A micro-grid power market bilateral transaction method and system based on improved game theory

Abstract

The application discloses a kind of based on improved game theory's microgrid electric power market bilateral transaction method and system;The present application is directed to the determination problem of real-time transaction electricity price strategy of electric power market participated by multiple microgrids, establishes the double-layer optimization model based on game theory, according to the characteristics of model, in the empire competition algorithm is combined with the train of thought of brainstorm optimization, and in a more flexible way to improve the evolutionary path, form a new improved empire competition algorithm, to determine the real-time transaction electricity price strategy of electric power market and the power allocated by each microgrid.The invention idea has strong novelty, creativity and feasibility, and its purpose is to provide an efficient and economical method for obtaining real-time transaction electricity price of electric power market, which lays a foundation for in-depth study of supply and demand game of electric power market.The invention content has wide application range and strong practicality, and has important significance for research and application fields such as electric power economic market, and provides an effective way for further improvement of production efficiency of electric power industry.

Description

Technical Field

[0001] This invention relates to the technical field of bilateral trading methods and systems for microgrid electricity markets that combine an improved imperial competition algorithm with game theory, specifically a bilateral trading method and system for microgrid electricity markets based on improved game theory. Background Technology

[0002] With increasingly severe environmental problems and the rapid depletion of non-renewable resources, the utilization rate of renewable energy is increasing. Distributed renewable energy sources are interconnected to form microgrids. Microgrids improve the utilization rate of distributed energy by coordinating and controlling various energy sources within the microgrid. Due to differences in the types of distributed energy sources among different microgrids, information exchange between microgrids can reduce the dependence of multiple microgrids on the main grid and enhance power supply reliability. The increase in the number of microgrids leads to the aggregation of more microgrids into multi-microgrid systems, which participate in electricity market transactions. During the operation of multi-microgrid systems, factors such as real-time power transmission between microgrids and between distributed energy sources and microgrids affect the operation of the multi-microgrid system. By studying the real-time electricity price trading problem between the multi-microgrid side and the user side, a win-win situation can be achieved for both the multi-microgrid and the users. Therefore, a trading method and system are needed in the electricity market involving multi-microgrids to ensure reasonable power allocation and economic benefits among the microgrids.

[0003] Game theory is the primary theory and methodology for studying bilateral trading problems in microgrid electricity markets. It considers the predictive and actual behaviors of individuals during the game process and studies their optimal strategies. Bilateral trading models in microgrid electricity markets based on game theory mainly include cooperative game-based models and non-cooperative game-based models. In cooperative game-based models, resource information is shared within the microgrid, and participants coordinate their strategies to maximize the profits of all participants. In non-cooperative game-based models, participants coordinate their strategies to maximize their own profits. Both types of models exhibit nonlinearity and non-convexity, making them difficult to solve using traditional mathematical methods. Heuristic methods provide feasible approaches to solving these models.

[0004] The Imperialist Competitive Algorithm (ICA) is a heuristic optimization algorithm based on the mechanism of imperialist colonial competition, proposed in 2007 by Atashpaz-Gargari, Lucas, and other scholars. This method boasts fast convergence speed and high convergence accuracy, and has been applied in practical fields such as scheduling and mechanical design. However, as the number of empires decreases during the competition, the diversity of the population diminishes, making it prone to getting trapped in local optima. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a bilateral trading method and system for microgrid electricity markets based on improved game theory, which solves the following problems:

[0006] 1. Existing bilateral trading models for microgrid electricity markets based on game theory mainly include trading models based on cooperative game theory and trading models based on non-cooperative game theory. Both types of models have characteristics such as nonlinearity and nonconvexity, which are technical problems that are difficult to solve using traditional mathematical methods.

[0007] 2. The Imperial Competition Algorithm is a heuristic optimization algorithm based on the imperialist colonial competition mechanism. As the number of empires decreases continuously during the competition, the diversity of the group decreases, making it easy to fall into the technical problem of local optima.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a bilateral trading method for microgrid electricity market based on improved game theory, which establishes a real-time trading price strategy model for each microgrid based on the information parameters of each microgrid; determines consumers and suppliers based on the information parameters and load demand of generating units within each microgrid; and establishes a real-time trading price strategy model between consumers and suppliers, taking into account the interests of different stakeholders, with the goal of maximizing social welfare within the microgrid.

[0009] Preferably, based on game theory, a two-level optimal scheduling model is established for cooperative / non-cooperative game among multiple microgrids; considering energy mutual assistance among microgrids, the lower-level optimization model is established with the goal of maximizing the total revenue of the multi-microgrid system and consumers and the constraint of safe operation of each microgrid; the upper-level optimization model is established with the goal of maximizing the profit of individuals (each microgrid and consumer) and the constraint of upper and lower limits of each generator.

[0010] Ideally, brainstorming and other optimization ideas should be incorporated to improve the Empire Competition algorithm;

[0011] (1) Utilize the brainstorming optimization thinking convergence (clustering) strategy to maintain the diversity of empire members in the empire competition algorithm, different regions of the solution space of different empire exploration / development optimization problems, in order to approach the global optimum;

[0012] (2) By using the brainstorming optimization strategy, multiple empires in the empire competition algorithm are combined and perturbed to achieve collaborative competition among multiple empires.

[0013] (3) Improve the way the colonies evolve by moving some colonies along a curve (e.g., a spiral) toward the imperialists, introduce a mutation mechanism that is triggered by a certain probability, use entropy data to guide the mutation operation, and use chaotic step size to improve search accuracy.

[0014] Preferably, the improved Empire Competition algorithm is used to solve the two-level optimal scheduling model of the multi-microgrid cooperative / non-cooperative game. The improved algorithm is used to solve the two-level optimal scheduling model of the multi-microgrid cooperative / non-cooperative game, to find the trading strategies of each microgrid and consumers, and then to obtain the output of controllable distributed power sources in each microgrid, while determining the actual power demand of each consumer.

[0015] A microgrid electricity market bilateral trading system based on improved game theory includes an acquisition module for acquiring data of the original problem (electricity market bilateral trading problem);

[0016] Construction module: used to construct problem models (real-time trading strategy model and multi-microgrid optimal scheduling model) based on the original problem data;

[0017] Solution calculation module: The solution calculation module calculates the problem model according to the solution algorithm (improved empire competition algorithm) to obtain an approximate optimal solution.

[0018] Beneficial effects

[0019] This invention provides a bilateral trading method and system for microgrid electricity markets based on improved game theory. Addressing the problem of determining real-time electricity pricing strategies in electricity markets involving multiple microgrids, this invention establishes a two-level optimization model based on game theory. Considering the characteristics of the model, it incorporates brainstorming optimization ideas into the Empire Competition algorithm and improves the evolutionary path in a more flexible way, forming a novel improved Empire Competition algorithm. This algorithm is used to determine the real-time electricity pricing strategy and the power allocated to each microgrid. The invention possesses strong novelty, inventiveness, and feasibility. Its purpose is to provide an efficient and economical method for obtaining real-time electricity market prices, laying the foundation for in-depth research on supply and demand game theory in electricity markets. The invention has a wide range of applications and strong practicality, and is of great significance to research and application fields such as electricity economics and markets, providing an effective way to further improve the production efficiency of the power industry. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the steps of a bilateral trading method and system for microgrid electricity markets based on improved game theory, as described in this invention.

[0021] Figure 2 This is a flowchart illustrating the iterative solution steps of a bilateral trading method and system for microgrid electricity markets based on improved game theory, as described in this invention.

[0022] Figure 3 This is a diagram illustrating the energy trading framework of a bilateral trading method and system for microgrid electricity markets based on improved game theory, as described in this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see Figure 1 This invention provides a technical solution: a bilateral trading method for microgrid electricity market based on improved game theory. The method establishes a real-time trading price strategy model for each microgrid based on its information parameters; determines consumers and suppliers based on the information parameters and load demand of generating units within each microgrid; and establishes a real-time trading price strategy model between consumers and suppliers, taking into account the interests of different stakeholders (consumers and suppliers) with the goal of maximizing social welfare within the microgrid.

[0025] Based on game theory, a two-level optimal scheduling model for cooperative / non-cooperative game among multiple microgrids is established. Considering energy mutual assistance among microgrids, a lower-level optimization model is established with the goal of maximizing the total revenue of the multi-microgrid system and consumers and the constraint of safe operation of each microgrid. An upper-level optimization model is established with the goal of maximizing the profit of individuals (each microgrid and consumer) and the constraint of upper and lower limits of each generator.

[0026] By incorporating ideas such as brainstorming optimization, the Empire Competition algorithm was improved.

[0027] (1) Utilize the brainstorming optimization thinking convergence (clustering) strategy to maintain the diversity of empire members in the empire competition algorithm, different regions of the solution space of different empire exploration / development optimization problems, in order to approach the global optimum;

[0028] (2) By using the brainstorming optimization strategy, multiple empires in the empire competition algorithm are combined and perturbed to achieve collaborative competition among multiple empires.

[0029] (3) Improve the way the colonies evolve by moving some colonies along a curve (e.g., a spiral) toward the imperialists, introduce a mutation mechanism that is triggered by a certain probability, use entropy data to guide the mutation operation, and use chaotic step size to improve search accuracy.

[0030] An improved imperial competition algorithm is used to solve a two-level optimal scheduling model of cooperative / non-cooperative game among multiple microgrids. The improved algorithm is used to solve the two-level optimal scheduling model of cooperative / non-cooperative game among multiple microgrids, to find the trading strategies of each microgrid and consumers, and then to obtain the output of controllable distributed power sources in each microgrid, while determining the actual power demand of each consumer.

[0031] A microgrid electricity market bilateral trading system based on improved game theory includes an acquisition module for acquiring data of the original problem (electricity market bilateral trading problem);

[0032] Module for building problems (real-time trading strategy model and multi-microgrid optimal scheduling model) based on the original problem data;

[0033] The solution calculation module calculates the problem model based on the solution algorithm (improved empire competition algorithm) to obtain an approximate optimal solution.

[0034] Its detailed connection method is a well-known technology in this field. The following mainly introduces the working principle and process, and the specific work is as follows.

[0035] Example: This is illustrated using a non-cooperative game theory-based bilateral electricity market transaction as an example:

[0036] Taking a non-cooperative game-theoretic bilateral electricity market transaction as an example, to maximize the interests of individual participants, each participant optimizes their own trading strategy based on the trading strategies that competitors might use. Participants possess complete information on their own and their competitors' actual generation costs and know the range of competitors' strategy spaces from historical data. Based on the information parameters of each microgrid, a real-time trading strategy model for the microgrid is established in the form of slope and intercept, allowing for greater flexibility in adjusting strategy variables. The real-time trading strategy model is as follows:

[0037]

[0038]

[0039] Among them, b i c i d is the marginal cost coefficient of the i-th generator; j e j P represents the utility function coefficient for the j-th consumer. i q represents the actual output power of the generator. j This represents the actual power required by the load. Let be the game coefficient for the j-th consumer; Let be the game coefficient of the i-th generator; Trading strategies for each generator; Transaction strategies for consumers; b′ i c′ i It is the static coefficient of the marginal cost coefficient of the i-th generator; d′ j 、e′ j It is the static coefficient of the j-th demand curve;

[0040] Based on non-cooperative game theory, a two-level optimal scheduling model for non-cooperative game theory in multi-microgrids is established.

[0041] Upper-level model:

[0042]

[0043] Lower-level model:

[0044] in, Let a be the nodal marginal price of the i-th generator and the nodal marginal price of the j-th consumer, respectively; i b i c i This is the cost coefficient for the generator; These are the lower and upper bounds of the game coefficients for the i-th generator, respectively; δ represents the lower and upper bounds of the game coefficients for the j-th consumer, respectively; k The node voltage angle is given. Formula (3) maximizes generator profits, formula (4) maximizes consumer profits, and formula (5) maximizes consumer profits minus generator costs, i.e., maximizes the total revenue of all microgrids and consumers.

[0045] A two-level optimal scheduling model for the non-cooperative game of the multi-microgrid system is solved based on an improved multi-empire cooperative competition algorithm. (See also...) Figure 2 The steps are as follows:

[0046] Step 1. Initialize decision variables: The decision variables of the objective function shown in formulas (3), (4), and (5) are the static coefficients b of the generator marginal cost coefficient. i c i And the consumer's utility function coefficient d j e j A solution to the objective function is represented by an Imperialist or Colony in the Imperialist competition algorithm. The solution set consists of K empires and NK colonies. The N solutions are initialized using chaos or other strategies. The objective function values ​​(i.e., the power in the Imperialist competition algorithm) of each N solution are calculated. These solutions are clustered into K classes in the solution space. The solution with the largest power (i.e., the largest objective function value in the maximization problem) in each class is selected as the Imperialist. Colonies are randomly allocated to Imperialists proportionally based on their power (the greater the power, the larger the allocation ratio). Each Imperialist and their assigned colonies form an Empire.

[0047] Step 2. Colonial / Imperial Perturbation: For each empire, perform a cooperative operation on the imperialists within the empire with probability p1, that is, combine (weight) the imperialist with another randomly selected imperialist and apply a random perturbation, as shown in formula (6):

[0048] imp i =α·imp i +(1-α)imp j +U(0, β) (6)

[0049] Among them, imp i For the current imperialists, imp j Let α be another randomly selected imperialist, and U(0, β) be the applied perturbation. 1-p1 moves the colony toward the imperialist along a curve (such as a spiral) with probability (introducing a mutation mechanism with a trigger probability of p3, using entropy data to guide the mutation operation, and employing a chaotic step size to expand the search range, improve search accuracy, and approach the global optimum);

[0050] Step 3. Imperialist Update and Total Imperial Power Calculation: For each empire, select the most powerful imperialists and colonies as the new imperialists; calculate the total power of the empire as shown in Formula (7):

[0051]

[0052] Among them, Q i The total power of Empire i K represents the standardized total power of the empire, and K represents the number of empires.

[0053] Step 4. Imperialist Competition: Empires are redistributed with probability p2 to maintain diversity in the solution space and sustain exploration / development of different regions within the solution space. Specifically, all current solutions (i.e., imperialists and colonies) are clustered into K classes in the solution space, and the solution with the greatest power in each class is selected as the new imperialist. Colonies are randomly allocated to imperialists proportionally based on their power. Each imperialist and their assigned colony form an empire. Competition between imperialist groups is conducted with probability 1-p2, i.e., a colony is selected from the weakest empire, and all empires compete to occupy this colony. In particular, when the weakest empire has only one colony left, a forced redistribution of empires is performed to simultaneously maintain exploration / development of different regions of the solution space by the K empires, approximating the global optimum.

[0054] Step 5. Iterate through steps 2, 3, and 4 until the algorithm's stopping condition is met. Output the optimal solution found during the algorithm's execution. This solution is the final solution of the objective function (3)(4)(5) (i.e., b). i c i d j e j The final solution sought (i.e., b) i c i d j e j Substituting into formulas (1) and (2), the real-time trading strategy of the microgrid is obtained, and then the actual output power P of the controllable distributed power source of each microgrid is obtained. i and the actual power demand q of the load j .

[0055] It should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

Claims

1. A bilateral trading method for microgrid electricity markets based on improved game theory, characterized in that, Based on the information parameters of each microgrid, a real-time electricity trading strategy model for the microgrid is established. Consumers and suppliers are determined based on the information parameters of generators and load demand within each microgrid. With the goal of maximizing social welfare within the microgrid, and considering the interests of different stakeholders (consumers and suppliers), a real-time electricity trading strategy model between consumers and suppliers is established. Based on game theory, a two-layer optimization scheduling model for cooperative / non-cooperative game theory among multiple microgrids is established. Considering energy mutual assistance among microgrids, with the goal of maximizing the total revenue of the multi-microgrid system and consumers, and constrained by the safe operation of each microgrid, a lower-level optimization model is established. With the goal of maximizing the profits of individual microgrids and consumers, and constrained by the upper and lower limits of each generator, an upper-level optimization model is established. Brainstorming optimization ideas are introduced to improve the Empire Competition algorithm. (1) By using the convergence clustering strategy of brainstorming optimization ideas, we can maintain the diversity of empire members in the empire competition algorithm and different regions of the solution space of different empires' exploration / development optimization problems in order to approach the global optimum. (2) By using the brainstorming optimization strategy, multiple empires in the empire competition algorithm are combined and perturbed to achieve collaborative competition among multiple empires. (3) Improve the evolution of colonies by moving some colonies along a curve toward the imperialists, introduce a mutation mechanism that is triggered by a certain probability, use entropy data to guide the mutation operation, and use chaotic step size to improve search accuracy.

2. The bilateral trading method for microgrid electricity market based on improved game theory according to claim 1, characterized in that, The improved Imperial Competition algorithm is used to solve the two-level optimal scheduling model of the multi-microgrid cooperative / non-cooperative game. The improved algorithm is used to solve the two-level optimal scheduling model of the multi-microgrid cooperative / non-cooperative game, to find the trading strategies of each microgrid and consumers, and then to obtain the output of controllable distributed power sources in each microgrid, while determining the actual power demand of each consumer.

3. The microgrid electricity market bilateral trading system based on improved game theory according to claim 1, characterized in that, Includes an acquisition module: used to acquire raw problem data, wherein the raw problem is a bilateral transaction problem in the electricity market; Construction module: used to construct a problem model based on the original problem data, wherein the problem model is a real-time trading strategy model and a multi-microgrid optimal scheduling model; The solution calculation module calculates the solution to the problem model according to the solution algorithm, which is an improved empire competition algorithm, to obtain an approximate optimal solution.

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