for the demand subsystem involves determining the power demand guarantee level for each province and city. The power demand guarantee level reflects the degree to which the system meets the power demand of each province and city, and its extraction formula is as follows: , This represents the optimal power demand guarantee level for the i-th province / city. The value range is usually between 0.9 and 1.1. A value of 1 indicates that the baseline demand is fully met, a value greater than 1 indicates that there is a certain margin for demand growth, and a value less than 1 indicates that there is a certain supply-demand gap but it is within an acceptable range.

[0182] The configuration parameters of the energy storage subsystem are extracted in the following way: , This represents the optimal energy storage capacity for the i-th province / city.

[0183] The performance indicators of the configuration scheme are calculated, including overall system synergy, total investment cost, annualized operating cost, renewable energy utilization rate, and power supply reliability. Overall synergy and total investment cost are calculated using the corresponding formulas mentioned above. Annualized operating cost mainly includes the operation and maintenance costs of renewable energy equipment and energy storage equipment, power loss costs of transmission lines, and costs generated from electricity trading. Operation and maintenance costs are calculated based on the installed capacity of various equipment and the corresponding annualized operation and maintenance rates. Transmission loss costs are determined based on line transmission capacity, loss rate, and electricity price. Electricity trading costs are calculated based on inter-provincial electricity exchange volume and market electricity prices. Renewable energy utilization rate is measured by the ratio of actual renewable energy generation to installed capacity in each province and city. Power supply reliability is assessed based on the comparison between the system's total power supply capacity and peak load demand.

[0184] A complete supply-transportation-demand-storage coordinated optimization configuration report includes a configuration scheme overview, a detailed configuration parameter table for each subsystem, a summary of key performance indicators, economic analysis, technical feasibility assessment, and implementation recommendations.

[0185] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for collaborative optimization configuration of renewable energy power system supply-distribution-demand-storage, characterized in that, The method comprises the following steps: S1, obtaining basic data of multiple provinces and cities, including renewable energy resource data and auxiliary data, performing standardization processing on the renewable energy resource data, defining decision variables of four subsystems of supply, transmission, demand and energy storage, and obtaining unified resource evaluation basis and decision variable system; S2, establishing a coordination degree calculation model, calculating the coordination degree of each subsystem respectively, calculating the overall coordination degree based on the coordination degree of each subsystem, and constructing a subsystem coordination strength matrix to quantify the coupling relationship between each subsystem; S3, establishing a multi-subpopulation collaborative optimization architecture, dividing the population into four subpopulations according to the four subsystems of supply, transmission, demand and energy storage, and using a coordination degree oriented initialization strategy to generate an initial solution set for each subpopulation; S4, using an improved sparrow optimization algorithm to perform parallel optimization on each subpopulation, taking the coordination degree and total cost as double objectives to evaluate the fitness, and performing collaborative optimization between subpopulations through adaptive parameter adjustment and information exchange mechanism, and obtaining the optimal configuration result when the convergence condition is reached; wherein the improved sparrow optimization algorithm considers the coordination degree feedback, convergence state and iteration process to calculate the adaptive step parameter, calculates the dynamic early warning value based on the adaptive step parameter, and adjusts the search mode of the discoverer through the dynamic early warning value; the information exchange mechanism dynamically adjusts the information exchange frequency based on the difference degree of the convergence state of each subpopulation to determine the information exchange time; S5, determining the renewable energy installed capacity, energy storage device configuration, power transmission scheme and demand response strategy of each province and city according to the optimal configuration result, and outputting the supply-transmission-demand-storage collaborative optimization configuration scheme of the renewable energy power system.

2. The renewable energy power system supply-distribution-demand-storage collaborative optimization configuration method according to claim 1, characterized in that, Step S2 comprises: S21, respectively establishing the coordination degree calculation formula of the supply subsystem, the transmission subsystem, the demand subsystem and the energy storage subsystem, wherein the coordination degrees of the transmission subsystem, the demand subsystem and the energy storage subsystem are obtained by weighted summation based on the influence indexes and their weights of each subsystem, and the supply subsystem needs to additionally deduct the uncertainty coefficient of renewable energy output, and the uncertainty coefficient is quantified by calculating the variance of renewable energy power generation of each province and city; S22, calculating the coordination degree of each subsystem by the geometric mean method to obtain the overall coordination degree; S23, evaluating the internal uncertainty degree by calculating the entropy value of each subsystem, calculating the weight coefficient of each subsystem based on the entropy value, and constructing a 4x4 symmetric coordination strength matrix combining the Pearson correlation coefficient and the weight coefficient between subsystems to quantify the mutual influence degree between each subsystem.

3. The renewable energy power system supply-distribution-demand-storage collaborative optimization configuration method according to claim 1, characterized in that, Step S3 comprises: S31, establishing a multi-subpopulation collaborative optimization architecture, wherein the supply subpopulation is responsible for optimizing the renewable energy installed capacity of each province and city, including the installed capacity decision variables of various renewable energy sources in each province and city; the transmission subpopulation is responsible for optimizing the transmission capacity of the power transmission line; the demand subpopulation is responsible for optimizing the power demand guarantee level of each province and city; and the energy storage subpopulation is responsible for optimizing the energy storage installed capacity of each province and city; S32, calculating system characteristic factors of each subsystem based on auxiliary data of each province and city, wherein the system characteristic factor of the supply sub-population is a resource endowment factor, the system characteristic factor of the transmission sub-population is a transmission demand coefficient, the system characteristic factor of the demand sub-population is a load characteristic coefficient, and the system characteristic factor of the energy storage sub-population is a configuration strategy coefficient; S33, generating an initial solution set of each sub-population based on the coordination strength matrix and the system characteristic factors of each subsystem.

4. The renewable energy power system supply-distribution-demand-storage collaborative optimization configuration method according to claim 1, characterized in that, Step S4 includes: S41, constructing a double-target fitness function by simultaneously considering the two targets of maximum coordination degree and minimum cost; S42, calculating initial fitness values of each individual in each sub-population, performing role assignment based on fitness ranking to determine the number of discoverers, followers and sentries; S43, based on an adaptive step size parameter Compute dynamic early warning value: ， wherein denotes the pre-warning value in the denotes the initial pre-warning value, denotes the pre-warning value in the denotes the adjustment factor of the denotes the pre-warning value in the denotes the adaptive step size parameter of the denotes the adaptive step size parameter of the​ S44, the discoverer adopts different search patterns according to the warning value, adopts global search strategy when , adopts local search strategy when , wherein, represents the security threshold; the follower adopts different following ways according to the individual ranking, the individual ranking in and before follows the discoverer, the individual ranking after performs random search, represents the size of the th subpopulation; the alarm adopts conservative or aggressive position update strategy according to the relative level of fitness; S45, performing constraint processing on the individuals after position updating by using a hierarchical constraint processing mechanism, including boundary layer constraint processing to ensure that the decision variables are within the allowed range, balance layer constraint processing to ensure power supply-demand balance constraints, and coordination layer constraint processing to ensure energy storage configuration proportion constraints and transmission capacity constraints; S46, dynamically calculating information exchange frequency based on the differences in convergence states of each sub-population, combining the current optimal individuals of each sub-population to form a candidate global solution when the exchange conditions are met, calculating the overall fitness of the candidate global solution, and updating the global optimal solution and feeding back the update information to each sub-population if the candidate global solution is better than the current global optimal solution; S47, checking whether the convergence conditions are met, and outputting the optimal configuration result if the maximum number of iterations is reached or the fitness improvement amplitude is less than a set threshold, otherwise, setting t=t+1 and returning to S43 for continued iteration optimization.

5. The renewable energy power system supply-distribution-demand-storage collaborative optimization configuration method according to claim 4, characterized in that, Adaptive step size parameter Considering the coordination feedback, convergence state and iteration process, the calculation formula is: ， wherein, represents an initial step parameter, represents the degree of cooperation of the th subpopulation at the th iteration, represents the maximum degree of cooperation of the th subpopulation, represents the maximum number of iterations, represents the convergence state evaluation value of the th subpopulation at the th iteration.

6. The renewable energy power system supply-distribution-demand-storage collaborative optimization configuration method according to claim 4, characterized in that, Step S46 includes: Dynamically calculating information exchange frequency based on the differences in convergence states of each sub-population, increasing the exchange frequency to promote coordination when the convergence degree differences are large, and reducing the exchange frequency to maintain diversity when the convergence degrees are similar, and the information exchange frequency calculation formula is: ， wherein, represents the initial exchange frequency, represents the convergence status evaluation value of the th subpopulation at the th iteration, represents a small constant to avoid division by zero; When the information exchange conditions are met, combining the optimal individuals of each sub-population to form a candidate solution and calculating the overall fitness of the candidate solution, and updating the global optimal solution and feeding back the update information to each sub-population if the candidate solution is better than the current global optimal solution.

7. The renewable energy power system supply-distribution-demand-storage collaborative optimization configuration method according to claim 4, characterized in that, The hierarchical constraint processing mechanism includes: boundary layer constraint processing to truncate the decision variables that exceed the variable boundary, so that all decision variables are within the allowed range; balance layer constraint processing to ensure power supply-demand balance constraints of the power system, based on power demand data in the auxiliary data, when a power balance constraint violation is detected, the violation degree is calculated and repaired by adjusting the charging and discharging power of the energy storage and exchanging power across provinces; and coordination layer constraint processing to ensure energy storage configuration proportion constraints and transmission capacity constraints, so that the ratio of energy storage capacity to renewable energy installed capacity is within a reasonable range, and the fitness value of an individual that violates the constraints is adjusted by a penalty function.

8. The renewable energy power system supply-distribution-demand-storage collaborative optimization configuration method according to claim 4, characterized in that, The calculation formula of the double-target fitness function is as follows: ， ， In the formula, Indicates the first The first in the subpopulation Individual; Represents an individual fitness value; Represents an individual The overall synergy of the corresponding configuration scheme; This represents the theoretical maximum value of the degree of synergy, used for normalization; Represents an individual The total cost of the corresponding configuration scheme; This represents the maximum cost, used for normalization. and These represent the weighting coefficients for synergy and cost, respectively, satisfying... ; Indicates from an individual The decision variable vector corresponding to the a-th subsystem is extracted from the position vector; Let represent the cost function of the a-th subsystem.

9. The renewable energy power system supply-distribution-demand-storage collaborative optimization configuration method according to claim 1, characterized in that, The auxiliary data in step S1 includes: Power demand data, including historical power load data, load growth trend data, peak-valley load difference coefficient, load forecasting data, and demand response potential data of each province and city, with data time granularity covering annual, monthly, daily, and hourly level data; transmission network data, including the existing transmission line rated transmission capacity, line impedance parameters, line length, transmission loss coefficient between each province and city, and the construction planning data and transmission capacity expansion potential data of cross-regional transmission channels; energy storage related data, including the installed capacity of existing energy storage facilities in each province and city, technical type distribution, charging and discharging efficiency parameters, and investment cost and operation and maintenance cost data of energy storage equipment; economic development data, energy consumption structure data, and policy support intensity data of each province and city.

10. The renewable energy power system supply-distribution-demand-storage collaborative optimization configuration method according to claim 1, characterized in that, Step S5 includes: Extracting specific configuration parameters of each subsystem from the global optimal solution, including the installed capacity of each type of renewable energy in each province and city of the supply subsystem, the transmission capacity configuration of each transmission line of the transmission subsystem, the power demand guarantee level of each province and city of the demand subsystem, and the energy storage installed capacity of each province and city of the energy storage subsystem; calculating the key performance indicators of the configuration scheme, including the overall coordination degree of the system, the total investment cost, the annualized operation cost, the renewable energy utilization rate, and the power supply reliability; generating a complete supply-transmission-demand-storage coordinated optimization configuration report.

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