A random regulation method for district shared energy storage and distributed new energy based on grid-connection performance game

Abstract

The present application relates to the technical field of shared energy storage optimization regulation, in particular to a kind of random regulation method of district shared energy storage and distributed new energy based on grid-connection efficiency game, the method first generates typical scene, and constructs the grid-connection efficiency master-slave game random regulation model with shared energy storage as leader, and distributed new energy as follower, proposes the grid-connection efficiency evaluation index of district distributed new energy, and obtains the comprehensive weight of grid-connection efficiency evaluation index, multiple grid-connection efficiency evaluation indexes are mapped into single grid-connection efficiency value, finally the stable equilibrium solution of regulation model is solved, reaches game equilibrium.The present application first quantifies the key physical performance index of distributed new energy grid-connection into single grid-connection efficiency value through subjective and objective weighting, and includes the core driving target of district shared energy storage and distributed new energy game decision, solves the physical system optimization problem under the competition of multiple new energy subjects, and maximizes the overall performance of district.

Description

Technical Field

[0001] This invention relates to the field of shared energy storage optimization and control technology, specifically a random control method for shared energy storage and distributed new energy in transformer substations based on grid-connected efficiency game theory. Background Technology

[0002] Renewable energy sources, represented by distributed wind and solar power, have experienced rapid development and large-scale integration into distribution substations. The integration of these distributed power sources into low-voltage distribution substations provides crucial support for energy structure transformation and user-side energy self-sufficiency. However, the inherent volatility, randomness, and intermittency of distributed renewable energy output pose serious challenges to the safe, stable, and economical operation of traditional distribution substations. Specifically, in high-penetration scenarios, problems such as node voltage exceeding limits, reverse overload of transformers, and increased network losses are easily triggered. These problems severely restrict the local absorption capacity of renewable energy, leading to frequent instances of wind and solar curtailment and reducing the utilization efficiency of grid and user assets.

[0003] To address these challenges, shared energy storage has emerged as a new business model and technological approach. By configuring a shared energy storage system at the distribution transformer area, its flexible and rapid power throughput capabilities can be effectively utilized to achieve multiple functions, including peak shaving and valley filling, smoothing fluctuations, supporting voltage, and promoting the local consumption of distributed renewable energy. Compared to independently configured energy storage, shared energy storage has significant advantages such as economies of scale, high resource utilization, and ease of unified control, and is considered one of the key technologies for solving the problem of grid connection and consumption of distributed renewable energy in distribution transformer areas.

[0004] However, shared energy storage's power support for a particular distributed renewable energy source alters the power flow distribution and voltage status of the local power grid. This change directly impacts the grid connection environment of other renewable energy sources and their demand for supporting resources, resulting in significant coupling and mutual influence between renewable energy sources and their shared energy storage applications. This strong interdependence in decision-making makes traditional centralized or independent optimization methods difficult to fully apply, necessitating the introduction of game theory. However, traditional game theory models are limited by a single economic objective. Economic game strategies driven by peak-valley arbitrage and grid connection revenue fail to consider the performance evaluation requirements and real-time security constraints of distributed renewable energy grid connection, thus failing to achieve optimal performance support from shared energy storage.

[0005] Therefore, establishing a stochastic control method that fully considers the grid connection efficiency of shared energy storage and reflects the mutual influence and game relationship among multiple new energy entities, and realizing the optimal scheduling of power supported by shared energy storage, is the key to ensuring the safe and reliable operation of the power grid in the distribution area and promoting the consumption of distributed new energy. It is also a core technical problem that urgently needs to be solved in this field. Summary of the Invention

[0006] This invention aims to solve the problem of optimal power scheduling of shared energy storage that takes into account the mutual influence and game relationship among multiple entities in the distributed renewable energy in the transformer area, and provides a random control method for shared energy storage and distributed renewable energy in the transformer area based on grid-connected efficiency game theory.

[0007] This invention is achieved using the following technical solution: a method for stochastic regulation of distributed energy storage and renewable energy in transformer substations based on grid-connected efficiency game theory, comprising the following steps:

[0008] Step 1: Generate typical scenarios containing multiple distributed new energy sources based on Copula function and K-means clustering algorithm;

[0009] Step 2: Based on the typical scenario generated in Step 1, construct a master-slave game stochastic control model for grid connection efficiency with shared energy storage as the leader and distributed new energy as the follower. Optimize the support power and pricing of shared energy storage for distributed new energy. In the control model, the objective function of shared energy storage is to maximize the total grid connection expected efficiency of distributed new energy and its own expected net income, while the objective function of distributed new energy is to maximize its own grid connection expected efficiency.

[0010] Step 3: Propose grid connection efficiency evaluation indicators for distributed renewable energy in the distribution area, including average voltage deviation at the grid connection point, power fluctuation, absorption rate, grid-connected power generation revenue, service fees paid to shared energy storage, and cost of wind and solar curtailment penalties.

[0011] Step 4: Use the analytic hierarchy process (AHP) to obtain the subjective weights of each grid connection efficiency evaluation index, and use the entropy weight method to obtain the objective weights of each grid connection efficiency evaluation index. Based on the comprehensive weight combining subjective and objective weights, map multiple grid connection efficiency evaluation indexes into a single grid connection efficiency value. This single grid connection efficiency value is output to the shared energy storage and distributed new energy objective functions in Step 2 to form a closed-loop feedback optimization.

[0012] Step 5: Use a distributed iterative framework based on backward induction to solve the stable equilibrium solution of the above master-slave game stochastic control model, thus achieving game equilibrium.

[0013] The above-mentioned method for random control of shared energy storage and distributed new energy in transformer substations based on grid-connected efficiency game theory involves the following steps: In step one, a marginal distribution function of the prediction error is fitted based on the prediction error sequence of distributed new energy; the marginal distribution functions of each independent prediction error are combined based on the Copula function to construct a joint distribution function and generate an original random scenario set; the original random scenario set is clustered using the K-means clustering algorithm and reduced to typical scenarios, providing an uncertain input basis for the game optimization in step two.

[0014] The specific process of step one of the above-mentioned method for stochastic regulation of distributed energy storage and renewable energy based on grid-connected efficiency game theory is as follows:

[0015] 1) The historical power prediction error data of distributed renewable energy sources are cleaned and normalized to obtain the prediction error sequence. ,in For distributed new energy The processed historical power prediction error data were used; the kernel density estimation method was employed to fit the prediction error sequence of distributed renewable energy sources, thereby obtaining... An independent prediction error marginal distribution function ,in For distributed new energy The marginal distribution function of the prediction error;

[0016] 2) Constructing representations based on Copula functions The marginal distribution function of each prediction error depends on the joint distribution function of the structure. In the formula, For the joint distribution function, For Copula functions;

[0017] 3) Sample the constructed joint distribution function to generate a dimension of... A random vector, where each dimension of the random vector corresponds to a distributed new energy source, the th... The dimensional random vector corresponds to the th A distributed new energy source; for each dimension of random vector, the inverse function of the corresponding prediction error marginal distribution function is used to perform an inverse transformation, which transforms it into the true prediction error value; the true prediction error value is added to the original prediction value of the historical power prediction error data to generate the original random scenario set of distributed new energy sources;

[0018] 4) The original random scene set is compressed using the K-means clustering algorithm, reducing it to... A typical scenario and its probability of occurrence.

[0019] The specific process of step two in the above-mentioned method for stochastic regulation of distributed energy storage and renewable energy based on grid-connected efficiency game theory is as follows:

[0020] Considering all typical scenarios, the objective function of shared energy storage is to maximize the total grid-connected expected efficiency of distributed new energy and its own expected net income. The expected net income includes the service fee for shared energy storage to provide supporting power to distributed new energy, the energy storage peak-valley arbitrage income, and the energy storage investment and operation and maintenance costs. , , In the formula, To share the energy storage objective function, Indicates the first A typical scenario This represents the total number of typical scenarios. Indicates the first The probability of a typical scenario; and They represent the first The weighting coefficients of expected net income and expected efficiency of total grid connection of distributed new energy in a typical scenario. Indicates shared energy storage in the first Expected net profit under a typical scenario; Indicates the first Total grid connection efficiency of distributed new energy in a typical scenario; , and These represent the shared energy storage in the first... Service fees for providing supporting power to distributed renewable energy in a typical scenario, energy storage peak-valley arbitrage income and energy storage investment and operation and maintenance costs; Indicates the first A distributed new energy source This indicates the total number of distributed renewable energy sources in the area. Indicates the first time, Indicates the total duration of regulation; and These respectively represent the shift from shared energy storage to distributed new energy sources. exist Pricing for charging and discharging services at any time; and These respectively represent the shift from shared energy storage to distributed new energy sources. In the In a typical scenario The charging and discharging power at any given time; and These represent shared energy storage. Charging and discharging electricity prices that constantly interact with the power grid; and These represent the shared energy storage... In a typical scenario The charging and discharging power that constantly interacts with the power grid; Indicates the shared energy storage operation and maintenance coefficient; This represents the unit capacity cost of shared energy storage. Indicates the rated capacity of shared energy storage. Indicates the discount rate. Indicates the shared energy storage float charging lifetime; Indicates the first The first distributed new energy source in the first The grid connection efficiency in a typical scenario is the average grid voltage deviation. Power fluctuation , absorption rate Electricity revenue Service fees paid to shared energy storage and the cost of penalties for abandoning wind and solar power The function;

[0021] The objective function for distributed renewable energy is to maximize its own grid-connected expected efficiency. In the formula, Indicates the first A distributed new energy objective function, Indicates the first The first distributed new energy source in the first Grid connection efficiency in a typical scenario.

[0022] The aforementioned method for stochastic regulation of shared energy storage and distributed renewable energy in transformer substations based on grid-connected efficiency game theory includes the following constraints for shared energy storage:

[0023] Shared energy storage power constraints: In the formula, and These represent the shared energy storage in the first... In a typical scenario Total charging power and total discharging power at any given time; and These represent the upper limits of the shared energy storage charging power and the upper limit of the discharging power, respectively.

[0024] Shared energy storage SOC constraints: In the formula, and They represent the first In a typical scenario Time and The state of charge of the energy storage is shared at all times; and These represent the charging efficiency and discharging efficiency of shared energy storage, respectively. and These represent the maximum and minimum values ​​of the state of charge of the shared energy storage, respectively.

[0025] Pricing constraints for shared energy storage charging and discharging services: In the formula, and These are the pricing for the maximum discharge service and the minimum discharge service of shared energy storage, respectively. and These are the pricing for the maximum and minimum charging services of shared energy storage, respectively.

[0026] Distributed renewable energy power constraints: In the formula, Indicates the first The first distributed new energy source in the first In a typical scenario Predicted power generation at any given time; Indicates the first The rated output power of each distributed new energy source; Indicates the first The first distributed new energy source in the first In a typical scenario The power generation and grid connection capacity at any given moment; Indicates the first The first distributed new energy source in the first In a typical scenario The amount of wind and solar power curtailed at any given moment; Indicates the first The first distributed new energy source In a typical scenario The grid-connected power at any given time should take into account the supporting role of shared energy storage.

[0027] To ensure that the proposed control method conforms to the physical laws of the power grid, the game optimization also needs to meet the basic power flow constraints for stable operation of the distribution area: node power balance constraints, node voltage constraints, line capacity constraints, etc.

[0028] The above-mentioned method for stochastic regulation of shared energy storage and distributed new energy in transformer substations based on grid-connected efficiency game theory, specifically includes the following grid-connected efficiency evaluation indicators proposed in step three:

[0029] Average voltage deviation at grid connection point: In the formula, Indicates the first The first distributed new energy source in the first Average voltage deviation at grid connection point in a typical scenario Indicates the first The first distributed new energy source in the first A typical scenario Voltage deviation at grid connection point at any time Indicates the first The first distributed new energy source in the first Nominal voltage at grid connection point in a typical scenario;

[0030] Power fluctuation: In the formula, Indicates the first The first distributed new energy source in the first Power fluctuations at the grid connection point in a typical scenario. and They represent the first The first distributed new energy source in the first In a typical scenario Time and Grid-connected power at any given time;

[0031] Consumption rate indicator: In the formula, Indicates the first The first distributed new energy source in the first The absorption rate in a typical scenario;

[0032] Electricity generation revenue: In the formula, Indicates the first The first distributed new energy source in the first Power generation revenue in a typical scenario Indicates the first Distributed new energy sources The on-grid electricity price at any given time;

[0033] Energy storage service fee: In the formula, Indicates the first The first distributed new energy source in the first Energy storage service fees in a typical scenario;

[0034] The cost of curtailing wind and solar power: In the formula, Indicates the first The first distributed new energy source in the first The penalty costs for wind and solar power curtailment in a typical scenario; For the first Distributed new energy sources The price of electricity is penalized for curtailing wind and solar power.

[0035] The specific process of step four in the above-mentioned method for stochastic regulation of distributed energy storage and renewable energy based on grid-connected efficiency game theory is as follows:

[0036] 1) Regarding the first In a typical scenario The six grid connection efficiency evaluation indicators for distributed renewable energy sources are ranked from most important to least important, and an evaluation matrix is ​​established. All grid connection efficiency evaluation indicators are positively oriented, and the positively oriented evaluation matrix is ​​then standardized to construct a standardized matrix. ;

[0037] 2) Determine the subjective weights of the indicators: Based on the analytic hierarchy process (AHP), compare the importance of adjacent grid connection efficiency evaluation indicators in turn to determine the scale value. … Construct the judgment matrix A = [ 1 t 1 t 1 t 2 ⋯ t 1 t 2 . . . t 5 1 / t 1 1 t 2 ⋯ t 2 . . . t 5 1 / t 1 t 2 1 / t 2 1 ⋯ t 3 . . . t 5 ⋮ ⋮ ⋮ ⋮ 1 / t 1 t 2 . . . t 5 1 / t 2 . . . t 5 1 / t 3 . . . t 5 ⋯ 1 ] Then the subjective weight is: In the formula: For the first In the first typical scenario Subjective weights of individual grid connection efficiency evaluation indicators To determine the matrix Elements in;

[0038] 3) Determine the objective weights of the indicators: Use the entropy weight method to calculate the first... The grid connection efficiency evaluation indicators are in the first... The weight of the distributed new energy sources is then the weight of the first one. Information entropy of each grid connection efficiency evaluation indicator and its utility value for: Then the objective weight is: In the formula: For the first In the first typical scenario The objective weights of each grid connection efficiency evaluation indicator For the standardized matrix Elements in;

[0039] 4) Determine the overall weight of the indicators: The overall weight is a combination of subjective and objective weights. In the formula, Indicates the first In the first typical scenario The comprehensive weight of each grid connection efficiency evaluation indicator;

[0040] 5) The overall evaluation results are as follows: { X i , k = [ d U i , k , D P i , average , k , or i , k , Q i , k , C i , k SES , C i , k C ], Q i , k = [ q 1 , k , q 2 , k , q 3 , k , q 4 , k , q 5 , k , q 6 , k ] E i . k eff ( d U i , k , D P i , average , k , or i , k , Q i , k , C i , k SES , C i , k C ) = X i , k × Q i , k T In the formula, Indicates the first The first distributed new energy source in the first Indicator vectors in typical scenarios Indicates the first The first distributed new energy source in the first Indicator weight vectors for a typical scenario.

[0041] The specific process of step five in the above-mentioned method for stochastic regulation of distributed energy storage and renewable energy based on grid-connected efficiency game theory is as follows:

[0042] 1) Pricing for arbitrary charging services proposed for shared energy storage Pricing of discharge services First, the optimal response of all distributed renewable energy sources is solved in the objective function of distributed renewable energy, resulting in a set of responses dependent on charging service pricing. Pricing of discharge services Optimal charging service power and optimal discharge service power ;

[0043] 2) This set of optimal charging service power and optimal discharge service power Substituting the objective function of shared energy storage, the problem is transformed into a decision variable containing only itself. and This is a single-variable optimization problem. By solving this single-variable optimization problem, the optimal charging service pricing can be obtained. and optimal discharge service pricing ;

[0044] 3) Price the best charging service and optimal discharge service pricing Substitute back into the objective function of distributed renewable energy and repeat step 1). When the optimal pricing and optimal power no longer change, the game equilibrium is reached.

[0045] The above variables , , , , , These are intermediate variables used in the algorithm's iterative process to temporarily store the temporary solution results for the shared energy storage charging and discharging service prices and power in each iteration. They are used for convergence judgment and parameter updates during the iteration process. When these intermediate variables remain essentially unchanged after two or more consecutive iterations, they are considered to have converged and are output as the final decision term.

[0046] Advantages of this invention:

[0047] This invention differs from traditional economic game theory based on electricity prices or costs. For the first time, it quantifies the key physical performance indicators of distributed renewable energy grid connection into a single grid connection efficiency value through subjective and objective weighting. This value is then incorporated into the core driving objective of the decision-making process for shared energy storage and distributed renewable energy in the distribution area. This solves the problem of physical system optimization under competition among multiple renewable energy entities and maximizes the overall performance of the distribution area. Attached Figure Description

[0048] Figure 1 This is a structural diagram of shared energy storage and distributed new energy in power distribution areas based on grid connection efficiency game theory.

[0049] Figure 2 This is a flowchart of the operation of the random control method for shared energy storage and distributed new energy in the transformer substation based on grid connection efficiency game theory. Detailed Implementation

[0050] The present invention will now be described in further detail with reference to the accompanying drawings.

[0051] A stochastic control method for shared energy storage and distributed renewable energy in transformer substations based on grid-connected efficiency game theory includes the following steps:

[0052] Step 1: Generate typical scenarios with multiple distributed new energy sources based on the Copula function and K-means clustering algorithm, assuming the transformer area has... A distributed new energy source involves the following steps:

[0053] 1) Perform data cleaning and normalization on the historical power prediction error data of distributed renewable energy sources to obtain the prediction error sequence. ,in For distributed new energy The processed historical power prediction error data were used; the kernel density estimation method was employed to fit the prediction error sequence of distributed renewable energy sources, thereby obtaining... An independent prediction error marginal distribution function ,in For distributed new energy The marginal distribution function of the prediction error;

[0054] 2) Constructing representations based on Copula functions The joint distribution function of the prediction error marginal distribution function depends on the structure, as shown in the following equation:

[0055]

[0056] In the formula, For the joint distribution function, This refers to the Copula function.

[0057] 3) Sample the constructed joint distribution function to generate a large-scale dimension of... A random vector, where each dimension of the random vector corresponds to a distributed new energy source, the th... The dimensional random vector corresponds to the th For each distributed renewable energy source, an inverse transformation is performed on the corresponding prediction error marginal distribution function of the random vector to convert it into the true prediction error value. The true prediction error value is then added to the original prediction value of the historical power prediction error data to generate a large-scale set of original random scenarios for distributed renewable energy sources. Each set of original random scenarios represents a distributed renewable energy source with its output power. Dimensional vector.

[0058] 4) The original random scene set is compressed using the K-means clustering algorithm, reducing it to... A number of typical scenarios and their probabilities of occurrence, among which the first... The probability of a typical scenario is .

[0059] The typical scenarios generated above will serve as the input basis for subsequent steps two (random master-slave game optimization) and three (grid connection efficiency index calculation), and will be used to characterize the impact of the uncertainty and correlation of distributed new energy output on the safety and economy of the transformer area.

[0060] Step 2: Based on the typical scenario generated in Step 1, construct a master-slave game stochastic control model for grid connection efficiency with shared energy storage as the leader and distributed new energy as the follower. Optimize the support power and pricing of shared energy storage for distributed new energy. In the control model, the objective function of shared energy storage is to maximize the total expected grid connection efficiency of distributed new energy and its own expected net income, while the objective function of distributed new energy is to maximize its own expected grid connection efficiency.

[0061] 1) Shared energy storage objective function

[0062] Considering all typical scenarios, the objective function of shared energy storage is to maximize the total grid-connected expected efficiency of distributed renewable energy and its own expected net revenue. The expected net revenue includes the service fee for shared energy storage to provide supporting power to distributed renewable energy, the energy storage peak-valley arbitrage revenue, and the energy storage investment and operation and maintenance costs. The decision variables are the supporting power and pricing of shared energy storage for distributed renewable energy.

[0063]

[0064]

[0065]

[0066] In the formula, To share the energy storage objective function, Indicates the first A typical scenario This represents the total number of typical scenarios. Indicates the first The probability of a typical scenario; and They represent the first The weighting coefficients of expected net income and expected efficiency of total grid connection of distributed new energy in a typical scenario. Indicates shared energy storage in the first Expected net profit under a typical scenario; Indicates the first Total grid connection efficiency of distributed new energy in a typical scenario; , and These represent the shared energy storage in the first... Service fees for providing supporting power to distributed renewable energy in a typical scenario, energy storage peak-valley arbitrage income and energy storage investment and operation and maintenance costs; Indicates the first A distributed new energy source This indicates the total number of distributed renewable energy sources in the area. Indicates the first time, Indicates the total duration of regulation; and These respectively represent the shift from shared energy storage to distributed new energy sources. exist Pricing for charging and discharging services at any time; and These respectively represent the shift from shared energy storage to distributed new energy sources. In the In a typical scenario The charging and discharging power at any given time; and These represent shared energy storage. Charging and discharging electricity prices that constantly interact with the power grid; and These represent the shared energy storage... In a typical scenario The charging and discharging power that constantly interacts with the power grid; Indicates the shared energy storage operation and maintenance coefficient; This represents the unit capacity cost of shared energy storage. Indicates the rated capacity of shared energy storage. Indicates the discount rate. This indicates the shared energy storage float charging lifetime. Indicates the first The first distributed new energy source in the first The grid connection efficiency in a typical scenario is the average grid voltage deviation. Power fluctuation , absorption rate Electricity revenue Service fees paid to shared energy storage and the cost of penalties for abandoning wind and solar power The function.

[0067] 2) Constraints of Shared Energy Storage

[0068] Shared energy storage power constraints:

[0069]

[0070] In the formula, and These represent the shared energy storage in the first... In a typical scenario Total charging power and total discharging power at any given time; and These represent the upper limits of the shared energy storage charging power and the upper limit of the discharging power, respectively.

[0071] Shared energy storage SOC constraints:

[0072]

[0073] In the formula, and They represent the first In a typical scenario Time and The state of charge of the energy storage is shared at all times; and These represent the charging efficiency and discharging efficiency of shared energy storage, respectively. and These represent the maximum and minimum states of charge of the shared energy storage, respectively.

[0074] Pricing constraints for shared energy storage charging and discharging services:

[0075]

[0076] In the formula, and These are the pricing for the maximum discharge service and the minimum discharge service of shared energy storage, respectively. and These are the pricing for the maximum and minimum charging services of shared energy storage, respectively.

[0077] 3) Objective function of distributed new energy

[0078] The objective function of the follower distributed renewable energy is to maximize its own grid-connected expected efficiency, and the decision variable is the grid-connected power of the distributed renewable energy.

[0079]

[0080] In the formula, Indicates the first A distributed new energy objective function, Indicates the first The first distributed new energy source in the first The grid connection efficiency in a typical scenario is the average grid voltage deviation. Power fluctuation , absorption rate Electricity revenue Service fees paid to shared energy storage and the cost of penalties for abandoning wind and solar power The function.

[0081] It should be noted that grid connection efficiency is not pre-calculated, but dynamically updated during the game process: when the leader adjusts the shared energy storage support power or the followers adjust the grid connection power, it will trigger grid power flow calculation, resulting in real-time changes in technical indicators such as voltage and volatility, which in turn affects the grid connection efficiency value.

[0082] 2) Constraints of Distributed New Energy Sources

[0083] Distributed renewable energy power constraints:

[0084]

[0085] In the formula, Indicates the first The first distributed new energy source in the first In a typical scenario Predicted power generation at any given time; Indicates the first The rated output power of each distributed new energy source; Indicates the first The first distributed new energy source in the first In a typical scenario The power generation and grid connection capacity at any given moment; Indicates the first The first distributed new energy source in the first In a typical scenario The amount of wind and solar power curtailed at any given moment; Indicates the first The first distributed new energy source In a typical scenario The grid-connected power at any given time should take into account the supporting role of shared energy storage.

[0086] To ensure that the proposed control strategy conforms to the physical laws of the power grid, the game optimization needs to meet the basic power flow constraints for stable operation of the distribution area: node power balance constraints, node voltage constraints, line capacity constraints, etc.

[0087] Step 3: Propose evaluation indicators for the grid connection efficiency of distributed renewable energy in the distribution area, including average voltage deviation at the grid connection point, power fluctuation, absorption rate, grid-connected power generation revenue, service fees paid to shared energy storage, and the cost of wind and solar curtailment penalties.

[0088] Average voltage deviation at grid connection point:

[0089]

[0090] In the formula, Indicates the first The first distributed new energy source in the first Average voltage deviation at grid connection point in a typical scenario Indicates the first The first distributed new energy source in the first A typical scenario Voltage deviation at grid connection point at any time Indicates the first The first distributed new energy source in the first The nominal voltage at the grid connection point under a typical scenario. This indicator is calculated from the power flow of the transformer substation.

[0091] Power fluctuation:

[0092]

[0093] In the formula, Indicates the first The first distributed new energy source in the first Power fluctuations at the grid connection point in a typical scenario. and They represent the first The first distributed new energy source in the first In a typical scenario Time and The grid-connected power at any given time.

[0094] Consumption rate indicator:

[0095]

[0096] In the formula, Indicates the first The first distributed new energy source in the first The absorption rate under a typical scenario.

[0097] Electricity generation revenue:

[0098]

[0099] In the formula, Indicates the first The first distributed new energy source in the first Power generation revenue in a typical scenario Indicates the first Distributed new energy sources The electricity price at any given time.

[0100] Energy storage service fee:

[0101]

[0102] In the formula, Indicates the first The first distributed new energy source in the first Energy storage service fees in a typical scenario.

[0103] The cost of curtailing wind and solar power:

[0104]

[0105] In the formula, Indicates the first The first distributed new energy source in the first The penalty costs for wind and solar power curtailment in a typical scenario; For the first Distributed new energy sources The price of electricity is penalized for curtailing wind and solar power.

[0106] Step 4: Based on the subjective and objective weighting method, construct the mapping relationship between the grid connection efficiency of distributed renewable energy in the transformer area and the grid connection efficiency evaluation indicators, mapping multiple grid connection efficiency evaluation indicators to a single grid connection efficiency value:

[0107] 1) Regarding the first In a typical scenario The six grid connection efficiency evaluation indicators of the distributed renewable energy sources to be evaluated are ranked from most important to least important, and an evaluation matrix is ​​established. All grid connection efficiency evaluation indicators are positively oriented. The positively oriented evaluation matrix is ​​then standardized to construct a standardized matrix. .

[0108] 2) Determine the subjective weights of the indicators: Based on the AHP (Analog-Philosophy of Hierarchy Process) method, compare the importance of adjacent grid connection efficiency evaluation indicators in turn to determine the scale value. … Construct the judgment matrix Its expression is as follows:

[0109] A = [ 1 t 1 t 1 t 2 ⋯ t 1 t 2 . . . t 5 1 / t 1 1 t 2 ⋯ t 2 . . . t 5 1 / t 1 t 2 1 / t 2 1 ⋯ t 3 . . . t 5 ⋮ ⋮ ⋮ ⋮ 1 / t 1 t 2 . . . t 5 1 / t 2 . . . t 5 1 / t 3 . . . t 5 ⋯ 1 ]

[0110] The subjective weight is then:

[0111]

[0112] In the formula: For the first In the first typical scenario Subjective weights of individual grid connection efficiency evaluation indicators To determine the matrix Elements in;

[0113] 3) Determine the objective weights of the indicators: Use the EWM method (entropy weight method) to calculate the first... The grid connection efficiency evaluation indicators are in the first... The weight of the distributed new energy sources is then the weight of the first one. Information entropy of each grid connection efficiency evaluation indicator and its utility value for:

[0114]

[0115] The objective weights are:

[0116] ;

[0117] In the formula: For the first In the first typical scenario The objective weights of each grid connection efficiency evaluation indicator For the standardized matrix The elements in.

[0118] 4) Determine the overall weight of the indicators: The overall weight is a combination of subjective and objective weights, as shown in the following formula:

[0119]

[0120] In the formula, Indicates the first In the first typical scenario The comprehensive weight of each grid connection efficiency evaluation indicator.

[0121] 5) The overall evaluation results are as follows:

[0122] { X i , k = [ d U i , k , D P i , average , k , or i , k , Q i , k , C i , k SES , C i , k C ], Q i , k = [ q 1 , k , q 2 , k , q 3 , k , q 4 , k , q 5 , k , q 6 , k ] E i . k eff ( d U i , k , D P i , average , k , or i , k , Q i , k , C i , k SES , C i , k C ) = X i , k × Q i , k T

[0123] In the formula, Indicates the first The first distributed new energy source in the first Indicator vectors in typical scenarios Indicates the first The first distributed new energy source in the first Indicator weight vectors for a typical scenario.

[0124] This step transforms the distributed, non-uniform grid connection efficiency evaluation index into a single grid connection efficiency value, which is the basis for the game optimization in step two.

[0125] Step 5: Use a distributed iterative framework based on backward induction to solve for the stable equilibrium solution of the above master-slave game stochastic control model:

[0126] 1) Pricing for arbitrary charging services proposed for shared energy storage Pricing of discharge services First, the optimal response of all distributed renewable energy sources is solved in the objective function of distributed renewable energy, resulting in a set of responses dependent on charging service pricing. Pricing of discharge services Optimal charging service power and optimal discharge service power ;

[0127] 2) This set of optimal charging service power and optimal discharge service power Substituting the objective function of shared energy storage, the problem is transformed into a decision variable containing only itself. and This is a single-variable optimization problem. By solving this single-variable optimization problem, the optimal charging service pricing can be obtained. and optimal discharge service pricing ;

[0128] 3) Price the best charging service and optimal discharge service pricing Substitute back into the objective function of distributed renewable energy and repeat step 1). When the optimal pricing and optimal power no longer change, the game equilibrium is reached.

Claims

1. A method for stochastic regulation of distributed energy storage and renewable energy in transformer substations based on grid-connected efficiency game theory, characterized in that: Includes the following steps: Step 1: Generate typical scenarios containing multiple distributed new energy sources based on Copula function and K-means clustering algorithm; Step 2: Based on the typical scenario generated in Step 1, construct a master-slave game stochastic control model for grid connection efficiency with shared energy storage as the leader and distributed new energy as the follower. Optimize the support power and pricing of shared energy storage for distributed new energy. In the control model, the objective function of shared energy storage is to maximize the total grid connection expected efficiency of distributed new energy and its own expected net income, while the objective function of distributed new energy is to maximize its own grid connection expected efficiency. Step 3: Propose grid connection efficiency evaluation indicators for distributed renewable energy in the distribution area, including average voltage deviation at the grid connection point, power fluctuation, absorption rate, grid-connected power generation revenue, service fees paid to shared energy storage, and cost of wind and solar curtailment penalties. Step 4: Use the analytic hierarchy process (AHP) to obtain the subjective weights of each grid connection efficiency evaluation indicator, and use the entropy weight method to obtain the objective weights of each grid connection efficiency evaluation indicator. Based on the comprehensive weights combining subjective and objective weights, map multiple grid connection efficiency evaluation indicators to a single grid connection efficiency value. This single grid-connected efficiency value is output to the shared energy storage and distributed new energy objective functions in step two, forming a closed-loop feedback optimization. Step 5: Use a distributed iterative framework based on backward induction to solve the stable equilibrium solution of the above master-slave game stochastic control model, thus achieving game equilibrium.

2. The method for stochastic regulation of distributed energy storage and renewable energy based on grid-connected efficiency game theory according to claim 1, characterized in that: In step one, the prediction error marginal distribution function is fitted based on the prediction error sequence of distributed new energy; the independent prediction error marginal distribution functions are combined based on the Copula function to construct a joint distribution function and generate the original random scene set; the K-means clustering algorithm is used to cluster the original random scene set and reduce it to typical scenes.

3. The method for stochastic regulation of distributed energy storage and renewable energy based on grid-connected efficiency game theory according to claim 2, characterized in that: The specific process of step one is as follows: 1) The historical power prediction error data of distributed renewable energy sources are cleaned and normalized to obtain the prediction error sequence. ,in For distributed new energy The processed historical power prediction error data were used; the kernel density estimation method was employed to fit the prediction error sequence of distributed renewable energy sources, thereby obtaining... An independent prediction error marginal distribution function ,in For distributed new energy The marginal distribution function of the prediction error; 2) Constructing representations based on Copula functions The marginal distribution function of each prediction error depends on the joint distribution function of the structure. In the formula, For the joint distribution function, For Copula functions; 3) Sample the constructed joint distribution function to generate a dimension of... A random vector, where each dimension of the random vector corresponds to a distributed new energy source, the th... The dimensional random vector corresponds to the th A distributed new energy source; for each dimension of random vector, the inverse function of the corresponding prediction error marginal distribution function is used to perform an inverse transformation, which transforms it into the true prediction error value; the true prediction error value is added to the original prediction value of the historical power prediction error data to generate the original random scenario set of distributed new energy sources; 4) The original random scene set is compressed using the K-means clustering algorithm, reducing it to... A typical scenario and its probability of occurrence.

4. The method for stochastic regulation of distributed energy storage and renewable energy based on grid-connected efficiency game theory according to claim 3, characterized in that: The specific process of step two is as follows: Considering all typical scenarios, the objective function of shared energy storage is to maximize the total grid-connected expected efficiency of distributed new energy and its own expected net income. The expected net income includes the service fee for shared energy storage to provide supporting power to distributed new energy, the energy storage peak-valley arbitrage income, and the energy storage investment and operation and maintenance costs. ， ， In the formula, To share the energy storage objective function, Indicates the first A typical scenario This represents the total number of typical scenarios. Indicates the first The probability of a typical scenario; and They represent the first The weighting coefficients of expected net income and expected efficiency of total grid connection of distributed new energy in a typical scenario. Indicates shared energy storage in the first Expected net profit under a typical scenario; Indicates the first Total grid connection efficiency of distributed new energy in a typical scenario; , and These represent the shared energy storage in the first... Service fees for providing supporting power to distributed renewable energy in a typical scenario, energy storage peak-valley arbitrage income and energy storage investment and operation and maintenance costs; Indicates the first A distributed new energy source This indicates the total number of distributed renewable energy sources in the area. Indicates the first time, Indicates the total duration of regulation; and These respectively represent the shift from shared energy storage to distributed new energy sources. exist Pricing for charging and discharging services at any time; and These respectively represent the shift from shared energy storage to distributed new energy sources. In the In a typical scenario The charging and discharging power at any given time; and These represent shared energy storage. Charging and discharging electricity prices that constantly interact with the power grid; and These represent the shared energy storage... In a typical scenario The charging and discharging power that constantly interacts with the power grid; Indicates the shared energy storage operation and maintenance coefficient; This represents the unit capacity cost of shared energy storage. Indicates the rated capacity of shared energy storage. Indicates the discount rate. Indicates the shared energy storage float charging lifetime; Indicates the first The first distributed new energy source in the first The grid connection efficiency in a typical scenario is the average grid voltage deviation. Power fluctuation , absorption rate Electricity revenue Service fees paid to shared energy storage and the cost of penalties for abandoning wind and solar power The function; The objective function for distributed renewable energy is to maximize its own grid-connected expected efficiency. In the formula, Indicates the first A distributed new energy objective function, Indicates the first The first distributed new energy source in the first Grid connection efficiency in a typical scenario.

5. The method for stochastic regulation of distributed energy storage and renewable energy based on grid-connected efficiency game theory according to claim 4, characterized in that: Constraints for shared energy storage include: Shared energy storage power constraints: In the formula, and These represent the shared energy storage in the first... In a typical scenario Total charging power and total discharging power at any given time; and These represent the upper limits of the shared energy storage charging power and the upper limit of the discharging power, respectively. Shared energy storage SOC constraints: In the formula, and They represent the first In a typical scenario Time and The state of charge of the energy storage is shared at all times; and These represent the charging efficiency and discharging efficiency of shared energy storage, respectively. and These represent the maximum and minimum values ​​of the state of charge of the shared energy storage, respectively. Pricing constraints for shared energy storage charging and discharging services: In the formula, and These are the pricing for the maximum discharge service and the minimum discharge service of shared energy storage, respectively. and These are the pricing for the maximum and minimum charging services of shared energy storage, respectively. Distributed renewable energy power constraints: In the formula, Indicates the first The first distributed new energy source in the first In a typical scenario Predicted power generation at any given time; Indicates the first The rated output power of each distributed new energy source; Indicates the first The first distributed new energy source in the first In a typical scenario The power generation and grid connection capacity at any given moment; Indicates the first The first distributed new energy source in the first In a typical scenario The amount of wind and solar power curtailed at any given moment; Indicates the first The first distributed new energy source In a typical scenario The grid-connected power at any given time.

6. The method for stochastic regulation of distributed energy storage and renewable energy based on grid-connected efficiency game theory according to claim 5, characterized in that: The specific grid connection efficiency evaluation indicators proposed in step three are as follows: Average voltage deviation at grid connection point: In the formula, Indicates the first The first distributed new energy source in the first Average voltage deviation at grid connection point in a typical scenario Indicates the first The first distributed new energy source in the first A typical scenario Voltage deviation at grid connection point at any time Indicates the first The first distributed new energy source in the first Nominal voltage at grid connection point in a typical scenario; Power fluctuation: In the formula, Indicates the first The first distributed new energy source in the first Power fluctuations at the grid connection point in a typical scenario. and They represent the first The first distributed new energy source in the first In a typical scenario Time and Grid-connected power at any given time; Consumption rate indicator: In the formula, Indicates the first The first distributed new energy source in the first The absorption rate in a typical scenario; Electricity generation revenue: In the formula, Indicates the first The first distributed new energy source in the first Power generation revenue in a typical scenario Indicates the first Distributed new energy sources The on-grid electricity price at any given time; Energy storage service fee: In the formula, Indicates the first The first distributed new energy source in the first Energy storage service fees in a typical scenario; The cost of curtailing wind and solar power: In the formula, Indicates the first The first distributed new energy source in the first The penalty costs for wind and solar power curtailment in a typical scenario; For the first Distributed new energy sources The price of electricity is penalized for curtailing wind and solar power.

7. The method for stochastic regulation of distributed energy storage and renewable energy based on grid-connected efficiency game theory according to claim 6, characterized in that: The specific process of step four is as follows: 1) Regarding the first In a typical scenario The six grid connection efficiency evaluation indicators for distributed renewable energy sources are ranked from most important to least important, and an evaluation matrix is ​​established. All grid connection efficiency evaluation indicators are positively oriented, and the positively oriented evaluation matrix is ​​then standardized to construct a standardized matrix. ; 2) Determine the subjective weights of the indicators: Based on the analytic hierarchy process (AHP), compare the importance of adjacent grid connection efficiency evaluation indicators in turn to determine the scale value. … Construct the judgment matrix Then the subjective weight is: In the formula: For the first In the first typical scenario Subjective weights of individual grid connection efficiency evaluation indicators To determine the matrix Elements in; 3) Determine the objective weights of the indicators: Use the entropy weight method to calculate the first... The grid connection efficiency evaluation indicators are in the first... The weight of the distributed new energy sources is then the weight of the first one. Information entropy of each grid connection efficiency evaluation indicator and its utility value for: Then the objective weight is: In the formula: For the first In the first typical scenario The objective weights of each grid connection efficiency evaluation indicator For the standardized matrix Elements in; 4) Determine the overall weight of the indicators: The overall weight is a combination of subjective and objective weights. In the formula, Indicates the first In the first typical scenario The comprehensive weight of each grid connection efficiency evaluation indicator; 5) The overall evaluation results are as follows: In the formula, Indicates the first The first distributed new energy source in the first Indicator vectors in typical scenarios Indicates the first The first distributed new energy source in the first Indicator weight vectors for a typical scenario.

8. The method for stochastic regulation of distributed energy storage and renewable energy based on grid-connected efficiency game theory according to claim 7, characterized in that: The specific process of step five is as follows: 1) Pricing for arbitrary charging services proposed for shared energy storage Pricing of discharge services First, the optimal response of all distributed renewable energy sources is solved in the objective function of distributed renewable energy, resulting in a set of responses dependent on charging service pricing. Pricing of discharge services Optimal charging service power and optimal discharge service power ; 2) This set of optimal charging service power and optimal discharge service power Substituting the objective function of shared energy storage, the problem is transformed into a decision variable containing only itself. and This is a single-variable optimization problem. By solving this single-variable optimization problem, the optimal charging service pricing can be obtained. and optimal discharge service pricing ; 3) Price the best charging service and optimal discharge service pricing Substitute back into the objective function of distributed renewable energy and repeat step 1). When the optimal pricing and optimal power no longer change, the game equilibrium is reached.

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