Multi-agent game based optimal scheduling method for distribution network-shared energy storage-multi-microgrid

Abstract

The application discloses a power distribution network-shared energy-multiple microgrid optimization scheduling method based on multi-agent game, and particularly relates to the following: a multi-microgrid shared energy multi-objective optimization configuration model is constructed by taking into account the energy storage investment cost and load fluctuation; a multi-objective particle swarm optimization algorithm is used to solve the multi-microgrid shared energy multi-objective optimization configuration model, and the charging and discharging power of the energy storage to each microgrid is obtained through optimization; a one-master-two-slave game scheduling model is established, in which the power distribution network is the master, and the multiple microgrids and the shared energy are the followers; a genetic algorithm embedded with a solver is used to solve the one-master-two-slave game scheduling model, and the optimal scheduling scheme is obtained. The application constructs a one-master-two-slave multi-agent game scheduling framework, which is conducive to promoting the cooperation and coordination among the power distribution network, the shared energy and the multiple microgrids, realizing the cooperation of the three-party scheduling, and further realizing the balanced distribution of the interests of the power distribution network, the shared energy and the multiple microgrids, so as to improve the overall benefit.

Description

Technical Field

[0001] This invention belongs to the field of power system optimization scheduling technology, specifically involving a distribution network-shared energy storage-multiple microgrid optimization scheduling method based on multi-stakeholder game theory. Background Technology

[0002] Under the "dual-carbon" strategy, integrated energy microgrids have achieved rapid development due to their significant advantages in clean energy substitution and promoting low-carbon sustainable energy development. Consequently, the optimal scheduling of integrated energy microgrids has become a research focus. With the continuous increase in the number of system microgrids, a new development pattern of multi-microgrid joint scheduling is gradually emerging. However, the coupling and interaction mechanisms between multiple microgrids and multiple energy flows are complex, and the superposition of various uncertainties such as source loads presents unprecedented challenges to traditional scheduling methods.

[0003] Currently, many new scheduling methods have been proposed by scholars at home and abroad. However, their research on the participation of shared energy storage in the scheduling of multi-microgrids focuses on the capacity configuration and operation strategy of shared energy storage, and fails to consider the issue of benefit distribution between shared energy storage and multi-microgrids, nor does it seek a balance between using energy storage to smooth out fluctuations and improve economic efficiency.

[0004] In addressing energy trading issues, game theory, as an effective tool, provides valuable insights into the complex problem of distributing interests among multiple stakeholders. However, existing methods, when exploring the game relationship between microgrids and shared energy storage, have failed to fully consider the participation of the distribution network, nor have they fully utilized the characteristics of microgrid grid connection to incorporate shared energy storage, microgrids, and the distribution network into the scope of game-based scheduling considerations. Summary of the Invention

[0005] The purpose of this invention is to provide an optimized scheduling method for distribution network-shared energy storage-multiple microgrids based on multi-party game theory, so as to achieve a balanced distribution of interests among the distribution network, shared energy storage and multiple microgrids, thereby improving the overall operational efficiency.

[0006] The technical solution adopted in this invention is a multi-stakeholder game-based optimization scheduling method for distribution networks, shared energy storage, and multiple microgrids, which is implemented according to the following steps:

[0007] Step 1: Construct a multi-objective optimization configuration model for shared energy storage across multiple microgrids, taking into account both energy storage investment costs and load fluctuations;

[0008] Step 2: The multi-objective particle swarm optimization algorithm is used to solve the multi-microgrid shared energy storage multi-objective optimization configuration model in Step 1, and the charging and discharging power of energy storage to each microgrid is optimized.

[0009] Step 3: Establish a game-theoretic scheduling model with the distribution network as the leader and multiple microgrids and shared energy storage as followers;

[0010] Step 4: Based on the optimized calculation of the charging and discharging power of energy storage to each microgrid, a genetic algorithm with nested solvers is used to solve the master-slave game scheduling model to obtain the optimal scheduling scheme.

[0011] The invention is further characterized in that,

[0012] In step 1, specifically: Step 1.1, establish two optimization objectives: the lowest energy storage investment cost and the smallest net load standard deviation;

[0013] (1) Microgrid i has the lowest energy storage cost, as shown in equation (1):

[0014] C SES,i =min(C INV,i +C OM,i (1);

[0015] In the formula: C SES.i For the energy storage investment cost of microgrid i, C INV.i For the energy storage investment costs borne by microgrid i, C OM.i The energy storage operation and maintenance costs borne by microgrid i;

[0016] 1) The energy storage investment cost borne by microgrid i is shown in equation (2):

[0017]

[0018] In the formula: λ P , λ E These represent the unit power investment cost and unit capacity investment cost of shared energy storage, respectively. T represents the maximum value of the energy storage power and capacity requirements of microgrid i, respectively; E Indicates the lifespan of shared energy storage;

[0019] 2) The energy storage operation and maintenance costs borne by microgrid i are shown in equation (3):

[0020]

[0021] In the formula: P SES,C,i (t), P SES,D,i (t) represents the charging and discharging power of energy storage to microgrid i during time period t; δ OM The microgrid i bears the unit power operation and maintenance cost of energy storage; T is the scheduling cycle; Δt is the time interval;

[0022] (2) The net load standard deviation of microgrid i is the smallest, as shown in equations (4)-(5):

[0023]

[0024] Step 1.2: Determine the constraints of the two optimization objectives and optimize the charging and discharging power of the shared energy storage for each microgrid; the constraints include the coupling constraints shared with microgrid i and the charging and discharging power constraints.

[0025] (1) The coupling constraints shared with microgrid i are shown in equation (6):

[0026]

[0027] The upper limit of the energy storage power demand of microgrid i is shown in equation (7):

[0028]

[0029] In the formula: These represent the upper limits of the charging and discharging power of energy storage to microgrid i, respectively; U SES,C,i (t) = 1 indicates charging, and 0 indicates discharging; U SES,D,i (t) is 1 when it represents discharging and 0 when it represents charging;

[0030] (2) Charge and discharge power constraints, as shown in equations (8)-(10):

[0031] E i (t)=E i (t-1)+(η SES,C P SES,C,i (t)-P SES,D,i (t) / η SES,D )Δt (8);

[0032]

[0033] Step 3 specifically involves:

[0034] (1) Establish the main distribution network dispatch model

[0035] 1) Objective function

[0036] The decision variable for optimal dispatching of distribution networks is the purchase and sale price of electricity, and the objective of optimal operation is the total cost C. DN The minimum, as shown in equation (11):

[0037] C DN =min(C grid +C MMG,DN +C SES,DN (11);

[0038] (a) The cost of purchasing electricity from the main grid is shown in equation (12):

[0039]

[0040] Where: δ sell,grid (t) represents the electricity price on the main grid during time period t; P buy,DN (t) represents the power purchased by the distribution network from the main grid during time period t;

[0041] (b) The energy interaction cost with the multi-microgrid is shown in Equation (13):

[0042]

[0043] (c) The energy interaction cost with shared energy storage is shown in Equation (14):

[0044]

[0045] In the formula: P buy,SES (t), P sell,SES (t) represents the power purchased and sold by the shared energy storage from the distribution network during time period t;

[0046] 2) Constraints

[0047] (a) Power flow balance constraint, as shown in equation (15):

[0048]

[0049] (b) State variable constraints, as shown in equation (16):

[0050]

[0051] (c) Access to distributed energy constraints, as shown in equation (17):

[0052]

[0053] (d) Time-of-use pricing constraints;

[0054] The electricity purchase and sale price set by the distribution network for microgrids and shared energy storage is a time-of-use price. The electricity sales price and purchase price during off-peak, normal, and peak periods satisfy the following relationship, as shown in equations (18)-(19):

[0055]

[0056] The electricity sales price of the distribution network must be higher than the electricity purchase price at each time period, as shown in equation (20):

[0057]

[0058] The average electricity sales price within the dispatch period T should not exceed the upper limit of the electricity sales price of the distribution network, as shown in equation (21):

[0059]

[0060] (2) Establish a multi-microgrid scheduling model that includes shared energy storage;

[0061] 1) Establish a shared energy storage dispatch model

[0062] (a) The objective function is shown in equation (22):

[0063] C SES =min(C INV +C OM -C SES,DN ) (twenty two);

[0064] a) Investment cost of shared energy storage C INV As shown in equation (23):

[0065]

[0066] b) The operation and maintenance cost of shared energy storage is shown in equation (24):

[0067]

[0068] c) The cost of energy interaction with the distribution network, as shown in equation (14) above;

[0069] (b) Constraints

[0070] a) Shared energy storage constraints, as shown in equation (25):

[0071]

[0072] b) Shared energy storage power balance constraints, as shown in equation (26):

[0073]

[0074] In the formula: U buy,SES (t) = 1 indicates electricity purchase, and 0 indicates electricity sale; U sell,SES When (t) is 0, it indicates purchasing electricity; when it is 1, it indicates selling electricity.

[0075] 2) Establish a multi-micronet scheduling model

[0076] (a) The objective function is shown in equation (27):

[0077] C MMG =min(C TU -C MMG,DN (27);

[0078] a) The start-up, shutdown, and coal consumption costs of small coal-fired power units are shown in equation (28):

[0079]

[0080] In the formula: P TU,i (t) represents the dispatch output of small and medium-sized coal-fired power units in microgrid i during time period t; a1, b1, and c1 are the coal consumption coefficients of small and medium-sized coal-fired power units, respectively; U TU,i (t) = 1 indicates startup, U TU,i (t) = 0 indicates shutdown; δ TU This is the start-up and shutdown cost coefficient for the unit;

[0081] b) The cost of energy interaction with the distribution network, as shown in equation (13) above;

[0082] (b) Constraints

[0083] a) Operating constraints for small coal-fired power units, as shown in equation (29):

[0084]

[0085] b) Constraints on inter-microgrid power trading, as shown in equation (30):

[0086]

[0087] In the formula: The upper limit of electrical power for interaction between microgrid i and other microgrids;

[0088] c) Microgrid power balance constraints, as shown in equation (31):

[0089]

[0090] In the formula: P ij (t) represents the interaction power between microgrids i and j in time period t, P ij (t)>0 indicates that microgrid i sells electricity to microgrid j, U buy,i (t) = 1 indicates electricity purchase, and 0 indicates electricity sale; U sell,i When (t) is 0, it indicates purchasing electricity; when it is 1, it indicates selling electricity.

[0091] d) Tie line power constraints, as shown in equation (32):

[0092]

[0093] The beneficial effects of this invention are:

[0094] (1) By constructing a multi-objective optimization configuration model and optimization algorithm, a balanced consideration of all objectives is achieved, which effectively smooths out the fluctuations of new energy access in each microgrid and reduces the shared energy storage costs that each microgrid needs to bear.

[0095] (2) By constructing a multi-subject game scheduling framework with one master and two slaves, it is conducive to promoting cooperation and coordination between the distribution network and shared energy storage and multiple microgrids, realizing the coordination of the three parties' scheduling, and thus achieving a balanced distribution of the interests of the distribution network, shared energy storage and multiple microgrids, thereby improving the overall benefits. Attached Figure Description

[0096] Figure 1 This is the flowchart of the multi-agent game-based optimal scheduling method for distribution network-shared energy storage-multi-microgrid in this invention;

[0097] Figure 2 This is a flowchart of the game optimization model solution in the multi-agent game-based optimal scheduling method for distribution network-shared energy storage-multi-microgrid in this invention;

[0098] Figure 3 This is a graph showing the net load fluctuation of the microgrid 1 before and after the shared energy storage charging and discharging process.

[0099] Figure 4 This is a graph showing the net load fluctuation of the microgrid 2 before and after the shared energy storage charging and discharging process of the present invention.

[0100] Figure 5 This is a graph showing the net load fluctuation of the microgrid 3 before and after the shared energy storage charging and discharging process. Detailed Implementation

[0101] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0102] This invention is based on a multi-agent game theory-based optimal scheduling method for distribution networks, shared energy storage, and multiple microgrids, such as... Figure 1 As shown, please follow these steps:

[0103] Step 1: Construct a multi-objective optimization configuration model for shared energy storage across multiple microgrids, taking into account both energy storage investment costs and load fluctuations. Specifically:

[0104] Step 1.1: Establish two optimization objectives: minimizing energy storage investment cost and minimizing net load standard deviation;

[0105] (1) Microgrid i has the lowest energy storage cost, as shown in equation (1):

[0106] C SES,i =min(C INV,i +C OM,i (1);

[0107] In the formula: C SES.i For the energy storage investment cost of microgrid i, C INV..i For the energy storage investment costs borne by microgrid i, C OM.i The energy storage operation and maintenance costs borne by microgrid i;

[0108] The formulas for calculating each cost are as follows:

[0109] 1) Ignoring the impact of economic development and inflation, the energy storage investment cost borne by microgrid i is shown in equation (2):

[0110]

[0111] In the formula: λ P , λ E These represent the unit power investment cost and unit capacity investment cost of shared energy storage, respectively. T represents the maximum value of the energy storage power and capacity requirements of microgrid i, respectively; E This indicates the lifespan of the shared energy storage.

[0112] 2) The energy storage operation and maintenance costs borne by microgrid i are shown in equation (3):

[0113]

[0114] In the formula: P SES,C,i (t), P SES,D,i (t) represents the charging and discharging power of energy storage to microgrid i during time period t; δ OM The microgrid i bears the unit power operation and maintenance cost of energy storage; t=1,2,3…T, where T is the scheduling period; Δt is the time interval;

[0115] (2) The net load standard deviation of microgrid i is the smallest, as shown in equations (4)-(5):

[0116]

[0117] In the formula: S i P represents the net load standard deviation of microgrid i. load,i (t) represents the load value of microgrid i during time period t; P WT,i (t), P PV,i (t) represents the wind power and photovoltaic power consumed by microgrid i during time period t; The equivalent load average of microgrid i;

[0118] Step 1.2: Determine the constraints of the two optimization objectives and optimize the charging and discharging power of the shared energy storage for each microgrid; the constraints include the coupling constraints shared with microgrid i and the charging and discharging power constraints.

[0119] (1) The coupling constraints shared with microgrid i are shown in equation (6):

[0120]

[0121] The upper limit of the energy storage power demand of microgrid i is shown in equation (7):

[0122]

[0123] In the formula: P SES,C,i (t), P SES,D,i (t) represents the charging and discharging power of energy storage to microgrid i during time period t; These represent the upper limits of the charging and discharging power of energy storage to microgrid i, respectively; U SES,C,i (t), U SES,D,i (t) Ensure that energy storage can only charge or discharge microgrid i at any given time during the scheduling cycle, U SES,C,i (t) is 1 when charging and 0 when discharging;

[0124] (2) Charge and discharge power constraints, as shown in equations (8)-(10):

[0125] E i (t)=E i (t-1)+(η SES,C P SES,C,i (t)-P SES,D,i (t) / η SES,D )Δt (8);

[0126]

[0127] In the formula: η SES,C η SES,D These represent the charging and discharging efficiencies of energy storage, respectively; E i (t) represents the storage capacity required for energy storage to charge and discharge microgrid i during time period t; To limit the capacity requirements of microgrid i for energy storage, μ E Capacity margin for shared energy storage in microgrids.

[0128] Step 2: Solve the multi-objective optimization configuration model of shared energy storage in step 1 using the multi-objective particle swarm optimization algorithm, such as... Figure 2 As shown, the optimized charging and discharging power of the energy storage to each microgrid is obtained; specifically:

[0129] Step 2.1: Initialize the positions, velocities, pbest, and gbest of N particles, with the number of iterations inter = 0;

[0130] Step 2.2: Calculate the fitness values ​​of the initial N populations, i.e., the energy storage investment cost and the root mean square error of the net load of the multi-microgrid;

[0131] Step 2.3: Update the particle's velocity and position, set the dynamic inertia weight, and update the population's individual extreme value pbest;

[0132] Step 2.4: Find the Pareto optimal solution for the new particle, merge it into the external archive set, find the Pareto optimal solution in the external archive set, and remove the non-Pareto optimal solutions;

[0133] Step 2.5: Determine whether the number of optimal solutions in the external set exceeds the specified capacity m. If so, select m particles based on the crowding distance; otherwise, use the roulette wheel method to select the best.

[0134] Step 2.6: The particle undergoes a small-probability mutation within a 30% range at its original position;

[0135] Step 2.7: Determine whether inter = inter + 1 and inter = intermax are satisfied. If so, output the Pareto optimal solution set and record the optimal charging and discharging strategy for each microgrid. Otherwise, return to step 2.3.

[0136] Step 3: Establish a game-theoretic scheduling model with the distribution network as the leader and multiple microgrids and shared energy storage as followers, to achieve a balanced distribution of interests among the distribution network, shared energy storage, and multiple microgrids, thereby improving overall benefits; specifically:

[0137] (1) Establish the main distribution network dispatch model

[0138] 1) Objective function

[0139] The decision variable for optimal dispatching of distribution networks is the purchase and sale price of electricity, and the objective of optimal operation is the total cost C. DN The minimum, as shown in equation (11):

[0140] C DN =min(C grid +C MMG,DN +C SES,DN (11);

[0141] In the formula: C DN C represents the total cost of the distribution network. grid The cost of purchasing electricity from the main grid for the distribution network; C MMG,DN The cost of energy interaction between the distribution network and multiple microgrids; C SES,DN This refers to the energy exchange costs between the distribution network and shared energy storage. The formulas for calculating each cost are as follows:

[0142] (a) The cost of purchasing electricity from the main grid is shown in equation (12):

[0143]

[0144] Where: δ sell,grid (t) represents the electricity price on the main grid during time period t; P buy,DN (t) represents the power purchased by the distribution network from the main grid during time period t;

[0145] (b) The energy interaction cost with the multi-microgrid is shown in Equation (13):

[0146]

[0147] Where: δ bu、y,DN (t) represents the optimized electricity purchase price for the distribution network from microgrids and shared energy storage within time period t; δ sell,DN (t) represents the optimized electricity price from the distribution network to the microgrid and shared energy storage; P buy,i (t), P sell,i (t) represents the power purchased and sold by microgrid i from the distribution network during time period t.

[0148] (c) The energy interaction cost with shared energy storage is shown in Equation (14):

[0149]

[0150] In the formula: P buy,SES (t), P sell,SES (t) represents the power purchased and sold by the shared energy storage from the distribution network during time period t.

[0151] 2) Constraints

[0152] The constraints include power flow balance constraints, state variable constraints, distributed energy access constraints, and time-of-use pricing constraints.

[0153] (a) Power flow balance constraint, as shown in equation (15):

[0154]

[0155] In the formula: n is the number of nodes in the distribution network; P p Q p U p These represent the active and reactive power injected into node p, and the voltage amplitude, respectively; G pq B pq δ pq U represents the conductance, susceptance, and voltage phase difference between nodes p and q, respectively. q It is the voltage amplitude at node q;

[0156] (b) State variable constraints, as shown in equation (16):

[0157]

[0158] In the formula: These are the upper and lower limits of the voltage amplitude injected at node p, respectively; G pq B pq δ pq These represent the conductance, susceptance, and voltage phase difference between nodes p and q, respectively; Sl , These represent the power and power limit of distribution network branch l, respectively.

[0159] (c) Access to distributed energy constraints, as shown in equation (17):

[0160]

[0161] In the formula: P DG (t), Q DG (t) represents the active and reactive power outputs of distributed energy sources during time period t; These represent the upper and lower limits of the active power output of distributed energy sources within time period t; These represent the upper and lower limits of reactive power output of distributed energy sources within time period t.

[0162] (d) Time-of-use pricing constraints;

[0163] The electricity purchase and sale price set by the distribution network for microgrids and shared energy storage is a time-of-use price. The electricity sales price and purchase price during off-peak, normal, and peak periods satisfy the following relationship, as shown in equations (18)-(19):

[0164]

[0165] In the formula: T v T f T p These represent the trough period, the normal period, and the peak period, respectively; δ buyv,DN (t), δ buyf,DN (t) and δ buyp,DN (t) represents the distribution network purchase price during off-peak, normal, and peak periods, respectively; δ sellv,DN (t), δ sellf,DN (t) and δ sellp,DN (t) represents the distribution network electricity price during off-peak, normal, and peak periods, respectively;

[0166] To ensure the profitability of the distribution network, the electricity sales price must be higher than the purchase price at each time period, as shown in equation (20):

[0167]

[0168] To prevent the main distribution network from raising electricity prices for its own benefit during the game, the average electricity price within the dispatch period T should not exceed the upper limit of the distribution network's electricity price, as shown in equation (21):

[0169]

[0170] In the formula: This is the upper limit for the electricity sales price on the distribution network.

[0171] (2) Establish a multi-microgrid scheduling model that includes shared energy storage;

[0172] 1) Establish a shared energy storage dispatch model

[0173] (a) Objective function

[0174] Step 2 has optimized the charging and discharging power P of the shared energy storage to microgrid i. SES,C,i (t), P SES,D,i (t), therefore, the decision variable of the shared energy storage dispatch model is the interaction power with the distribution network, and the objective of optimized operation is the total cost C. SES The minimum, as shown in equation (22):

[0175] C SES =min(C INV +C OM -C SES,DN ) (twenty two);

[0176] In the formula: C SES Total cost of shared energy storage; C INV Investment costs for shared energy storage; C OM The operation and maintenance costs of shared energy storage; C SES,DN The cost of energy exchange between shared energy storage and the distribution network is calculated using the following formulas:

[0177] a) Investment cost of shared energy storage C INV As shown in equation (23):

[0178]

[0179] In the formula: λ P and λ E These are the unit power investment cost and unit capacity investment cost of shared energy storage, respectively. and These are the power and capacity limits for shared energy storage, respectively; T E The lifespan of shared energy storage;

[0180] b) The operation and maintenance cost of shared energy storage is shown in equation (24):

[0181]

[0182] In the formula: P SES,C (t), P SES,D (t) represent the charging and discharging power of the stored energy during time period t, respectively; δ OM The unit power operation and maintenance cost of energy storage;

[0183] c) The cost of energy interaction with the distribution network, as shown in equation (14) above;

[0184] (b) Constraints

[0185] a) Shared energy storage constraints include inherent constraints on shared energy storage charging and discharging, upper and lower limits of energy capacity, and constraints on the relationship between energy capacity in two adjacent time periods, as shown in equation (25):

[0186]

[0187] In the formula: E SES (t) represents the capacity of shared energy storage within time period t; η SES,C η dSES,D These represent the charging and discharging efficiencies of energy storage, respectively; P SES,C (t), P SES,D (t) represents the charging and discharging power of the stored energy during time period t; E SES (0) and E SES (24) represent the energy stored at the beginning and end of the scheduling cycle, respectively; This indicates the upper limit of the energy storage capacity. Indicates the upper limit of the charging and discharging power of energy storage; U SES,C (t), U SES,D (t) Ensure that energy storage can only be charged or discharged at any given time during the scheduling cycle, U SES,C (t) = 1 indicates charging, and 0 indicates discharging.

[0188] b) Shared energy storage power balance constraints, as shown in equation (26):

[0189] This utilizes the shared energy storage optimized in step 2 to charge and discharge power P to microgrid i. SES,C,i (t), P SES,D,i (t);

[0190]

[0191] In the formula: U buy,SES (t), U sell,SES (t) Ensure that energy storage can only purchase or sell electricity to the distribution network at any given time during the dispatch cycle, U buy,SES When (t) is 1, it indicates purchasing electricity; when it is 0, it indicates selling electricity. buy,SES (t), P sell,SES (t) represents the power purchased and sold from the distribution network by the shared energy storage during time period t.

[0192] 2) Establish a multi-micronet scheduling model

[0193] (a) The objective function is shown in equation (27):

[0194] C MMG =min(CTU -C MMG,DN (27);

[0195] In the formula: C MMG The total cost of multiple microgrids; C TU For the start-up, shutdown, and coal consumption costs of small coal-fired power units; C MMG,DN This refers to the energy exchange costs between the microgrids and the distribution network. The formulas for calculating each cost are as follows:

[0196] a) The start-up, shutdown, and coal consumption costs of small coal-fired power units are shown in equation (28):

[0197]

[0198] In the formula: P TU,i (t) represents the dispatch output of small and medium-sized coal-fired power units in microgrid i during time period t; a1, b1, and c1 are the coal consumption coefficients of small and medium-sized coal-fired power units, respectively; U TU,i (t) is a binary variable, U TU,i (t) = 1 indicates startup, U TU,i (t) = 0 indicates shutdown; δ TU represents the start-up and shutdown cost coefficient of the unit; i = 1, 2, 3…N, where N is a positive integer;

[0199] b) The cost of energy interaction with the distribution network, as shown in equation (13) above;

[0200] (b) Constraints

[0201] a) Operating constraints for small coal-fired power units, as shown in equation (29):

[0202]

[0203] In the formula: These are the upper and lower limits of the output power of small coal-fired power units; These represent the upper and lower limits of the ramp-up power output of small coal-fired power units.

[0204] b) Constraints on inter-microgrid power trading, as shown in equation (30):

[0205]

[0206] In the formula: The upper limit of electrical power for microgrid i to interact with other microgrids.

[0207] c) Microgrid power balance constraints, as shown in equation (31):

[0208]

[0209] In the formula: P ij(t) represents the interaction power between microgrids i and j in time period t, P ij (t)>0 indicates that microgrid i sells electricity to microgrid j, U buy,i (t), U sell,i (t) Ensure that the microgrid can only interact with the distribution network at a time during the dispatch cycle, U buy,i (t) = 1 indicates purchasing electricity, and 0 indicates selling electricity.

[0210] d) Tie line power constraints, as shown in equation (32):

[0211]

[0212] In the formula: This represents the maximum transmission power of the tie line;

[0213] Step 4: Based on the optimized calculation of the charging and discharging power of energy storage to each microgrid, a genetic algorithm with nested solvers is used to solve the master-slave game scheduling model to obtain the optimal scheduling scheme; specifically:

[0214] Step 4.1: Initialize (update) the genetic population parameters and generate (update) the time-of-use electricity price for the distribution network;

[0215] Step 4.2: Use Gurobi to solve for the minimum cost of shared energy storage and multiple microgrids respectively, and record the optimal power purchase and sale strategy for distribution network and shared energy storage and the optimal power purchase and sale strategy for distribution network and multiple microgrids.

[0216] Step 4.3: Calculate the minimum cost of the distribution network with global optimality of the genetic population, and record the electricity price;

[0217] Step 4.4: Determine if the cost is lower than the previously recorded cost. If yes, update the optimal cost of the distribution network; otherwise, return to step 4.1.

[0218] Step 4.5: Determine if the iteration limit has been reached. If yes, output the optimal scheduling strategy for each subject; otherwise, return to step 4.1.

[0219] Step 4.6: Allocate costs across multiple micronets based on the Shapley value. The specific calculation process is as follows:

[0220] The Shapley value method is a commonly used cost allocation method. If the total cost C of a multi-micronetwork is calculated based on the conventional Shapley value method... MMG If the cost is allocated, the cost allocated to microgrid i can be expressed as equation (33):

[0221]

[0222] In the formula: N is the number of microgrids; S i S represents the set consisting of all subsets of micronet i;i = {1,2,…,N}; |s| represents the number of micronets contained in consortium s; v(s) represents the cost generated by consortium s; v(s\{i}) represents the cost generated by consortium s after removing micronet i.

[0223] This invention employs an improved Shapley value method, specifically utilizing the interaction power P between microgrid i and microgrid j. ij (t) Measure contributions to the consortium, and thus redefine the contribution share B of microgrid i. i As shown in equation (34):

[0224]

[0225] The sum of the proportions of N microgrids satisfies The difference ΔB between the conventional and improved proportions of microgrid i i =B i -1 / N, using ΔB i The allocation results of the conventional Shapley value method can be corrected by a correction amount of ΔB. i The allocated cost of microgrid i after correction is shown in equation (35):

[0226]

[0227] Example 1

[0228] To verify the effectiveness of the established model and method, this invention conducted simulation tests using an improved IEEE 33-node system. Node 1 of the distribution network is connected to the upstream grid and is equipped with one wind farm and one photovoltaic power station, connected to nodes 19 and 9 respectively. Three microgrids, MG1, MG2, and MG3, are connected to nodes 29, 32, and 15 respectively, and the shared energy storage is connected to node 13. Table 1 shows the parameters of the distribution network and the shared energy storage. During the simulation, the population size of the genetic algorithm was set to 20, and the number of iterations was set to 100.

[0229] Table 1. Distribution Network and Shared Energy Storage Parameters

[0230]

[0231]

[0232] Table 2 shows the time-of-use electricity prices set for the distribution network. Prices during off-peak, flat, and peak periods show a gradual upward trend. Within each specific time period, the electricity price sold to users by the distribution network is always higher than the price they purchase. This pricing mechanism effectively ensures the economic and stable operation of the distribution network.

[0233] Table 2 Time-of-use pricing for distribution networks

[0234]

[0235] Example 2

[0236] Figures 3-5 The graph shows the net load fluctuations of each microgrid before and after shared energy storage charging and discharging. The black bars represent the charging and discharging power of energy storage in microgrid i. A value greater than 0 indicates energy storage charging, resulting in an increase in the microgrid's net load; a value less than 0 indicates energy storage discharging, resulting in a decrease in the microgrid's net load. It is evident that the established model effectively incentivizes the role of energy storage and effectively mitigates the net load fluctuations caused by the anti-peak-shaving characteristics of new energy sources. This mechanism helps balance the supply and demand differences in the power grid, significantly improving the stability and reliability of the grid.

[0237] Example 3

[0238] To verify the effectiveness of the proposed one-leader-two-follower game model, the distribution network is taken as the leader, and the question of whether shared energy storage and multiple microgrids respond as followers to the time-of-use pricing set by the distribution network is considered. Three operating schemes are set up for comparative analysis, as shown in Table 3. Based on the solution results, the three-party costs of the three operating schemes are compared, as shown in Table 4.

[0239] Table 3 Comparison of the three operating schemes

[0240] 1 yes yes 2 no yes 3 no no

[0241] Table 4. Comparison of Tripartite Costs for Three Operating Schemes

[0242] 1 59624 2173 8664 2 60213 2456 8672 3 63452 3863 10457

[0243] In Scheme 2, the distribution network and the microgrids form a master-slave game. Compared to Scheme 1, the shared energy storage cannot respond to the time-of-use pricing set by the distribution network and cannot utilize its remaining capacity after charging and discharging the microgrids. Therefore, the costs of the distribution network and the shared energy storage increase, while the cost of the microgrids remains almost unchanged. In Scheme 3, the shared energy storage and the microgrids are dispatched separately according to a fixed price. Compared to Scheme 1, the shared energy storage and the microgrids cannot respond to the time-of-use pricing set by the distribution network and can only adjust their own power purchase and sale strategies with the distribution network, i.e., purchasing electricity when the price is low and selling electricity when the price is high. Therefore, the costs of all three parties increase. Thus, it is proven that the strategy of Scheme 1, where the distribution network acts as the leader and the shared energy storage and microgrids act as followers, forming a master-slave game, is the most reasonable.

[0244] Example 4

[0245] The effectiveness analysis of the improved Shapley value method is as follows:

[0246] Table 5 shows a cost comparison between microgrids before and after joining the alliance. The total operating cost after the alliance was established was RMB 8,664.06, representing a 3.6% decrease compared to the total cost of each microgrid operating independently before the alliance. Furthermore, the cost shared by each microgrid after the alliance was also reduced compared to independent operation, which fully demonstrates that the alliance reached a game equilibrium and optimized economic benefits.

[0247] Table 5. Cost Comparison Before and After Microgrid Joins the Alliance

[0248] Micronet 1 2318.09 2149.63 Microgrid 2 3052.88 3021.97 Micronet 3 3625.92 3492.40 total 8996.89 8664.06

[0249] Table 6 compares the traditional Shapley algorithm with the improved Shapley algorithm. It can be seen that, compared to the traditional Shapley algorithm, the improved Shapley algorithm measures contribution to the coalition by the interaction power between micronets. Because micronet 2 has lower interaction power with other micronets, its cost increases by 14.7%. Micronets 1 and 3 have higher interaction power with other micronets, thus reducing their costs by 12.0% and 5.06% respectively. The overall cost of multiple micronets decreases by 1.06%, demonstrating not only the advantages of individual micronets in the coalition but also providing an effective approach to the problem of multi-micronet benefit distribution.

[0250] Table 6 Comparison of Traditional Shapley Algorithm and Improved Shapley Algorithm

[0251]

Claims

1. A multi-agent game-based optimal scheduling method for distribution network-shared energy storage-multiple microgrids, characterized in that, The specific steps are as follows: Step 1: Construct a multi-objective optimization configuration model for shared energy storage across multiple microgrids, taking into account both investment costs and load fluctuations; specifically: Step 1.1: Establish two optimization objectives: minimizing energy storage investment cost and minimizing net load standard deviation; (1) Microgrid i The energy storage cost is the lowest, as shown in equation (1): （1）； In the formula: C SES.i For micro-network i Energy storage investment cost, C INV.i For micro-network i The energy storage investment cost borne by C OM.i For micro-network i The energy storage operation and maintenance costs borne; The formulas for calculating each cost are as follows: 1) Microgrid i The energy storage investment cost borne is shown in equation (2): （2）； In the formula: λ P , λ E These represent the unit power investment cost and unit capacity investment cost of shared energy storage, respectively. , They represent microgrids respectively. i The maximum required energy storage power and capacity; T E Indicates the lifespan of shared energy storage; 2) Microgrid i The energy storage operation and maintenance costs borne are shown in equation (3): （3）； In the formula: P SES,C,i ( t ), P SES,D,i ( t ) represent time periods respectively t Internal energy storage to microgrid i The charging and discharging power; δ OM For micro-network i Bearing the unit power operation and maintenance cost of energy storage; T It is the scheduling cycle; It is a time interval; (2) Microgrid i The net load standard deviation is the smallest, as shown in equations (4)-(5): （4）； （5）； In the formula: S i For micro-network i The standard deviation of net load; P load,i ( t )for t Time-based microgrid i The load value; P WT,i ( t ), P PV,i ( t ) are respectively t Time-based microgrid i The wind and solar power absorbed; For micro-network i The equivalent load average; Step 1.2: Determine the constraints for the two optimization objectives, and optimize the charging and discharging power of the shared energy storage for each microgrid; the constraints include those related to the microgrid. i Shared coupling constraints and charge / discharge power constraints; (1) With microgrid i The shared coupling constraints are shown in equation (6): （6）； For micro-network i The upper limit of energy storage power demand is shown in equation (7): （7）； In the formula: , These represent energy storage to microgrids. i The upper limit of charging and discharging power; U SES,C,i ( t A value of 1 indicates charging, and a value of 0 indicates discharging. U SES,D,i ( t A value of 1 indicates discharging, and a value of 0 indicates charging; (2) Charge and discharge power constraints, as shown in equations (8)-(10): （8）； （9）； （10）； In the formula: η SES,C , η SES,D These represent the charging and discharging efficiencies of energy storage, respectively. E i ( t () indicates a time period t Internal energy storage to microgrid i Storage capacity required for charging and discharging; For micro-network i Utilizing the upper limit of energy storage capacity demand, μ E Capacity margin for shared energy storage in microgrids; Step 2: The multi-objective particle swarm optimization algorithm is used to solve the multi-microgrid shared energy storage multi-objective optimization configuration model in Step 1, and the charging and discharging power of energy storage to each microgrid is optimized. Step 3: Establish a game-theoretic scheduling model with the distribution network as the leader and multiple microgrids and shared energy storage as followers; Step 4: Based on the optimized calculation of the charging and discharging power of energy storage to each microgrid, a genetic algorithm with nested solvers is used to solve the master-slave game scheduling model to obtain the optimal scheduling scheme.

2. The method for optimal scheduling of distribution network-shared energy storage-multiple microgrids based on multi-agent game theory as described in claim 1, characterized in that, Step 2 specifically involves: Step 2.1, Initialization N The particle position, velocity, pbest, and gbest are set, with the number of iterations inter=0; Step 2.2, Calculate Initialization N The fitness values ​​of each population, i.e., the energy storage investment cost and the root mean square deviation of the net load of the microgrid; Step 2.3: Update the particle's velocity and position, set the dynamic inertia weight, and update the population's individual extreme value pbest; Step 2.4: Find the Pareto optimal solution for the new particle, merge it into the external archive set, find the Pareto optimal solution in the external archive set, and remove the non-Pareto optimal solutions; Step 2.5: Determine whether the number of optimal solutions in the external set exceeds the specified capacity m. If so, select m particles based on the crowding distance; otherwise, use the roulette wheel method to select the best. Step 2.6: The particle undergoes a small-probability mutation within a 30% range at its original position; Step 2.7: Determine whether inter=inter+1 and inter=intermax are satisfied. If yes, output the Pareto optimal solution set and record the optimal charging and discharging strategy for each microgrid. Otherwise, return to step 2.

3.

3. The method for optimal scheduling of distribution network-shared energy storage-multiple microgrids based on multi-agent game theory as described in claim 2, characterized in that, Step 3 specifically involves: (1) Establish the main distribution network dispatch model 1) Objective function The decision variable for optimal dispatching of distribution networks is the purchase and sale price of electricity, and the objective of optimal operation is the total cost. C DN The minimum is as shown in equation (11): （11）； In the formula: C DN The total cost of the distribution network; C grid The cost of purchasing electricity from the main grid for the distribution network; C MMG,DN The cost of energy interaction between the distribution network and multiple microgrids; C SES,DN The cost of energy interaction between the distribution network and shared energy storage; the formulas for calculating each cost are as follows: (a) The cost of purchasing electricity from the main grid is shown in equation (12): （12）； In the formula: δ sell,grid ( t () are time periods t The electricity price on the main grid; P buy,DN ( t (Time period) t The internal distribution network purchases power from the main grid; (b) The energy interaction cost with the multi-microgrid is shown in Equation (13): （13）； In the formula: δ buy,DN ( t (Time period) t The optimized distribution network uses electricity purchase prices from microgrids and shared energy storage; δ sell,DN ( t This refers to the optimized electricity sales price from the distribution network to microgrids and shared energy storage. P buy,i ( t ), P sell,i ( t (Time period) t Intranet i From the power purchased and sold in the distribution network; (c) The energy interaction cost with shared energy storage is shown in Equation (14): （14）； In the formula: P buy,SES ( t ), P sell,SES ( t (Time period) t Shared energy storage utilizes the power purchased and sold from the power distribution network; 2) Constraints The constraints include power flow balance constraints, state variable constraints, distributed energy access constraints, and time-of-use pricing constraints. (a) Power flow balance constraint, as shown in equation (15): （15）； In the formula: n This refers to the number of nodes in the distribution network. P p , Q p , U p They are nodes p Injected active and reactive power and voltage amplitude; G pq , B pq , δ pq They are nodes p and q The conductance, susceptance, and voltage phase difference between them; U q It is a node q The voltage amplitude; (b) State variable constraints, as shown in equation (16): （16）； In the formula: , They are nodes p The upper and lower limits of the injected voltage amplitude; G pq , B pq , δ pq They are nodes p and q The conductance, susceptance, and voltage phase difference between them; S l , These are distribution network branches l Power and power limit; (c) Access to distributed energy constraints, as shown in equation (17): （17）； In the formula: P DG ( t ), Q DG ( t () are time periods t The active and reactive power output of distributed energy sources within the system; , Time periods t Upper and lower limits of active power output of internal distributed energy sources; , Time periods t Upper and lower limits of reactive power output of internal distributed energy resources; (d) Time-of-use pricing constraints; The electricity purchase and sale price set by the distribution network for microgrids and shared energy storage is a time-of-use price. The electricity sales price and purchase price during off-peak, normal, and peak periods satisfy the following relationship, as shown in equations (18)-(19): （18）； （19）； In the formula: T v , T f , T p These are respectively the off-peak period, the normal period, and the peak period; δ buyv,DN ( t ), δ buyf,DN ( t )and δ buyp,DN ( t These are the distribution network purchase prices for off-peak, normal, and peak periods, respectively. δ sellv,DN ( t ), δ sellf,DN ( t )and δ sellp,DN ( t These are the distribution network electricity prices for off-peak, normal, and peak periods, respectively. The electricity sales price of the distribution network must be higher than the electricity purchase price at each time period, as shown in equation (20): （20）； The average electricity sales price within the dispatch period T should not exceed the upper limit of the electricity sales price of the distribution network, as shown in equation (21): （21）； In the formula: This is the upper limit for the electricity sales price in the distribution network; (2) Establish a multi-microgrid scheduling model that includes shared energy storage; 1) Establish a shared energy storage scheduling model (a) Objective function Step 2 has optimized the shared energy storage to microgrid i Charging and discharging power P SES,C,i ( t ), P SES,D,i ( t Therefore, the decision variable of the shared energy storage dispatch model is the interaction power with the distribution network, and the objective of optimized operation is the total cost C. SES The minimum, as shown in equation (22): （22）； In the formula: C SES The total cost of shared energy storage; C INV Investment costs for shared energy storage; C OM The operation and maintenance costs of shared energy storage are calculated using the following formulas: a) Investment costs of shared energy storage C INV As shown in equation (23): （23）； In the formula: λ P and λ E These are the unit power investment cost and unit capacity investment cost of shared energy storage, respectively. and These are the power and capacity limits for shared energy storage, respectively. T E The lifespan of shared energy storage; b) The operation and maintenance cost of shared energy storage is shown in equation (24): （24）； In the formula, δ OM The unit power operation and maintenance cost of energy storage; P SES,C ( t ), P SES,D ( t ) represent time periods respectively t The charging and discharging power of the internal energy storage; c) The cost of energy interaction with the distribution network, as shown in equation (14) above; (b) Constraints a) Shared energy storage constraints, as shown in equation (25): （25） In the formula: E SES ( t () indicates a time period t Internal shared energy storage capacity; η SES,C , η dSES,D These represent the charging and discharging efficiencies of energy storage, respectively. E SES (0) and E SES (24) represent the energy stored at the beginning and end of the scheduling cycle, respectively; b) Shared energy storage power balance constraints, as shown in equation (26): （26）； In the formula: U buy,SES ( t A value of 1 indicates purchasing electricity, and a value of 0 indicates selling electricity. U sell,SES ( t A value of 0 indicates electricity purchase, and a value of 1 indicates electricity sale. 2) Establish a multi-micronet scheduling model (a) The objective function is shown in equation (27): （27）； In the formula: C MMG The total cost of multiple microgrids; C TU The start-up, shutdown, and coal consumption costs of small coal-fired power units; C MMG,DN The cost of energy interaction between the multi-microgrid and the distribution network; the formulas for calculating each cost are as follows: a) The start-up, shutdown, and coal consumption costs of small coal-fired power units are shown in equation (28): (28); In the formula: P TU,i ( t (Time period) t Intranet i Dispatch and output of small and medium-sized coal-fired power units; a 1. b 1. c 1 represents the coal consumption coefficient of a small coal-fired power unit; U TU,i ( t )=1 indicates startup. U TU,i ( t )=0 indicates that the machine is stopped; δ TU This is the start-up and shutdown cost coefficient for the generating unit; b) The cost of energy interaction with the distribution network, as shown in equation (13) above; (b) Constraints a) Operating constraints for small coal-fired power units, as shown in equation (29): （29）； In the formula: , These are the upper and lower limits of the output power of small coal-fired power units; , These are the upper and lower limits of the ramp-up power output of small coal-fired power units; b) Constraints on inter-microgrid power trading, as shown in equation (30): （30）； In the formula: For micro-network i Upper limit of electrical power for interaction with other microgrids; c) Microgrid power balance constraints, as shown in equation (31): （31）； In the formula: P ij ( t (Time period) t microgrid i , j Interaction power between P ij ( t )>0 indicates microgrid i To Micro Network j Electricity sales U buy,i ( t A value of 1 indicates purchasing electricity, and a value of 0 indicates selling electricity. U sell,i ( t A value of 0 indicates electricity purchase, and a value of 1 indicates electricity sale. d) Tie line power constraints, as shown in equation (32): （32）； In the formula: This represents the maximum transmission power of the tie line.

4. The method for optimal scheduling of distribution network-shared energy storage-multiple microgrids based on multi-agent game theory as described in claim 3, characterized in that, Step 4 specifically involves: Step 4.1: Initialize genetic population parameters and generate time-of-use electricity prices for the distribution network; Step 4.2: Use Gurobi to solve for the minimum cost of shared energy storage and multiple microgrids respectively, and record the optimal power purchase and sale strategy for distribution network and shared energy storage and the optimal power purchase and sale strategy for distribution network and multiple microgrids. Step 4.3: Calculate the minimum cost of the distribution network with global optimality of the genetic population, and record the electricity price; Step 4.4: Determine if the cost is lower than the previously recorded cost. If yes, update the optimal cost of the distribution network; otherwise, return to step 4.

1. Step 4.5: Determine if the iteration limit has been reached. If yes, output the optimal scheduling strategy for each subject; otherwise, return to step 4.

1. Step 4.6: Allocate costs across multiple microgrids based on the Shapley value, i.e., utilize microgrids. i With micro-network j Inter-transaction power P ij ( t ) Measure contributions to the alliance, and thus redefine microgrids i The proportion of B i As shown in equation (34): （34）； in ，N The sum of the proportions of individual microgrids satisfies micronet i The difference between the conventional and improved proportions ,use The allocation result is adjusted by the amount of adjustment. The corrected microgrid i The allocated cost is shown in equation (35): （35）。

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