Multi-microgrid bidirectional time-of-use electricity price optimization method based on multilevel game

Abstract

The invention discloses a multi-microgrid bidirectional time-of-use electricity price optimization method based on a multi-level game, and the method comprises the steps: firstly, designing a bidirectional time-of-use electricity price incentive mechanism which faces a power generator and a user at the same time in the electric energy transaction with a multi-microgrid system as a background; secondly, establishing a multi-level differentiated game model which takes a power generator, an MGCC and a micro-grid user as main bodies and is coupled with a non-cooperative game and a master-slave game; and finally, solving the established multi-level game model by adopting a double-layer particle swarm algorithm in combination with a Cplex solver to obtain an optimal bidirectional time-of-use electricity price optimization strategy. According to the time-of-use electricity price optimization method provided by the invention, energy management can be optimized, the system operation cost and the user electricity consumption cost are reduced, and the economic benefits of power generators are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-microgrid optimized operation, and in particular to a multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game. Background Art

[0002] With the continuous growth of energy demand and the increasing prominence of environmental problems, microgrids, as an important carrier of renewable energy technology, play an increasingly important role in the energy system. Multi-microgrids, as a further development form of microgrids, have higher complexity and flexibility. By rationally arranging energy exchanges between microgrids and maximizing the use of renewable energy, we can reduce dependence on traditional energy and promote sustainable energy development. However, the economic operation efficiency of multi-microgrid systems is closely related to user participation, which relies on an effective price incentive mechanism to mobilize the enthusiasm of users. Therefore, demand response (DR) has been widely used.

[0003] In the prior art: Reference [1]: "Cooperative Game Model of Multi-Microgrid System and Its Optimal Dispatching Strategy" (Li Demin, Wu Zaijun, Zhao Bo. Cooperative Game Model of Multi-Microgrid System and Its Optimal Dispatching Strategy [J]. Proceedings of the CSEE, 2022, 42(14): 5140-5154.) sets all microgrids in the multi-microgrid system as energy management entities with equal status, grants each microgrid the pricing power and transaction power decision-making power for power transactions with other microgrids, and constructs a distributed energy management architecture without a central management unit, effectively realizing the distributed energy management of the multi-microgrid system. This study uses the game theory framework to achieve the optimal operation of multi-microgrids, but does not consider the impact of user participation in demand response on the system optimization dispatch.

[0004] Reference [2]: "Reliability assessment of incentive-and price-based demand response programs in restructured power systems" (Nikzad, M., Mozafari, B. Reliability assessment of incentive-and price-based demand response programs in restructured power systems. International Journal of Electrical Power & Energy Systems, 2014, 56: 83-96.) Incentives and penalties are set for users who respond to and do not respond to load reduction, and an incentive-based demand response model is constructed based on this to verify the improvement of the reliability of the restructured power system by the demand response program. This study considers the impact of DR on grid operation and dispatch, and provides an important reference for operators' dispatch decisions. However, the design and implementation of current demand response strategies rely more on relatively simplified and idealized user behavior models. These models usually assume that users' responses to price signals or incentives are immediate and linear, while ignoring the economic and psychological factors in the user's decision-making process.

[0005] Reference [3]: "Study on multi-type flexible load control method of active distribution network based on dynamic time-sharing electricity price" (Cui, J., Ran, Z., Shen, W., Xin, Y. Study on multi-type flexible load control method of active distribution network based on dynamic time-sharing electricity price. Applied Energy, 2024, 357: 122479.) proposed a dynamic time-of-use electricity price game model that takes into account the wishes of multiple users to maximize user satisfaction and benefits. However, in this literature study, the power generator did not adopt the time-of-use electricity price mechanism, but maintained a unified electricity sales price. This approach ignores the subjective initiative of the operators on the power generation side and the potential advantages of formulating time-of-use electricity prices on the power generation side in improving energy utilization efficiency. Summary of the invention

[0006] In view of the above problems, the present invention discloses a multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game. First, in the electric energy transaction with a multi-microgrid system as the background, the method designs a bidirectional time-of-use electricity price incentive mechanism for both power generators and users; secondly, a multi-level differentiated game model is established with power generators, MGCCs and microgrid users as the main body, coupling non-cooperative games and master-slave games. Finally, a two-layer particle swarm algorithm combined with a Cplex solver is used to solve the established multi-level differentiated game model to obtain the optimal bidirectional time-of-use electricity price optimization strategy. The time-of-use electricity price optimization method proposed in the present invention can optimize energy management, reduce system operating costs and user electricity costs, and improve the economic benefits of power generators.

[0007] The technical solution adopted by the present invention is:

[0008] The multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game includes the following steps:

[0009] Step 1: In the electric energy transaction based on the multi-microgrid system, a two-way time-of-use electricity price incentive mechanism is designed for both power generators and users;

[0010] Step 2: A multi-level differentiated game model is established, which is based on the power generator, microgrid control center MGCC and microgrid users, and couples non-cooperative game and master-slave game;

[0011] Step 3: The double-layer particle swarm algorithm is combined with the Cplex solver to solve the multi-level differentiated game model established in step 2 and obtain the optimal two-way time-of-use electricity price optimization strategy.

[0012] In step 1, in a multi-microgrid system, reasonable adjustment of electricity prices is one of the key means to achieve efficient energy utilization and cost optimization. The present invention promotes active interaction between power generators, MGCCs and users, optimizes power distribution, and promotes the use of renewable energy by setting a two-way time-of-use electricity price incentive mechanism. The two-way time-of-use electricity price incentive mechanism encourages users to use electricity when electricity prices are low, and vice versa, to reduce electricity consumption, thereby balancing the supply and demand relationship, reducing system operating costs, and improving energy efficiency.

[0013] The two-way time-of-use electricity price incentive mechanism includes two parts: the two-way time-of-use electricity price period division and the two-way time-of-use electricity price optimization decision model. Among them, the two-way time-of-use electricity price period division is as follows:

[0014] The time-of-use electricity price for MGCC sales needs to be clustered based on the MGCC purchase volume to determine the peak, flat and valley time-of-use period on the power generation side; the time-of-use electricity price for microgrid users needs to be clustered based on the user purchase volume. Since the purchase volume is one-dimensional data, the k-means clustering algorithm is used to determine the time-of-use period. The objective function of the k-means clustering algorithm is the sum of the mean square error of the data and the cluster center to which it belongs.

[0015] Based on the k-means clustering algorithm, the clustering mark of the purchased electricity is obtained, and the peak, flat and valley time periods are determined according to the clustering mark. The expression is as follows:

[0016] S a =[δ1,δ2,…,δ 24 ]

[0017]

[0018] T F ∪T P ∪T G ={0,1,2,…,23}

[0019]

[0020] In the formula, S a is the clustering label vector of the purchased electricity; t is the clustering mark of the purchased electricity and generated electricity in period t; δ F ,δ P ,δ G are the clustering marks for peak, flat and valley time periods respectively; T F 、T P 、T G They are respectively a collection of peak, flat and valley time periods of purchased electricity.

[0021] The core goal of time-of-use electricity pricing is to promote the smoothing of the system load curve by reducing the peak-to-valley difference of the load. Therefore, the construction of the two-way time-of-use electricity pricing optimization decision model should consider minimizing the peak value and peak-to-valley difference of the load, while considering the economic benefit constraints of both supply and demand sides to ensure a balance of interests between the two sides;

[0022] When deciding the electricity price of power generators, the objective function includes:

[0023] (1) Minimize the peak value F1 of the power generation curve of the power generator;

[0024] (2) Maximizing the valley value F2 of the power generation curve of the power generator;

[0025] (3) Minimize the peak-to-valley difference F3 of the generator’s power generation curve,

[0026] The specific objective function is as follows:

[0027]

[0028] In the above formula, P e t The amount of electricity generated for power generators; It indicates the maximum power generation of a power generator in a single period of 24 hours; it indicates a period of 24 hours; 0-23 represents the 24 hours in a day.

[0029] When deciding the MGCC electricity sales price, the objective function is:

[0030] (1) Minimize the peak value F4 of the microgrid user load curve;

[0031] (2) Maximize the valley value F5 of the microgrid user load curve;

[0032] (3) Minimize the peak-to-valley difference F6 of microgrid user load,

[0033] The specific objective function is as follows:

[0034]

[0035] In the above formula, The amount of electricity purchased by the mth microgrid user from MGCC; It is expressed as the maximum amount of electricity purchased by the user from MGCC in a single period of 24 hours; It is expressed as the minimum value of electricity purchased from MGCC in a single period of 24 hours.

[0036] The constraints of the game equilibrium cover the revenue of power generators, the benefits of MGCC and the comprehensive satisfaction of users. After the time-of-use electricity price is adjusted, the interests or satisfaction of all relevant parties should at least remain at the level before the adjustment to ensure that no party suffers losses under the new pricing mechanism. The specific constraints are as follows:

[0037] C F,after ≥C F,before

[0038] C Fy,after ≤C Fy,before

[0039] C M,after ≥C M,before

[0040] F U,after ≥F U,before

[0041]

[0042] In the above formula, C F,after represents the revenue of the power generator after the electricity price adjustment, CF,before represents the revenue of the power generator before the electricity price adjustment; C Fy,after is the operation and maintenance cost of the power generator after the electricity price adjustment; C Fy,before is the operation and maintenance cost of the power generator before the electricity price adjustment; C M,after is the revenue of MGCC after electricity price adjustment, C M,before is the revenue of MGCC before electricity price adjustment; F U,after is the comprehensive satisfaction of users after electricity price adjustment, F U,before It is the comprehensive satisfaction of users with electricity consumption before the electricity price adjustment; It is the amount of electricity purchased by microgrid users from MGCC after the electricity price adjustment. It is the amount of electricity purchased by microgrid users from MGCC before the electricity price adjustment;

[0043] Formula C F,after ≥C F,before , Formula C M,after ≥C M,before Japanese style F U,after ≥F U,before It means that the objective function of each subject should be optimized after adjustment;

[0044] Formula C Fy,after ≤C Fy,before It indicates that the operation and maintenance costs of power generators need to be reduced after the electricity price adjustment;

[0045] Mode It means that the user’s unit electricity purchasing cost cannot increase after the electricity price adjustment.

[0046] In step 2, the multi-level game mechanism is as follows:

[0047] In the multi-level game mechanism, there is a non-cooperative game between the power generator and the MGCC, and a master-slave game relationship between the MGCC and the microgrid users; the power generator sets the power sales price according to the power demand of each microgrid, taking into account the fixed cost of power generation and the operation and maintenance cost; the MGCC conducts day-ahead planning and dispatching, giving priority to the consumption of renewable energy output and energy storage within the microgrid. If there is still demand, it will purchase electricity from other microgrids and finally from the power generator. MGCC considers its own benefits and sets the load-side time-of-use electricity price; users adjust their electricity consumption behavior in different time periods according to the load-side time-of-use electricity price and psychological expectations. The three stakeholders, the power generator, MGCC and the user, influence each other through decision-making on-grid time-of-use electricity price, load-side time-of-use electricity price and time-of-use electricity consumption behavior, and obtain the optimal equilibrium solution through game theory;

[0048] The game specifically includes the following steps:

[0049] Step 2.1: The power generator sets the initial grid-connected time-of-use electricity price on the power generation side.

[0050] Step 2.2: MGCC formulates power purchase strategy and load-side time-of-use electricity price based on time-of-use electricity price and power demand.

[0051] Step 2.3: Microgrid users independently shift or reduce loads based on the load-side time-of-use electricity price.

[0052] Step 2.4: MGCC updates the dispatch strategy and power purchase plan based on the user's load time-sharing demand.

[0053] Step 2.5: The power generator adjusts the power generation plan and updates the grid-connected time-of-use electricity price according to the power purchase plan of MGCC.

[0054] Step 2.6: Repeat steps 2.2 to 2.5 until the grid-connected time-of-use electricity price of the power generator, the power purchasing strategy of the MGCC, the load-side time-of-use electricity price and the user's electricity consumption plan remain stable and the game equilibrium is reached.

[0055] Step 2.7: Energy trading is performed with the game equilibrium solution as the final result.

[0056] In step 2, the multi-level differentiated game model includes:

[0057] (1): Non-cooperative game model:

[0058] The multi-level game proposed in this invention includes the non-cooperative game between the power generator and the MGCC and the master-slave game between the MGCC and the user. The two parties involved in the non-cooperative game use the grid-connected time-of-use electricity price and the power purchase plan as game items, and establish a non-cooperative game economic optimization model with multi-microgrid as the background.

[0059] 1) Participant collection:

[0060] Y={F,MGCC1,MGCC2,iii,MGCC m ,MGCC M}

[0061] Where, F represents the generator; MGCC m represents the mth microgrid control center; MGCC1 represents the first microgrid control center; MGCC2 represents the second microgrid control center; MGCC M represents the Mth microgrid control center.

[0062] 2) Strategy set:

[0063] The strategy set of the generator is expressed as:

[0064]

[0065] In the formula, Represents the power generation of the power generator during period t; Ω Frepresents the strategy set of the power generator; Respectively represent the upper and lower limits of power generation; represents the time-of-use electricity price sold by the power generator during period t; They respectively represent the upper and lower limits of the electricity sales price of power generators.

[0066] The strategy set of the mth MGCC is expressed as:

[0067]

[0068] In the formula, Indicates the amount of electricity purchased by the mth MGCC from the generator during period t; Ω M,m represents the strategy set of the mth MGCC; Respectively represent the upper and lower limits of electricity purchase amount.

[0069] 3) Utility function:

[0070] Power generators pursue their own benefits in the game process F The maximum is expressed as follows:

[0071] C F =C Mbuy -C Fy -C Fe

[0072] In the formula, C F is the revenue of the power generator; C Mbuy is the electricity purchase cost of MGCC and the electricity sales revenue of the generator; C Fy is the operation and maintenance cost of the generator; C Fe It is the environmental protection cost of power generators.

[0073] MGCC pursues benefits in the game process M The maximum is expressed as follows:

[0074] C M,m =C Ubuy,m -(C PV,m +C WT,m +C GT,m +C ESS,m +C Mbuy,m +C Ms,m +C Me,m )

[0075] In the formula, C M,m is the total revenue of MGCC in the mth microgrid system; C Ubuy,m is the electricity cost of microgrid user m, which is equivalent to the electricity sales revenue of the mth MGCC; C PV,m is the operation and maintenance cost of photovoltaic equipment; C WT,m is the operation and maintenance cost of wind power equipment; CGT,m The power generation and operation and maintenance cost of gas turbine; C ESS,m The charging and discharging operation and maintenance cost of energy storage equipment; C Mbuy,m is the electricity purchase cost of MGCC; C Ms,m is the interaction cost between microgrids; C Me,m is the environmental protection cost of MGCC.

[0076] 4) Nash equilibrium:

[0077] When the game reaches equilibrium, the generator and MGCC need to meet the following conditions:

[0078]

[0079] In the formula, It represents the revenue of the generator after the generator's power generation, the electricity sales price set by the generator and the amount of electricity purchased by the mth MGCC from the generator reach a game equilibrium. It represents the power generation of the power supplier when the game reaches equilibrium; It represents the electricity selling price set by the power generator when the game reaches equilibrium; It represents the amount of electricity purchased by the mth MGCC from the generator when the game reaches equilibrium.

[0080] It represents the revenue of the generator only when the electricity sales price set by the generator and the amount of electricity purchased by the mth MGCC from the generator reach a game equilibrium; It represents the power generation of the power supplier when the game equilibrium is not reached.

[0081] It represents the revenue of the generator only when the power generation of the generator and the power purchase amount of the mth MGCC from the generator reach the game equilibrium; It refers to the electricity sales price set by the power generator when the game equilibrium is not reached.

[0082] It means the revenue of the generator only when the power generation of the generator and the electricity price set by the generator reach the game equilibrium; It indicates the amount of electricity purchased by the mth MGCC from the generator when the game equilibrium is not reached. From the above formula, it can be seen that when the non-cooperative game between the generator and the MGCC reaches the Nash equilibrium, no participant can unilaterally change the strategy to obtain a better benefit function without affecting other participants.

[0083] (2): Master-slave game model:

[0084] The master-slave game model proposed in the present invention is a decision-making process in which MGCC and microgrid users participate in the game as leaders and followers, pursuing the optimal goal of each other. The master-slave game model can be expressed as:

[0085] 1) Participant collection:

[0086] Y={MGCC m ,U m}

[0087] Where, MGCC m represents the mth MGCC; U m represents the mth microgrid user.

[0088] 2) Strategy set:

[0089] The strategy of the leader MGCC is Ω M,m represents the strategy set of the mth MGCC;

[0090] The strategy of the follower microgrid user is Ω U,m represents the strategy set of the m-th microgrid user;

[0091] in, They represent the upper and lower limits of the electricity sales price of MGCC respectively; They are respectively the upper and lower limits of the amount of electricity that microgrid users can purchase.

[0092] 3) Utility function:

[0093] As a leader, MGCC seeks to maximize its own benefits during the game, as shown below:

[0094] C M,m =C Ubuy,m -(C PV,m +C WT,m +C GT,m +C ESS,m +C Mbuy,m +C Ms,m +C Me,m )

[0095] Microgrid users pursue the highest comprehensive satisfaction with electricity consumption during the game, as shown in the following formula:

[0096]

[0097]

[0098] In the formula, α t Indicates the user's satisfaction with electricity consumption behavior; β t Indicates the user's satisfaction with electricity cost;t The comprehensive satisfaction of users is measured; ω1 and ω2 represent the weight coefficients of user satisfaction with electricity consumption behavior and user satisfaction with electricity cost, respectively, and ω1+ω2=1; is the original load before demand response; ρ1 and ρ2 are the user electricity efficiency coefficients, with values ​​of ρ1 = 0.0009 and ρ2 = 1.5; is the original electricity price before demand response.

[0099] 4) Stcakelberg equilibrium:

[0100] When the follower microgrid users make the best response according to the strategy of the leader MGCC, and MGCC also accepts this response, the game reaches the Stcakelberg equilibrium; if When it is the equilibrium solution of the master-slave game, it must satisfy:

[0101]

[0102] In the formula, It represents the revenue of MGCC when the time-of-use electricity price set by MGCC and the amount of electricity purchased by microgrid users from MGCC reach a game equilibrium; It represents the time-of-use electricity price set by MGCC when the game equilibrium is reached;

[0103] It represents the revenue of MGCC only when the time-of-use electricity price set by MGCC reaches the game equilibrium.

[0104] Ω M,m represents the strategy set of the mth MGCC; Ω U,m represents the strategy set of the m-th microgrid user.

[0105] After the master-slave game between MGCC and microgrid users reaches an equilibrium solution, neither party can obtain greater benefits by unilaterally changing strategies.

[0106] In step 3, a two-layer particle swarm algorithm combined with a Cplex solver is used to solve the established multi-level differentiated game model. The upper-level game is a non-cooperative game between the power generator and the MGCC, and the game objectives include maximizing the revenue of the power generator and the MGCC, and determining the power sales price and power generation plan of the power generator. The lower-level game is a master-slave game between the MGCC and the user, and the game objectives include maximizing the benefits of the MGCC and the comprehensive satisfaction of the user, determining the MGCC power purchase plan and power sales price, and the user's power consumption plan.

[0107] The solution of the multi-level differentiated game model includes the following steps:

[0108] S1: Find the equilibrium point of the upper game:

[0109] S1-1: Initialize the particle swarm. Set the decision range of the generator and MGCC, and randomly initialize the position and speed of the particle swarm. Each particle represents a set of possible strategy combinations, that is, a set of decisions of the generator and MGCC.

[0110] S1-2: Evaluate fitness. For each possible strategy combination, calculate the corresponding power generator and MGCC benefits.

[0111] S1-3: Update individual and global optimal solutions. For each particle (strategy combination), update its individual optimal solution according to the benefits, that is, save the historical best position and the corresponding benefit value. Then, find the global optimal solution in the entire particle swarm, that is, the optimal game strategy combination.

[0112] S1-4: Update particle position and velocity. Update the particle position and velocity based on the individual historical optimal position and the global optimal position.

[0113] S1-5: Convergence judgment. Judge whether the maximum number of iterations has been reached. If not, return to S1-2 to continue iterating.

[0114] S1-6: Call the Cplex solver. The equilibrium point found by the particle swarm algorithm is input into the Cplex solver as the initial solution to obtain the game result.

[0115] S2: Find the equilibrium point of the lower-level game:

[0116] S2-1: Reinitialize the particle swarm. Set the decision range of MGCC and user, and randomly initialize the position and speed of the particle swarm. Each particle represents a set of possible strategy combinations, that is, a set of MGCC and user decisions.

[0117] S2-2: Evaluate fitness. Calculate the objective function of each strategy. Input the result of the upper game as a parameter to solve the lower game.

[0118] S2-3: Update individual and global optimal solutions. For each particle (strategy combination), update its individual optimal solution according to the benefits, that is, save the historical best position and the corresponding benefit value. Then, find the global optimal solution in the entire particle swarm, that is, the optimal game strategy combination.

[0119] S2-4: Update particle position and velocity. Update the particle position and velocity based on the individual historical optimal position and the global optimal position.

[0120] S2-5: Convergence judgment. Judge whether the maximum number of iterations has been reached. If not, return to S2-2 to continue iteration. S2-6: Call Cplex solver. Input the equilibrium point found by the particle swarm algorithm as the initial solution into the Cplex solver to obtain the game result; each particle represents a group of MGCC and user decisions.

[0121] S3: Feedback and integration: Feedback the lower-level game results to the upper-level game and readjust the upper-level strategy. Perform comprehensive optimization of the upper-level and lower-level games to ensure that the final results meet the overall goals.

[0122] S4: Re-solve: Based on the feedback results and adjusted parameters, repeat the solution process S1-S2 of the upper and lower level games.

[0123] S5: Verify whether the result after optimization adjustment is better. If it meets the requirements, the entire solution process is completed.

[0124] Through the game process, the optimal solutions of the upper and lower level games are obtained, including the optimal electricity sales price and power generation plan of the power generator, the optimal electricity purchase plan and electricity sales price of the MGCC, and the optimal electricity consumption plan of the user.

[0125] In step 3, the optimal two-way time-of-use electricity price optimization strategy specifically refers to: when the upper and lower level games reach a game equilibrium, the time-of-use electricity sales price of the power generator to the MGCC and the time-of-use electricity sales price of the MGCC to the user.

[0126] The present invention provides a multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game, and the technical effects are as follows:

[0127] (1) Step 1 of the present invention proposes a two-way time-of-use electricity price incentive mechanism, which has the following advantages compared to traditional electricity price strategies:

[0128] a. Price flexibility: The two-way time-of-use electricity price mechanism adjusts the electricity price according to the supply and demand situation in different time periods, while the traditional electricity price strategy usually adopts a fixed electricity price or a simple peak and valley electricity price without detailed time period division.

[0129] b. Optimize resource allocation: The two-way time-of-use electricity price mechanism more effectively matches electricity supply and demand, optimizes resource allocation, and reduces waste. Traditional electricity price strategies often cannot be dynamically adjusted to cope with actual demand changes.

[0130] c. Promote demand response: Two-way time-of-use electricity prices encourage users to use electricity during periods of low electricity prices, reduce electricity consumption during peak hours, and promote demand response; traditional electricity price strategies lack this dynamic adjustment mechanism.

[0131] d. Improve power generation efficiency: The two-way time-of-use electricity price mechanism supports power generators to adjust their power generation plans according to electricity prices and improve power generation efficiency; traditional electricity price strategies cannot provide such flexibility.

[0132] e. Enhance grid stability: The two-way time-of-use electricity price mechanism balances the load by adjusting the electricity price and enhances the stability of the grid. Traditional electricity price strategies may lead to power shortages or surpluses.

[0133] (2) Step 2 of the present invention proposes a multi-level differentiated game model. The two-way time-of-use electricity price incentive mechanism in step 1 is also designed based on this model. The specific advantages of this model are as follows:

[0134] 1) Simulate real market behavior: The game model can truly reflect the interaction and strategy selection between power generators, microgrid control centers (MGCCs) and users, and simulate the competition and cooperation behaviors in the actual market.

[0135] 2) Considering strategy interaction: The game model can handle the mutual influence and optimization of the strategies of all parties, so that the design of electricity price strategy is more in line with the dynamic adjustment needs in the actual game.

[0136] 3) Multi-level decision optimization: The game model is suitable for dealing with multi-level decision-making problems. It optimizes power generation, power sales and power consumption strategies through game analysis at different levels, thereby optimizing electricity price settings more comprehensively.

[0137] Solving complex problems: Game models can handle complex strategic interactions and optimization problems, and are suitable for solving multi-party games and nonlinear optimization problems that are difficult to handle with traditional methods.

[0138] (3) Step 3 of the present invention specifically involves solving the multi-level differentiated game model of step 2 using a double-layer particle swarm algorithm. The specific advantages of this algorithm are as follows:

[0139] ①. Efficient optimization: The particle swarm algorithm converges quickly by simulating group behavior and can efficiently find the optimal solution, which is suitable for dealing with complex multi-level game problems.

[0140] ②. Multi-level adaptability: The two-layer particle swarm algorithm can be optimized at different levels to ensure the coordination and optimization of upper and lower level decisions.

[0141] Flexible processing: Able to handle nonlinear and multi-objective optimization problems and adapt to different electricity price strategy requirements.

[0142] ③. Dynamic adjustment: Adapt to market dynamics, continuously update strategies through an iterative process, and optimize electricity price settings.

[0143] ④. Global search capability: The particle swarm algorithm has strong global search capabilities, which can avoid local optimal solutions and improve the solution quality of the overall game model.

[0144] (4) The time-of-use electricity price optimization method proposed in the present invention can optimize energy management, reduce system operating costs and user electricity costs, improve the economic benefits of power generators and the environmental protection of multi-microgrid systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0145] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0146] Figure 1 This is a flow chart of the multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game.

[0147] Figure 2 This is the optimized time-of-use electricity price diagram.

[0148] Figure 3 This is the load optimization result of microgrid user 1.

[0149] Figure 4 This is the load optimization result of microgrid user 2.

[0150] Figure 5 This is the load optimization result of microgrid user 3. DETAILED DESCRIPTION

[0151] The present invention will be further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0152] Figure 1 This is a flow chart of the optimization method proposed in the present invention. First, in the electric energy transaction based on the multi-microgrid system, the present invention designs a two-way time-of-use electricity price incentive mechanism for both power generators and users; secondly, a multi-level differentiated game model is established with power generators, MGCCs and microgrid users as the main body, coupling non-cooperative games and master-slave games. Finally, a two-layer particle swarm algorithm is combined with a Cplex solver to solve the established multi-level game model, and the optimal two-way time-of-use electricity price optimization strategy is obtained. The time-of-use electricity price optimization method proposed in the present invention can optimize energy management, reduce system operating costs and user electricity costs, and improve the economic benefits of power generators.

[0153] Figure 2 is the optimized time-of-use electricity price diagram. Figure 2It can be seen that the generator sets the time-sharing period according to the power generation demand, and the MGCC sets the time-sharing period according to the user load. Therefore, the time-sharing period of the generator is different from the time-sharing period of the MGCC power sales price and the MGCC interactive power price. In the early morning period (0:00-4:00), the power demand after demand response increases compared with before optimization, the photovoltaic output is 0 and there is no available power in the energy storage device. At this time, the generator's power generation plan is at a high level, and the power sales price of the generator is in the peak period. In the period of 4:00-9:00, the photovoltaic equipment gradually starts to work, and the energy storage equipment plays a scheduling function of low charging and high discharge. The power generation plan of the generator is lower than the peak period, so the time-sharing power sales price of the generator is in the normal period. In the period of 10:00-16:00, the photovoltaic output reaches its peak, and the user load after demand response tends to be flat. At this time, the MGCC's power demand for the generator is low, and the time-sharing power price is in the valley period. During the period of 18:00-21:00, the photovoltaic equipment stops generating electricity, and the output of MGCC's distributed power sources, gas turbines, etc. is insufficient to cover user demand. Therefore, the power generation plan of the power generator is relatively high, and the time-of-use electricity price is in the peak period. During the period of 0:00-7:00 and 20:00-23:00, the user load is relatively low, so the MGCC electricity sales price and the MGCC interactive electricity price are both in the valley period. During the period of 7:00-11:00 and 16:00-20:00, the output of photovoltaic equipment, wind power equipment and gas turbines is insufficient to cover user demand. MGCC needs to buy electricity from the power generator, and the power generator's electricity sales price is high at this time, MGCC's electricity purchase cost increases, so MGCC's electricity sales price also increases accordingly. During the period of 11:00-16:00, the output of photovoltaic equipment increases, and MGCC's electricity purchase cost decreases, so MGCC's electricity sales price is in the valley period.

[0154] Figure 3 to Figure 5 They are the load optimization results of microgrid users 1, 2, and 3 respectively. Figure 3 to Figure 5 Analysis shows that the user loads of the three microgrids were reduced during optimization. Demand response was carried out during the period with higher electricity prices (10:00-22:00), and part of the electricity load was transferred to the period with lower electricity prices, reducing the power demand during peak hours. At the same time, the peak load was reduced and the peak-to-valley difference was reduced, thereby saving energy costs. This optimization strategy not only alleviates the pressure on the power grid, but also improves the economic benefits of MGCC and microgrid users.

[0155] Table 1 Multi-scheme system optimization results

[0156]

[0157] Table 1 shows the optimization results of the multi-scheme system. In order to verify the economic and environmental performance of the method proposed in the present invention, four schemes were set up for comparative analysis.

[0158] Scheme 1 does not consider the game process, and both the generator and the MGCC set a fixed time-of-use electricity price;

[0159] Option 2 considers the game process between power generators and MGCC, and a fixed time-of-use electricity price is set between MGCC and users;

[0160] Scheme 3 considers the game process between MGCC and users, and sets a fixed time-of-use electricity price between the power generator and MGCC;

[0161] Scheme 4 considers that the power generator, MGCC and users all participate in the game process, which is the method proposed in the present invention.

[0162] Comprehensively comparing Scheme 1 and Scheme 4, Scheme 1 does not have a game process, then the time-sharing period of the power generation company's electricity sales price is almost the same as the time-sharing period of the MGCC electricity sales price. Scheme 4 has a multi-level game process. When the game equilibrium solution is reached, the power generation company's electricity sales price shows the rule that the electricity price is high when the electricity sales volume is high and the electricity price is low when the electricity sales volume is low, and the peak-to-valley difference of the electricity price is reduced. The gas turbine cost of Scheme 4 is reduced by 8111.6 yuan compared with Scheme 1, a decrease of 25.6%; the environmental protection cost is reduced by 222.8 yuan, a decrease of 19.1%; the total benefit of MGCC is increased by 15343.7 yuan, an increase of 54.3%; the income of the power generation company is increased by 12951.3 yuan, an increase of 27.3%; the cost of microgrid users is reduced by 43028.6 yuan, a decrease of 31.6%; the comprehensive satisfaction of users is improved by 0.627.

[0163] From the above data analysis, it can be seen that since the power generator, MGCC and users all participate in the game process in Scheme 4, the energy consumption behavior of users is guided by electricity prices to respond to demand, which reduces electricity costs. The power generation plan of the power generator has obtained higher profits after adjustment. Compared with Scheme 1, all costs of Scheme 4 are reduced by about 20%, and the income of the power generator and the total benefit of MGCC are greatly improved, which reflects the absolute advantages of Scheme 4 in terms of economy and environmental protection.

Claims

1. A multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game, characterized by The following steps are involved: Step 1: In the electric energy transaction based on the multi-microgrid system, a two-way time-of-use electricity price incentive mechanism is designed for both power generators and users; Step 2: A multi-level differentiated game model coupling non-cooperative game and master-slave game was established with power generators, microgrid control center MGCC and microgrid users as the main bodies; Step 3: Use a two-layer particle swarm algorithm combined with a solver to solve the multi-level differentiated game model established in step 2 and obtain the optimal two-way time-of-use electricity price optimization strategy.

2. The multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game according to claim 1 is characterized by: In step 1, the two-way time-of-use electricity price incentive mechanism includes two parts: two-way time-of-use electricity price period division and two-way time-of-use electricity price optimization decision model; wherein, the two-way time-of-use electricity price period division is specifically as follows: The time-of-use electricity price for MGCC sales needs to be clustered based on the amount of electricity purchased by MGCC to determine the peak, flat and valley time-of-use periods on the power generation side; the time-of-use electricity price for microgrid users needs to be clustered based on the amount of electricity purchased by users; since the amount of electricity purchased is one-dimensional data, the k-means clustering algorithm is used to determine the time-of-use period; the objective function of the k-means clustering algorithm is the sum of the mean square error of the data and the cluster center to which it belongs; Based on the k-means clustering algorithm, the clustering mark of the purchased electricity is obtained, and the peak, flat and valley time periods are determined according to the clustering mark. The expression is as follows: S a =[δ1,δ2,…,δ 24 ] T F ∪T P ∪T G ={0,1,2,…,23} In the formula, S a is the clustering label vector of the purchased electricity; t is the clustering mark of the purchased electricity and generated electricity in period t; δ F , δ P , δ G are the clustering marks for peak, flat and valley time periods respectively; T F , T P , T G They are the peak, flat and valley time periods of the purchased electricity respectively; The construction of the two-way time-of-use electricity price optimization decision model takes into account the minimization of load peak and peak-to-valley difference, while considering the economic benefit constraints of both supply and demand sides to ensure a balance of interests between the two sides; When deciding the electricity price of power generators, the objective function includes: (1) Minimize the peak value F1 of the power generation curve of the power generator; (2) Maximizing the valley value F2 of the power generation curve of the power generator; (3) Minimize the peak-to-valley difference F3 of the generator’s power generation curve, The specific objective function is as follows: In the above formula, P e t The amount of electricity generated for power generators; It indicates the maximum power generation of a generator in a single period of 24 hours; it indicates a period of 24 hours; When deciding the MGCC electricity sales price, the objective function is: (1) Minimize the peak value F4 of the microgrid user load curve; (2) Maximize the valley value F5 of the microgrid user load curve; (3) Minimize the peak-to-valley difference F6 of microgrid user load, The specific objective function is as follows: In the above formula, The amount of electricity purchased by the mth microgrid user from MGCC; It is expressed as the maximum amount of electricity purchased by the user from MGCC in a single period of 24 hours; It is expressed as the minimum value of electricity purchased from MGCC in a single period of 24 hours.

3. The multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game according to claim 2 is characterized by: The constraints of the game equilibrium include the revenue of power generators, the benefits of MGCC and the comprehensive satisfaction of users. The constraints are as follows: C F,after ≥C F,before C Fy,after ≤C Fy,before C M,after ≥C M,before F U,after ≥F U,before In the above formula, C F,after represents the revenue of the power generator after the electricity price adjustment, C F,before represents the revenue of the power generator before the electricity price adjustment; C Fy,after is the operation and maintenance cost of the power generator after the electricity price adjustment; C Fy,before is the operation and maintenance cost of the power generator before the electricity price adjustment; C M,after is the revenue of MGCC after electricity price adjustment, C M,before is the revenue of MGCC before electricity price adjustment; F U,after is the comprehensive satisfaction of users after electricity price adjustment, F U,before It is the comprehensive satisfaction of users with electricity consumption before the electricity price adjustment; It is the amount of electricity purchased by microgrid users from MGCC after the electricity price adjustment. It is the amount of electricity purchased by microgrid users from MGCC before the electricity price adjustment; Formula C F,after ≥C F,before , Formula C M,after ≥C M,before Japanese style F U,after ≥F U,before It means that the objective function of each subject should be optimized after adjustment; Formula C Fy,after ≤C Fy,before It indicates that the operation and maintenance costs of power generators need to be reduced after the electricity price adjustment; Mode It means that the user’s unit electricity purchasing cost cannot increase after the electricity price adjustment.

4. The multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game according to claim 1 is characterized by: In step 2, the multi-level game mechanism is as follows: In the multi-level game mechanism, there is a non-cooperative game between the power generator and the MGCC, and a master-slave game relationship between the MGCC and the microgrid users; the power generator sets the power sales price according to the power demand of each microgrid, taking into account the fixed cost of power generation and the operation and maintenance cost; the MGCC conducts day-ahead planning and dispatching, giving priority to the consumption of new energy output and energy storage within the microgrid. If there is still demand, it will purchase electricity from other microgrids and finally from the power generator; the MGCC considers its own benefits and sets the load-side time-of-use electricity price; the user adjusts the electricity consumption behavior in each time period according to the load-side time-of-use electricity price and psychological expectations; the three stakeholders, the power generator, MGCC and the user, influence each other through decision-making on-grid time-of-use electricity price, load-side time-of-use electricity price and time-of-use electricity consumption behavior, and obtain the optimal equilibrium solution through game.

5. The multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game according to claim 4 is characterized by: The game specifically includes the following steps: Step 2.1: The power generator sets the initial grid-connected time-of-use electricity price on the power generation side; Step 2.2: MGCC formulates power purchase strategy and load-side time-of-use electricity price based on time-of-use electricity price and power demand; Step 2.3: Microgrid users independently shift or reduce loads according to the load-side time-of-use electricity price; Step 2.4: MGCC updates the dispatch strategy and power purchase plan according to the user's load time-sharing demand; Step 2.5: The power generator adjusts the power generation plan and updates the grid-connected time-of-use electricity price according to the power purchase plan of MGCC; Step 2.6: Repeat steps 2.2 to 2.5 until the power supplier's on-grid time-of-use electricity price, MGCC's power purchase strategy, load-side time-of-use electricity price and the user's electricity consumption plan remain stable and unchanged, reaching a game equilibrium; Step 2.7: Energy trading is performed with the game equilibrium solution as the final result.

6. The multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game according to claim 5 is characterized by: In step 2, the multi-level differentiated game model includes: (1): Non-cooperative game model: The multi-level game proposed in the present invention includes the non-cooperative game between the power generator and the MGCC and the master-slave game process between the MGCC and the user; wherein, the two parties participating in the non-cooperative game use the grid-connected time-of-use electricity price and the power purchase plan as game items respectively, and establish a non-cooperative game economic optimization model with multi-microgrid as the background; 1) Participant collection: Y={F,MGCC1,MGCC2,iii,MGCC m ,MGCC M } Where, F represents the generator; MGCC m represents the mth microgrid control center; MGCC1 represents the first microgrid control center; MGCC2 represents the second microgrid control center; MGCC M represents the Mth microgrid control center; 2) Strategy set: The strategy set of the generator is expressed as: In the formula, Represents the power generation of the power generator during period t; Ω F represents the strategy set of the power generator; Respectively represent the upper and lower limits of power generation; represents the time-of-use electricity price sold by the power generator during period t; They represent the upper and lower limits of the electricity price sold by power generators respectively; The strategy set of the mth MGCC is expressed as: In the formula, Indicates the amount of electricity purchased by the mth MGCC from the generator during period t; Ω M,m represents the strategy set of the mth MGCC; Respectively represent the upper and lower limits of the amount of electricity purchased; 3) Utility function: Power generators pursue their own benefits in the game process F The maximum is expressed as follows: C F =C Mbuy -C Fy -C Fe In the formula, C F is the revenue of the power generator; C Mbuy is the electricity purchase cost of MGCC and the electricity sales revenue of the generator; C Fy is the operation and maintenance cost of the generator; C Fe Environmental protection costs for power generators; MGCC pursues benefits in the game process M The maximum is expressed as follows: C M,m =C Ubuy,m -(C PV,m +C WT,m +C GT,m +C ESS,m +C Mbuy,m +C Ms,m +C Me,m ) In the formula, C M,m is the total revenue of MGCC in the mth microgrid system; C Ubuy,m is the electricity cost of microgrid user m, which is equivalent to the electricity sales revenue of the mth MGCC; C PV,m is the operation and maintenance cost of photovoltaic equipment; C WT,m is the operation and maintenance cost of wind power equipment; C GT,m is the power generation and operation and maintenance cost of gas turbine; C ESS,m The charging and discharging operation and maintenance cost of energy storage equipment; C Mbuy,m is the electricity purchase cost of MGCC; C Ms,m is the interaction cost between microgrids; C Me,m The environmental protection cost of MGCC; 4) Nash equilibrium: When the game reaches equilibrium, the generator and MGCC need to meet the following conditions: In the formula, It represents the revenue of the generator after the generator's power generation, the electricity sales price set by the generator and the amount of electricity purchased by the mth MGCC from the generator reach a game equilibrium; It represents the power generation of the power supplier when the game reaches equilibrium; It represents the electricity selling price set by the power generator when the game reaches equilibrium; It represents the amount of electricity purchased by the mth MGCC from the generator when the game reaches equilibrium; It represents the revenue of the generator only when the electricity sales price set by the generator and the amount of electricity purchased by the mth MGCC from the generator reach a game equilibrium; It represents the power generation of the power generator when the game equilibrium is not reached; It represents the revenue of the generator only when the power generation of the generator and the power purchase amount of the mth MGCC from the generator reach the game equilibrium; It indicates the electricity price set by the power generator when the game equilibrium is not reached; It means the revenue of the generator only when the power generation of the generator and the electricity price set by the generator reach the game equilibrium; It represents the amount of electricity purchased by the mth MGCC from the generator when the game equilibrium is not reached; (2): Master-slave game model: The master-slave game model is a decision-making process in which MGCC and microgrid users participate in the game as leaders and followers to pursue the optimal goals of each other; the master-slave game model is expressed as: 1) Participant collection: Y={MGCC m ,U m } Where, MGCC m represents the mth MGCC; U m represents the mth microgrid user; 2) Strategy set: The strategy of the leader MGCC is Ω M,m represents the strategy set of the mth MGCC; the strategy of the follower microgrid user is Ω U,m represents the strategy set of the m-th microgrid user; in, They represent the upper and lower limits of the electricity sales price of MGCC respectively; They are the upper and lower limits of the amount of electricity that microgrid users can purchase, respectively; 3) Utility function: As a leader, MGCC seeks to maximize its own benefits during the game, as shown below: C M,m =C Ubuy,m -(C PV,m +C WT,m +C GT,m +C ESS,m +C Mbuy,m +C Ms,m +C Me,m ) Microgrid users pursue the highest comprehensive satisfaction with electricity consumption during the game, as shown in the following formula: In the formula, α t Indicates the user's satisfaction with electricity consumption behavior; β t Indicates the user's satisfaction with electricity costs; t The comprehensive satisfaction of users is measured; ω1 and ω2 represent the weight coefficients of user satisfaction with electricity consumption behavior and user satisfaction with electricity cost, respectively, and ω1+ω2=1; is the original load before demand response; ρ1 and ρ2 are the user electricity efficiency coefficients, with values ​​of ρ1 = 0.0009 and ρ2 = 1.5; is the original electricity price before demand response; 4) Stcakelberg equilibrium: When the follower microgrid users make the best response according to the strategy of the leader MGCC, and MGCC also accepts this response, the game reaches the Stcakelberg equilibrium; if When it is the equilibrium solution of the master-slave game, it must satisfy: In the formula, It represents the revenue of MGCC when the time-of-use electricity price set by MGCC and the amount of electricity purchased by microgrid users from MGCC reach game equilibrium; It represents the time-of-use electricity price set by MGCC when the game equilibrium is reached; It represents the revenue of MGCC only when the time-of-use electricity price set by MGCC reaches the game equilibrium; Ω M,m represents the strategy set of the mth MGCC; Ω U,m represents the strategy set of the m-th microgrid user; After the master-slave game between MGCC and microgrid users reaches an equilibrium solution, neither party can obtain greater benefits by unilaterally changing strategies.

7. The multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game according to claim 1 is characterized by: In step 3, a double-layer particle swarm algorithm combined with a Cplex solver is used to solve the established multi-level differentiated game model; wherein the upper-level game is a non-cooperative game between the power generator and the MGCC, and the game objectives include maximizing the profits of the power generator and the MGCC, and determining the power sales price and power generation plan of the power generator; the lower-level game is a master-slave game between the MGCC and the user, and the game objectives include maximizing the benefits of the MGCC and the comprehensive satisfaction of the user, and determining the power purchase plan and power sales price of the MGCC, as well as the power consumption plan of the user.

8. The multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game according to claim 7 is characterized by: The solution of the multi-level differentiated game model includes the following steps: S1: Find the equilibrium point of the upper game: S1-1: Initialize the particle swarm; set the decision range of the generator and MGCC, and randomly initialize the position and speed of the particle swarm; each particle represents a set of possible strategy combinations, that is, a set of decisions of the generator and MGCC; S1-2: Evaluate fitness; for each possible strategy combination, calculate the corresponding power generator and MGCC benefits; S1-3: Update individual and global optimal solutions; for each particle, update its individual optimal solution according to the benefit, that is, save the historical best position and the corresponding benefit value; then, find the global optimal solution in the entire particle swarm, that is, the optimal game strategy combination; S1-4: Update particle position and velocity; update the particle position and velocity according to the individual historical optimal position and the global optimal position; S1-5: Convergence judgment: judge whether the maximum number of iterations has been reached; if not, return to S1-2 to continue iterating; S1-6: Call the Cplex solver; input the equilibrium point found by the particle swarm algorithm into the Cplex solver as the initial solution to obtain the game result; S2: Find the equilibrium point of the lower-level game: S2-1: Set the decision range of MGCC and user, and randomly initialize the position and speed of the particle swarm; each particle represents a set of possible strategy combinations, that is, a set of decisions of MGCC and user; S2-2: Calculate the objective function of each strategy group; input the result of the upper game as a parameter to solve the lower game; S2-3: For each particle, update its individual optimal solution according to the benefit, that is, save the historical best position and the corresponding benefit value; then, find the global optimal solution in the entire particle group, that is, the optimal game strategy combination; S2-4: Update the position and velocity of the particle according to the individual historical optimal position and the global optimal position; S2-5: Determine whether the maximum number of iterations has been reached; if not, return to S2-2 to continue iterating; S2-6: Input the equilibrium point found by the particle swarm algorithm as the initial solution into the Cplex solver to obtain the game result; S3: Feedback and integration: Feedback the results of the lower-level game to the upper-level game and readjust the upper-level strategy; Conduct comprehensive optimization of upper and lower level games to ensure that the final result meets the overall goal; S4: Re-solve: Based on the feedback results and the adjusted parameters, the solving process S1-S2 of the upper and lower game is repeated; S5: Verify whether the result after optimization adjustment is better. If it meets the requirements, the whole solution process is completed; Through the game process, the optimal solutions of the upper and lower level games are obtained, including the optimal electricity sales price and power generation plan of the power generator, the optimal electricity purchase plan and electricity sales price of the MGCC, and the optimal electricity consumption plan of the user.

9. The multi-microgrid bidirectional time-of-use electricity price optimization method based on multi-level game according to claim 8 is characterized by: In step 3, the optimal two-way time-of-use electricity price optimization strategy specifically refers to: when the upper and lower level games reach a game equilibrium, the time-of-use electricity sales price of the power generator to the MGCC and the time-of-use electricity sales price of the MGCC to the user.

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