Park micro-grid operation optimization method considering power-carbon dual excitation and multi-agent game

Abstract

The invention discloses a park micro-grid operation optimization method considering power-carbon dual excitation and multi-agent game, and the method comprises the steps: 1) constructing a park micro-grid system in which agents including managers, suppliers and users participate, and constructing a decision model corresponding to a micro-grid based on each agent; 2) establishing a physical operation constraint of the micro-grid; step 3), introducing a time sequence change electricity-carbon dual excitation price; and 4) determining an optimal operation strategy of each main body through a multi-main body game, and operating the park micro-grid according to a balanced optimal result of the game process. According to the method, power transaction and carbon emission constraint are combined, behavior modeling is carried out on each main body by using a game theory model, an operation strategy is optimized, and each main body is guided through price signals of a power market and a carbon market to realize micro-grid operation efficiency improvement and carbon emission.

Description

Technical Field

[0001] The present invention relates to the field of low-carbon technology, and in particular to a method for optimizing the operation of a park microgrid taking into account dual incentives of electricity and carbon and multi-agent game. Background Art

[0002] As a diversified energy system integrating multiple energy forms, campus microgrids have received extensive attention. Microgrids can flexibly dispatch multiple energy resources (such as photovoltaics, wind energy, etc.). However, the traditional microgrid operation mode mainly focuses on economy and power management, ignoring the carbon emissions and control of the system, and it is difficult to meet the requirements of low-carbon and green development under the current "dual carbon" goal.

[0003] In order to better guide the various entities in the microgrid to achieve a good optimization between electricity consumption and carbon emissions, it is necessary to introduce a dual incentive mechanism of electricity and carbon. This mechanism can not only guide various entities to optimize energy utilization through economic incentives, but also promote low-carbon operation through incentives and constraints on carbon emissions. On this basis, a multi-agent game and operation optimization method for campus microgrids that can comprehensively consider dual carbon incentives is studied, aiming to form a reasonable energy transaction and carbon emission optimization strategy through game interaction among multiple entities, so as to achieve efficient and low-carbon operation of the microgrid. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art, and to provide a method for optimizing the operation of a park microgrid that takes into account the dual incentives of electricity and carbon and multi-agent game, so as to solve the problem that the coordinated operation of the existing park microgrid system only considers the low-carbon nature or economy of the system and does not clearly divide the subjects in the system. The method is specifically implemented by the following technical solutions:

[0005] The method for optimizing the operation of a park microgrid considering dual incentives of electricity and carbon and multi-agent game comprises: step 1) constructing a park microgrid system in which managers, suppliers and users participate, wherein managers influence suppliers through energy purchase and sale price and quantity adjustment strategies to form an upper strategy layer; suppliers influence users through energy purchase behaviors, and users feedback managers through energy use behaviors to form a lower strategy layer for following the upper layer; respectively constructing a supplier decision model of the microgrid and a user decision model of the microgrid based on each subject;

[0006] Step 2) Establishing the physical operating constraints of the microgrid;

[0007] Step 3) Introducing a time-varying dual incentive price for electricity and carbon;

[0008] Step 4) Determine the optimal operation strategy of each subject through multi-agent game, and operate the park microgrid according to the equilibrium optimal result of the game process.

[0009] The further design of the park microgrid operation optimization method considering the dual incentives of electricity and carbon and multi-agent game is that the manager decision model of the microgrid in step 1) constructs the objective function according to formula (1) based on the operation income

[0010]

[0011] In formula (1), t represents the time period, T is the total time period, The revenue from electricity sales to users for energy managers, and are the electricity purchase costs of energy managers from energy suppliers and the grid, respectively; and They are the electricity price compensation and carbon compensation costs provided by energy managers to users; Carbon trading costs for outsourced electricity for energy managers.

[0012] The further design of the park microgrid operation optimization method considering the dual incentives of electricity and carbon and multi-agent game is that the supplier decision model of the microgrid in step 1) constructs the objective function according to formula (2) based on maximizing its own profit

[0013]

[0014] In formula (2), and Represent the operation and maintenance costs of energy storage, wind power and photovoltaic respectively. Among them, the operation and maintenance cost of energy storage Calculate according to formula (3):

[0015]

[0016] In formula (3), and are the discharge benefit and charging cost of energy storage respectively, The operation and maintenance cost of energy storage.

[0017] The further design of the park microgrid operation optimization method considering the dual incentives of electricity and carbon and multi-agent game is that the user decision model of the microgrid is based on the maximization of user cost-effectiveness, and the user's electricity cost and electricity satisfaction are compared to construct the objective function according to formula (4):

[0018]

[0019] In formula (4), is the user electricity satisfaction index, calculated according to formula (5):

[0020]

[0021] In formula (5), and They are the actual power consumption and ideal power consumption of the user respectively.

[0022] The further design of the park microgrid operation optimization method considering the dual incentives of electricity and carbon and multi-agent game is that the physical operation constraints established in step 2) include:

[0023] According to formula (6), the energy manager establishes the price constraints for purchasing and selling electricity.

[0024]

[0025] In formula (6), and are the prices for energy managers to purchase and sell electricity from the power grid, and The prices at which energy managers purchase and sell electricity from energy suppliers and users, respectively;

[0026] According to formula (7), the energy manager needs to be responsible for the power balance constraint in the microgrid.

[0027]

[0028] In formula (7), The remaining power for energy managers, and They are the amount of electricity interacted between energy managers and power grids and energy suppliers respectively;

[0029] According to formula (8), the interactive power constraint between the energy manager and the external grid connection line is established:

[0030]

[0031] In formula (8), The upper limit of power for interaction with the grid;

[0032] According to formula (9), the power constraint of the energy supplier output in period t is established:

[0033]

[0034] In formula (9), P WT and are the output power of WT and PV respectively, and are the power of discharging and charging the energy storage respectively;

[0035] According to formula (10), the constraint condition of the current energy state of energy storage is established:

[0036]

[0037] In formula (10), η BT,chr , η BT,dis are the charging and discharging efficiencies of the energy storage device, respectively; and are the minimum and maximum values ​​of the energy storage device respectively; γ BT is the energy self-loss rate of the energy storage device;

[0038] According to formula (11), the user constraint condition is established.

[0039]

[0040] In formula (11), It indicates the maximum value of the load that can be adjusted by the user during period t. For fixed load, For adjustable load.

[0041] The further design of the park microgrid operation optimization method considering the dual incentives of electricity and carbon and multi-agent game is to introduce the dual incentive price of electricity and carbon with time series variation according to the modeling of formula (12):

[0042]

[0043] In formula (12), represents the electricity purchase price and carbon emission factor during period t; represents the electricity price compensation coefficient and carbon compensation coefficient in period t; It represents the user's electricity price and carbon emission incentive factor in period t. The compensation price composed of electricity price and carbon emission factor encourages users to adjust their energy consumption behavior in order to seek the balance between energy economy and low carbon.

[0044] The further design of the park microgrid operation optimization method considering the dual incentives of electricity and carbon and multi-agent game is to determine the optimal operation strategy of each subject through multi-agent game according to formula (13):

[0045]

[0046] In formula (13), α(t) is the action matrix; β(t) is the reward matrix; the energy manager’s actions include purchasing power Internal purchase price Electricity price compensation coefficient provided to users and carbon compensation coefficient Energy suppliers' actions include selling power User actions as power consumption The payoff matrix includes the benefits of energy managers Energy supplier profits and energy cost-effectiveness for users During the game, when no interested party can unilaterally change the strategy of the equilibrium solution to obtain benefits, the game reaches the Stackelberg equilibrium and satisfies the condition of formula (14):

[0047]

[0048] but is the optimal equilibrium outcome of the Stackelberg game at time t.

[0049] The present invention also provides an electronic device, comprising a memory, a processor and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the park microgrid operation optimization method considering dual incentives of electricity and carbon and multi-agent game.

[0050] The present invention also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the park microgrid operation optimization method that considers dual incentives of electricity and carbon and multi-agent game.

[0051] The advantages of the present invention are as follows:

[0052] The park microgrid operation optimization method considering dual incentives of electricity and carbon and multi-agent game of the present invention combines electricity trading with carbon emission constraints, uses game theory models to model the behavior of each subject, optimizes the operation strategy, and guides each subject to achieve efficiency improvement and carbon emissions through price signals from the electricity market and carbon market; it not only improves the economic benefits of microgrid operation, but also provides technical support for the carbon neutrality goals at the park level. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Schematic diagram of the architecture of the campus microgrid system of the present invention.

[0054] Figure 2 Schematic diagram of the game solving process.

[0055] Figure 3 This is a schematic diagram of the simulation topology of the park microgrid. DETAILED DESCRIPTION

[0056] The technical solution of the present invention is described in detail below.

[0057] Embodiment 1

[0058] This embodiment provides a method for optimizing the operation of a park microgrid considering dual incentives of electricity and carbon and multi-agent game, the method comprising:

[0059] Step 1) Build a campus microgrid system that includes managers, suppliers, and users. Managers influence suppliers through energy purchase and sale price and quantity adjustment strategies to form an upper strategy layer; suppliers influence users through energy purchase behavior, and users feedback managers through energy consumption behavior to form a lower strategy layer that follows the upper layer. Figure 1 ; Construct the decision model corresponding to the microgrid based on each subject;

[0060] Step 2) Establishing the physical operating constraints of the microgrid;

[0061] Step 3) Introducing a time-varying dual incentive price for electricity and carbon;

[0062] Step 4) Determine the optimal operation strategy of each subject through multi-agent game, and operate the park microgrid according to the equilibrium optimal result of the game process.

[0063] In step 1), the manager decision model of the microgrid is based on the operation benefits and constructs the objective function according to formula (1):

[0064]

[0065] In formula (1), t represents the time period, T is the total time period, The revenue from electricity sales to users for energy managers, and are the electricity purchase costs of energy managers from energy suppliers and the grid, respectively; and They are the electricity price compensation and carbon compensation costs provided by energy managers to users; Carbon trading costs for outsourced electricity for energy managers.

[0066] In step 1), the supplier decision model of the microgrid is based on maximizing its own profit and constructing the objective function according to formula (2):

[0067]

[0068] In formula (2), and Represent the operation and maintenance costs of energy storage, wind power and photovoltaic respectively. Among them, the operation and maintenance cost of energy storage Calculate according to formula (3):

[0069]

[0070] In formula (3), and are the discharge benefit and charging cost of energy storage respectively, The operation and maintenance cost of energy storage.

[0071] The user decision model of the microgrid is based on maximizing user cost-effectiveness. It compares the user's electricity cost with electricity satisfaction and constructs the objective function according to formula (4):

[0072]

[0073] In formula (4), is the user electricity satisfaction index, calculated according to formula (5):

[0074]

[0075] In formula (5), and They are the actual power consumption and ideal power consumption of the user respectively.

[0076] The physical operating constraints established in step 2) include:

[0077] According to formula (6), the energy manager establishes the price constraints for purchasing and selling electricity.

[0078]

[0079] In formula (6), and are the prices for energy managers to purchase and sell electricity from the power grid, and The prices at which energy managers purchase and sell electricity from energy suppliers and users, respectively;

[0080] According to formula (7), the energy manager needs to be responsible for the power balance constraint in the microgrid.

[0081]

[0082] In formula (7), The amount of electricity purchased from the grid for energy managers, and They are the electricity sales from energy managers to users and energy suppliers, respectively;

[0083] According to formula (8), the interactive power constraint between the energy manager and the external grid connection line is established:

[0084]

[0085] In formula (8), The upper limit of power for interaction with the grid;

[0086] According to formula (9), the power constraint of the energy supplier output in period t is established:

[0087]

[0088] In formula (9), P WT and are the output power of WT and PV respectively, and are the power of discharging and charging the energy storage respectively;

[0089] According to formula (10), the constraint condition of the current energy state of energy storage is established:

[0090]

[0091] In formula (10), η BT,chr , η BT,dis are the charging and discharging efficiencies of the energy storage device, respectively; and are the minimum and maximum values ​​of the energy storage device respectively; γ BT is the energy self-loss rate of the energy storage device;

[0092] According to formula (11), the user constraint condition is established.

[0093]

[0094] In formula (11), It indicates the maximum value of the load that can be adjusted by the user during period t. For fixed load, For adjustable load.

[0095] According to the modeling of formula (12), the time-varying electricity-carbon dual incentive price is introduced:

[0096]

[0097] In formula (12), represents the electricity purchase price and carbon emission factor during period t; represents the electricity price compensation coefficient and carbon compensation coefficient in period t; It represents the user's electricity price and carbon emission incentive factor in period t. The compensation price composed of electricity price and carbon emission factor encourages users to adjust their energy consumption behavior in order to seek the balance between energy economy and low carbon.

[0098] According to formula (13), the optimal operation strategy of each subject is determined through multi-agent game.

[0099]

[0100] In formula (13), α is the action matrix; β is the reward matrix; the energy manager’s actions include the purchased power P grid ; Internal purchase electricity price p EM ; Electricity price compensation η provided to users e and carbon compensation ηc ; The energy supplier's actions include selling power P EP ; The user's action is the power consumption P L ; The payoff matrix includes the energy manager’s benefit E EM , the profit of energy suppliers EP and the energy cost-effectiveness E of users user ;

[0101] During the game, when no interested party can unilaterally change the strategy of the equilibrium solution to obtain benefits, the game reaches the Stackelberg equilibrium and satisfies the condition of formula (14):

[0102]

[0103] but is the optimal equilibrium result of the Stackelberg game.

[0104] like Figure 2 , the specific steps for solving the optimal equilibrium result are:

[0105] Step 4-1) Input initial data and set parameters.

[0106] Step 4-2) Initialize population a and set the number of iterations K=0.

[0107] Step 4-3) The energy manager sends the optimized purchase and sale energy prices to the lower-level followers. Energy suppliers and users obtain the optimal power results of photovoltaics, wind turbines, energy storage, and users based on their own optimal benefits, and send the optimization results to the upper-level leaders.

[0108] Step 4-4) The energy manager calculates its own benefit U1 according to the follower's strategy.

[0109] Step 4-5) Perform crossover and mutation operations on population a to obtain a new population b.

[0110] Step 4-6) Repeat steps 3 to 4, and the energy manager calculates his own benefit U2.

[0111] Step 4-7) Select operation, if U2>U1, then a=b, U1=U2, if U2<U1, then remain unchanged. Step 4-8) Determine operation, if the required number of iterations is reached, output the result, otherwise jump to step 4-5).

[0112] The following is a specific example of a microgrid in a certain park. Based on the peak and valley electricity prices of the power grid and the real-time carbon emission factors, the daily carbon emission curve of the purchased electricity in the internal park is obtained, and combined with the park's own distributed energy, the internal energy consumption of the park is optimized. Considering that the park's electricity consumption methods include purchased electricity, local photovoltaics, wind power, energy storage, etc., in order to analyze the optimal operation strategy of the system under different goals, this example sets three scenarios:

[0113] 1) Multi-agent game operation strategy considering electricity price and carbon price incentives;

[0114] 2) Multi-agent game operation strategy that only considers electricity price incentives;

[0115] 3) The multi-agent game operation strategy that only considers carbon price incentives.

[0116] The total daily energy consumption of the three scheduling operation methods is shown in Table 1.

[0117] Table 1 Comparison of energy consumption of three scheduling methods (unit: kWh)

[0118]

[0119] According to the comparison of energy consumption results, under the dual compensation incentive mechanism of electricity price and carbon price, the microgrid increases the purchased power and energy storage power. On the one hand, increasing the purchased power enables the microgrid to purchase electricity from the main grid during periods of low electricity prices or low carbon emissions from the grid, thereby reducing overall electricity costs and reducing dependence on fossil fuel power generation. On the other hand, increasing the energy storage power can more efficiently store renewable energy or reduce the cost of electricity, release electricity during peak electricity price periods, and optimize energy scheduling. Through the synergy of purchased electricity and energy storage, the microgrid can flexibly respond to load fluctuations, reduce carbon emissions, and improve the economic and environmental benefits of operation.

[0120] The daily total cost and carbon emissions of the three scheduling methods are shown in Table 2.

[0121] Table 2 Comparison of overall cost and carbon emissions of three scheduling methods

[0122]

[0123] According to the overall cost comparison, under the dual compensation incentive mechanism of electricity price and carbon price, the overall optimization of the system considering economy and low carbon can be achieved. The system can ensure that carbon emissions are effectively controlled while reducing economic expenses. It avoids ignoring environmental costs due to the pursuit of economic benefits alone, and avoids unnecessary economic burdens caused by excessive attention to carbon emission reduction. This balance allows the system to take into account both cost-effectiveness and environmental protection goals in long-term sustainable development, achieving dual optimization.

[0124] In the single-objective scheduling method, since only the optimized target is considered, other indicators are relatively ignored and it is easy to fall into the local optimum. Among them, only the economic scheduling strategy is considered, which tends to use more coal-fired power generation units and ignores the large-scale application of carbon capture technology, resulting in higher overall carbon emissions; while pure low-carbon scheduling sets too high a target for net carbon emissions, and has a higher utilization rate for cleaner energy such as gas, which is relatively costly. The optimal comprehensive multi-agent game method proposed in this application takes into account the mutual dominance characteristics and generalized cost optimality among multiple objectives, and realizes a global optimal solution that comprehensively considers the constraints and contradictions of multiple objectives. It can achieve the lowest greenhouse gas and pollutant gas emissions as much as possible without causing too much economic pressure. For the specific simulation topology diagram of this embodiment, see Figure 3 .

[0125] Embodiment 2

[0126] This embodiment provides an electronic device, including a memory, a processor, and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the above-mentioned campus microgrid operation optimization method considering dual incentives of electricity and carbon and multi-agent game.

[0127] Embodiment 3

[0128] This embodiment also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the above-mentioned park microgrid operation optimization method considering dual incentives of electricity and carbon and multi-agent game.

[0129] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimizing the operation of a park microgrid considering dual incentives of electricity and carbon and multi-agent game, characterized in that: include: Step 1) Construct a campus microgrid system with the participation of managers, suppliers, and users. Managers influence suppliers through energy purchase and sale price and quantity adjustment strategies to form an upper strategy layer; suppliers influence users through energy purchase behavior, and users feedback managers through energy use behavior to form a lower strategy layer that follows the upper layer; construct a supplier decision model and a user decision model of the microgrid based on each subject respectively; Step 2) Establishing the physical operating constraints of the microgrid; Step 3) Introducing a time-varying dual incentive price for electricity and carbon; Step 4) Determine the optimal operation strategy of each subject through multi-agent game, and operate the park microgrid according to the equilibrium optimal result of the game process.

2. The park microgrid operation optimization method considering dual incentives of electricity and carbon and multi-agent game according to claim 1 is characterized in that In step 1), the manager decision model of the microgrid is based on the operation benefits and constructs the objective function according to formula (1): In formula (1), t represents the time period, T is the total time period, The revenue from electricity sales to users for energy managers, and are the electricity purchase costs of energy managers from energy suppliers and the grid, respectively; and They are the electricity price compensation and carbon compensation costs provided by energy managers to users; Carbon trading costs for outsourced electricity for energy managers.

3. The park microgrid operation optimization method considering dual incentives of electricity and carbon and multi-agent game according to claim 1 is characterized in that In step 1), the supplier decision model of the microgrid is based on maximizing its own profit and constructing the objective function according to formula (2): In formula (2), and Represent the operation and maintenance costs of energy storage, wind power and photovoltaic respectively. Among them, the operation and maintenance cost of energy storage Calculate according to formula (3): In formula (3), and are the discharge benefit and charging cost of energy storage respectively, The operation and maintenance cost of energy storage.

4. The park microgrid operation optimization method considering dual incentives of electricity and carbon and multi-agent game according to claim 1 is characterized in that The user decision model of the microgrid is based on maximizing user cost-effectiveness. It compares the user's electricity cost with electricity satisfaction and constructs the objective function according to formula (4): In formula (4), is the user electricity satisfaction index, calculated according to formula (5): In formula (5), and They are the actual power consumption and ideal power consumption of the user respectively.

5. The park microgrid operation optimization method considering dual incentives of electricity and carbon and multi-agent game according to claim 1 is characterized in that: The physical operating constraints established in step 2) include: According to formula (6), the energy manager establishes the price constraints for purchasing and selling electricity. In formula (6), and are the prices for energy managers to purchase and sell electricity from the power grid, and The prices at which energy managers purchase and sell electricity from energy suppliers and users, respectively; According to formula (7), the energy manager needs to be responsible for the power balance constraint in the microgrid. In formula (7), The remaining power for energy managers, and They are the amount of electricity interacted between energy managers and power grids and energy suppliers respectively; According to formula (8), the interactive power constraint between the energy manager and the external grid connection line is established: In formula (8), The upper limit of power for interaction with the grid; According to formula (9), the power constraint of the energy supplier output in period t is established: In formula (9), P WT and are the output power of WT and PV respectively, and are the power of discharging and charging the energy storage respectively; According to formula (10), the constraint condition of the current energy state of energy storage is established: In formula (10), η BT,chr , η BT,dis are the charging and discharging efficiencies of the energy storage device, respectively; and are the minimum and maximum values ​​of the energy storage device respectively; γ BT is the energy self-loss rate of the energy storage device; According to formula (11), the user constraint condition is established. In formula (11), It indicates the maximum value of the load that can be adjusted by the user during period t. For fixed load, For adjustable load.

6. The park microgrid operation optimization method considering dual incentives of electricity and carbon and multi-agent game according to claim 1 is characterized in that: According to the modeling of formula (12), the time-varying electricity-carbon dual incentive price is introduced: In formula (12), represents the electricity purchase price and carbon emission factor during period t; represents the electricity price compensation coefficient and carbon compensation coefficient in period t; It represents the user's electricity price and carbon emission incentive factor in period t. The compensation price composed of electricity price and carbon emission factor encourages users to adjust their energy consumption behavior in order to seek the balance between energy economy and low carbon.

7. The park microgrid operation optimization method considering dual incentives of electricity and carbon and multi-agent game according to claim 1 is characterized in that: According to formula (13), the optimal operation strategy of each subject is determined through multi-agent game. In formula (13), α(t) is the action matrix; β(t) is the reward matrix; the energy manager’s actions include purchasing power Internal purchase price Electricity price compensation coefficient provided to users and carbon compensation coefficient Energy suppliers' actions include selling power User actions as power consumption The payoff matrix includes the benefits of energy managers Energy supplier profits and energy cost-effectiveness for users During the game, when no interested party can unilaterally change the strategy of the equilibrium solution to obtain benefits, the game reaches the Stackelberg equilibrium and satisfies the condition of formula (14): The * in Formula 14 represents the optimal strategy value of the corresponding parameter; but is the optimal equilibrium outcome of the Stackelberg game at time t.

8. An electronic device, characterized in that: It includes a memory, a processor and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the park microgrid operation optimization method considering the dual incentives of electricity and carbon and multi-agent game as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is executed by a processor to implement the park microgrid operation optimization method considering dual incentives of electricity and carbon and multi-agent game as described in any one of claims 1-7.

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