Multi-microgrid system optimization control method based on master-slave game

Abstract

The invention provides a multi-microgrid system optimization control method based on a master-slave game. The method comprises the following steps: acquiring the electricity price of a multi-microgrid aggregator for purchasing electricity from a power distribution network, the electricity price of the multi-microgrid aggregator for selling electricity to each microgrid, energy storage charging efficiency and energy storage discharging efficiency, and the energy storage capacity of energy storage equipment; obtaining an equilibrium solution by using an equivalent mixed integer linear programming model of a pre-obtained master-slave game model; configuring an electricity price making strategy of the multi-microgrid aggregator and an electricity purchasing and selling strategy of each microgrid based on the equilibrium solution; the equivalent mixed integer linear programming model of the master-slave game model is obtained by converting a pre-constructed master-slave game model into a mixed integer linear programming problem; in the master-slave game model, the upper-layer leader and the lower-layer follower perform master-slave games with own benefit maximization as a target. The master-slave game model is converted into a mixed integer linear programming problem, so that a game equilibrium solution of a single optimization problem can be solved, and optimal configuration of the micro-grid system is realized.

Description

Technical Field

[0001] The present invention relates to the field of new energy, and in particular to a multi-microgrid system optimization control method based on master-slave game. Background Art

[0002] As renewable energy sources like photovoltaics and wind power are connected to the grid and combined with energy storage devices to form microgrids, which interact deeply with distribution networks, and the number of microgrids increases, multi-microgrid aggregators will play a key role in the operation and management of these microgrids. Therefore, optimizing the configuration of these multi-microgrid systems has become a pressing issue in this field. Summary of the Invention

[0003] The present invention provides a multi-microgrid system optimization control method based on master-slave game.

[0004] In a first aspect, an embodiment of the present invention provides a multi-microgrid system optimization control method, comprising:

[0005] Obtain the electricity price that the multi-microgrid aggregator purchases from the distribution network, the electricity price that the multi-microgrid aggregator sells to each microgrid, the energy storage charging efficiency and energy storage discharge efficiency, and the energy storage capacity of the energy storage equipment;

[0006] Based on the electricity price that the multi-microgrid aggregator purchases from the distribution network, the electricity price that the multi-microgrid aggregator sells to each microgrid, the energy storage charging efficiency and energy storage discharge efficiency, and the energy storage capacity of the energy storage equipment, an equilibrium solution is obtained using the equivalent mixed integer linear programming model of the pre-obtained master-slave game model.

[0007] Based on the balanced solution, configure the electricity price setting strategy of the multi-microgrid aggregator and the electricity purchase and sales strategy of each microgrid;

[0008] Among them, the equivalent mixed integer linear programming model of the master-slave game model is obtained by converting the pre-constructed master-slave game model into a mixed integer linear programming problem; in the master-slave game model, the upper leader is the multi-microgrid aggregator, and the lower followers are the microgrids in the multi-microgrid system. The upper leader and the lower followers respectively play the master-slave game with the goal of maximizing their own interests; the equilibrium solution includes the electricity purchase price and electricity sales price of the microgrid formulated by the multi-microgrid aggregator, the power of electricity purchased from the distribution network and the power of electricity sold by the multi-microgrid aggregator, the power of electricity purchased from the multi-microgrid aggregator and the power of electricity sold, the energy storage charging power and the energy storage discharging power, the interaction power between the microgrid and the multi-microgrid aggregator, and the power actually absorbed by renewable energy.

[0009] In some implementations, the method further includes:

[0010] Constructing a master-slave game model, the master-slave game model including a multi-microgrid aggregator electricity price optimization model and a microgrid operation optimization model, wherein the multi-microgrid aggregator electricity price optimization model aims to maximize the multi-microgrid aggregator's revenue; and the microgrid operation optimization model aims to maximize the microgrid's revenue;

[0011] Converting the master-slave game model into a mixed integer linear programming problem to obtain an equivalent mixed integer linear programming model of the master-slave game model;

[0012] The step of converting the master-slave game model into a mixed integer linear programming problem to obtain an equivalent mixed integer linear programming model of the master-slave game model includes:

[0013] Applying KKT conditions to perform equivalent transformation on the microgrid operation optimization model, and transforming the microgrid operation optimization model into a linear programming problem;

[0014] The microgrid operation optimization model converted into a linear programming problem is merged with the multi-microgrid aggregator electricity price optimization model to obtain an equivalent mixed integer linear programming model of the master-slave game model.

[0015] In some implementations, the multi-microgrid aggregator electricity price optimization model satisfies the power purchase and sale constraints when the multi-microgrid aggregator interacts with the distribution network. Before merging the microgrid operation optimization model converted into a linear programming problem with the multi-microgrid aggregator electricity price optimization model to obtain an equivalent mixed integer linear programming model of the master-slave game model, the method further includes:

[0016] The power purchase and sales constraints when multi-microgrid aggregators interact with the distribution network are converted into equivalent linear power.

[0017] In some implementations, the equivalent mixed integer linear programming model of the master-slave game model is expressed as follows:

[0018]

[0019] Where N represents the number of microgrids in the microgrid system, i represents the i-th microgrid, represents the maximum charging power of energy storage, T represents the length of a scheduling cycle, t represents the time, express The Lagrangian factor of Indicates the maximum discharge power of energy storage, express Lagrangian factor, SOC ES,i,0 represents the initial remaining capacity of the energy storage device scheduling period, Indicates the minimum remaining capacity of the energy storage device, express The Lagrangian factor of Indicates the maximum remaining capacity of the energy storage device, express The Lagrangian factor, P DG,i,t Represents the actual output power of renewable energy, Indicates P DG,i,t The Lagrangian factor of Indicates the maximum power sold by the microgrid, express The Lagrangian factor of Indicates the maximum power purchase of the microgrid, express The Lagrangian factor, P Load,i,t Indicates load power, μ eq,i,t Indicates P Load,i,t The dual variable of represents the price of electricity purchased by the multi-microgrid aggregator from the distribution network, It represents the power that the multi-microgrid aggregator purchases from the distribution network. represents the electricity price sold by the multi-microgrid aggregator, It represents the power sold by the multi-microgrid aggregator, and Δt represents the length of unit time.

[0020] In some implementations, the constraints of the equivalent mixed integer linear programming model of the master-slave game model include:

[0021] Constraints on microgrid electricity purchase and sales prices set by multi-microgrid aggregators;

[0022] The restriction that the electricity sales price is higher than the electricity purchase price;

[0023] Power purchase and sales constraints when multi-microgrid aggregators interact with the distribution grid;

[0024] The relationship constraints between the interaction power between the microgrid and the multi-microgrid aggregator and the power purchased and sold by the microgrid;

[0025] The constraints that the KKT conditions must satisfy.

[0026] In some implementations, applying the KKT condition to perform equivalent transformation on the microgrid operation optimization model, transforming the microgrid operation optimization model into a linear programming problem, includes:

[0027] Construct the Lagrangian function and analyze the constraints that the microgrid operation optimization model needs to meet based on the KKT conditions;

[0028] When the electricity price is given, the microgrid operation optimization model is relaxed into a linear programming problem. According to the strong duality theorem, the optimal solution of the linear programming problem corresponding to the microgrid operation optimization model is determined to be consistent with the optimal solution of the dual problem.

[0029] In some implementations, the microgrid operation optimization model satisfies the complementary constraints of energy storage charging and discharging states and the complementary constraints of electricity purchasing and selling states, and the complementary constraints of energy storage charging and discharging states and the complementary constraints of electricity purchasing and selling states have non-convex characteristics; before applying the KKT condition to perform an equivalent transformation on the microgrid operation optimization model and converting the microgrid operation optimization model into a linear programming problem, the complementary constraints of energy storage charging and discharging states and the complementary constraints of electricity purchasing and selling states are also subjected to convex optimization processing.

[0030] In a second aspect, an embodiment of the present invention provides a multi-microgrid system optimization control device, comprising:

[0031] An acquisition module is used to obtain the electricity price that the multi-microgrid aggregator purchases from the distribution network, the electricity price that the multi-microgrid aggregator sells to each microgrid, the energy storage charging efficiency and energy storage discharging efficiency, and the energy storage capacity of the energy storage equipment;

[0032] A solution module is used to obtain an equilibrium solution based on the electricity price purchased by the multi-microgrid aggregator from the distribution network, the electricity price sold by the multi-microgrid aggregator to each microgrid, the energy storage charging efficiency and energy storage discharge efficiency, and the energy storage capacity of the energy storage equipment, using an equivalent mixed integer linear programming model of the pre-obtained master-slave game model;

[0033] A configuration module, configured to configure the electricity price setting strategy of the multi-microgrid aggregator and the electricity purchase and sales strategy of each microgrid based on the balanced solution;

[0034] The equivalent mixed integer linear programming model of the master-slave game model is obtained by converting the pre-constructed master-slave game model into a mixed integer linear programming problem; in the master-slave game model, the upper-level leader is the multi-microgrid aggregator, and the lower-level followers are the microgrids in the multi-microgrid system. The upper-level leader and the lower-level followers respectively play the master-slave game with the goal of maximizing their own interests; the equilibrium solution includes the electricity price set by the multi-microgrid aggregator for microgrid purchases. and electricity prices The power purchased by multi-microgrid aggregators from the distribution grid and the power of electricity sold The power that the microgrid purchases from multi-microgrid aggregators and the power of electricity sold Energy storage charging power and energy storage discharge power Interaction power between microgrids and multi-microgrid aggregators The power actually consumed by renewable energy P DG,u .

[0035] In some implementations, the multi-microgrid system optimization control device further includes:

[0036] A construction module is used to construct a master-slave game model, wherein the master-slave game model includes a multi-microgrid aggregator electricity price optimization model and a microgrid operation optimization model, wherein the multi-microgrid aggregator electricity price optimization model aims to maximize the multi-microgrid aggregator's revenue; and the microgrid operation optimization model aims to maximize the microgrid's revenue;

[0037] a conversion module, configured to convert the master-slave game model into a mixed integer linear programming problem, and obtain an equivalent mixed integer linear programming model of the master-slave game model;

[0038] Wherein, the conversion module includes:

[0039] An equivalent transformation submodule, configured to perform equivalent transformation on the microgrid operation optimization model by applying KKT conditions, thereby transforming the microgrid operation optimization model into a linear programming problem;

[0040] The merging submodule is used to merge the microgrid operation optimization model converted into a linear programming problem with the multi-microgrid aggregator electricity price optimization model to obtain an equivalent mixed integer linear programming model of the master-slave game model.

[0041] In some implementations, the multi-microgrid aggregator electricity price optimization model satisfies the power purchase and sale constraints when the multi-microgrid aggregator interacts with the distribution network electricity; the equivalent conversion submodule is also used to: perform equivalent linear conversion processing on the power purchase and sale constraints when the multi-microgrid aggregator interacts with the distribution network electricity.

[0042] In some implementations, the equivalent mixed integer linear programming model of the master-slave game model is expressed as follows:

[0043]

[0044] Where N represents the number of microgrids in the microgrid system, i represents the i-th microgrid, represents the maximum charging power of energy storage, T represents the length of a scheduling cycle, t represents the time, express The Lagrangian factor of Indicates the maximum discharge power of energy storage, express Lagrangian factor, SOC ES,i,0 represents the initial remaining capacity of the energy storage device scheduling period, Indicates the minimum remaining capacity of the energy storage device, express The Lagrangian factor of Indicates the maximum remaining capacity of the energy storage device, express The Lagrangian factor, P DG,i,t Represents the actual output power of renewable energy, Indicates P DG,i,t The Lagrangian factor of Indicates the maximum power sold by the microgrid, express The Lagrangian factor of Indicates the maximum power purchase of the microgrid, express The Lagrangian factor, P Load,i,t Indicates load power, μ eq,i,t Indicates P Load,i,t The dual variable of represents the price of electricity purchased by the multi-microgrid aggregator from the distribution network, It represents the power that the multi-microgrid aggregator purchases from the distribution network. represents the electricity price sold by the multi-microgrid aggregator, It represents the power sold by the multi-microgrid aggregator, and Δt represents the length of unit time.

[0045] In some implementations, the constraints of the equivalent mixed integer linear programming model of the master-slave game model include:

[0046] Constraints on microgrid electricity purchase and sales prices set by multi-microgrid aggregators;

[0047] The restriction that the electricity sales price is higher than the electricity purchase price;

[0048] Power purchase and sales constraints when multi-microgrid aggregators interact with the distribution grid;

[0049] The relationship constraints between the interaction power between the microgrid and the multi-microgrid aggregator and the power purchased and sold by the microgrid;

[0050] The constraints that the KKT conditions must satisfy.

[0051] In some implementations, the equivalent conversion submodule is used to:

[0052] Construct the Lagrangian function and analyze the constraints that the microgrid operation optimization model needs to meet based on the KKT conditions;

[0053] When the electricity price is given, the microgrid operation optimization model is relaxed into a linear programming problem. According to the strong duality theorem, the optimal solution of the linear programming problem corresponding to the microgrid operation optimization model is determined to be consistent with the optimal solution of the dual problem.

[0054] In some implementations, the microgrid operation optimization model satisfies a complementary constraint between energy storage charging and discharging states and a complementary constraint between electricity purchase and sales states, and the complementary constraint between energy storage charging and discharging states and the complementary constraint between electricity purchase and sales states have non-convex characteristics; the device further comprises:

[0055] The optimization module is used to perform an equivalent transformation on the microgrid operation optimization model under the KKT condition, and before converting the microgrid operation optimization model into a linear programming problem, perform convex optimization processing on the complementary constraints of the energy storage charging and discharging states and the complementary constraints of the power purchase and sales states.

[0056] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by at least one processor, the method described in the first aspect is implemented.

[0057] In a fourth aspect, an embodiment of the present invention provides an electronic device, comprising a memory and at least one processor, wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in the first aspect is implemented.

[0058] The embodiments of the present invention can at least bring the following beneficial effects:

[0059] The present invention provides an interactive framework between a multi-microgrid aggregator and multiple microgrids. In a master-slave game model with one master and multiple followers between the upper-level multi-microgrid aggregator and the lower-level multi-microgrids, both the multi-microgrid aggregator (leader) and the microgrids (followers) participate in the game with the goal of maximizing their own interests. Since the master-slave game model is converted into a mixed integer linear programming problem, the game equilibrium solution of a single optimization problem can be solved, thereby achieving optimal configuration of the microgrid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope.

[0061] Figure 1 1 is a schematic diagram of the structure of a multi-microgrid system provided by an embodiment of the present invention;

[0062] Figure 2 This is a flow chart of a multi-microgrid system optimization control method provided by an embodiment of the present invention;

[0063] Figure 3 This is a schematic diagram of a master-slave game structure provided by an embodiment of the present invention;

[0064] Figure 4 This is a schematic diagram of a typical structure of a microgrid provided by an embodiment of the present invention;

[0065] Figure 5 These are the DG and load power curves of three typical microgrid scenarios provided by the embodiments of the present invention;

[0066] Figure 6This is a multi-microgrid aggregated commercial electricity purchase and sales price curve provided by an embodiment of the present invention;

[0067] Figure 7 It is the optimal electricity price curve formulated by the upper-layer multi-microgrid aggregator provided by the embodiment of the present invention;

[0068] Figure 8 The optimal energy storage control strategy for the lower photovoltaic microgrid, wind power microgrid, and photovoltaic + wind power microgrid provided by the embodiment of the present invention;

[0069] Figure 9 This is the electricity purchase and sales strategy provided by the embodiment of the present invention;

[0070] Figure 10 It is the revenue of the microgrid aggregator and the cost of the three microgrids provided by the embodiment of the present invention. DETAILED DESCRIPTION

[0071] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0072] Example 1

[0073] This embodiment provides a multi-microgrid system optimization control method, such as Figure 2 Shown, including:

[0074] Step S101: Obtain the electricity price that the multi-microgrid aggregator purchases from the distribution network The price of electricity sold by the multi-microgrid aggregator to each microgrid Energy storage charging efficiency and energy storage discharge efficiency Energy storage capacity E of energy storage equipment ES,i .

[0075] Step S102: The electricity price purchased from the distribution network by the multi-microgrid aggregator The price of electricity sold by the multi-microgrid aggregator to each microgrid Energy storage charging efficiency and energy storage discharge efficiency Energy storage capacity E of energy storage equipment ES,i, using the equivalent mixed integer linear programming model of the pre-obtained master-slave game model, the equilibrium solution is obtained.

[0076] Step S103: configuring the electricity price setting strategy of the multi-microgrid aggregator and the electricity purchase and sales strategy of each microgrid based on the balanced solution.

[0077] Among them, the equivalent mixed integer linear programming model of the master-slave game model is obtained by converting the pre-constructed master-slave game model into a mixed integer linear programming problem; in the master-slave game model, the upper leader is the multi-microgrid aggregator, and the lower followers are the microgrids in the multi-microgrid system. The upper leader and the lower followers respectively play the master-slave game with the goal of maximizing their own interests; the equilibrium solution includes the electricity price set by the multi-microgrid aggregator for the microgrid to purchase electricity and electricity prices The power purchased by multi-microgrid aggregators from the distribution grid and the power of electricity sold The power that the microgrid purchases from multi-microgrid aggregators and the power of electricity sold Energy storage charging power and energy storage discharge power Interaction power between microgrids and multi-microgrid aggregators The power actually consumed by renewable energy P DG,u .

[0078] The method of this embodiment provides an interactive framework between a multi-microgrid aggregator and multiple microgrids. In a master-slave game model with one master and multiple followers between the upper-level multi-microgrid aggregator and the lower-level multi-microgrids, both the multi-microgrid aggregator (leader) and the microgrids (followers) participate in the game with the goal of maximizing their own interests. Since the master-slave game model is converted into a mixed integer linear programming problem, the game equilibrium solution of a single optimization problem can be solved, thereby achieving optimal configuration of the microgrid system.

[0079] The physical scenario of the master-slave game problem considered in this embodiment is that multiple microgrids containing distributed energy and energy storage equipment are connected to the distribution network, and the dynamic optimization configuration of the multi-microgrid aggregator game is considered. The system structure is as follows Figure 1 shown.

[0080] The participants in the master-slave game are the distribution multi-microgrid aggregator and microgrid users. The distribution network sets fixed electricity purchase and sales prices to the multi-microgrid aggregator, effectively eliminating the distribution network from the game. The multi-microgrid aggregator, acting as the link between the distribution network and microgrid users and the manager of the microgrid, sets electricity prices for microgrid users by collecting information about distribution network electricity prices and microgrid users' purchase and sales plans. Based on this information, smart terminals in the microgrid automatically calculate and execute the optimal operating strategy without intervention from the aggregator.

[0081] Based on the above operating mechanism, the operating model of a multi-microgrid aggregator is as follows:

[0082] 1) The microgrid determines its operating strategy based on the predicted PV power and load power, as well as the initial electricity price provided by the multi-microgrid aggregator. It then submits its electricity purchase and sales strategy to the multi-microgrid aggregator. The multi-microgrid aggregator aggregates and determines electricity demand based on the reported demand.

[0083] 2) The multi-microgrid aggregator signs a power purchase and sales contract with the distribution network in the market, determines the amount of electricity to be purchased and sold for each period, and publishes the electricity price information for each period to microgrid users. The average retail electricity price set by the multi-microgrid aggregator must not be higher than the average retail electricity price set by the distribution network to protect the interests of users.

[0084] Under the aforementioned operating mechanism, the pricing set by the multi-microgrid aggregator is coupled with the microgrid's operational strategy: the microgrid determines its power purchase and sales plan and operational strategy based on the price set by the multi-microgrid aggregator; the multi-microgrid aggregator sets its price based on the microgrid's power purchase and sales plan. According to the agreed-upon price, the average retail price is fixed. If the multi-microgrid aggregator raises the price during a certain period, prices during other periods will inevitably fall below the average. The microgrid's intelligent terminals will automatically adjust their power purchase and sales strategies, which in turn will affect the price set by the multi-microgrid aggregator. This naturally creates a master-slave relationship between the multi-microgrid aggregator and microgrid users.

[0085] In addition, in actual operation scenarios, multiple microgrids may form cooperative alliances due to the fact that they belong to the same investment entity and can reduce operating costs through energy complementarity.

[0086] The key to pricing and optimizing scheduling strategies for multi-microgrid systems lies in handling the master-slave game relationship between the multi-microgrid aggregator and the microgrids and finding the Nash equilibrium solution. In the master-slave game model, the upper leader is the multi-microgrid aggregator and the lower followers are the microgrids. The master-slave game structure is as follows: Figure 3 shown.

[0087] Therefore, this embodiment further includes a process of constructing an equivalent mixed integer linear programming model of the master-slave game model from step S201 to step S202:

[0088] Step S201: Construct a master-slave game model. The master-slave game model includes a multi-microgrid aggregator electricity price optimization model and a microgrid operation optimization model. The multi-microgrid aggregator electricity price optimization model aims to maximize the multi-microgrid aggregator's revenue; the microgrid operation optimization model aims to maximize the microgrid's revenue.

[0089] (a) Multi-microgrid aggregator electricity price optimization model

[0090] As the upper-level leader, the multi-microgrid aggregator mainly guides the lower-level follower microgrids to participate in price response by setting electricity prices. The revenue of the multi-microgrid aggregator is considered to be the profit obtained from selling electricity to users and the upper-level distribution network, and the difference between the cost of purchasing electricity from the microgrid and the upper-level distribution network, as shown in formula (1). The operating goal of the multi-microgrid aggregator is to maximize revenue. In addition, it should be noted that the multi-microgrid aggregator aggregates the electricity demand of multiple microgrids through power purchase contracts, which presents a deterministic demand to the distribution network, reducing the difficulties caused by randomness in the distribution network scheduling. Therefore, it will receive subsidies from the distribution network, but this part is not yet considered in the model.

[0091]

[0092] Where, and The electricity purchase price and electricity sales price of the microgrid set by the multi-microgrid aggregator at time t are decision variables; and The price at which the multi-microgrid aggregator purchases and sells electricity from the distribution network at time t is set by the upper-level distribution network and is a fixed value; and is the power purchased and sold by the multi-microgrid aggregator to the distribution network at time t; and is the power purchased and sold by microgrid i from the multi-microgrid aggregator at time t; T represents the length of a scheduling cycle, which is set to 24 hours; Δt is the unit time length, which is set to 1 hour in this embodiment; and N represents the number of microgrids in the microgrid cluster.

[0093] To fully protect the interests of microgrid users, the electricity prices set by multi-microgrid aggregators should meet the following constraints:

[0094]

[0095]

[0096]

[0097]

[0098] Where, represents the upper and lower bounds of the microgrid purchase and sales prices set by the multi-microgrid aggregator; Represents the average of the microgrid purchase and sales prices set by multiple microgrid aggregators. To encourage the consumption of renewable energy and the grid connection of surplus electricity, the sales price is generally higher than the purchase price:

[0099]

[0100] Considering that a multi-microgrid aggregator can only be in one of the states of selling or purchasing electricity from the distribution network at the same time, the following power constraints need to be met when the multi-microgrid aggregator interacts with the distribution network:

[0101]

[0102] Where, and It represents the maximum power purchase and maximum power sales when the multi-microgrid aggregator interacts with the distribution network. t Indicates the state of electricity purchase and sale by multiple microgrids. t For 1 hour, the microgrid aggregator purchases electricity from the distribution network and sells electricity vice versa.

[0103] Since the multi-microgrid aggregator does not include energy storage devices, it needs to meet the power balance constraints:

[0104]

[0105] In summary, the electricity price optimization model of the upper-level player multi-microgrid aggregator can be modeled as:

[0106]

[0107] Among them, the decision variables are z.

[0108] (b) Microgrid operation optimization model

[0109] As a lower-level follower, the microgrid responds to the optimal operation strategy based on the electricity price set by the multi-microgrid aggregator. For any microgrid i, the typical structure is as follows Figure 4 As shown in Figure 1, a microgrid consists of distributed energy sources (DG) (such as photovoltaic and wind power), local loads (LOAD), energy storage (ES), and a central controller. DN represents the distribution network.

[0110] The constraints of the microgrid operation optimization model include:

[0111] (1) Energy storage equipment operation constraints

[0112] During microgrid operation, energy storage needs to meet the following constraints:

[0113]

[0114]

[0115]

[0116] SOC ES,i,t+1 =SOC ES,i,t +ΔSOCES,i,t (13)

[0117]

[0118]

[0119]

[0120] Where, equations (10) and (11) are the charge and discharge power constraints of the energy storage device. and Indicates the energy storage charging and discharging power, and are the maximum charge and discharge power and discharge power of energy storage, respectively; Equation (12) represents the complementary constraint of energy storage charge and discharge states. At a single moment, energy storage can only be in one state, either charging or discharging. Constraints (13), (14), (15) and (16) represent constraints related to the remaining capacity of ES. and Indicates the energy storage charge and discharge efficiency, SOC ES,i,0 Indicates the initial remaining capacity of the ES scheduling period, and is the minimum and maximum remaining capacity of ES. ES,i is the energy storage capacity of the ES. Constraint (15) prevents the ES from being overcharged or over-discharged, which affects its service life. Constraint (16) ensures that the remaining capacity in the last time period is equal to the initial remaining capacity, which facilitates the periodic scheduling of energy storage.

[0121] (2) Distributed energy operation constraints

[0122] For distributed energy in microgrids, considering the possibility of wind and solar power abandonment due to incomplete absorption of renewable energy due to operating status restrictions, at time t, the predicted power P of renewable energy actually absorbed is DG,u,i,t and the predicted power of renewable energy actual output P DG,i,t The following constraints should be met:

[0123] 0≤P DG,u,i,t ≤P DG,i,t (17)

[0124] Renewable energy consumption rate η DG,i,t The calculation formula is: DG,i,t =P DG,u,i,t / P DG,i,t .

[0125] (3) Microgrid interaction model

[0126] In the non-cooperative game mode, the microgrid only interacts with the multi-microgrid aggregator. The operating cost of the microgrid is considered to be the difference between the cost of purchasing electricity from the multi-microgrid aggregator and the profit obtained from selling electricity to the multi-microgrid aggregator. The calculation is shown in formula (18):

[0127]

[0128] The power balance constraint (19) and the interactive power constraints (20), (21), and (22) must be satisfied during the interaction between the microgrid and the multi-microgrid aggregator:

[0129]

[0130]

[0131]

[0132]

[0133] Where, P Load,i,t is the load power at time t. and represents the maximum power purchase and sales of the microgrid. Equation (22) constrains the microgrid to be in only one state at a time, namely, the power purchase or sales state complementarity constraint.

[0134] The operation goal of the lower-level player microgrid is to minimize the operation cost. The microgrid operation optimization model can be expressed as:

[0135]

[0136] Among them, the decision variables are P DG,u,i .

[0137] Step S202: Convert the master-slave game model into a mixed integer linear programming problem to obtain an equivalent mixed integer linear programming model of the master-slave game model.

[0138] By converting the master-slave game model into a mixed integer linear programming problem, the multi-microgrid aggregator electricity price optimization model and the microgrid operation optimization model can be combined into a single optimization problem, and then the optimal solution can be obtained. This optimal solution is the equilibrium point of the game.

[0139] The master-slave game model is transformed into a mixed integer linear programming problem, and an equivalent mixed integer linear programming model of the master-slave game model is obtained, including:

[0140] Step S202a: Apply the KKT condition to perform equivalent transformation on the microgrid operation optimization model, and transform the microgrid operation optimization model into a linear programming problem.

[0141] In some implementations, applying the KKT condition to perform an equivalent transformation on the microgrid operation optimization model, transforming the microgrid operation optimization model into a linear programming problem, further includes:

[0142] Step S202a-1: Construct a Lagrangian function and analyze the constraints that the microgrid operation optimization model needs to meet based on the KKT conditions;

[0143] Step S202a-2: When the electricity price is given, the microgrid operation optimization model is relaxed into a linear programming problem, and the optimal solution of the linear programming problem corresponding to the microgrid operation optimization model is determined to be consistent with the optimal solution of the dual problem according to the strong duality theorem.

[0144] In one example, the KKT condition is applied to transform the underlying optimization model and construct the Lagrangian function as shown in (B1).

[0145] Lagrangian function of the underlying optimization problem:

[0146]

[0147] Among them, μ soc,i 、μ eq,i,t is the dual variable, and λ is the Lagrange factor.

[0148] In one example, the constraints that need to be satisfied when analyzing the underlying optimization problem based on the KKT conditions are shown in (B2).

[0149] The constraints that need to be met by the KKT conditions are:

[0150]

[0151] Linearize the nonlinear terms in the KKT condition. For the multiplication of two non-negative variables, such as pq = 0, the transformation can be performed by introducing the 01 variable θ, as shown in formula (B3), where M is a sufficiently large positive real number:

[0152]

[0153] The expression after transformation of formula (B2) is shown in formula (B4). At this time, the problem is a mixed integer linear programming problem, which can be solved by commercial solvers.

[0154]

[0155] Given a given electricity price, the relaxed lower-level optimization problem is a (KKT conditional) linear programming problem. According to the strong duality theorem, the optimal solution to the lower-level problem is consistent with the optimal solution to the dual problem. This expression is shown in Equation (B5).

[0156] The equivalent formula of the optimal solution of the lower-level problem obtained by the strong duality theorem is:

[0157]

[0158] In some implementations, the microgrid operation optimization model satisfies the energy storage charging and discharging state complementary constraint (12) and the electricity purchasing and selling state complementary constraint (22), and the energy storage charging and discharging state complementary constraint and the electricity purchasing and selling state complementary constraint have non-convex characteristics; before applying the KKT condition to perform an equivalent transformation on the microgrid operation optimization model and transforming the microgrid operation optimization model into a linear programming problem, it is also necessary to perform convex optimization processing on the energy storage charging and discharging state complementary constraint and the electricity purchasing and selling state complementary constraint.

[0159] Under the master-slave game framework, after the upper-level multi-microgrid aggregator sets the electricity price, the microgrid operation optimization problem at the lower level is a deterministic problem. However, the pricing of the upper-level multi-microgrid aggregator needs to consider the reaction strategy of the microgrid users to the price. There is a strategy coupling between the upper and lower optimization problems, so the upper-level optimization problem cannot be solved directly. Therefore, this embodiment transforms the lower-level problem based on the KKT (Karush-Kuhn-Tucker) condition and its dual theory, and merges it with the upper-level problem into a single optimization problem, and then the optimal solution can be obtained. This optimal solution is the equilibrium point of the game. However, the state complementary constraint formula in the lower-level optimization problem - the energy storage charging and discharging state complementary constraint (12) and the power purchase and sales state complementary constraint (22) contain the form of variable multiplication, which has a strong non-convex characteristic and cannot be transformed by applying the KKT condition. Therefore, it is necessary to perform convex optimization processing on the non-convex state complementary constraint.

[0160] In some implementations, the multi-microgrid aggregator electricity price optimization model satisfies the power purchase and sale power constraints (7) when the multi-microgrid aggregator interacts with the power of the distribution network; before merging the microgrid operation optimization model converted into a linear programming problem with the multi-microgrid aggregator electricity price optimization model to obtain an equivalent mixed integer linear programming model of the master-slave game model, the method of this embodiment also includes: performing equivalent linearization conversion processing on the power purchase and sale power constraints (7) when the multi-microgrid aggregator interacts with the power of the distribution network.

[0161] For the non-convex energy storage charge and discharge state complementary constraint (12), the microgrid operation optimization model satisfies the relaxation condition and can accurately relax the energy storage charge and discharge state complementary constraint (12). At the same time, this embodiment provides the following proof that the relaxation condition is satisfied. Numerical experimental results in multiple scenarios also demonstrate the accuracy of the relaxation.

[0162] The following is an explanation of the looseness of the energy storage complementarity constraint. In the model of this embodiment, for the case where the energy storage is charged within a unit scheduling period, there are two charging and discharging schemes and their corresponding costs:

[0163] ① Energy storage only The power value is charged.

[0164] ② Energy storage The power value is charged, and The power value is discharged.

[0165] The two solutions require the same amount of power to be replenished per unit time, which means:

[0166]

[0167] The cost of plan ① is

[0168] The cost of plan ② is

[0169] Among them, π MG Indicates the electricity purchase price or electricity sales price at this time.

[0170] The difference between the two options is:

[0171]

[0172] Therefore, solution ① is more economical, so it must satisfy The same explanation can be applied to the case where energy storage is discharged within a unit dispatch period.

[0173] For the non-convex microgrid's electricity purchase and sales state complementary constraint (Equation 22), considering the difference in electricity purchase and sales prices, there is no direct and accurate relaxation condition. Since the difference in electricity purchase and sales state is only in the microgrid objective function (Equation 18), the microgrid electricity purchase and sales variables (the interaction power between the microgrid and the multi-microgrid aggregator) can be equivalently transformed as follows, rewriting Equations (20) to (22) as Equations (24) to (25):

[0174]

[0175]

[0176] Where, Represents the interaction power between the microgrid and the multi-microgrid aggregator. The positive time indicates that the microgrid purchases electricity. When it is negative, it means the microgrid is selling electricity. and The following algebraic relations are satisfied:

[0177]

[0178] The power balance equation (8) of the multi-microgrid aggregator can be rewritten as:

[0179]

[0180] The microgrid operation objective function (18) can be rewritten as:

[0181]

[0182] Formula (28) contains nonlinear terms in the form of absolute values, and the new variable And constraint formula (29) is linearized, and the microgrid operation objective function after linearization is shown in formula (30):

[0183]

[0184]

[0185] After relaxation, the operation optimization model of the microgrid can be expressed as shown in Equation (31), and the decision variables are P DG,u,i .

[0186]

[0187] Step S202b: merge the microgrid operation optimization model converted into a linear programming problem with the multi-microgrid aggregator electricity price optimization model to obtain an equivalent mixed integer linear programming model of the master-slave game model.

[0188] In some implementations, the KKT condition converts the lower-level problem into an equivalent one and merges it with the upper-level problem. The resulting master-slave game model between the multi-microgrid aggregator and the microgrid can be converted into a mixed integer linear programming problem (equivalent mixed integer linear programming model of the master-slave game model) as shown in Equation (32):

[0189]

[0190] Where N represents the number of microgrids in the microgrid system, i represents the i-th microgrid, represents the maximum charging power of energy storage, T represents the length of a scheduling cycle, t represents the time, express The Lagrangian factor of Indicates the maximum discharge power of energy storage, express Lagrangian factor, SOC ES,i,0 represents the initial remaining capacity of the energy storage device scheduling period, Indicates the minimum remaining capacity of the energy storage device, express The Lagrangian factor of Indicates the maximum remaining capacity of the energy storage device, express The Lagrangian factor, P DG,i,t Represents the actual output power of renewable energy, Indicates P DG,i,t The Lagrangian factor of Indicates the maximum power sold by the microgrid, express The Lagrangian factor of Indicates the maximum power purchase of the microgrid, express The Lagrangian factor, P Load,i,t Indicates load power, μ eq,i,t Indicates P Load,i,t The dual variable of represents the price of electricity purchased by the multi-microgrid aggregator from the distribution network, It represents the power that the multi-microgrid aggregator purchases from the distribution network. represents the electricity price sold by the multi-microgrid aggregator, It represents the power sold by the multi-microgrid aggregator, and Δt represents the length of unit time.

[0191] The constraints of the equivalent mixed integer linear programming model of the master-slave game model include:

[0192] The constraints on the microgrid purchase and sales prices set by the multi-microgrid aggregator are (Equations (2) to (5));

[0193] The constraint that the electricity sales price is higher than the electricity purchase price, formula (6);

[0194] The power purchase and sale constraints when multiple microgrid aggregators interact with the distribution network, formula (7);

[0195] The relationship constraint between the interaction power between the microgrid and the multi-microgrid aggregator and the power purchased and sold by the microgrid is (26).

[0196] The constraints that the KKT condition needs to satisfy are shown in formula (B4).

[0197] Regarding the master-slave game, if the Nash equilibrium solution exists and is unique, the game model must meet the following three conditions: 1) The strategy sets of the leader and followers are non-empty compact convex sets; 2) When the leader's strategy is given, all followers have a unique optimal solution; 3) When the follower's strategy is given, the leader has a unique optimal solution. After relaxation, the lower-level optimization model is a linear model, and the non-convex constraints (7) in the upper-level model are also linearized to achieve convex optimization processing. Therefore, both the upper-level and lower-level optimization models are convex models, so it can be assumed that equilibrium exists. The equilibrium point of the game is the optimal optimization configuration solution for the game.

[0198] The following examples illustrate the effectiveness of the model and solution method of this embodiment.

[0199] 1. Example system configuration

[0200] To simplify the example without loss of generality, it is assumed that there are three microgrids in the multi-microgrid system, namely, a microgrid whose renewable energy is photovoltaic (referred to as "photovoltaic microgrid"), a microgrid whose renewable energy is wind power (referred to as "wind power microgrid"), and a microgrid whose renewable energy is photovoltaic and wind power (referred to as "photovoltaic + wind power microgrid"). The distributed energy data and load data are from the Eila data website, and the electricity price data is from the PJM data website. After obtaining one year of data, the Kmeans clustering method is used to use the clustering results as typical day data for the example, and it is assumed that the various state parameters in each time period remain unchanged. The power curves of renewable energy and load power of the three microgrids on a typical day are as follows: Figure 5 shown.

[0201] It can be seen from the renewable energy power and load curves that the renewable energy power output of the photovoltaic microgrid is generally lower than the load demand, which is a low-power microgrid. The renewable energy power output of the wind power microgrid is generally approximately the same as the load demand, which is a balanced microgrid. The renewable energy power output of the photovoltaic + wind power microgrid is generally higher than the load demand, which is a high-power microgrid.

[0202] In this example, it is assumed that in the day-ahead market, the electricity sales price of the multi-microgrid aggregator is 1.1 times the electricity purchase price, that is, The electricity price curve is as follows Figure 6 As shown; Assume that the lower bound of the microgrid electricity price set by the multi-microgrid aggregator satisfies The average electricity price of micro-online shopping meets the

[0203] 2. Solution of the master-slave game model

[0204] In the master-slave game model, each microgrid operates independently, and there is no electricity interaction. Solving the master-slave game equivalent mixed integer linear programming problem (32), we find that the maximum revenue of the upper-level multi-microgrid aggregator is $1.63, the minimum cost of the photovoltaic microgrid is $7.17, the minimum cost of the wind power microgrid is $1.26, and the minimum cost of the photovoltaic + wind power microgrid is -$3.37.

[0205] The optimal electricity price curve formulated by the upper-level multi-microgrid aggregator is as follows: Figure 7 The lower layer, the optimal control strategy for energy storage of photovoltaic microgrid, wind power microgrid and photovoltaic + wind power microgrid is shown as follows: Figure 8 As shown in the figure, the electricity purchase and sales strategy is as follows Figure 9 shown.

[0206] Figure 7In this scenario, the purchase and sales prices of microgrid aggregators are fixed by the distribution network. The purchase and sales prices of microgrids are determined by the aggregators, and the optimal price is determined through a master-slave game. The electricity price results show that the purchase and sales prices of microgrids are generally higher than those of the aggregators during the 4-12 hour period, and lower during the 12-24 hour period.

[0207] Depend on Figure 8 The results show that at a single moment, the microgrid energy storage is only in one state: charging, discharging, or neither charging nor discharging, which illustrates the accuracy of the relaxed energy storage state complementarity constraint.

[0208] comprehensive Figure 7 、 Figure 8 and Figure 9 As a result, the microgrid's energy storage charging and discharging strategies, along with its power purchase and sales strategies, are coordinated with the pricing of electricity by the multi-microgrid aggregator, achieving an equilibrium in a master-slave game model. To maximize their profits, the multi-microgrid aggregator sets higher electricity prices during the 4-12 hour period. During this period, when the microgrid's renewable energy power is insufficient, the microgrid purchases electricity to charge the energy storage and reserve energy for later discharge. To minimize their own operating costs, the microgrid flexibly regulates energy storage charging and discharging, selling electricity during the 6:00 PM to 10:00 PM period when electricity prices are higher, thereby maximizing profits and reducing costs.

[0209] 3. Impact of energy storage scale on the optimal solution

[0210] The energy storage capacity installed in a microgrid is a key parameter that needs to be studied in the microgrid planning stage. To analyze the impact of energy storage capacity on the operating costs of microgrids and multi-microgrid aggregators in the master-slave game problem, the maximum energy storage capacity of the three microgrids is set to vary from 14kWh to 22kWh, while other parameters remain unchanged. The multi-microgrid aggregator's revenue and the costs of the three microgrids are calculated as follows: Figure 10 shown.

[0211] from Figure 10It can be seen that the operating costs of different types of microgrids show different trends in response to the increase in energy storage capacity. For photovoltaic microgrids, the operating cost is lower when the energy storage capacity is small; for wind power microgrids, the operating cost is lower when the energy storage capacity is large; for photovoltaic + wind power microgrids, the energy storage capacity has no significant effect on the cost value. The increase in energy storage capacity can enhance the microgrid's ability to flexibly regulate, but due to the energy storage operation constraint (16), the total amount of electricity purchased and sold by the microgrid will also increase, which may lead to an increase in operating costs. Under the master-slave game mechanism, the purchase and sale prices of the microgrid are also inconsistent due to different energy storage capacity settings. If the enhanced benefits of microgrid flexibility regulation are not as good as the cost brought by the increase in electricity purchase and sale, the overall operating cost will increase. Photovoltaic microgrids are low-power microgrids. Increasing energy storage capacity enhances the microgrid's flexible regulation capabilities, but it is still not enough to make up for the electricity shortage. At this time, the enhanced benefits of microgrid flexibility regulation are not as good as the cost brought by the increase in electricity purchase and sale. Therefore, for photovoltaic microgrids, the operating cost is lower when the energy storage capacity is small. Wind power microgrids are balanced microgrids. The benefits of increasing storage capacity, which enhances the microgrid's flexible control capabilities, outweigh the costs of increased electricity purchases and sales. Therefore, a wind power microgrid with a larger storage capacity has lower operating costs. PV+wind power microgrids are multi-grid microgrids with more abundant electricity. The benefits of increasing storage capacity, which enhances the microgrid's flexible control capabilities, are equal to the costs of increased electricity purchases and sales. Therefore, the storage capacity of a PV+wind power microgrid has no significant impact on operating costs. This result suggests that, within the master-slave game framework, increasing storage capacity does not necessarily lead to increased benefits for different microgrid types. Therefore, energy storage capacity planning should be analyzed in conjunction with actual scenarios.

[0212] This embodiment establishes a master-slave game model with one master and multiple slaves between the upper-level multi-microgrid aggregator and the lower-level multi-microgrids. The multi-microgrid aggregator (leader) and the microgrid (follower) both participate in the game with the goal of maximizing their own interests. The KKT (Karush-Kuhn-Tucker) condition, duality theorem, and linear relaxation technology are applied to transform the master-slave game model into a mixed integer linear programming problem, and the game equilibrium solution is solved to achieve the optimal configuration of the microgrid system. The microgrid energy storage charging and discharging strategy and the power purchase and sales strategy are coordinated with the multi-microgrid aggregator electricity price formulation to achieve the equilibrium of the master-slave game model. Under the master-slave game framework, increasing the energy storage capacity of different microgrid types does not necessarily lead to increased profits. Therefore, in the process of energy storage capacity planning, analysis should be conducted in combination with actual scenarios.

[0213] Example 2

[0214] This embodiment provides a multi-microgrid system optimization control device, including:

[0215] Acquisition module, used to obtain the electricity price purchased by multi-microgrid aggregators from the distribution network The price of electricity sold by the multi-microgrid aggregator to each microgrid Energy storage charging efficiency and energy storage discharge efficiency Energy storage capacity E of energy storage equipment ES,i ;

[0216] Solving module for the electricity price purchased from the distribution network by multi-microgrid aggregators The price of electricity sold by the multi-microgrid aggregator to each microgrid Energy storage charging efficiency and energy storage discharge efficiency Energy storage capacity E of energy storage equipment ES,i , using the equivalent mixed integer linear programming model of the pre-obtained master-slave game model, the equilibrium solution is obtained;

[0217] Configuration module, used to configure the electricity price setting strategy of multi-microgrid aggregators and the power purchase and sales strategy of each microgrid based on the equilibrium solution;

[0218] Among them, the equivalent mixed integer linear programming model of the master-slave game model is obtained by converting the pre-constructed master-slave game model into a mixed integer linear programming problem; in the master-slave game model, the upper leader is the multi-microgrid aggregator, and the lower followers are the microgrids in the multi-microgrid system. The upper leader and the lower followers respectively play the master-slave game with the goal of maximizing their own interests; the equilibrium solution includes the electricity price set by the multi-microgrid aggregator for microgrid purchases and electricity prices The power purchased by multi-microgrid aggregators from the distribution grid and the power of electricity sold The power that the microgrid purchases from multi-microgrid aggregators and the power of electricity sold Energy storage charging power and energy storage discharge power Interaction power between microgrids and multi-microgrid aggregators The power actually consumed by renewable energy P DG,u .

[0219] In some implementations, the multi-microgrid system optimization control device of this embodiment further includes:

[0220] A construction module is used to construct a master-slave game model. The master-slave game model includes a multi-microgrid aggregator electricity price optimization model and a microgrid operation optimization model. The multi-microgrid aggregator electricity price optimization model aims to maximize the multi-microgrid aggregator's revenue; the microgrid operation optimization model aims to maximize the microgrid's revenue.

[0221] A conversion module is used to convert the master-slave game model into a mixed integer linear programming problem to obtain an equivalent mixed integer linear programming model of the master-slave game model;

[0222] The conversion module includes:

[0223] The equivalent transformation submodule is used to perform equivalent transformation on the microgrid operation optimization model by applying KKT conditions, and transform the microgrid operation optimization model into a linear programming problem;

[0224] The merging submodule is used to merge the microgrid operation optimization model converted into a linear programming problem with the multi-microgrid aggregator electricity price optimization model to obtain an equivalent mixed integer linear programming model of the master-slave game model.

[0225] In some implementations, the multi-microgrid aggregator electricity price optimization model satisfies the power purchase and sale constraints when the multi-microgrid aggregator interacts with the distribution network electricity; the equivalent conversion sub-module is also used to: perform equivalent linear conversion processing on the power purchase and sale constraints when the multi-microgrid aggregator interacts with the distribution network electricity.

[0226] In some implementations, the equivalent mixed integer linear programming model of the master-slave game model is expressed as follows:

[0227]

[0228] Where N represents the number of microgrids in the microgrid system, i represents the i-th microgrid, represents the maximum charging power of energy storage, T represents the length of a scheduling cycle, t represents the time, express The Lagrangian factor of Indicates the maximum discharge power of energy storage, express Lagrangian factor, SOC ES,i,0 represents the initial remaining capacity of the energy storage device scheduling period, Indicates the minimum remaining capacity of the energy storage device, express The Lagrangian factor of Indicates the maximum remaining capacity of the energy storage device, express The Lagrangian factor, P DG,i,t Represents the actual output power of renewable energy, Indicates P DG,i,t The Lagrangian factor of Indicates the maximum power sold by the microgrid, express The Lagrangian factor of Indicates the maximum power purchase of the microgrid, express The Lagrangian factor, P Load,i,t Indicates load power, μ eq,i,t Indicates P Load,i,t The dual variable of represents the price of electricity purchased by the multi-microgrid aggregator from the distribution network, It represents the power that the multi-microgrid aggregator purchases from the distribution network. represents the electricity price sold by the multi-microgrid aggregator, It represents the power sold by the multi-microgrid aggregator, and Δt represents the length of unit time.

[0229] In some implementations, the constraints of the equivalent mixed integer linear programming model of the master-slave game model include:

[0230] Constraints on microgrid electricity purchase and sales prices set by multi-microgrid aggregators;

[0231] The restriction that the electricity sales price is higher than the electricity purchase price;

[0232] Power purchase and sales constraints when multi-microgrid aggregators interact with the distribution grid;

[0233] The relationship constraints between the interaction power between the microgrid and the multi-microgrid aggregator and the power purchased and sold by the microgrid;

[0234] The constraints that the KKT conditions must satisfy.

[0235] In some implementations, the equivalent conversion submodule is used to:

[0236] Construct the Lagrangian function and analyze the constraints that the microgrid operation optimization model needs to meet based on the KKT conditions;

[0237] When the electricity price is given, the microgrid operation optimization model is relaxed into a linear programming problem. According to the strong duality theorem, the optimal solution of the linear programming problem corresponding to the microgrid operation optimization model is determined to be consistent with the optimal solution of the dual problem.

[0238] In some implementations, the microgrid operation optimization model satisfies the complementary constraints of energy storage charging and discharging states and the complementary constraints of electricity purchase and sales states, and the complementary constraints of energy storage charging and discharging states and the complementary constraints of electricity purchase and sales states have non-convex characteristics; the apparatus of this embodiment further includes:

[0239] The optimization module is used to perform equivalent transformation on the microgrid operation optimization model by applying KKT conditions. Before transforming the microgrid operation optimization model into a linear programming problem, the complementary constraints of energy storage charging and discharging states and the complementary constraints of power purchase and sales states are subjected to convex optimization.

[0240] The specific implementation of each module in this embodiment is shown in Example 1, which will not be described in detail in this embodiment. This embodiment at least has all the beneficial effects of Example 1.

[0241] Example 3

[0242] This embodiment provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by at least one processor, the method of the first embodiment is implemented.

[0243] Example 4

[0244] This embodiment provides an electronic device, including a memory and at least one processor. The memory stores a computer program, and when the computer program is executed by the at least one processor, the method of the first embodiment is implemented.

[0245] Among them, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0246] The processor can be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller unit (MCU), a microprocessor or other electronic components.

[0247] In the several embodiments provided in the embodiments of the present invention, it should be understood that the disclosed apparatus and method can also be implemented in other ways. The apparatus and method embodiments described above are merely illustrative.

[0248] It should be noted that, in this document, the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. The terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0249] Although the embodiments disclosed herein are as described above, the contents described herein are merely embodiments for facilitating understanding of the present invention and are not intended to limit the present invention. Any person skilled in the art may make any modifications and variations in the form and details of the embodiments without departing from the spirit and scope of the present invention. However, the scope of patent protection of the present invention shall remain subject to the scope defined by the appended claims.

Claims

1. A multi-microgrid system optimization control method, It is characterized in that include: Obtain the electricity price that the multi-microgrid aggregator purchases from the distribution network, the electricity price that the multi-microgrid aggregator sells to each microgrid, the energy storage charging efficiency and energy storage discharging efficiency, and the energy storage capacity of the energy storage equipment; Based on the electricity price that the multi-microgrid aggregator purchases from the distribution network, the electricity price that the multi-microgrid aggregator sells to each microgrid, the energy storage charging efficiency and the energy storage discharge efficiency, and the energy storage capacity of the energy storage equipment, the equivalent mixed integer linear programming model of the master-slave game model obtained in advance is used to obtain the equilibrium solution; Based on the balanced solution, configure the electricity price setting strategy of the multi-microgrid aggregator and the electricity purchase and sales strategy of each microgrid; Among them, the equivalent mixed integer linear programming model of the master-slave game model is obtained by converting the pre-constructed master-slave game model into a mixed integer linear programming problem; in the master-slave game model, the upper leader is the multi-microgrid aggregator, and the lower follower is each microgrid in the multi-microgrid system, and the upper leader and the lower follower respectively play the master-slave game with the goal of maximizing their own interests; the equilibrium solution includes the electricity purchase price and electricity sales price of the microgrid set by the multi-microgrid aggregator, the power of electricity purchased from the distribution network and the power of electricity sold by the multi-microgrid aggregator, the power of electricity purchased from the multi-microgrid aggregator and the power of electricity sold by the microgrid to the multi-microgrid aggregator, the energy storage charging power and the energy storage discharging power, the interactive power between the microgrid and the multi-microgrid aggregator, and the power actually consumed by renewable energy.

2. The multi-microgrid system optimization control method according to claim 1, It is characterized in that Also includes: Constructing a master-slave game model, wherein the master-slave game model includes a multi-microgrid aggregator electricity price optimization model and a microgrid operation optimization model, wherein the multi-microgrid aggregator electricity price optimization model aims to maximize the multi-microgrid aggregator revenue; The microgrid operation optimization model aims to maximize the microgrid revenue; Converting the master-slave game model into a mixed integer linear programming problem to obtain an equivalent mixed integer linear programming model of the master-slave game model; The step of converting the master-slave game model into a mixed integer linear programming problem to obtain an equivalent mixed integer linear programming model of the master-slave game model includes: Applying KKT conditions to perform equivalent transformation on the microgrid operation optimization model, and transforming the microgrid operation optimization model into a linear programming problem; The microgrid operation optimization model converted into a linear programming problem is combined with the multi-microgrid aggregator electricity price optimization model to obtain an equivalent mixed integer linear programming model of the master-slave game model.

3. The multi-microgrid system optimization control method according to claim 2, It is characterized in that The multi-microgrid aggregator electricity price optimization model satisfies the power purchase and sale power constraints when the multi-microgrid aggregator interacts with the distribution network; before merging the microgrid operation optimization model converted into a linear programming problem with the multi-microgrid aggregator electricity price optimization model to obtain an equivalent mixed integer linear programming model of the master-slave game model, the method also includes: The power purchase and sales constraints when multi-microgrid aggregators interact with the distribution network are converted into equivalent linear ones.

4. The multi-microgrid system optimization control method according to claim 1, It is characterized in that The equivalent mixed integer linear programming model of the master-slave game model is expressed as follows: Where N represents the number of microgrids in the microgrid system, i represents the i-th microgrid, represents the maximum charging power of energy storage, T represents the length of a scheduling cycle, t represents the time, express The Lagrangian factor of Indicates the maximum discharge power of energy storage, express The Lagrangian factor, SOC ES,i,0 represents the initial remaining capacity of the energy storage device scheduling cycle, Indicates the minimum remaining capacity of the energy storage device, express The Lagrangian factor of Indicates the maximum remaining capacity of the energy storage device, express The Lagrangian factor, P DG,i,t Represents the actual output power of renewable energy. Indicates P DG,i,t The Lagrangian factor of represents the maximum power sold by the microgrid, express The Lagrangian factor of represents the maximum power purchase of the microgrid, express The Lagrangian factor, P Load,i,t Indicates load power, μ eq,i,t Indicates P Load,i,t The dual variable of represents the price of electricity purchased by multi-microgrid aggregators from the distribution network, It represents the power purchased by multi-microgrid aggregators from the distribution network. represents the electricity price sold by multi-microgrid aggregators, It represents the power sold by the multi-microgrid aggregator, and Δt represents the length of unit time.

5. The multi-microgrid system optimization control method according to claim 1, It is characterized in that The constraints of the equivalent mixed integer linear programming model of the master-slave game model include: The constraints on the microgrid electricity purchase and sales prices set by multiple microgrid aggregators; The restriction that the electricity sales price is higher than the electricity purchase price; Power purchase and sales constraints when multi-microgrid aggregators interact with distribution grids; The relationship constraints between the interaction power between the microgrid and the multi-microgrid aggregator and the power purchased and sold by the microgrid; The constraints that the KKT conditions need to satisfy.

6. The multi-microgrid system optimization control method according to claim 2, It is characterized in that Applying KKT conditions to perform equivalent transformation on the microgrid operation optimization model, the microgrid operation optimization model is transformed into a linear programming problem, including: Construct the Lagrangian function and analyze the constraints that the microgrid operation optimization model needs to meet according to the KKT conditions; When the electricity price is given, the microgrid operation optimization model is relaxed into a linear programming problem. According to the strong duality theorem, the optimal solution of the linear programming problem corresponding to the microgrid operation optimization model is determined to be consistent with the optimal solution of the dual problem.

7. The multi-microgrid system optimization control method according to claim 2, It is characterized in that The microgrid operation optimization model satisfies the complementary constraints of energy storage charging and discharging states and the complementary constraints of electricity purchase and sales states, and the complementary constraints of energy storage charging and discharging states and the complementary constraints of electricity purchase and sales states have non-convex characteristics; before applying the KKT condition to perform equivalent transformation on the microgrid operation optimization model and transforming the microgrid operation optimization model into a linear programming problem, the complementary constraints of energy storage charging and discharging states and the complementary constraints of electricity purchase and sales states are also subjected to convex optimization processing.

8. A multi-microgrid system optimization control device, It is characterized in that include: An acquisition module is used to obtain the electricity price that the multi-microgrid aggregator purchases from the distribution network, the electricity price that the multi-microgrid aggregator sells to each microgrid, the energy storage charging efficiency and energy storage discharging efficiency, and the energy storage capacity of the energy storage equipment; A solution module is used to obtain an equilibrium solution based on the electricity price purchased by the multi-microgrid aggregator from the distribution network, the electricity price sold by the multi-microgrid aggregator to each microgrid, the energy storage charging efficiency and the energy storage discharging efficiency, and the energy storage capacity of the energy storage equipment by using an equivalent mixed integer linear programming model of the master-slave game model obtained in advance; A configuration module, used to configure the electricity price setting strategy of the multi-microgrid aggregator and the electricity purchase and sale strategy of each microgrid based on the balanced solution; Among them, the equivalent mixed integer linear programming model of the master-slave game model is obtained by converting the pre-constructed master-slave game model into a mixed integer linear programming problem; in the master-slave game model, the upper leader is the multi-microgrid aggregator, and the lower follower is each microgrid in the multi-microgrid system, and the upper leader and the lower follower respectively play the master-slave game with the goal of maximizing their own interests; the equilibrium solution includes the electricity purchase price and electricity sales price of the microgrid set by the multi-microgrid aggregator, the power of electricity purchased from the distribution network and the power of electricity sold by the multi-microgrid aggregator, the power of electricity purchased from the multi-microgrid aggregator and the power of electricity sold by the microgrid to the multi-microgrid aggregator, the energy storage charging power and the energy storage discharging power, the interactive power between the microgrid and the multi-microgrid aggregator, and the power actually consumed by renewable energy.

9. The multi-microgrid system optimization control device according to claim 8, It is characterized in that Also includes: A construction module is used to construct a master-slave game model, wherein the master-slave game model includes a multi-microgrid aggregator electricity price optimization model and a microgrid operation optimization model, wherein the multi-microgrid aggregator electricity price optimization model aims to maximize the multi-microgrid aggregator's revenue; The microgrid operation optimization model aims to maximize the microgrid revenue; A conversion module, used for converting the master-slave game model into a mixed integer linear programming problem, and obtaining an equivalent mixed integer linear programming model of the master-slave game model; Wherein, the conversion module comprises: An equivalent conversion submodule is used to apply KKT conditions to perform equivalent conversion on the microgrid operation optimization model, and convert the microgrid operation optimization model into a linear programming problem; The merging submodule is used to merge the microgrid operation optimization model converted into a linear programming problem with the multi-microgrid aggregator electricity price optimization model to obtain an equivalent mixed integer linear programming model of the master-slave game model.

10. The multi-microgrid system optimization control device according to claim 9, It is characterized in that The multi-microgrid aggregator electricity price optimization model meets the power purchase and sale constraints when the multi-microgrid aggregator interacts with the distribution network electricity; the equivalent conversion submodule is also used to: perform equivalent linear conversion processing on the power purchase and sale constraints when the multi-microgrid aggregator interacts with the distribution network electricity.

11. The multi-microgrid system optimization control device according to claim 9, It is characterized in that The equivalent mixed integer linear programming model of the master-slave game model is expressed as follows: Where N represents the number of microgrids in the microgrid system, i represents the i-th microgrid, represents the maximum charging power of energy storage, T represents the length of a scheduling cycle, t represents the time, express The Lagrangian factor of Indicates the maximum discharge power of energy storage, express The Lagrangian factor, SOC ES,i,0 represents the initial remaining capacity of the energy storage device scheduling cycle, Indicates the minimum remaining capacity of the energy storage device, express The Lagrangian factor of Indicates the maximum remaining capacity of the energy storage device, express The Lagrangian factor, P DG,i,t Represents the actual output power of renewable energy. Indicates P DG,i,t The Lagrangian factor of represents the maximum power sold by the microgrid, express The Lagrangian factor of represents the maximum power purchase of the microgrid, express The Lagrangian factor, P Load,i,t Indicates load power, μ eq,i,t Indicates P Load,i,t The dual variable of represents the price of electricity purchased by multi-microgrid aggregators from the distribution network, It represents the power purchased by multi-microgrid aggregators from the distribution network. represents the electricity price sold by multi-microgrid aggregators, It represents the power sold by the multi-microgrid aggregator, and Δt represents the length of unit time.

12. The multi-microgrid system optimization control device according to claim 9, It is characterized in that The constraints of the equivalent mixed integer linear programming model of the master-slave game model include: The constraints on the microgrid electricity purchase and sales prices set by multiple microgrid aggregators; The restriction that the electricity sales price is higher than the electricity purchase price; Power purchase and sales constraints when multi-microgrid aggregators interact with distribution grids; The relationship constraints between the interaction power between the microgrid and the multi-microgrid aggregator and the power purchased and sold by the microgrid; The constraints that the KKT conditions need to satisfy.

13. The multi-microgrid system optimization control device according to claim 9, It is characterized in that The equivalent conversion submodule is used for: Construct the Lagrangian function and analyze the constraints that the microgrid operation optimization model needs to meet according to the KKT conditions; When the electricity price is given, the microgrid operation optimization model is relaxed into a linear programming problem. According to the strong duality theorem, the optimal solution of the linear programming problem corresponding to the microgrid operation optimization model is determined to be consistent with the optimal solution of the dual problem.

14. The multi-microgrid system optimization control device according to claim 9, It is characterized in that The microgrid operation optimization model satisfies the complementary constraints of energy storage charging and discharging states and the complementary constraints of electricity purchase and sale states, and the complementary constraints of energy storage charging and discharging states and the complementary constraints of electricity purchase and sale states have non-convex characteristics; the device also includes: The optimization module is used to perform equivalent transformation on the microgrid operation optimization model under the KKT condition, and before converting the microgrid operation optimization model into a linear programming problem, perform convex optimization processing on the complementary constraints of the energy storage charging and discharging states and the complementary constraints of the power purchase and sales states.

15. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the method according to any one of claims 1 to 7 is implemented.

16. An electronic device, It is characterized in that The method comprises a memory and at least one processor, wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method according to any one of claims 1 to 7 is implemented.

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