A power distribution network-microgrid coordination optimization method and related device

Abstract

The embodiment of the application provides a power distribution network-multi-microgrid collaborative optimization method and related equipment, and belongs to the field of power system optimization scheduling. The method comprises the following steps: each operation subject constructs a chaotic polynomial proxy model of operation cost based on a random response surface method, and quantitatively obtains an expected independent operation cost of each subject considering uncertainty risk; the cost is used to reconstruct a negotiation breakdown point of a cooperative game; a Nash bargaining model is established based on a new benchmark, and is decomposed into power and price optimization sub-problems, distributed iterative solution is carried out by using an alternating direction multiplier method, and an optimal collaborative transaction strategy is obtained; and finally, the strategy is executed and dynamically updated under an intra-day rolling framework. The application realizes endogenous quantification and fair allocation of risk cost by deeply integrating the random response surface method and the game theory, overcomes the defects of the traditional "risk-game separation" paradigm, and effectively improves the overall economy, operation stability and benefit distribution fairness of the system.

Description

Technical Field

[0001] This application relates to the field of power system optimization and dispatching technology, and in particular to a method and related equipment for collaborative optimization of distribution network and multiple microgrids. Background Technology

[0002] With the increasing penetration rate of renewable distributed power sources such as wind power and photovoltaics, distributed energy clusters, represented by multi-microgrids, have become an important form of modern power distribution networks. While improving energy utilization efficiency and power supply reliability, this form also presents new challenges to system operation and scheduling due to its multi-entity and high randomness characteristics.

[0003] Currently, operation scheduling generally adopts a multi-timescale coordination framework of "day-ahead-intraday-real-time". Intraday rolling optimization, as a key link connecting day-ahead planning and real-time operation, can effectively smooth out ultra-short-term fluctuations in renewable energy output and load demand through online feedback correction mechanisms. However, distribution networks and multiple microgrids often belong to different stakeholders, and traditional centralized optimization methods are difficult to coordinate the independent interests of each party. Therefore, distributed coordination mechanisms based on game theory have become an effective way to ensure the autonomous decision-making rights of each stakeholder and improve overall operational efficiency. Existing research mostly uses distributed algorithms such as the alternating direction multiplier method to construct the coordination framework, which effectively solves the problems of information privacy protection and computational efficiency.

[0004] In terms of uncertainty handling methods, existing technologies mainly present two typical paths: one is the robust optimization-based method, which ensures the feasibility of decision-making in the worst case by setting an uncertainty set, but often leads to overly conservative scheduling schemes; the other is the stochastic programming method, which meticulously characterizes uncertainty through scenario analysis and other means, but faces the "curse of dimensionality" problem caused by the increase in the dimension of random variables.

[0005] However, current research faces a key technical bottleneck: most methods separate uncertainty handling from the game-theoretic decision-making process, forming a traditional paradigm of "risk-game separation," which manifests itself in the following ways: 1) Existing collaborative optimization frameworks fail to endogenously embed uncertainty and risk into the game interaction process and lack an effective mechanism to quantify the risk costs of each subject into game-like transaction signals.

[0006] 2) When microgrids provide regulation services, the additional operating costs they bear are significantly random. Existing cooperative game models lack accurate probabilistic equivalence modeling methods and cannot accurately quantify the actual risk costs borne by each microgrid.

[0007] 3) In terms of the design of the benefit distribution mechanism, existing models generally use the determined independent operating cost as the point of negotiation breakdown, without considering the effect of uncertainty risk on the actual cost of each entity's independent operation. This results in the benefit distribution failing to accurately reflect the actual risk contribution of each entity, affecting the long-term stability of the cooperative alliance. Summary of the Invention

[0008] The main objective of this application is to propose a distribution network-multi-microgrid collaborative optimization method, electronic equipment, storage medium, and program product based on stochastic response surface methodology and cooperative game theory. By deeply integrating stochastic response surface methodology with cooperative game theory, a risk-endogenous collaborative decision-making paradigm is constructed, thereby accurately quantifying the risk costs borne by each microgrid in mitigating random fluctuations. Based on this, the negotiation benchmark of cooperative game theory is reconstructed to achieve a fairer and more efficient distribution of benefits, ultimately improving the overall economic efficiency, operational robustness, and alliance stability of the system.

[0009] To achieve the above objectives, one aspect of this application proposes a distribution network-multi-microgrid collaborative optimization method, the method comprising: S1: Individual risk cost quantification: Each operating entity constructs a chaotic multinomial proxy model of its operating cost based on the stochastic response surface method, performs local stochastic optimization with the upper bound of the confidence interval as the objective, and solves for its expected independent operating cost taking into account uncertainty risk. S2: Reconstruction of the benchmark for cooperative game: The expected independent operating costs of each subject obtained in step S1 are established as the negotiation breakdown point for cooperative game; S3: Distributed Nash Bargaining Solution: Based on the aforementioned negotiation breakdown point, a Nash bargaining cooperative game model is established and decomposed into a sub-problem of optimizing the traded electricity volume and a sub-problem of optimizing the traded electricity price. The distributed iterative solution is performed using the alternating direction multiplier method to obtain the optimal collaborative trading strategy. S4: Intraday Rolling Optimization Execution: Implement the optimal collaborative trading strategy at the current execution time, and repeat steps S1 to S3 based on the updated information at the next rolling time.

[0010] In some embodiments, step S1, constructing the chaotic multinomial proxy model based on the stochastic response surface method, includes: Obtain the expected value and standard deviation of the uncertainties of the sources and loads within each subject; The Nataf transformation is used to convert the correlated original random input variables into independent standard normal distribution variables; Based on Hermite chaotic polynomials, a proxy model is constructed that maps independent standard normal variables to stochastic responses with runtime costs.

[0011] In some embodiments, the objective function of the local stochastic optimization in step S1, which targets the upper bound of the confidence interval, is:

[0012] in, For the operating costs of each microgrid, and These are the expected value and standard deviation of the operating cost calculated using the proxy model, respectively. For a given confidence level The corresponding standard normal quantile, To optimize decision-making time in a rolling manner, The time when the rolling optimization ends is T, where T is the total optimization time. The mathematical expectation operator represents taking the expected value of a variable. Let m be the electricity purchase cost of the microgrid during time period t. Let m be the operating cost of the microgrid during time period t. The dispatch cost of SOC deficit or surplus power caused by the current strategy, where m is the microgrid number and M is the total number of microgrids in the multi-microgrid system.

[0013] In some embodiments, in step S2, the point of breakdown in negotiations includes: the independent operating cost of the distribution network. And the expected independent operating cost of each microgrid m .

[0014] In some embodiments, in step S3, the objective function of the Nash bargaining cooperative game model is to maximize the product of the cooperative surpluses of all participants:

[0015] in, The independent operating cost of the distribution network during time period t. The negotiated operating cost of the distribution network during time period t. Let m be the independent operating cost of microgrid m during time period t. This represents the operating cost of microgrid m during time period t after negotiation.

[0016] In some embodiments, step S3 specifically involves: The Nash bargaining cooperative game model is decomposed into a first subproblem and a second subproblem; The first subproblem is to optimize the traded electricity volume with the objective of minimizing the total system operating cost, given a specific electricity price. The second subproblem is to optimize the electricity trading price with the goal of maximizing payment benefits, given a certain amount of electricity. The augmented Lagrangian functions of the first and second subproblems are constructed respectively, and the solutions are obtained iteratively by the alternating direction multiplier method. During the iteration process, each subject only exchanges the boundary coupling variables and the Lagrangian multipliers.

[0017] In some embodiments, in step S1, the constraints of the local stochastic optimization include energy storage state of charge security constraints and grid connection point power fluctuation constraints expressed in the form of chance constraints, and are transformed into deterministic second-order cone constraints for solution through the chaotic polynomial surrogate model.

[0018] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0019] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0020] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0021] Compared with the prior art, this application has the following beneficial effects: 1) Accurate quantification of endogenous risk: By constructing a proxy model using the stochastic response surface methodology, the risk costs borne by each entity in coping with uncertainty during independent operation are efficiently and accurately quantified, providing a real and reliable quantitative basis for cooperative game theory.

[0022] 2) Fairer distribution of benefits: Using the risk-adjusted expected independent operating cost as the negotiation benchmark ensures that the final distribution of benefits accurately reflects the actual risk contribution of each entity in the cooperation, avoiding the unfairness caused by ignoring risk differences in traditional methods and enhancing the stability of the cooperative alliance.

[0023] 3) Improved overall economic efficiency: By using a game-theoretic framework with endogenous risk, the system guides all parties to conduct transactions based on a full understanding of their own risk costs, thereby achieving optimal risk cost allocation and reducing the overall operating cost of the system.

[0024] 4) Enhanced operational robustness: The use of an objective function and opportunity constraints based on the upper bound of the confidence interval in local optimization makes the scheduling scheme more robust to uncertain fluctuations, effectively suppresses the risk of power default at the grid connection point, and improves the stability of system operation.

[0025] 5) Privacy protection and scalability: It adopts a fully distributed Nash bargaining solution framework, in which each subject only exchanges necessary boundary information during the iteration process, thus protecting business privacy; the modular design facilitates the access of new microgrid subjects and has good scalability. Attached Figure Description

[0026] Figure 1 A flowchart of a distributed solution using the stochastic response surface method is provided for an embodiment of this application.

[0027] Figure 2 This is a schematic diagram of the IEEE 33-node system architecture on which the embodiments of this application are based.

[0028] Figure 3 This is a comparison of the probability density distribution of the total system operating cost when using the method of this application and the traditional robust optimization method.

[0029] Figure 4 This is a comparison of the probability density distribution of the total cooperative surplus (payment benefit) of the system when using the method of this application and the traditional robust optimization method.

[0030] Figure 5 This is a comparison diagram of the probability density distribution of power at the point of common coupling of each microgrid when using the method of this application and the traditional robust optimization method.

[0031] Figure 6 This is a comparison of the probability density distribution of operating costs and benefits for each microgrid under the method described in this application.

[0032] Figure 7 The flowchart is for the distribution network-multi-microgrid collaborative optimization method provided in the embodiments of this application.

[0033] Figure 8 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0036] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0037] 1) A distribution network refers to a power grid that receives electrical energy from the transmission network or regional power plants and distributes it locally or in stages according to voltage to various users through distribution facilities. It consists of overhead lines, cables, poles, distribution transformers, disconnect switches, reactive power compensators, and some auxiliary facilities, and plays a crucial role in distributing electrical energy within the power grid. With the increasing penetration rate of renewable distributed power sources, distributed energy clusters, represented by multi-microgrids, have become an important development direction for modern power distribution networks. While improving energy utilization efficiency and power supply reliability, this new network form also brings new challenges to system operation due to its multi-entity and high randomness characteristics.

[0038] At the operational scheduling level, a multi-timescale coordination framework of "day-ahead-intraday-real-time" is currently widely adopted. Intraday rolling optimization, as a key link connecting day-ahead planning and real-time operation, can effectively mitigate ultra-short-term fluctuations in renewable energy output and load demand through online feedback correction mechanisms. However, distribution networks and multiple microgrids often belong to different stakeholders, making it difficult to coordinate the interests of all parties using traditional centralized optimization methods. Therefore, distributed coordination mechanisms based on game theory have become an effective way to ensure the autonomous decision-making rights of each stakeholder and improve overall operational efficiency. Existing research often uses distributed algorithms such as the alternating direction multiplier method to construct the coordination framework, which effectively addresses the issues of information privacy protection and computational efficiency.

[0039] In handling uncertainty, existing technologies mainly present two typical paths: one is based on robust optimization, which ensures the feasibility of decisions in the worst case by setting an uncertainty set, but often leads to overly conservative scheduling schemes; the other uses stochastic programming methods, which meticulously characterize uncertainty through scenario analysis and other means, but faces the "curse of dimensionality" problem caused by the increase in the dimensionality of random variables. The proposal of approximation techniques such as stochastic response surface methodology, by constructing a surrogate model between the input random variables and the output response, obtains the output statistical characteristics at a lower computational cost, providing a feasible solution to high-dimensional stochastic optimization problems.

[0040] However, current research faces a key technical bottleneck: most methods separate uncertainty handling from the game-theoretic decision-making process, forming a traditional paradigm of "risk-game separation," which manifests itself in the following three aspects: First, existing collaborative optimization frameworks fail to intrinsically embed uncertainty and risk into the game-theoretic interaction process. Even with advanced techniques such as stochastic response surface methodology, they are limited to risk assessment or opportunity constraint verification for a single agent, lacking an effective mechanism to quantify the risk costs of each agent into game-theoretic transaction signals.

[0041] Second, in the process of microgrids participating in system regulation, adjusting energy storage charging and discharging plans to mitigate power fluctuations and deviating from the day-ahead benchmark operating point inevitably incurs additional operating costs. This cost increment has significant stochastic characteristics, and existing cooperative game models, lacking precise probabilistic equivalence modeling methods, cannot accurately quantify the actual risk costs borne by each microgrid when providing regulation services.

[0042] Third, in terms of the design of the benefit distribution mechanism, existing models generally use the determined independent operating cost as the point of negotiation breakdown, without considering the effect of uncertainty risk on the actual cost of each entity's independent operation. This results in the benefit distribution failing to accurately reflect the actual risk contribution of each entity, affecting the long-term stability of the cooperative alliance.

[0043] Therefore, constructing a cooperative game framework that can accurately quantify the risk costs of microgrid regulation, realize the endogenous transmission of risk value, and distribute benefits fairly based on actual risk-bearing has become a key technical problem that urgently needs to be solved to improve the collaborative operation efficiency of multi-microgrid clusters.

[0044] In view of this, this application provides a distribution network-multi-microgrid collaborative optimization method, electronic equipment, storage medium and program product based on stochastic response surface methodology and cooperative game theory. The core purpose of this solution is to construct a risk-endogenous collaborative decision-making paradigm by deeply integrating stochastic response surface methodology and cooperative game theory, thereby accurately quantifying the risk costs borne by each microgrid in mitigating random fluctuations, and thereby reconstructing the negotiation benchmark of cooperative game theory to achieve a fairer and more efficient distribution of benefits, ultimately improving the overall economic efficiency, operational robustness and alliance stability of the system.

[0045] The distribution network-multi-microgrid collaborative optimization method provided in this application relates to the field of power system optimization and dispatching technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smartwatch, or vehicle-mounted terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the distribution network-multi-microgrid collaborative optimization method, but is not limited to the above forms.

[0046] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0047] like Figure 7 As shown, this embodiment provides a method for coordinated optimization of a distribution network and multiple microgrids, including the following steps: Step S1: Endogenous quantification of individual risk costs based on SRSM.

[0048] Each operating entity (microgrid m∈M) executes in parallel: 1) Input: Get the current position of itself within a scrolling window (e.g., time period). The expected value and standard deviation of the forecasts for uncertain sources such as photovoltaics and loads.

[0049] 2) Modeling: A Hermite chaotic multinomial surrogate model is constructed using SRSM to map uncertain inputs to stochastic outputs of operating costs (such as electricity purchase costs and maintenance costs). The implementation process of SRSM includes the following five key steps: 2.1) Hermite chaotic polynomial According to SRSM theory, a stochastic process with multiple dimensions of independent standard normal random input variables can be mapped to a Hermite chaotic polynomial: (1) In the formula: —The random response output corresponding to the m-th random sample; —The undetermined coefficients of the chaotic polynomial, ; —The number of undetermined coefficients in the chaotic polynomial, where For random input dimensions, The order of the chaotic polynomial; —The m-th random standard sample in the n-th dimension.

[0050] 2.2) Nataf transform for handling correlation Based on the principle of probabilistic equivalence and Choleskey factorization, the Nataf transformation is used to convert correlated multidimensional uncertain variables into samples that follow an independent standard normal distribution. This allows chaotic polynomials to be applied to random processes that map non-independent standard normal distributions. (2) In the formula: —Independent standard normal distribution samples; —Three-dimensional random input; the Nataf transformation parameters are determined by the probability distribution function of the random input and its correlation coefficient matrix. These parameters are obtained by fitting historical data and are known values ​​in the optimization calculation.

[0051] 2.3) Locating points and sample generation Based on the principle of linearly independent collocation: utilizing the eigenvalues ​​of Hermite's third-order orthogonal polynomials [ ] Forming 27 unique, standard normal distribution samples Construct candidate collocation points to form the original Hermite matrix. After Gaussian elimination, we can obtain a full-rank rank with a rank equal to 1. orthogonal matrix The collocation points corresponding to the row numbers of the matrix remaining after Gaussian elimination are the standard samples of SRSM. .

[0052] (3) In the formula: —Nataf inverse transform function; — The Gaussian distribution parameters that the random input quantity in time period t follows.

[0053] 2.4) Construction and coefficient solution of linear systems Standard Sample of SRSM Once confirmed, The values ​​of each element are also determined, and equation (13) is written in matrix form: (4) Based on the above equation, the characteristic coefficients of the chaotic polynomial Compared with the random output variables in the original stochastic process model There is a linear mapping relationship between them, that is: (5) 2.5) Statistical Analysis and Confidence Interval Estimation Utilizing the probabilistic and statistical properties of chaotic polynomials, through the characteristic coefficients Directly calculate the output response The statistical moments. The upper / lower bounds of the confidence interval for the response output can be expressed as: (6) In the formula: , —The upper and lower bounds of the confidence interval for the random response output; — Random response output Expected value; ——Random response output The standard deviation can be calculated from the characteristic coefficients: (7) 3) Optimization: With the objective of minimizing the upper bound of the confidence interval of the operating cost at confidence level α, local stochastic optimization is performed while satisfying its own operating constraints (such as dynamic energy storage SOC, equipment power limits, and grid connection point power opportunity constraints). The objective function of the microgrid considering uncertainties is shown in equation (8): (8) in, and To efficiently calculate the expected value and standard deviation of operating costs using the aforementioned SRSM proxy model, The standard normal quantiles at the 95% confidence level. The dispatch cost of SOC deficit or surplus power caused by the current strategy.

[0054] The objective function of the distribution network is shown in equation (9): (9) In the formula, To optimize the total operating cost of independent operation of the distribution network within a time scale; For distribution network t The total revenue generated from selling electricity to the microgrid at all times; As the balancing node of the distribution network t The expenditure on purchasing electricity from the superior power grid at all times; This is to cover the power generation losses and operation and maintenance costs of the distribution network itself.

[0055] 4) Output: The solution yields the operating cost of the distribution network. and the independent operating cost of microgrids taking into account uncertainty risks This cost accurately represents the risk that each entity must bear when operating independently and dealing with all fluctuations on its own.

[0056] Step S2: Reconstruct the cooperative game negotiation benchmark that takes into account risk costs.

[0057] Based on the stochastic optimization model established in S1, the optimal operating costs of the distribution network and each microgrid are calculated separately under the conditions of independent operation without considering cooperation. The key innovation lies in the fact that the "independent operating cost" here is not a deterministic cost in the traditional sense, but rather an expected independent operating cost that takes into account the uncertainty risk of the source-load relationship. This cost serves as a new "negotiation breakdown point," constituting a negotiation benchmark for cooperative games, ensuring that the subsequent distribution of benefits truly reflects the risk costs that each entity would have to bear if it did not cooperate.

[0058] Step S3: Solve the distributed Nash bargaining problem based on the new benchmark.

[0059] 1) Modeling: Based on the new benchmark mentioned above, a Nash bargaining model that maximizes the total cooperative surplus of the system is established, as shown in Equation (10): (10) In the formula: This represents the total operating cost of the distribution network within the trading window after price negotiation. For microgrids m The total operating cost within the transaction window after negotiation.

[0060] 2) Decomposition: Decompose the original problem into two subproblems that can be solved in a distributed manner: Subproblem P1: Minimize social cost (given electricity price, optimize electricity consumption).

[0061] To achieve distributed solution, Lagrange multipliers and penalty coefficients are introduced to construct an augmented Lagrange function. The coupling variable is the negotiated electricity volume between the distribution network and multiple microgrids, which must satisfy the following constraints: (11) Therefore, based on the objective function of the distribution network shown in equation (9), its augmented Lagrangian function can be constructed as follows: (12) In the formula: —The Lagrange multipliers traded between the distribution network and the m-th microgrid during time period t; —The penalty coefficient for the first subproblem; —Number of iterations; — The number of iterations in the previous round.

[0062] Based on equation (8), the augmented Lagrangian function of the microgrid objective function is constructed as follows: (13) The update rules for Lagrange multipliers are as follows: (14) Equations (12) and (13) are solved by their respective operators. The optimal trading volume can be obtained by simply exchanging the decision variables and iterating. .

[0063] Subproblem P2: Maximize payment benefits (given electricity volume, optimize electricity price).

[0064] In the objective function of this subproblem, the coupling variable is the electricity trading price, which must satisfy the following constraints: (15) In the formula: —The transaction price is ultimately determined by the cooperative game between the distribution network and multiple microgrids during time period t.

[0065] Similarly, to achieve distributed solution, Lagrange multipliers and penalty coefficients are introduced into equation (10) to construct an augmented Lagrange function. The augmented Lagrange function of the distribution network objective function is: (16) In the formula: —The Lagrange multipliers traded between the distribution network and the m-th microgrid during time period t; —The penalty coefficient for the second subproblem; —Number of iterations; — The number of iterations in the previous round.

[0066] The augmented Lagrangian function of the microgrid objective function is: (17) The update rules for Lagrange multipliers are as follows: (18) Equations (16) and (17) are solved by their respective operators. The optimal electricity price can be obtained by simply exchanging the decision variables and iterating. .

[0067] 3) Solution: The alternating direction multiplier method based on the Lagrange augmented matrix is ​​used to solve the two subproblems in a distributed iterative manner. The specific solution process is as follows: Figure 1 As shown.

[0068] During the iteration process, each entity only needs to exchange boundary coupling variables (such as the proposed value of the traded electricity volume and the expected value of the traded electricity price) and Lagrange multipliers, without disclosing internal cost functions and private operating constraints. Thus, while protecting the commercial privacy of all parties, they can collaboratively converge to the globally optimal trading strategy. ).

[0069] Step S4: Perform intraday rolling optimization.

[0070] During each rolling optimization moment within the operating day, based on the latest ultra-short-term forecast information, S1 to S3 are repeatedly executed to dynamically update the trading plan and scheduling instructions for the next few hours, and the decision at the current moment is executed immediately, thereby achieving online feedback correction and smoothing out random fluctuations.

[0071] Below, using specific application examples, this embodiment, based on the IEEE 33-node system, details the specific application and verification process of the above method.

[0072] Step 1: Basic Scene Setup This embodiment uses the IEEE 33-node system as the basis for constructing a computational model, integrating three photovoltaic-storage-charging microgrids participating in distribution network-multi-microgrid collaborative optimization into nodes 6, 15, and 30. The energy storage and SVC system access points directly managed by the distribution network and the distribution network structure are as follows: Figure 2 As shown, Figure 2 Load1, load2, and load3 are the main loads of the distribution network, with large capacities, and their load fluctuations are considered due to uncertainty. The energy storage configuration in both the distribution network and the microgrid has a capacity of 1 MW·h, a maximum charging / discharging power of 1.5MW, an operating capacity range of [0.2~0.9] MW·h, an energy storage charging / discharging efficiency of 90%, and a self-discharge rate of 2% / month. The system is set to run until 7:00 AM of the day, initiating intraday rolling optimization. The optimization window is divided into: execution period (… ), and forward-looking optimization period ( , ) and the remaining time period (after 11:00). Optimize the confidence level setting to .

[0073] Step 2: Endogenous Quantification of Individual Risk Costs Based on SRSM 1) Input: At 7:00, each entity obtains the latest ultra-short-term forecast data. Taking MG1 as an example, the expected value of each uncertainty source during the period from 7:00 to 10:00 is... with standard deviation As shown in Tables 1 and 2. The data structures of MG2 and MG3 are similar, and the numerical values ​​are omitted.

[0074] Table 1 Expected values ​​of microgrid source load

[0075] Table 2 Standard Deviation of Microgrid Source Load

[0076] 2) Modeling: Based on the data in the table above, MG1 calculates its photovoltaic output. ,load Electric vehicle charging power Consider as a three-dimensional correlated random input vector The joint probability distribution (assumed to be Gaussian) and correlation coefficient matrix are fitted using historical data. Then, through Nataf transformation, the... It is transformed into an independent standard normal random variable. Subsequently, a second-order Hermite chaotic polynomial is constructed as its total running cost. The proxy model.

[0077] 3) Optimization: MG1 constructs a local random optimization problem.

[0078] 3.1) Objective function: Minimize the upper bound of the 95% confidence interval of the running cost during the execution period and the look-ahead optimization period (7:00-10:00), and include the expected cost penalty for the remaining period.

[0079] 3.2) Key constraints (all expressed as chance constraints and transformed into deterministic second-order cone constraints for solution using SRSM): ① Energy storage SOC safety constraints:

[0080] ②PCC power fluctuation constraints: To allow for volatility, we take 5%.

[0081] ③ Conventional constraints such as upper and lower limits of equipment power and power balance.

[0082] 4) Output: By solving the above optimization problem, MG1 obtains its optimal local scheduling scheme and calculates its expected independent operating cost taking into account uncertainty risks. Similarly, the parallel calculations of microgrids MG2 and MG3 yield the results. As shown in Table 3.

[0083] Table 3 Microgrid Costs

[0084] Meanwhile, the distribution network (D) performs optimization calculations to obtain its operating costs. As shown in Table 4.

[0085] Table 4 Distribution Network Costs

[0086] Step 3: Reconstruct the cooperative game negotiation benchmark that takes into account risk costs.

[0087] This step involves a logical transformation. The four costs output from step S1 will be transformed... This will be established as the new negotiation benchmark (negotiation breakdown point) for this round of cooperative game (7:00-10:00 window). Any final cooperative solution must ensure that the actual cost of cooperation for all parties does not exceed their corresponding [values / costs]. }

[0088] Step 4: Solve the distributed Nash bargaining problem based on the new benchmark.

[0089] 1) Modeling: Based on the new benchmark, a Nash bargaining cooperative game model is constructed. Its mathematical objective is to maximize the product of the cost savings (i.e. cooperative surplus) of all participants relative to their risk benchmark, as shown in Equation (10).

[0090] 2) Decomposition and solution: Distributed solution is performed using the alternating direction multiplier method (ADMM) based on the Lagrange augmented matrix.

[0091] 3) Output: After distributed iteration, the optimal collaborative trading strategy for 7:00-10:00 is finally achieved, as shown in Tables 5 and 6.

[0092] Table 5 Transaction Electricity under the Method Presented

[0093] Table 6. Electricity Price under the Method Presented in This Paper

[0094] Step 5: Perform intraday rolling optimization and verify the results. At 7:00, all entities immediately execute according to ( , The system formulates scheduling and trading instructions. When the time advances to 8:00, the system repeats steps S1 to S3 based on the latest forecast information at 8:00, generates a new optimization strategy for the 8:00-11:00 window, and executes the instructions at 8:00. This cycle repeats to achieve online rolling optimization.

[0095] Effect verification (comparative experiment): The method of this application was compared with the traditional method using robust optimization in Monte Carlo simulation (100,000 samples).

[0096] Economy and Risk: Simulation results as follows Figure 3 , Figure 4 As shown in the figure, the blue curve represents the traditional method, and the red curve represents the method of this application. As can be seen from the figure, the 95th percentile of the total system operating cost under the method of this application is 2888.34 yuan, which is 423.85 yuan lower than the 3312.19 yuan of the traditional method. Meanwhile, the 95% confidence lower bound of the total cooperative surplus under the method of this application is 1205.16 yuan, which is higher than the 1179.25 yuan of the traditional method.

[0097] Operational stability: This embodiment presents PDF graphs showing the PCC port power and operating cost of each microgrid at time 7 under two strategies, as shown below. Figure 5 As shown, the power distribution of each microgrid common connection point (PCC) under the method of this application is more closely related to the contract value, and the fluctuation range is significantly smaller than that of the traditional method, proving that its embedded opportunity constraint effectively suppresses the risk of power interaction default.

[0098] Fairness: by Figure 6 As can be seen, under the strategy proposed in this application, the probability distribution of operating costs and benefits of each microgrid is more concentrated, and the distribution locations and shapes of different microgrids are highly similar. This indicates that each microgrid can obtain a comparable level of economic benefit, without the unfair phenomenon of some members profiting excessively while others benefit meagerly.

[0099] Energy storage status: After optimization, the method in this application guides the energy storage SOC of each microgrid to remain at a reasonable high level (as shown in Table 7), reserving sufficient adjustment capacity for smoothing fluctuations in subsequent periods.

[0100] Table 7 Energy Storage SOC Strategy Values

[0101] In summary, the method proposed in this application can effectively improve the system's economy, operational stability, and allocation fairness.

[0102] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0103] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0104] Please see Figure 8 , Figure 8 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 801 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 802 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 802 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 802 and is called and executed by the processor 801 using the methods described in the embodiments of this application. The 803 input / output interface is used to implement information input and output. The communication interface 804 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 805 transmits information between various components of the device (e.g., processor 801, memory 802, input / output interface 803, and communication interface 804); The processor 801, memory 802, input / output interface 803, and communication interface 804 are connected to each other within the device via bus 805.

[0105] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0106] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0107] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0108] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0109] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented in the embodiments of this program product are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments. The executable computer program code or "code" used to perform the various embodiments can be written in high-level programming languages ​​such as C, C++, Python, Smalltalk, Java, JavaScript, Visual Basic, Structured Query Language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0110] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0111] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0112] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0113] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0114] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0115] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0117] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0118] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0119] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for coordinated optimization of distribution network and multiple microgrids, characterized in that, The method includes the following steps: S1: Individual risk cost quantification: Each operating entity constructs a chaotic multinomial proxy model of its operating cost based on the stochastic response surface method, performs local stochastic optimization with the upper bound of the confidence interval as the objective, and solves for its expected independent operating cost taking into account uncertainty risk. S2: Reconstruction of the benchmark for cooperative game: The expected independent operating costs of each subject obtained in step S1 are established as the negotiation breakdown point for cooperative game; S3: Distributed Nash Bargaining Solution: Based on the aforementioned negotiation breakdown point, a Nash bargaining cooperative game model is established and decomposed into a sub-problem of optimizing the traded electricity volume and a sub-problem of optimizing the traded electricity price. The distributed iterative solution is performed using the alternating direction multiplier method to obtain the optimal collaborative trading strategy. S4: Intraday Rolling Optimization Execution: Implement the optimal collaborative trading strategy at the current execution time, and repeat steps S1 to S3 based on the updated information at the next rolling time.

2. The method according to claim 1, characterized in that, In step S1, constructing the chaotic multinomial surrogate model based on the stochastic response surface method includes: Obtain the expected value and standard deviation of the uncertainties of the sources and loads within each subject; The Nataf transformation is used to convert the correlated original random input variables into independent standard normal distribution variables; Based on Hermite chaotic polynomials, a proxy model is constructed that maps independent standard normal variables to stochastic responses with runtime costs.

3. The method according to claim 2, characterized in that, In step S1, the objective function of the local stochastic optimization targeting the upper bound of the confidence interval is: in, For the operating costs of each microgrid, and These are the expected value and standard deviation of the operating cost calculated using the proxy model, respectively. For a given confidence level The corresponding standard normal quantile, To optimize decision-making time in a rolling manner, The time when the rolling optimization ends is T, where T is the total optimization time. The mathematical expectation operator represents taking the expected value of a variable. Let m be the electricity purchase cost of the microgrid during time period t. The operating cost of microgrid m during time period t. The dispatch cost of SOC deficit or surplus power caused by the current strategy, where m is the microgrid number and M is the total number of microgrids in the multi-microgrid system.

4. The method according to claim 1, characterized in that, In step S2, the points of breakdown in negotiations include: the independent operating costs of the distribution network. And the expected independent operating cost of each microgrid m .

5. The method according to claim 1, characterized in that, In step S3, the objective function of the Nash bargaining cooperative game model is to maximize the product of the cooperative surpluses of all participants: in, The independent operating cost of the distribution network during time period t. The negotiated operating cost of the distribution network during time period t. Let m be the independent operating cost of microgrid m during time period t. This represents the operating cost of microgrid m during time period t after negotiation.

6. The method according to claim 5, characterized in that, In step S3, the distributed Nash bargaining solution specifically involves: The Nash bargaining cooperative game model is decomposed into a first subproblem and a second subproblem; The first subproblem is to optimize the traded electricity volume with the objective of minimizing the total system operating cost, given a specific electricity price. The second subproblem is to optimize the electricity trading price with the goal of maximizing payment benefits, given a certain amount of electricity. The augmented Lagrangian functions of the first and second subproblems are constructed respectively, and the solutions are obtained iteratively by the alternating direction multiplier method. During the iteration process, each subject only exchanges the boundary coupling variables and the Lagrangian multipliers.

7. The method according to claim 1, characterized in that, In step S1, the constraints of the local stochastic optimization include energy storage state of charge security constraints and grid connection point power fluctuation constraints expressed in the form of chance constraints, and are transformed into deterministic second-order cone constraints through the chaotic polynomial surrogate model for solution.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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