Multi-park integrated energy system optimization scheduling method and system based on game theory

Abstract

The invention provides a multi-park integrated energy system optimization scheduling method and system based on a game theory, and relates to the field of integrated energy systems, and the method comprises the steps: building a scheduling model for a to-be-scheduled multi-park integrated energy system with the optimal overall performance as a target; performing sub-problem division on the scheduling model, and solving the sub-problems to obtain an optimal scheduling strategy; the scheduling model comprises a multi-layer game model and an energy supplier robust optimization model, the energy supplier robust optimization model describes the fluctuation range of the source load by using a multi-interval uncertainty set, finds the worst value in the fluctuation range of the source load as the worst environment condition, and solves the optimal operation decision under the worst environment condition. According to the method, the multi-layer game model of the integrated energy system is established to solve the problem of interest relation and energy interaction of all subjects, the uncertainty of new energy power generation and power utilization loads is processed through robust optimization, and a new theoretical framework and method approach are provided for collaborative scheduling of the multi-park integrated energy system.

Description

Technical Field

[0001] The present disclosure relates to the field of integrated energy systems, and in particular to a multi-park integrated energy system optimization scheduling method and system based on game theory. Background Art

[0002] An Integrated Energy System (IES) is a multi-energy, complementary system that integrates electricity, heat, gas, and other energy sources. Its key feature is achieving efficient energy utilization and clean energy transformation through the coordinated optimization of production and load across multiple energy sources. IES offers a variety of functions, including multi-scenario coordinated energy scheduling, real-time optimization and control, and improved system reliability. By tightly integrating energy production, transmission, storage, and consumption, IES can effectively improve the overall efficiency of the energy system, reduce operating costs, promote the low-carbon transformation of the energy structure, and meet diverse user needs.

[0003] The scheduling of integrated energy systems presents two major challenges due to their inherent characteristics. First, as energy systems develop and scale, conflicts of interest often arise between various stakeholders, including power generation companies, users, and energy service providers. For example, energy suppliers seek to maximize profits, while users seek to minimize energy costs. These complex interactions among these stakeholders are difficult to effectively address using traditional optimization methods. Second, the operation of integrated energy systems often faces significant uncertainties on the source and load sides, such as the volatility of renewable energy and the randomness of energy demand. This challenges the reliability and robustness of scheduling schemes. Traditional optimization methods often fail to fully consider extreme scenarios when dealing with these uncertainties, potentially leading to reduced economic efficiency and safety of system operation.

[0004] In addressing the aforementioned challenges, existing research has largely focused on the game relationship between a single energy supplier and user, as well as robust optimization strategies in a single scenario. However, in the actual operation of an integrated energy system, the interest interactions between multiple energy suppliers and multiple users are more complex, and the energy flow and coordination requirements between different parks also increase the difficulty of system optimization. At the same time, faced with high uncertainty on the source and load side, relying solely on traditional game theory or single robust optimization is difficult to achieve optimal operation of the system as a whole. Existing research has not fully considered the collaborative and competitive characteristics between multiple parks and users, nor has it combined game theory with robust optimization methods, making it impossible to simultaneously address the complex multi-agent interaction relationship and the source-load uncertainty challenges.

[0005] In summary, the multi-park integrated energy system scheduling technology still has problems of multi-subject interest interaction and source-load uncertainty. Summary of the Invention

[0006] In order to solve the above problems, the present disclosure proposes a multi-park integrated energy system optimization scheduling method and system based on game theory, establishes a multi-layer game model of the integrated energy system, solves the interest relationship and energy interaction of various subjects, and uses robust optimization to deal with the uncertainty of renewable energy power generation and load, providing a new theoretical framework and methodological approach for the coordinated scheduling of multi-park integrated energy systems.

[0007] According to some embodiments, the present disclosure adopts the following technical solutions:

[0008] The multi-park integrated energy system optimization scheduling method based on game theory includes:

[0009] With the goal of optimizing overall performance, a scheduling model is constructed for the multi-park integrated energy system to be scheduled;

[0010] The scheduling model is divided into sub-problems, and the sub-problems are solved by combining KKT conditions, ADMM, and C&CG algorithms to obtain the optimal scheduling strategy;

[0011] Among them, the scheduling model includes a multi-layer game model and an energy supplier robust optimization model. The multi-layer game model includes a cooperative game model between producers and consumers within the producer-consumer user alliance, a master-slave game model between the producer-consumer user alliance and the energy supplier, and a cooperative game model between energy suppliers. The energy supplier robust optimization model uses a multi-interval uncertainty set to describe the fluctuation range of the source load, finds the worst value within the fluctuation range of the source load as the worst environmental condition, and solves the optimal operation decision under the worst environmental conditions.

[0012] According to some embodiments, the present disclosure adopts the following technical solutions:

[0013] The multi-park integrated energy system optimization and dispatching system based on game theory includes:

[0014] The model building module is configured to: build a scheduling model for the multi-park integrated energy system to be scheduled with the goal of optimizing overall performance;

[0015] The model solving module is configured to: divide the scheduling model into sub-problems, solve the sub-problems by combining KKT conditions, ADMM, and C&CG algorithms, and obtain the optimal scheduling strategy;

[0016] Among them, the scheduling model includes a multi-layer game model and an energy supplier robust optimization model. The multi-layer game model includes a cooperative game model between producers and consumers within the producer-consumer user alliance, a master-slave game model between the producer-consumer user alliance and the energy supplier, and a cooperative game model between energy suppliers. The energy supplier robust optimization model uses a multi-interval uncertainty set to describe the fluctuation range of the source load, finds the worst value within the fluctuation range of the source load as the worst environmental condition, and solves the optimal operation decision under the worst environmental conditions.

[0017] According to some embodiments, the present disclosure adopts the following technical solutions:

[0018] A computer program product includes a computer program, which, when executed by a processor, implements the multi-park integrated energy system optimization scheduling method based on game theory.

[0019] According to some embodiments, the present disclosure adopts the following technical solutions:

[0020] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the multi-park integrated energy system optimization scheduling method based on game theory is implemented.

[0021] According to some embodiments, the present disclosure adopts the following technical solutions:

[0022] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device implements the multi-park integrated energy system optimization scheduling method based on game theory.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The present invention provides a multi-park integrated energy system optimization scheduling method and system based on game theory, which can effectively solve the problem of interest interaction and source-load uncertainty among multiple subjects in the multi-park integrated energy system, and achieve high efficiency, stability and economy of system operation. The specific advantages are:

[0025] (1) Construct a three-layer game system of "user cooperation - master-slave pricing - energy supplier cooperation". The bottom layer uses cooperative games to encourage producers and consumers to form alliances, share benefits, and achieve complementary energy surpluses and shortages; the middle layer uses master-slave games to characterize the dynamic prices between energy suppliers and users, so that price signals are accurately linked to real-time loads; the top layer uses cooperative games to organize cross-park energy suppliers to carry out horizontal energy transactions and cost sharing. This architecture captures both horizontal competition and cooperation and vertical quantity and price games, breaking through the limitations of traditional single-layer or two-layer models that are difficult to simultaneously take into account multi-dimensional interests, and provides a more fine-grained game method for multi-agent coordination of integrated energy systems.

[0026] (2) Using a multi-interval uncertainty set to describe the source-load uncertainty of the system, the random fluctuations are divided into several intervals and the source-load value in the worst case falls within the interval. Compared with the traditional polyhedron or box-type uncertainty set, it can more accurately capture the fluctuation shape and correlation, significantly reduce conservatism in the worst case, effectively suppress the increase in operating costs, and improve the flexibility of the scheduling plan and the ability to absorb new energy.

[0027] (3) Combining game theory with robust optimization, a three-layer game two-stage robust optimization model of "cooperation-master-slave-cooperation + multi-interval robustness" is constructed and solved using the ADMM nested C&CG solution algorithm. First, the KKT condition and duality theory are used to transform the master-slave game into a cooperative game between producers and consumers. C&CG is used to solve the uncertainty problem, and finally ADMM is used to solve the benefit allocation problem. The solution method can converge quickly and only requires the exchange of interactive power and price information, ensuring data privacy. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0029] Figure 1 This is a flow chart of the method of Example 1.

[0030] Figure 2 This is the structural diagram of the multi-park integrated energy system of Example 1.

[0031] Figure 3 This is a flowchart for solving the problem in Example 1.

[0032] Figure 4 This is the source load prediction diagram of each entity in Example 1.

[0033] Figure 5 This is the algorithm convergence result diagram of Example 1.

[0034] Figure 6 This is the operating result diagram of Example 1.

[0035] Figure 7 The worst source load value diagram obtained by calculation in Example 1. DETAILED DESCRIPTION

[0036] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0038] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "comprising" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0039] Example 1

[0040] In one embodiment of the present disclosure, a multi-park integrated energy system optimization scheduling method based on game theory is provided. Figure 1 Shown, including:

[0041] Step S1: Build a scheduling model for the multi-park integrated energy system to be scheduled with the goal of optimizing overall performance;

[0042] Step S2: Divide the scheduling model into sub-problems, and solve the sub-problems by combining KKT conditions, ADMM, and C&CG algorithms to obtain the optimal scheduling strategy;

[0043] Among them, the scheduling model includes a multi-layer game model and an energy supplier robust optimization model. The multi-layer game model includes a cooperative game model between producers and consumers within the producer-consumer user alliance, a master-slave game model between the producer-consumer user alliance and the energy supplier, and a cooperative game model between energy suppliers. The energy supplier robust optimization model uses a multi-interval uncertainty set to describe the fluctuation range of the source load, finds the worst value within the fluctuation range of the source load as the worst environmental condition, and solves the optimal operation decision under the worst environmental conditions.

[0044] As an embodiment, the disclosed multi-park integrated energy system optimization and scheduling method based on game theory establishes a multi-layered game model for the integrated energy system to resolve the interests and energy interactions of various entities, and uses robust optimization to handle the uncertainty of renewable energy generation and power load. This provides a new theoretical framework and methodological approach for the coordinated scheduling of multi-park integrated energy systems. The specific implementation process is as follows:

[0045] A multi-park integrated energy system is a complex energy network that integrates multiple park energy systems through physical or virtual interconnection, aiming to achieve energy efficiency, renewable energy consumption and low carbon goals, such as Figure 2 As shown, it consists of multiple parks, each of which includes an energy supplier and a prosumer user alliance composed of several prosumers. Therefore, the multi-park integrated energy system involves multiple stakeholders such as energy suppliers and prosumers.

[0046] In order to deal with the multi-agent interest interaction problem and source-load uncertainty problem in the integrated energy system, this paper proposes a robust optimization scheduling strategy for a multi-park integrated energy system based on a three-layer game. In the multi-agent interest relationship part, a three-layer game framework is constructed, and in the uncertainty processing part, a two-stage robust optimization model for the energy supplier is constructed. Specifically:

[0047] Step 1: Build a three-layer game framework: build a master-slave game model between the prosumer user alliance and the energy supplier, a cooperative game model within the prosumer user alliance, and a cooperative game model between energy suppliers.

[0048] 1. Master-slave game model between the prosumer user alliance and energy suppliers

[0049] This model is used to model a master-slave game between prosumers and energy suppliers. The energy supplier sets electricity and heat prices and sends them to a prosumer user alliance. The user alliance responds to these energy prices to calculate its own electricity and heat demand and sends it back to the energy supplier. A dynamic game is played between the two, with the energy supplier constantly adjusting its offer and the user alliance continuously responding to its strategy, ultimately reaching a master-slave equilibrium. In this scenario, neither the energy supplier nor the prosumer can unilaterally change their strategies to increase their own profits. Therefore, a mutually linked objective function for the energy supplier and the prosumer is designed to implement the master-slave game between them.

[0050] The operating goal of the energy supplier is to minimize its total operating cost, so the energy supplier's objective function is expressed as follows:

[0051]

[0052] Where, F EH,i is the total operating cost of energy supplier i, C gas,i 、C grid,i are the energy purchase costs of energy suppliers, gas grids, and power grids, respectively. ES,i is the energy storage loss cost, C DM,i The equipment operation and maintenance costs of the energy supplier, is the carbon emission cost, C AE,i C is the penalty cost for wind curtailment, EU,iThe energy supplier i sets its own external energy selling price when selling energy, and accepts the energy selling price of other energy suppliers when purchasing energy. EHp2p,i It represents the energy sales revenue of energy supplier i when interacting with other energy suppliers.

[0053] The operating goal of the prosumer is to minimize its total operating cost, mainly considering the energy purchase cost and demand response penalty. Therefore, the prosumer's objective function is as follows:

[0054]

[0055] Where, F EU,i,m represents the objective function of prosumer m under energy supplier i, Demand response penalty for the prosumer, They represent the purchased electrical energy and thermal energy power of prosumer m under energy supplier i, Energy interaction benefits between producers and consumers.

[0056] 2. Internal cooperative game model of the prosumer user alliance

[0057] It is used to model the cooperative game between producers and consumers. For the photovoltaic producer-consumer alliance, energy interaction will first be carried out within the alliance. Different photovoltaic producers and consumers will set the electricity price in the cooperative game process and pass it to other producers and consumers in the same alliance. Other producers and consumers accept the price and reasonably adjust their own demand response and electricity interaction volume based on the price of the energy supplier. With the goal of maximizing the alliance's profit under the asymmetric Nash bargaining model, they will set their own electricity quotation. Finally, the game equilibrium is reached, and the user demand response, electricity quotation between users, and electricity interaction between users are obtained at this time. To this end, with the goal of maximizing the overall profit of the producer-consumer alliance under asymmetric Nash bargaining, a producer-consumer cooperative game model is designed, which is expressed by the formula:

[0058]

[0059] Where, F EU,i,m,0 is the energy cost of prosumer m when operating independently under energy supplier i, θ i,m represents the asymmetric Nash bargaining factor of user m, which is determined by the interaction power of the user in the cooperation process.

[0060] 3. Cooperation game model between energy suppliers

[0061] It is used to model the cooperative game between energy suppliers. After the current two-level game reaches equilibrium, the energy suppliers, taking into account their own energy surplus or shortage at this time, also cooperate to maximize the overall profit under the asymmetric Nash bargaining model. Each energy supplier sets its own transaction price and transaction volume at this time, and transmits it to other energy suppliers. Other energy suppliers respond to this price and make their own decisions until the expected electricity price and volume of each energy supplier reach consistency. At this point, it is considered that the game convergence point has been reached, that is, the optimal solution. To this end, with the goal of maximizing the total profit of energy suppliers under asymmetric Nash bargaining, a cooperative game model for energy suppliers is designed, which can be expressed as follows:

[0062]

[0063] Where, F EH,i,0 is the operating cost of energy supplier i when it operates independently, F EH,i is the operating cost after cooperation with energy suppliers, θ i is an asymmetric Nash bargaining factor, which is determined by the interaction power of prosumers in the cooperation process.

[0064] Step 2: Construct a two-stage robust optimization model for energy suppliers.

[0065] The above three layers are about collaborative optimization between the two entities. When the energy supplier makes its own decisions, it uses robust optimization to deal with its own source-load uncertainty to ensure that the system can still maintain the expected performance level in the worst case. Therefore, a multi-interval uncertainty set is used to describe the fluctuation range of the source load, and the worst value (i.e., the worst environmental conditions) within the source-load fluctuation range is found. The equipment operation plan and energy supplier pricing under this worst value are solved to obtain the energy supplier's final operation strategy.

[0066] Among them, the multi-interval uncertainty set is defined as:

[0067]

[0068] Where U represents the uncertainty set of the energy supplier, and Respectively represent the actual source load value and the predicted source load value of the park, and Indicates the range of fluctuation between the actual value of the source load and the predicted value, which is divided into K intervals. and is a 0-1 variable representing the source charge deviation value. When it is 1, it means that the actual value is higher than the predicted value.

[0069] Based on the multi-interval uncertainty set defined above, and taking the minimum operating cost of the energy supplier under uncertainty as the goal, a two-stage robust optimization model for energy supplier i is constructed:

[0070] The optimization goal of the first stage is to maximize energy sales revenue and minimize decision-making costs, and to find the optimal energy supplier's energy sales price and equipment operating status. Specifically:

[0071]

[0072] Among them, δ i 、z i and y i are the first-stage decision variables of the energy supplier, which are 0-1 variables of the energy supplier's own operation, the price of energy sold to the prosumer alliance, and the energy interaction amount of cooperation between energy suppliers; w i It is the collection of energy demand response of the user alliance, including its own load reduction, load shifting and energy purchase from energy suppliers. It represents the revenue from energy sales from energy suppliers to prosumers. The actual solution process does not include the product of price and load reduction. i 、E i , I i are the relevant coefficient matrix and constant matrix respectively.

[0073] The second stage optimization goal is to optimize the cost under the worst conditions, and solve the equipment operation plan and the worst scenario. Specifically:

[0074]

[0075] Among them, x i is the decision variable of the second stage, is the equipment operation strategy of the energy supplier after eliminating the 0-1 variables, u i is the uncertainty variable, A i 、B i 、C i They are the cost coefficients and constant matrices of energy supplier operation and transactions between energy suppliers respectively. The second row represents the equipment operation constraints considering uncertainty, in which the equality constraints are also converted. G i 、H i 、J i , K i , L i are the relevant coefficient matrix and constant matrix respectively.

[0076] After solving the second stage, the optimized variables of the second stage are returned to the first stage, and the two are alternately iterated to solve the two-stage robust optimization model. After considering uncertainty, the Nash bargaining model of the energy supplier is transformed from deterministic optimization to maximizing the value of the energy supplier's profit improvement under uncertainty.

[0077] The energy constraint of the prosumer alliance is expressed as:

[0078]

[0079] The goal of the user alliance is to minimize energy costs, including energy purchase costs, demand response penalties, and energy trading benefits between users. M is the revenue from energy sales by energy suppliers to prosumers, i.e. the energy purchase cost for users. i w i is the user demand response penalty, v i is the energy transaction volume between users, M i 、N i , Q i 、R i 、S i are the constraint coefficient matrix and constant matrix of user alliance demand response and energy interaction, respectively.

[0080] Step 3: Solve the three-layer game model and the two-stage robust optimization model.

[0081] First, the model solution problem is decomposed into four sub-problems. The KKT condition is used to transform sub-problems 1 and 3, and the C&CG nested ADMM algorithm is used to solve them. The scheduling plan of the energy supplier under the worst conditions, the worst conditions at this time, and the master-slave game pricing of the prosumer alliance are obtained. At the same time, the energy consumption plan of the prosumer alliance and the interaction volume within the prosumer alliance are obtained. Then, the ADMM algorithm is used to directly solve sub-problems 2 and 4, and the energy pricing within the energy supplier alliance and the prosumer user alliance when the cost is optimal is obtained. The combination of the two is the optimal scheduling strategy for the multi-park integrated energy system, which is explained below.

[0082] 1. Problem decomposition

[0083] The objective functions expressed in the above formulas (3), (4), (6), (7), and (8) are decomposed into two sub-problems: the minimum total cost of the energy supplier (self-optimization) and the distribution of benefits (cooperative game); and the minimum comprehensive cost of the prosumer (self-optimization) and the distribution of benefits (cooperative game), as follows:

[0084]

[0085] Among them, θ i represents the Nash bargaining factor of energy supplier i, θ i,m It represents the Nash bargaining factor of prosumer m under energy supplier i.

[0086] For the sake of convenience, the above problems are referred to as sub-problems 1-4, representing the optimization problem of the energy supplier and the optimization problem of the prosumer user respectively.

[0087] 2. The alliance uses KKT conditions to transform the master-slave game problem between the energy supplier and the prosumer alliance and the cooperation problem within the prosumer alliance, that is, merge sub-problems 1 and 3, and incorporate the prosumer alliance cooperation problem into the energy supplier optimal solution problem.

[0088] The KKT condition of the user's energy constraint (8) is:

[0089]

[0090] The optimization objectives of the energy supplier after the conversion are:

[0091]

[0092] 3. After the KKT condition conversion, C&CG is first used to solve the uncertainty problem and obtain the robust optimization strategy for cooperation among energy suppliers. Then, ADMM is used to solve the benefit distribution problem, and finally the scheduling plan and uncertainty scenario of the energy supplier and prosumer alliance are obtained.

[0093] After the master-slave game problem is converted, subproblems 1 and 3 can be solved jointly. The following augmented Lagrangian function of energy supplier i is constructed to solve each subproblem separately:

[0094]

[0095] Based on the above augmented Lagrangian function, as Figure 3 As shown, the specific steps of solving are:

[0096] 3.1 Jointly solve subproblems 1 and 3 of minimizing the running cost, specifically:

[0097] (1) Assuming the parameters are known Based on the two-stage robust optimization model, the uncertainty problem of energy suppliers is solved by the C&CG algorithm, and the optimal operating cost variables of each entity are obtained.

[0098] 1) Solve the first stage

[0099] When solving the first stage, we first assume that the worst-case scenario and the energy interaction between energy suppliers in the second stage have been found. We then bring these into the first stage to solve the deterministic problem and obtain a more constrained lower bound. The expression and constraints of the first stage problem are as follows:

[0100]

[0101] Where η iThe auxiliary variable is introduced to represent the result of the second stage solution. When solving the first stage solution, it is assumed that the result of the second stage is known and it is used as a known quantity. Then the result of the first stage solution is brought into the second stage, and the cycle is repeated until convergence is achieved. k is the number of iterations, k max is the maximum number of iterations. These are the worst uncertain variables and the supplier's equipment operation strategy solved in the second stage. By solving the main problem, the supplier's equipment operation plan can be obtained when the worst uncertainty and equipment operation strategy are known. Pricing for energy user alliances and intermediate variables And get the optimization target constraint value of the second stage at this time Update the lower bound of the algorithm

[0102] 2) Solve the second stage

[0103] The solution to the first stage problem provides a lower bound for the entire two-stage problem, and at the same time obtains the running variables for solving the second stage. The second stage problem can be expressed as:

[0104]

[0105] Since this problem is a max-min nested problem and cannot be solved directly, the inner min problem can be converted into a max problem through duality theory and merged into the outer layer. The original problem is converted into a single-layer optimization problem through the KKT condition, and the nonlinear terms in the complementary relaxation condition are solved using the large M method; after the conversion, Equation (18) can be rewritten as:

[0106]

[0107] Where, π i is the dual variable, σ i , τ i This is the auxiliary 0-1 variable introduced by the Big M method; solving this transformed single-layer problem can obtain the optimal solution of the subproblem And update the upper bound

[0108] 3) Iteration loop

[0109] Determine the convergence condition UB-LB≤ε EH Whether the given error limit is met, if it is met, the supplier's operation result is given; if not, the optimization result of the second stage is added as a constraint to the first stage, and after returning to step 1), k=k+1 is set to continue the loop until the error meets the requirement or the loop limit is reached. The constraints added during the iteration are:

[0110]

[0111] (2) After obtaining the equipment operation strategy and the energy supplier interaction power, update the consistency variable and Lagrange multiplier:

[0112]

[0113] (3) Update the number of iterations k = k + 1 and determine the convergence of ADMM. If the residual requirement is met, the algorithm converges and the current running result is output. Otherwise, return to step (1) and iterate until the convergence condition is met or the maximum number of iterations is reached. The convergence condition is that both the original residual and the dual residual meet the accuracy requirements.

[0114] 3.2 Solve subproblems 2 and 4 of benefit distribution.

[0115] After jointly solving sub-problems 1 and 3, we obtain the transaction volume between energy suppliers when the total profit of the cooperative alliance is maximized. Based on this transaction volume, the ADMM algorithm is used to solve the transaction price. The solution process is similar to that of sub-problem 1 and will not be repeated here.

[0116] To demonstrate the effectiveness of the method in this embodiment, a simulation analysis is conducted on an integrated energy system consisting of three energy suppliers and nine prosumers. The example is based on a typical daily situation of an integrated energy system in a certain park. The renewable energy and load forecast curves of energy suppliers and prosumers are as follows: Figure 4 As shown in the figure, the confidence level of electric and thermal load is 0.15, the confidence level of photovoltaic and wind power is 0.1, the uncertainty is 12, and the uncertainty set value interval is 6. The source and load forecast values ​​of energy suppliers and energy users in Park 1 are as follows:

[0117] The proposed algorithm converges as Figure 5 ,It can be seen that the results can converge within a smaller number of iterations, ,which reflects the effectiveness of the algorithm proposed in this paper.

[0118] The operation results of the energy supplier park and the prosumer user are as follows: Figure 6 As shown in the figure, it can be seen that the proposed scheduling strategy can well obtain the equipment operation status of different participating entities. Energy suppliers and producers and consumers actively participate in transactions to reduce their own operating costs.

[0119] The worst source load condition obtained in this embodiment is as follows Figure 7As shown, its main characteristics are the increase in electric and thermal loads and the decrease in renewable energy output, and the worst value is always within the limited uncertainty range; this embodiment adopts a multi-interval uncertainty set. Unlike the traditional polyhedral uncertainty set that can only limit the worst value to the interval boundary, the multi-interval uncertainty set can more finely characterize the source-load uncertainty; for example, the values ​​of the electric load at 3:00, 12:00, 15:00, and 17:00 and the values ​​of the thermal load at 1:00, 9-10:00, and 15:00 all fall within the interval, thereby more comprehensively capturing various possible source-load fluctuations, enhancing the robustness of the system, and being consistent with the connotation of the worst scenario.

[0120] Example 2

[0121] In one embodiment of the present disclosure, a multi-park integrated energy system optimization and scheduling system based on game theory is provided, including:

[0122] The model building module is configured to: build a scheduling model for the multi-park integrated energy system to be scheduled with the goal of optimizing overall performance;

[0123] The model solving module is configured to: divide the scheduling model into sub-problems, solve the sub-problems by combining KKT conditions, ADMM, and C&CG algorithms, and obtain the optimal scheduling strategy;

[0124] Among them, the scheduling model includes a multi-layer game model and an energy supplier robust optimization model. The multi-layer game model includes a cooperative game model between producers and consumers within the producer-consumer user alliance, a master-slave game model between the producer-consumer user alliance and the energy supplier, and a cooperative game model between energy suppliers. The energy supplier robust optimization model uses a multi-interval uncertainty set to describe the fluctuation range of the source load, finds the worst value within the fluctuation range of the source load as the worst environmental condition, and solves the optimal operation decision under the worst environmental conditions.

[0125] Example 3

[0126] In one embodiment of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the multi-park integrated energy system optimization scheduling method based on game theory.

[0127] Example 4

[0128] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, which is used to store computer instructions. When the computer instructions are executed by a processor, the multi-park integrated energy system optimization scheduling method based on game theory is implemented.

[0129] Example 5

[0130] In one embodiment of the present disclosure, an electronic device is provided, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the multi-park integrated energy system optimization scheduling method based on game theory.

[0131] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0133] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A multi-park integrated energy system optimization scheduling method based on game theory, characterized by: include: With the goal of optimizing overall performance, a scheduling model is constructed for the multi-park integrated energy system to be scheduled; The scheduling model is divided into sub-problems, and the sub-problems are solved by combining KKT conditions, ADMM, and C&CG algorithms to obtain the optimal scheduling strategy; Among them, the scheduling model includes a multi-layer game model and an energy supplier robust optimization model. The multi-layer game model includes a cooperative game model between producers and consumers within the producer-consumer user alliance, a master-slave game model between the producer-consumer user alliance and the energy supplier, and a cooperative game model between energy suppliers. The energy supplier robust optimization model uses a multi-interval uncertainty set to describe the fluctuation range of the source load, finds the worst value within the fluctuation range of the source load as the worst environmental condition, and solves the optimal operation decision under the worst environmental conditions.

2. The multi-park integrated energy system optimization scheduling method based on game theory according to claim 1 is characterized in that: The cooperative game model between the producers and consumers is expressed as follows: Where, F EU,i,m,0 is the energy cost of prosumer m under energy supplier i when it operates independently, θ i,m represents the asymmetric Nash bargaining factor of prosumer m; The cooperative game model between energy suppliers is expressed as follows: Where, F EH,i,0 is the operating cost of energy supplier i when it operates independently, F EH,i The operating costs after cooperation with energy suppliers, is the asymmetric Nash bargaining factor.

3. The multi-park integrated energy system optimization scheduling method based on game theory according to claim 1 is characterized in that: The master-slave game model between the prosumer user alliance and the energy supplier includes the energy supplier objective function and the prosumer objective function, which are: Where, F EH,i is the total operating cost of energy supplier i, C gas,i 、C grid,i are the energy purchase costs of energy suppliers, gas grids, and power grids, respectively. ES,i is the energy storage loss cost, C DM,i The equipment operation and maintenance costs of the energy supplier, is the carbon emission cost, C AE,i C is the penalty cost for wind curtailment, EU,i represents the revenue from energy sales by energy supplier i to the prosumers it manages, C EHp2p,i represents the energy sales revenue when energy supplier i interacts with other energy suppliers; Where, F EU,i,m represents the objective function of prosumer m under the management of energy supplier i, Demand response penalty for the prosumer, They represent the purchased electrical energy and thermal energy power of prosumer m under energy supplier i, Energy interaction benefits between producers and consumers.

4. The multi-park integrated energy system optimization scheduling method based on game theory according to claim 1 is characterized in that: The multi-interval uncertainty set is expressed as: Where U represents the energy supplier’s multi-interval uncertainty set, and Respectively represent the actual source load value and the predicted source load value of the park, and Indicates the range of fluctuation between the actual value of the source load and the predicted value, which is divided into K intervals. and is a 0-1 variable representing the source charge deviation value.

5. The multi-park integrated energy system optimization scheduling method based on game theory according to claim 1 is characterized in that: The sub-problems of the division include two sub-problems: the total cost minimization and benefit distribution of energy suppliers, and two sub-problems: the comprehensive cost minimization and benefit distribution of prosumers, which are as follows:

6. The multi-park integrated energy system optimization scheduling method based on game theory as claimed in claim 3 is characterized in that: The sub-problems are solved by combining KKT conditions, ADMM, and C&CG algorithms, specifically: The KKT condition is used to merge the internal cooperation of the prosumer alliance and the master-slave game problem between the energy supplier and the prosumer alliance into the optimization problem of the energy supplier. The ADMM nested C&CG algorithm is used to solve the problem, and the ADMM is used to solve the benefit distribution problem of the cooperation between the energy supplier and the prosumer.

7. A multi-park integrated energy system optimization and scheduling system based on game theory, characterized by: include: The model building module is configured to: build a scheduling model for the multi-park integrated energy system to be scheduled with the goal of optimizing overall performance; The model solving module is configured to: divide the scheduling model into sub-problems, solve the sub-problems by combining KKT conditions, ADMM, and C&CG algorithms, and obtain the optimal scheduling strategy; Among them, the scheduling model includes a multi-layer game model and an energy supplier robust optimization model. The multi-layer game model includes a cooperative game model between producers and consumers within the producer-consumer user alliance, a master-slave game model between the producer-consumer user alliance and the energy supplier, and a cooperative game model between energy suppliers. The energy supplier robust optimization model uses a multi-interval uncertainty set to describe the fluctuation range of the source load, finds the worst value within the fluctuation range of the source load as the worst environmental condition, and solves the optimal operation decision under the worst environmental conditions.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the game theory-based multi-park integrated energy system optimization scheduling method described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the multi-park integrated energy system optimization and scheduling method based on game theory as described in any one of claims 1 to 6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the multi-park integrated energy system optimization scheduling method based on game theory as described in any one of claims 1 to 6.

Images