Multi-virtual power plant collaborative optimization scheduling method considering energy sharing

Abstract

A multi-virtual power plant collaborative optimization scheduling method considering energy sharing comprises the steps of constructing a master-slave game model, constructing an upper-layer game leader scheduling center model, constructing a lower-layer game follower multi-virtual power plant collaborative optimization scheduling model and a multi-virtual power plant income distribution model, and solving the models by adopting a particle swarm optimization algorithm and a Cplex solver. According to the invention, the scheduling center based on energy sharing and the master-slave game model of the multiple virtual power plants are provided, the scheduling center is used as a superior game leader, and the multiple virtual power plants are guided to perform optimal scheduling by dynamically adjusting the electricity price, so that energy sharing among the multiple virtual power plants is stimulated; effective interaction between the multiple virtual power plants and an upper-layer mechanism is achieved through electricity price adjustment; according to the method, the benefit relationship among the multiple virtual power plants is deeply studied, and the problem of benefit damage of part of main bodies in the collaborative operation process is solved based on the multi-virtual power plant collaborative operation benefit distribution model of the Shapley value method, so that the collaborative operation relationship of the multiple virtual power plants can be stably maintained.

Description

Technical Field

[0001] The present invention relates to the field of power system dispatching, and in particular to a multi-virtual power plant collaborative optimization dispatching method for alleviating the regulation pressure of a power grid control center through energy sharing among multiple entities. Background Art

[0002] At present, under the background of the construction of a new power system, my country has rapidly developed distributed resources such as wind power, photovoltaics, and energy storage. Wind power and photovoltaic installed capacity have become the world's first, but the problem of power consumption is prominent (power consumption: refers to the entire process of power transmission, distribution to end users and actual use from power stations, describing the activity of power being converted into other forms of energy at the terminal). On the other hand, the growth rate of my country's power load is significantly higher than the growth rate of power, and the peak-to-valley difference continues to increase.

[0003] In this context, virtual power plant technology came into being. Virtual power plants are oriented to multiple resources such as source, load and storage, and apply information technology and intelligent technologies such as big data, cloud computing, the Internet of Things, mobile Internet, artificial intelligence, blockchain, and edge computing to achieve extensive interconnection of distributed resources. They have the characteristics of comprehensive state perception, efficient information processing, and convenient and accurate scheduling.

[0004] At present, many researchers have conducted research on the operation and dispatch of virtual power plants and market transactions. The details are as follows:

[0005] 1. "Two-stage robust trading strategy of virtual power plants for multi-level electricity-carbon coupling market" [J / OL]. Automation of Electric Power Systems, 1-11 [2024-05-26]", "Two-level stochastic optimization of virtual power plants participating in energy and reserve markets based on conditional value at risk" [J / OL]. Power System Technology, 1-13 [2024-05-26]", "Joint optimization strategy of virtual power plants participating in primary and auxiliary markets considering uncertain risks and multi-agent collaboration" [J / OL]. Power System Technology, 1-16 [2024-05-26]". The trading model of virtual power plants participating in energy market, carbon market and ancillary service market was explored.

[0006] 2. "An Optimal Model of Virtual Power Plant Participating in Electricity Carbon Market Based on Multi-layer Benefit Sharing" [J]. Systems Engineering - Theory & Practice, 2024, 44(02): 661-683, "Economic Optimal Dispatch of Combined Heat and Power of Virtual Power Plant Based on Stochastic Optimization" [J]. Acta Energiae Solaris Sinica, 2023, 44(09): 57-65. The optimal dispatch model of virtual power plant considering uncertainty was studied.

[0007] However, the existing studies on virtual power plants mentioned in 1 and 2 above mostly focus on the optimization scheduling and market transactions within a single virtual power plant. However, with the development of virtual power plant technology, multiple virtual power plants will coexist in the same region, so studying only a single virtual power plant cannot adapt to the development trend.

[0008] 3. "Coordinated Optimal Dispatch of Building Virtual Power Plants Considering Electricity Sharing" [J]. Journal of Electric Power Science and Technology, 2022, 37(01): 96-105, "Multi-time Scale Coordinated Operation Strategy of Virtual Power Plant Cluster Considering Electricity Sharing" [J]. Power System Technology, 2022, 46(02): 642-656. Considering the electricity sharing, a coordinated dispatch model of virtual power plant cluster is proposed.

[0009] 4. "Multi-virtual power plant electricity-carbon-reserve P2P trading model based on conditional risk value" [J]. Automation of Electric Power Systems, 2022, 46(18): 147-157. With virtual power plants participating in multi-variety market transactions as the background, a multi-virtual power plant peer-to-peer trading model considering energy sharing is proposed.

[0010] However, the existing research on 3 and 4 above has not deeply considered the interest relationship among multiple subjects, which may cause some subjects to suffer losses in the process of collaborative operation.

[0011] 5. "Research on Virtual Power Plant Alliance Strategy and Revenue Distribution Mechanism Based on Cooperative Game" [J]. Smart Power, 2024, 52(01): 39-46+64, "Cooperative Optimization Operation Strategy of Multi-energy VPP Group Based on Nash Negotiation" [J / OL]. Energy Storage Science and Technology, 1-14 [2024-05-26]". Based on Nash bargaining theory, a multi-virtual power plant cooperative game model is proposed, and the Nash bargaining problem is decomposed into cluster revenue maximization problem and cluster price payment problem for solution.

[0012] Similarly, the multi-virtual power plant operation model based on cooperative game in the above-mentioned existing research takes into account the profit distribution among multiple subjects, but does not consider the interactive relationship between the superior dispatching agency and the lower-level virtual power plants. It can only passively accept the fixed electricity price of the dispatching agency, which affects the enthusiasm of the lower-level virtual power plants to respond to demand and share energy.

[0013] In summary, there are many defects in the deployment technology of virtual power plants in existing technologies / research, as follows:

[0014] 1. Existing research on virtual power plants focuses on resource aggregation, optimized scheduling and market transactions within virtual power plants. However, with the development of virtual power plant technology, multiple virtual power plants will coexist in the same region. Therefore, studying only a single virtual power plant cannot adapt to the development trend.

[0015] 2. In existing research on multi-agent optimization operation, the electricity price set by the superior dispatching center is mostly considered as a fixed value, and the subordinate entities passively accept the electricity price, without considering the interactive relationship between the two.

[0016] 3. In the existing multi-subject optimization operation, the interest relationship among multiple subjects has not been deeply considered, resulting in the possible loss of interests of some subjects during the collaborative operation process.

[0017] Therefore, there is an urgent need for a new multi-virtual power plant collaborative optimization scheduling method that can solve the above problems. Summary of the invention

[0018] In order to solve the various defects existing in the above-mentioned prior art / research, the present invention provides a collaborative optimization scheduling method for multiple virtual power plants taking into account energy sharing, by setting up a master-slave game model between a scheduling center and multiple virtual power plants, and forming effective interaction between the upper scheduling center and the lower multiple virtual power plants through dynamic electricity price adjustment.

[0019] 1. A method for collaborative optimization scheduling of multiple virtual power plants taking into account energy sharing, comprising the following steps:

[0020] 1) Construct a master-slave game model;

[0021] 2) Construct the upper-level game leader dispatch center model;

[0022] 3) Constructing a lower-level game follower multi-virtual power plant collaborative optimization scheduling model, which is divided into constructing a virtual power plant optimization scheduling model and constructing a multi-virtual power plant collaborative optimization scheduling model;

[0023] 4) Multi-virtual power plant profit distribution model.

[0024] According to a method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing of the present invention, it is characterized in that the master-slave game model of step 1) is constructed as follows:

[0025] The upper-level master-slave game model is divided into a dispatching center model and a multi-virtual power plant model. The dispatching center model is set as the game leader. The dispatching center model dynamically adjusts the electricity price with the goal of maximizing its own profits. The multi-virtual power plant model is then set as the game follower, and optimized scheduling is performed with the goal of minimizing group costs.

[0026] A collaborative optimization scheduling method for multiple virtual power plants taking into account energy sharing according to the present invention is characterized in that the multiple virtual power plant models trade electricity purchase and sale amounts with the scheduling center model, while the scheduling center model trades electricity purchase and sale prices with the multiple virtual power plant models.

[0027] According to a method for collaborative optimization scheduling of multiple virtual power plants taking into account energy sharing of the present invention, it is characterized in that the step 2) of constructing an upper-level game leader scheduling center model is specifically as follows:

[0028] In the above step 1), the dispatch center is used as the leader of the master-slave game, with the goal of maximizing system revenue, to formulate the purchase and sale prices of multiple virtual power plants. The objective function of the dispatch center is:

[0029]

[0030] Where: C DSO represents the revenue of the dispatching center; C grid It represents the cost of purchasing and selling electricity of the power grid, which can be expressed as follows:

[0031]

[0032] Where: c grid,buy 、c grid,sell Indicates the purchase and sale price of electricity traded with the power grid;

[0033] In the process of trading with multiple virtual power plants, the transaction price set by the dispatch center should meet the upper and lower limit constraints:

[0034]

[0035] Where: They are the upper and lower limits of the electricity purchase price of multiple virtual power plants; They are the upper and lower limits of the electricity sales prices of multiple virtual power plants respectively.

[0036] According to a method for collaborative optimization dispatching of multiple virtual power plants taking into account energy sharing of the present invention, it is characterized in that in the aforementioned "in the process of trading with multiple virtual power plants, the transaction electricity price formulated by the dispatching center should satisfy the upper and lower limit constraints", in order to avoid the dispatching center from excessively raising the electricity price, maximizing the system revenue and completely ignoring the interests of the lower virtual power plants, the electricity price should also satisfy the following average value constraint:

[0037]

[0038] According to a method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing of the present invention, it is characterized in that the step 3) of constructing a virtual power plant optimization scheduling model in the collaborative optimization scheduling model of multiple virtual power plants of the lower-level game follower is specifically as follows:

[0039] In this step, the virtual power plant aggregates various types of distributed resources, including distributed generators, photovoltaic units, wind turbines, energy storage, interruptible loads and transferable loads. To minimize the scheduling cost, the objective function of the virtual power plant i is as follows:

[0040] Scheduling costs Including: Distributed Generator DG operating costs Energy storage ES operating costs Interruptible Load IL Call Cost Transferable load TL call cost and transaction costs with the grid The formula is as follows:

[0041]

[0042] The specific expressions of each cost are as follows:

[0043]

[0044] Where: a i , b i and c i Indicates the cost coefficient of the unit;

[0045] P i DG Indicates the actual output of DG;

[0046] P i ES,ch , P i ES,dis Indicates ES charging and discharging power;

[0047] c ES,i It represents the unit operating cost of ES;

[0048] P i IL It indicates the load interruption amount of IL users;

[0049] P i TL,up , P i TL,down Indicates the load increase and decrease of TL users;

[0050] c IL,i 、c TL,i denote the unit compensation costs for load interruption and transfer of IL users and TL users respectively;

[0051] P i grid,buy , P i grid,sell Indicates the amount of electricity purchased and sold from the power grid;

[0052] c trade,buy 、c trade,sell It indicates the purchase and sale price of electricity traded with the power grid, which is set by the dispatching center.

[0053] According to a method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing of the present invention, it is characterized in that in the construction of the virtual power plant optimization scheduling model, the constraints that the operation of the virtual power plant i should satisfy are as follows:

[0054] 1) DG constraints:

[0055]

[0056] Where: Indicates the upper and lower limits of DG output; Indicates the climbing ability of DG;

[0057] 2) ES constraints:

[0058]

[0059] E i (t) = E i (t-1)

[0060] +P i ES,ch (t)η ch -P i ES,dis (t) / η dis

[0061]

[0062] Where: Represents the upper limit of ES output; E i represents ES capacity; ch , η dis Indicates ES charging and discharging efficiency; Indicates the upper and lower limits of ES capacity;

[0063] 3) IL / TL constraints:

[0064]

[0065] Where: Indicates the maximum interruption upper limit and transfer upper limit of load respectively;

[0066] 4) Grid / multiple virtual power plant transaction constraints:

[0067]

[0068] P i-j +P j-i =0

[0069] Where: Indicates the upper limit of the amount of electricity traded with the power grid; P i-jRepresents the shared electricity between virtual power plant i and virtual power plant j. A positive value indicates that virtual power plant i supplies energy to virtual power plant j, and a negative value indicates that virtual power plant j supplies energy to virtual power plant i. represents the upper limit of power sharing between virtual power plants;

[0070] 5) Power balance constraints:

[0071]

[0072] Where: P i new Indicates new energy output.

[0073] According to a method for collaborative optimization scheduling of multiple virtual power plants taking into account energy sharing of the present invention, it is characterized in that the step 3) of constructing a collaborative optimization scheduling model of multiple virtual power plants in the lower-level game follower collaborative optimization scheduling model is specifically as follows:

[0074] In this step, virtual power plants can maximize overall benefits through energy sharing. The objective function of the multi-virtual power plant collaborative optimization scheduling model is as follows:

[0075]

[0076] Where: N represents the number of virtual power plant members in the cooperative alliance.

[0077] It should be noted that in the construction of the multi-virtual power plant collaborative optimization scheduling model, when considering the virtual power plant scheduling cost, there is no need to consider the cost of energy sharing between virtual power plants. This is because when considering the overall benefits, the transactions between virtual power plants reach an internal balance of income and expenditure. Therefore, this part can be omitted in the objective function.

[0078] According to a method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing of the present invention, it is characterized in that the revenue distribution model of the multiple virtual power plants in step 4) is specifically:

[0079] The Shapley value method is used to realize the distribution of benefits from the coordinated operation of multiple virtual power plants. The calculation formula is as follows:

[0080]

[0081] δ(i,S)=v(S∪{i})-v(S)

[0082] Where: φ i (N) represents the Shapley value of participant i; represents the subset of the coalition that does not include participant i; δ(i,S) represents the marginal contribution.

[0083] It should be noted that in step 4), the profit distribution model of multiple virtual power plants is that after the master-slave game of steps 1) to 3), the dispatching center and the multiple virtual power plants reach an agreement on the purchase and sale prices of electricity, and the multiple virtual power plants also determine their dispatching plans. In the process of considering the collaborative optimization dispatch of multiple virtual power plants, since the benefits of internal shared electricity are negligible, compared with independent operation, some members will suffer losses in the process of collaborative operation, and profit distribution is needed. The Shapley value method is used to distribute the benefits according to the marginal contribution of the participants to the alliance to ensure the rationality and publicity of the distribution results.

[0084] According to a method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing of the present invention, it is characterized in that the step 5) adopts a particle swarm algorithm and a Cplex solver to solve the above model, which is specifically:

[0085] 1) Initialize the particle swarm parameters, including the number of particle swarms, the maximum number of iterations, etc.;

[0086] 2) Input the transaction parameters related to the virtual power plant, including equipment parameters, electricity price information, new energy and load forecast power, etc.;

[0087] 3) The dispatch center initializes the transaction electricity price based on the constraints and sends it to the virtual power plant;

[0088] 4) Multiple virtual power plants receive electricity price data for optimized dispatching, and use the Cplex solver to decide on the amount of electricity to be traded with the upper-level power grid with the goal of minimizing overall cost, and send it to the dispatching center;

[0089] 5) The dispatch center receives the transaction power data and calculates the revenue, and determines whether the maximum number of iterations has been reached. If not, the new round of electricity prices is updated and the process returns to step 4); if yes, the electricity price corresponding to the optimal revenue is output;

[0090] 6) The virtual power plant calculates the dispatch cost based on the optimal electricity price of the dispatch center and calculates the cost under different alliance combinations;

[0091] 7) Calculate the marginal revenue of each virtual power plant according to the cost under different alliance combinations, and distribute the revenue based on the Shapley value method.

[0092] The overall idea of ​​the present invention is: first, consider the dispatching center to maximize its own revenue, formulate the purchase and sale electricity prices of multiple virtual power plants, and establish the dispatching center model of the upper-level game leader. Then, model multiple types of distributed resources, analyze the output characteristics of multiple types of distributed power generation resources, and establish an optimization dispatching model within the virtual power plant. Then, on this basis, consider the energy sharing of multiple virtual power plants and construct a collaborative optimization dispatching model of multiple virtual power plants. Finally, considering the energy sharing among multiple virtual power plants, the Shapley value method is used to realize the redistribution of costs among multiple virtual power plants, and the enthusiasm of multiple virtual power plants to participate in energy sharing is encouraged.

[0093] The following beneficial effects are achieved by using a multi-virtual power plant collaborative optimization scheduling method taking into account energy sharing of the present invention:

[0094] 1. The present invention provides a collaborative optimization scheduling method for multiple virtual power plants taking into account energy sharing, and constructs a master-slave game model between a scheduling center and multiple virtual power plants. The scheduling center, as the superior game leader, guides multiple virtual power plants to perform optimized scheduling and encourages energy sharing among multiple virtual power plants by dynamically adjusting electricity prices.

[0095] 2. The present invention provides a collaborative optimization scheduling method for multiple virtual power plants taking into account energy sharing, conducts in-depth research on the interest relationship among multiple virtual power plants, and solves the problem of loss of interests of some entities during the collaborative operation process through the Shapley value method, so that the collaborative operation relationship of multiple virtual power plants can be stably maintained. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 A schematic diagram of the relationship between the dispatching center and multiple virtual power plants in the step of building a master-slave game model in a method for collaborative optimization dispatching of multiple virtual power plants taking into account energy sharing of the present invention;

[0097] Figure 2 It is a flow chart of a specific implementation method of a multi-virtual power plant collaborative optimization scheduling method taking energy sharing into account according to the present invention. DETAILED DESCRIPTION

[0098] The following is a further description of the technical means, creative features, objectives and effects achieved by a multi-virtual power plant collaborative optimization scheduling method taking into account energy sharing of the present invention in combination with the accompanying drawings and embodiments.

[0099] Example:

[0100] A method for collaborative optimization scheduling of multiple virtual power plants taking into account energy sharing comprises the following steps:

[0101] 1) Construct a master-slave game model;

[0102] 2) Construct the upper-level game leader dispatch center model;

[0103] 3) Constructing a lower-level game follower multi-virtual power plant collaborative optimization scheduling model, which is divided into constructing a virtual power plant optimization scheduling model and constructing a multi-virtual power plant collaborative optimization scheduling model;

[0104] 4) Multi-virtual power plant profit distribution model.

[0105] Step 1) constructs a master-slave game model, which is specifically as follows:

[0106] like Figure 1 As shown, the master-slave game model in the upper layer is divided into a dispatching center model and a multi-virtual power plant model, wherein the dispatching center model is set as the game leader, and the dispatching center model dynamically adjusts the electricity price with the goal of maximizing its own profit, and then the multi-virtual power plant model is set as the game follower, and optimizes the dispatching with the goal of minimizing the group cost.

[0107] The multi-virtual power plant model trades electricity purchase and sale with the dispatch center model, while the dispatch center model trades electricity purchase and sale prices with the multi-virtual power plant model.

[0108] Step 2) constructs the upper-level game leader dispatch center model, which is specifically as follows:

[0109] In the above step 1), the dispatch center is used as the leader of the master-slave game, with the goal of maximizing system revenue, to formulate the purchase and sale prices of multiple virtual power plants. The objective function of the dispatch center is:

[0110]

[0111] Where: C DSO represents the revenue of the dispatching center; C grid It represents the cost of purchasing and selling electricity of the power grid, which can be expressed as follows:

[0112]

[0113] Where: c grid,buy 、c grid,sell Indicates the purchase and sale price of electricity traded with the power grid;

[0114] In the process of trading with multiple virtual power plants, the transaction price set by the dispatch center should meet the upper and lower limit constraints:

[0115]

[0116] Where: They are the upper and lower limits of the electricity purchase price of multiple virtual power plants; They are the upper and lower limits of the electricity sales prices of multiple virtual power plants respectively.

[0117] In the “In the process of trading with multiple virtual power plants, the transaction price set by the dispatch center should meet the upper and lower limit constraints”, in order to prevent the dispatch center from excessively raising the price, maximizing the system revenue and completely ignoring the interests of the lower virtual power plants, the price should also meet the following average value constraint:

[0118]

[0119] Step 3) constructs the virtual power plant optimization scheduling model in the lower-level game follower multi-virtual power plant collaborative optimization scheduling model, which is specifically:

[0120] In this step, the virtual power plant aggregates various types of distributed resources, including distributed generators, photovoltaic units, wind turbines, energy storage, interruptible loads and transferable loads. To minimize the scheduling cost, the objective function of the virtual power plant i is as follows:

[0121] Scheduling costs Including: Distributed Generator DG operating costs Energy storage ES operating costs Interruptible Load IL Call Cost Transferable load TL call cost and transaction costs with the grid The formula is as follows:

[0122]

[0123] The specific expressions of each cost are as follows:

[0124]

[0125] Where: a i , b i and c i Indicates the cost coefficient of the unit;

[0126] P i DG Indicates the actual output of DG;

[0127] P i ES,ch , P i ES,dis Indicates ES charging and discharging power;

[0128] c ES,i It represents the unit operating cost of ES;

[0129] P i IL It indicates the load interruption amount of IL users;

[0130] P iTL,up , P i TL,down Indicates the load increase and decrease of TL users;

[0131] c IL,i 、c TL,i denote the unit compensation costs for load interruption and transfer of IL users and TL users respectively;

[0132] P i grid,buy , P i grid,sell Indicates the amount of electricity purchased and sold from the power grid;

[0133] c trade,buy 、c trade,sell It indicates the purchase and sale price of electricity traded with the power grid, which is set by the dispatching center.

[0134] In constructing the virtual power plant optimization dispatch model, the constraints that the operation of virtual power plant i should meet are as follows:

[0135] 1) DG constraints:

[0136]

[0137] Where: Indicates the upper and lower limits of DG output; Indicates the climbing ability of DG;

[0138] 2) ES constraints:

[0139]

[0140] E i (t) = E i (t-1)

[0141] +P i ES,ch (t)η ch -P i ES,dis (t) / η dis

[0142]

[0143] Where: Represents the upper limit of ES output; E i represents ES capacity; ch , η dis Indicates ES charging and discharging efficiency; Indicates the upper and lower limits of ES capacity;

[0144] 3) IL / TL constraints:

[0145]

[0146] Where: Indicates the maximum interruption upper limit and transfer upper limit of load respectively;

[0147] 4) Grid / multiple virtual power plant transaction constraints:

[0148]

[0149] P i-j +P j-i =0

[0150] Where: Indicates the upper limit of the amount of electricity traded with the power grid; P i-j Represents the shared electricity between virtual power plant i and virtual power plant j. A positive value indicates that virtual power plant i supplies energy to virtual power plant j, and a negative value indicates that virtual power plant j supplies energy to virtual power plant i. represents the upper limit of power sharing between virtual power plants;

[0151] 5) Power balance constraints:

[0152]

[0153] Where: P i new Indicates new energy output.

[0154] Step 3) of constructing the multi-virtual power plant collaborative optimization scheduling model in the lower-level game follower multi-virtual power plant collaborative optimization scheduling model is specifically as follows:

[0155] In this step, virtual power plants can maximize overall benefits through energy sharing. The objective function of the multi-virtual power plant collaborative optimization scheduling model is as follows:

[0156]

[0157] Where: N represents the number of virtual power plant members in the cooperative alliance.

[0158] The multi-virtual power plant revenue allocation model in step 4) is specifically as follows:

[0159] The Shapley value method is used to realize the distribution of benefits from the coordinated operation of multiple virtual power plants. The calculation formula is as follows:

[0160]

[0161] δ(i,S)=v(S∪{i})-v(S)

[0162] Where: φ i(N) represents the Shapley value of participant i; represents the subset of the coalition that does not include participant i; δ(i,S) represents the marginal contribution.

[0163] like Figure 2 As shown, step 5) uses particle swarm optimization and Cplex solver to solve the above model, which is specifically as follows:

[0164] 1) Initialize the particle swarm parameters, including the number of particle swarms, the maximum number of iterations, etc.;

[0165] 2) Input the transaction parameters related to the virtual power plant, including equipment parameters, electricity price information, new energy and load forecast power, etc.;

[0166] 3) The dispatch center initializes the transaction electricity price based on the constraints and sends it to the virtual power plant;

[0167] 4) Multiple virtual power plants receive electricity price data for optimized dispatching, and use the Cplex solver to decide on the amount of electricity to be traded with the upper-level power grid with the goal of minimizing overall cost, and send it to the dispatching center;

[0168] 5) The dispatch center receives the transaction power data and calculates the revenue, and determines whether the maximum number of iterations has been reached. If not, the new round of electricity prices is updated and the process returns to step 4); if yes, the electricity price corresponding to the optimal revenue is output;

[0169] 6) The virtual power plant calculates the dispatch cost based on the optimal electricity price of the dispatch center and calculates the cost under different alliance combinations;

[0170] 7) Calculate the marginal revenue of each virtual power plant according to the cost under different alliance combinations, and distribute the revenue based on the Shapley value method.

[0171] In the above embodiments, a collaborative optimization scheduling method for multiple virtual power plants taking into account energy sharing of the present invention proposes a master-slave game model between a scheduling center and multiple virtual power plants based on energy sharing, and realizes effective interaction between multiple virtual power plants and upper-level institutions through electricity price adjustment; and the present invention proposes a collaborative operation profit distribution model for multiple virtual power plants based on the Shapley value method to solve the problem of loss of interests of some entities during the collaborative operation process.

[0172] The present invention constructs a master-slave game model between the dispatch center and multiple virtual power plants. The dispatch center, as the superior game leader, guides multiple virtual power plants to optimize dispatch by dynamically adjusting electricity prices, and encourages energy sharing among multiple virtual power plants. The present invention conducts in-depth research on the interest relationship among multiple virtual power plants, and solves the problem of loss of interests of some entities in the collaborative operation process through the Shapley value method, so that the collaborative operation relationship of multiple virtual power plants can be stably maintained.

[0173] In addition, those skilled in the art should recognize that the above embodiments are only used to illustrate the present application, and are not used to limit the present application. As long as they are within the spirit of the present application, any changes or modifications to the above embodiments will fall within the scope of the claims of the present application.

Claims

1. A method for collaborative optimization scheduling of multiple virtual power plants taking into account energy sharing, comprising the following steps: 1) Construct a master-slave game model; 2) Construct the upper-level game leader dispatch center model; 3) Constructing a lower-level game follower multi-virtual power plant collaborative optimization scheduling model, which is divided into constructing a virtual power plant optimization scheduling model and constructing a multi-virtual power plant collaborative optimization scheduling model; 4) Multiple virtual power plant revenue allocation model; 5) Particle swarm optimization and Cplex solver are used to solve the above model.

2. A method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing as claimed in claim 1, characterized in that: The step 1) of constructing a master-slave game model is specifically as follows: The upper-level master-slave game model is divided into a dispatching center model and a multi-virtual power plant model. The dispatching center model is set as the game leader. The dispatching center model dynamically adjusts the electricity price with the goal of maximizing its own profits. The multi-virtual power plant model is then set as the game follower, and optimized scheduling is performed with the goal of minimizing group costs.

3. A method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing as claimed in claim 2, characterized in that: The multi-virtual power plant model trades electricity purchase and sale with the dispatch center model, while the dispatch center model trades electricity purchase and sale prices with the multi-virtual power plant model.

4. A method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing as claimed in claim 1, characterized in that: The step 2) of constructing the upper-level game leader dispatch center model is specifically as follows: In the above step 1), the dispatch center is used as the leader of the master-slave game, with the goal of maximizing system revenue, to formulate the purchase and sale prices of multiple virtual power plants. The objective function of the dispatch center is: Where: C DSO represents the revenue of the dispatching center; C grid It represents the cost of purchasing and selling electricity of the power grid, which can be expressed as follows: Where: c grid,buy 、c grid,sell Indicates the purchase and sale price of electricity traded with the power grid; In the process of trading with multiple virtual power plants, the transaction price set by the dispatch center should meet the upper and lower limit constraints: Where: They are the upper and lower limits of the electricity purchase price of multiple virtual power plants; They are the upper and lower limits of the electricity sales prices of multiple virtual power plants respectively.

5. A method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing as claimed in claim 4, characterized in that: In the aforementioned "In the process of trading with multiple virtual power plants, the transaction electricity price set by the dispatch center should meet the upper and lower limit constraints", in order to prevent the dispatch center from excessively raising the electricity price and maximizing the system revenue while completely ignoring the interests of the lower-level virtual power plants, the electricity price should also meet the following average value constraint:

6. A method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing as claimed in claim 1, characterized in that: The step 3) of constructing the virtual power plant optimization scheduling model in the lower-level game follower multi-virtual power plant collaborative optimization scheduling model is specifically as follows: In this step, the virtual power plant aggregates various types of distributed resources, including distributed generators, photovoltaic units, wind turbines, energy storage, interruptible loads and transferable loads. To minimize the scheduling cost, the objective function of the virtual power plant i is as follows: Scheduling costs Including: Distributed Generator DG operating costs Energy storage ES operating costs Interruptible Load IL Call Cost Transferable load TL call cost and transaction costs with the grid The formula is as follows: The specific expressions of each cost are as follows: Where: a i 、b i and c i Indicates the cost coefficient of the unit; P i DG Indicates the actual output of DG; P i ES,ch , P i ES,dis Indicates ES charging and discharging power; c ES,i It represents the unit operating cost of ES; P i IL It indicates the load interruption amount of IL users; P i TL,up , P i TL,down Indicates the load increase and decrease of TL users; c IL,i 、c TL,i denote the unit compensation costs for load interruption and transfer of IL users and TL users respectively; P i grid,buy , P i grid,sell Indicates the amount of electricity purchased and sold from the power grid; c trade,buy 、c trade,sell It indicates the electricity purchase and sale price with the power grid, which is set by the dispatching center.

7. A method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing as claimed in claim 6, characterized in that: In the construction of the virtual power plant optimization scheduling model, the constraints that the operation of the virtual power plant i should meet are as follows: 7.1) DG Constraints: Where: Indicates the upper and lower limits of DG output; Indicates the climbing ability of DG; 7.2) ES constraints: AND i (t)=E i (t-1)+P i ES,ch (t)η ch -P i ES,dis (t) / η dis During the ceremony: Display ES output upper limit; E i represents ES capacity; ch , η dis Indicates ES charging and discharging efficiency; Indicates the upper and lower limits of ES capacity; 7.3) IL / TL constraints: Where: Indicates the maximum interruption upper limit and transfer upper limit of load respectively; 7.4) Grid / multiple virtual power plant transaction constraints: P i-j +P j-i =0 Where: Indicates the upper limit of the amount of electricity traded with the power grid; P i-j Represents the shared electricity between virtual power plant i and virtual power plant j. A positive value indicates that virtual power plant i supplies energy to virtual power plant j, and a negative value indicates that virtual power plant j supplies energy to virtual power plant i. represents the upper limit of power sharing between virtual power plants; 7.5) Power balance constraints: Where: P i new Indicates new energy output.

8. The method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing according to claim 1, characterized in that: The step 3) of constructing the multi-virtual power plant collaborative optimization scheduling model in the lower-level game follower multi-virtual power plant collaborative optimization scheduling model is specifically as follows: In this step, virtual power plants can maximize overall benefits through energy sharing. The objective function of the multi-virtual power plant collaborative optimization scheduling model is as follows: Where: N represents the number of virtual power plant members in the cooperative alliance.

9. The method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing according to claim 1, characterized in that: The multi-virtual power plant revenue distribution model of step 4) is specifically as follows: The Shapley value method is used to realize the distribution of benefits from the coordinated operation of multiple virtual power plants. The calculation formula is as follows: δ(i,S)=v(S∪{i})-v(S) Where: φ i (N) represents the Shapley value of participant i; represents the subset of the coalition that does not include participant i; δ(i,S) represents the marginal contribution.

10. The method for collaborative optimization and scheduling of multiple virtual power plants taking into account energy sharing according to claim 1, characterized in that: The step 5) uses a particle swarm algorithm and a Cplex solver to solve the above model, which is specifically as follows: 10.1) Initialize the particle swarm parameters, including the number of particle swarms, maximum number of iterations, etc.; 10.2) Input the transaction parameters related to the virtual power plant, including equipment parameters, electricity price information, new energy and load forecast power, etc.; 10.3) The dispatch center initializes the transaction electricity price based on the constraints and sends it to the virtual power plant; 10.4) Multiple virtual power plants receive electricity price data for optimized dispatching, and use the Cplex solver to decide on the amount of electricity to be traded with the upper-level power grid with the goal of minimizing the overall cost, and send it to the dispatching center; 10.5) The dispatch center receives the transaction power data and calculates the revenue, and determines whether the maximum number of iterations has been reached. If not, a new round of electricity prices is updated and the process returns to step 10.4); if yes, the electricity price corresponding to the optimal revenue is output; 10.6) The virtual power plant calculates the dispatch cost based on the optimal electricity price of the dispatch center and calculates the cost under different alliance combinations; 10.7) Calculate the marginal revenue of each virtual power plant based on the costs under different alliance combinations, and distribute the revenue based on the Shapley value method.

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