Virtual power plant customized aggregation optimization method considering master-slave game

Abstract

The invention belongs to the technical field of power system demand response, and discloses a virtual power plant customized aggregation optimization method considering a master-slave game, and the method specifically comprises the following steps: constructing a game aggregation framework based on a virtual power plant operator and users owning distributed energy resources; constructing a virtual power plant operator benefit model by taking the minimum aggregation cost of the virtual power plant operator as a target; building a user benefit model by taking the maximization of the expected benefit of the user as a target; based on a game aggregation framework, in combination with a virtual power plant operator benefit model and a user benefit model, a master-slave game double-layer optimization model is established, and customized aggregation optimization of the virtual power plant is realized through dynamic iterative adjustment; the problem that in the prior art, a virtual power plant aggregation mechanism is difficult to dynamically coordinate the benefits of operators and users, so that the resource optimization configuration efficiency is low is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system demand response, and specifically relates to a customized aggregation optimization method for a virtual power plant considering master-slave game. Background Art

[0002] Virtual power plants (VPs) are an important means of aggregating flexible resources to participate in the operation of new power systems. They can tap the flexibility potential of flexible resources and optimize and regulate them at a low cost, thereby participating in the dispatch and market transactions of new power systems. In the process of participating in the market, more reasonable resource selection and aggregation architecture can better balance resource utilization efficiency and economic benefits, thereby enhancing the market competitiveness of VPs. Currently, VPs face two major technical bottlenecks when aggregating distributed energy resources to participate in the power market. First, the existing aggregation mechanism lacks an effective game framework and fails to fully consider the differentiated response characteristics of users to electricity price incentives, resulting in significant deviations between the actual response of highly sensitive users and the dispatch plan when prices fluctuate. Second, the traditional static aggregation model adopts a fixed priority or weight allocation strategy, which is difficult to dynamically adapt to the randomness of renewable energy output and the time-varying nature of load demand, seriously restricting the market competitiveness of VPs. Especially in power systems with a high proportion of renewable energy access, this rigid aggregation method cannot achieve the optimal configuration of flexible resources, resulting in wasted regulation capacity and loss of economic benefits. Therefore, the existing technology has the problem that the VP aggregation mechanism cannot dynamically coordinate the interests of operators and users, resulting in inefficient resource optimization and allocation. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a customized aggregation optimization method for virtual power plants that takes into account the master-slave game, which solves the problem in the existing technology that the virtual power plant aggregation mechanism is difficult to dynamically coordinate the interests of operators and users, resulting in inefficient resource optimization configuration.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A virtual power plant customized aggregation optimization method considering master-slave game specifically includes the following steps:

[0006] Build a game aggregation framework based on virtual power plant operators and users with distributed energy resources;

[0007] Taking the minimum aggregate cost of virtual power plant operators as the goal, a virtual power plant operator benefit model is constructed;

[0008] Build a user benefit model with the goal of maximizing user expected benefits;

[0009] Based on the game aggregation framework, combined with the virtual power plant operator benefit model and user benefit model, a master-slave game two-layer optimization model is established to achieve customized aggregation optimization of the virtual power plant through dynamic iterative adjustment;

[0010] Based on virtual power plant operators and users with distributed energy resources, a game aggregation framework is constructed, which includes the following steps:

[0011] With virtual power plant operators as leaders;

[0012] Multiple independent users with distributed energy resources are followers;

[0013] Users submit performance parameters to virtual power plant operators based on distributed energy resources;

[0014] The virtual power plant operator serves as the upper-level decision-making body, responsible for aggregated regulation and providing users with adjustable capacity prices;

[0015] A game aggregation framework is jointly constructed through two-way interaction between virtual power plant operators and multiple independent users;

[0016] Distributed energy resources include wind power and photovoltaic wind power units and adjustable loads;

[0017] Adjustable loads include energy storage equipment, air conditioners, and vehicle-to-charging pile units consisting of electric vehicles and charging piles;

[0018] Performance parameters include the operating status, adjustable power, adjustable power, and operating cost of distributed energy resources;

[0019] The VPP operator benefit model includes the VPP operator’s aggregated costs and constraints;

[0020] The aggregated costs of virtual power plant operators include fixed user access costs, adjustable capacity payment costs, and power deviation penalty costs;

[0021] Constraints include adjustable power constraints, user selection constraints, and call price constraints;

[0022] The aggregate cost expression of the virtual power plant operator in period t is as follows:

[0023] C vpp =C1+C2+C3

[0024] Where C VPP is the aggregation cost; C1 is the user's fixed access cost; C2 is the adjustable capacity payment cost; C3 is the power deviation penalty cost;

[0025] The user fixed access cost C1 is expressed as follows:

[0026]

[0027] Where, I is the user set; Ci is the user fixed access cost coefficient; x i ∈{0,1} is the user selection flag, indicating whether to aggregate the corresponding user;

[0028] The expression of capacity adjustment payment cost C2 is as follows:

[0029]

[0030] Where, are the upward and downward adjustable capacity prices in the adjustable capacity price paid by the virtual power plant operator to user i in period t respectively; are the adjustable power and adjustable power reported by user i to the virtual power plant operator during period t, respectively;

[0031] The power deviation penalty cost C3 is expressed as follows:

[0032]

[0033] Where ρ is the power deviation penalty coefficient of the virtual power plant; U t With D t Adjustable power targets and adjustable power targets customized for virtual power plant operators respectively;

[0034] Substitute C1, C2 and C3 into C VPP In the expression of , we get:

[0035]

[0036] The expression of the adjustable power constraint is as follows:

[0037]

[0038] Where M is the adjustable power deviation range allowed by the virtual power plant operator, but the power deviation will cause a power deviation penalty cost C3;

[0039] The number of users in the aggregation is less than or equal to the total number of users. The expression of the user selection constraint is as follows:

[0040]

[0041] Where N I is the total number of users;

[0042] The adjustable capacity price negotiated between the virtual power plant operator and each user has upper and lower limits, so the call price constraint expression is:

[0043]

[0044] Where, and The minimum and maximum adjustable capacity prices paid by the virtual power plant operator to user i in period t are respectively

[0045] are the minimum and maximum downward adjustable capacity prices paid by the virtual power plant operator to user i in period t.

[0046] The expression of user expected benefit B is as follows:

[0047]

[0048] Where S is the uncertainty scenario set of wind and solar power unit output and user demand for distributed energy resource load; π s is the probability of scenario s; E is the user's adjustable capability benefit; C DG 、C ES 、C AC and C EV They are respectively the operating cost of wind and solar units, the operating cost of energy storage equipment, the operating cost of air conditioning and the operating cost of vehicle-pile units; C grid The cost of purchasing and selling electricity from the upper network to the user; C cut The cost of curtailing wind and solar power generation;

[0049] Among them, E, C grid 、C DG 、C ES 、C AC 、C EV and C cut The expression is as follows:

[0050]

[0051] Where, and are the upward and downward adjustable capacity prices in the adjustable capacity price paid by the virtual power plant to the user during period t;

[0052]

[0053] Where, are the electricity purchase and sales prices in period t under scenario s;

[0054] are the power purchased and sold to the superior network by the user in time period t under scenario s;

[0055]

[0056] Where r i dg is the operation and maintenance cost coefficient of the wind and solar power unit;

[0057]

[0058] Where r i es is the depreciation cost coefficient of the energy storage equipment; in the optimization results, or Any one of them is zero;

[0059]

[0060] Where r i ac 、r i ev are the operation and maintenance cost coefficients of air conditioning and vehicle-pile unit respectively;

[0061]

[0062] Where r i cut is the cost coefficient of wind and solar power curtailment of wind and solar power generation units.

[0063] Based on the game aggregation framework, combined with the virtual power plant operator benefit model and user benefit model, a master-slave game two-layer optimization model is established. Through dynamic iterative adjustment, customized aggregation optimization of the virtual power plant is achieved. The specific steps include:

[0064] The upper-level model is constructed with the minimum aggregate cost of the virtual power plant operator as the goal. The minimum aggregate cost expression is as follows:

[0065]

[0066] The lower-level model is constructed with the goal of maximizing the user's expected benefit. The expression for maximizing the expected benefit is as follows:

[0067]

[0068] The upper model and the lower model constitute a master-slave game two-layer optimization model;

[0069] Based on the master-slave game two-layer optimization model, the virtual power plant operator, as the leader, takes the lead in automatically adjusting capacity prices and user selection results;

[0070] Followers optimize the performance parameters of their own distributed energy resources based on the adjustable capacity price and upload the performance parameters to the virtual power plant operator;

[0071] The virtual power plant operator adjusts the price of adjustable capacity based on user responses;

[0072] Virtual power plant operators and users make dynamic iterative adjustments to ultimately achieve the optimal aggregation state and maximize benefits for both parties.

[0073] Beneficial effects of the present invention:

[0074] The present invention constructs a master-slave game aggregation framework including virtual power plant operators and distributed energy users, establishes a two-way interaction mechanism based on adjustable capacity prices, and effectively solves the problem that the interests of operators and users are difficult to dynamically coordinate in traditional aggregation methods; by constructing a virtual power plant operator benefit model and a user benefit model, this method achieves the collaborative optimization of minimizing operator aggregation costs and maximizing user expected benefits, and significantly improves the aggregation efficiency of distributed energy resources such as wind and solar units, energy storage equipment, and vehicle-to-pile units. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0076] Figure 1 It is the overall flow chart of the present invention;

[0077] Figure 2 It is a schematic diagram of the game aggregation framework of the present invention. DETAILED DESCRIPTION

[0078] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0079] like Figures 1 to 2 As shown, a virtual power plant customized aggregation optimization method considering master-slave game specifically includes the following steps:

[0080] Build a game aggregation framework based on virtual power plant operators and users with distributed energy resources;

[0081] Taking the minimum aggregate cost of virtual power plant operators as the goal, a virtual power plant operator benefit model is constructed;

[0082] Build a user benefit model with the goal of maximizing user expected benefits;

[0083] Based on the game aggregation framework, combined with the virtual power plant operator benefit model and user benefit model, a master-slave game two-layer optimization model is established to achieve customized aggregation optimization of the virtual power plant through dynamic iterative adjustment;

[0084] Based on virtual power plant operators and users with distributed energy resources, a game aggregation framework is constructed, which includes the following steps:

[0085] With virtual power plant operators as leaders;

[0086] Multiple independent users with distributed energy resources are followers;

[0087] Users submit performance parameters to virtual power plant operators based on distributed energy resources;

[0088] The virtual power plant operator serves as the upper-level decision-making body, responsible for aggregated regulation and providing users with adjustable capacity prices;

[0089] A game aggregation framework is jointly constructed through two-way interaction between virtual power plant operators and multiple independent users;

[0090] Distributed energy resources include wind power and photovoltaic wind power units and adjustable loads;

[0091] Adjustable loads include energy storage equipment, air conditioners, and vehicle-to-charging pile units consisting of electric vehicles and charging piles;

[0092] Performance parameters include the operating status, adjustable power, adjustable power, and operating cost of distributed energy resources;

[0093] The VPP operator benefit model includes the VPP operator’s aggregated costs and constraints;

[0094] The aggregated costs of virtual power plant operators include fixed user access costs, adjustable capacity payment costs, and power deviation penalty costs;

[0095] Constraints include adjustable power constraints, user selection constraints, and call price constraints;

[0096] The aggregate cost expression of the virtual power plant operator in period t is as follows:

[0097] C vpp =C1+C2+C3

[0098] Where C VPP is the aggregation cost; C1 is the user's fixed access cost; C2 is the adjustable capacity payment cost; C3 is the power deviation penalty cost;

[0099] The user fixed access cost C1 is expressed as follows:

[0100]

[0101] Where I is the user set; C i is the user's fixed access cost coefficient; x i ∈{0,1} is the user selection flag, indicating whether to aggregate the corresponding user;

[0102] The expression of capacity adjustment payment cost C2 is as follows:

[0103]

[0104] Where, are the upward and downward adjustable capacity prices in the adjustable capacity price paid by the virtual power plant operator to user i in period t respectively; are the adjustable power and adjustable power reported by user i to the virtual power plant operator during period t, respectively;

[0105] The power deviation penalty cost C3 is expressed as follows:

[0106]

[0107] Where ρ is the power deviation penalty coefficient of the virtual power plant; U t With D t Adjustable power targets and adjustable power targets customized for virtual power plant operators respectively;

[0108] Substitute C1, C2 and C3 into C VPP In the expression of , we get:

[0109]

[0110] The expression of the adjustable power constraint is as follows:

[0111]

[0112] Where M is the adjustable power deviation range allowed by the virtual power plant operator, but the power deviation will cause a power deviation penalty cost C3;

[0113] The number of users in the aggregation is less than or equal to the total number of users. The expression of the user selection constraint is as follows:

[0114]

[0115] Where N I is the total number of users;

[0116] The adjustable capacity price negotiated between the virtual power plant operator and each user has upper and lower limits, so the call price constraint expression is:

[0117]

[0118] Where, and The minimum and maximum adjustable capacity prices paid by the virtual power plant operator to user i in period t are respectively

[0119] are the minimum and maximum downward adjustable capacity prices paid by the virtual power plant operator to user i in period t.

[0120] The expression of user expected benefit B is as follows:

[0121]

[0122] Where S is the uncertainty scenario set of wind and solar power unit output and user demand for distributed energy resource load; π s is the probability of scenario s; E is the user's adjustable capability benefit; C DG 、C ES 、C AC and C EV They are respectively the operating cost of wind and solar units, the operating cost of energy storage equipment, the operating cost of air conditioning and the operating cost of vehicle-pile units; C grid The cost of purchasing and selling electricity from the upper network to the user; C cut The cost of curtailing wind and solar power generation;

[0123] Among them, E, C grid 、C DG 、C ES 、C AC 、C EV and C cut The expression is as follows:

[0124]

[0125] Where, and are the upward and downward adjustable capacity prices in the adjustable capacity prices paid by the virtual power plant to users during period t respectively;

[0126]

[0127] Where, are the electricity purchase and sales prices in period t under scenario s;

[0128] are the power purchased and sold to the superior network by the user in time period t under scenario s;

[0129]

[0130] Where r i dg is the operation and maintenance cost coefficient of the wind and solar power unit;

[0131]

[0132] Where r i es is the depreciation cost coefficient of the energy storage equipment; in the optimization results, or Any one of them is zero;

[0133]

[0134] Where r i ac 、r i ev are the operation and maintenance cost coefficients of air conditioning and vehicle-pile unit respectively;

[0135]

[0136] Where r i cut is the cost coefficient of wind and solar power curtailment of wind and solar power generation units.

[0137] Based on the game aggregation framework, combined with the virtual power plant operator benefit model and user benefit model, a master-slave game two-layer optimization model is established. Through dynamic iterative adjustment, customized aggregation optimization of the virtual power plant is achieved. The specific steps include:

[0138] The upper-level model is constructed with the minimum aggregate cost of the virtual power plant operator as the goal. The minimum aggregate cost expression is as follows:

[0139]

[0140] The lower-level model is constructed with the goal of maximizing the user's expected benefit. The expression for maximizing the expected benefit is as follows:

[0141]

[0142] The upper model and the lower model constitute a master-slave game two-layer optimization model;

[0143] Based on the master-slave game two-layer optimization model, the virtual power plant operator, as the leader, takes the lead in automatically adjusting capacity prices and user selection results;

[0144] Followers optimize the performance parameters of their own distributed energy resources based on the adjustable capacity price and upload the performance parameters to the virtual power plant operator;

[0145] The virtual power plant operator adjusts the price of adjustable capacity based on user responses;

[0146] Virtual power plant operators and users make dynamic iterative adjustments to ultimately achieve the optimal aggregation state and maximize benefits for both parties.

[0147] The present invention constructs a master-slave game aggregation framework including virtual power plant operators and distributed energy users, establishes a two-way interaction mechanism based on adjustable capacity prices, and effectively solves the problem that the interests of operators and users are difficult to dynamically coordinate in traditional aggregation methods; by constructing a virtual power plant operator benefit model and a user benefit model, this method achieves the collaborative optimization of minimizing operator aggregation costs and maximizing user expected benefits, and significantly improves the aggregation efficiency of distributed energy resources such as wind and solar units, energy storage equipment, and vehicle-to-pile units.

[0148] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0149] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications are intended to fall within the scope of the present invention.

Claims

1. A virtual power plant customized aggregation optimization method considering master-slave game, characterized by: The specific steps include: Build a game aggregation framework based on virtual power plant operators and users with distributed energy resources; Taking the minimum aggregate cost of virtual power plant operators as the goal, a virtual power plant operator benefit model is constructed; Build a user benefit model with the goal of maximizing user expected benefits; Based on the game aggregation framework and combined with the virtual power plant operator benefit model and user benefit model, a master-slave game two-layer optimization model is established, and customized aggregation optimization of the virtual power plant is achieved through dynamic iterative adjustment.

2. The virtual power plant customized aggregation optimization method considering master-slave game according to claim 1 is characterized in that: Based on virtual power plant operators and users with distributed energy resources, a game aggregation framework is constructed, which includes the following steps: With virtual power plant operators as leaders; Multiple independent users with distributed energy resources are followers; Users submit performance parameters to virtual power plant operators based on distributed energy resources; The virtual power plant operator serves as the upper-level decision-making body, responsible for aggregated regulation and providing users with adjustable capacity prices; Through two-way interaction between virtual power plant operators and multiple independent users, a game aggregation framework is jointly constructed.

3. The virtual power plant customized aggregation optimization method considering master-slave game according to claim 2 is characterized in that: Distributed energy resources include wind power and photovoltaic wind power units and adjustable loads; Adjustable loads include energy storage equipment, air conditioners, and vehicle-pile units consisting of electric vehicles and charging piles.

4. The virtual power plant customized aggregation optimization method considering master-slave game according to claim 2 is characterized in that: Performance parameters include the operating status of distributed energy resources, adjustable power, adjustable power and operating costs.

5. The virtual power plant customized aggregation optimization method considering master-slave game according to claim 4 is characterized in that: The VPP operator benefit model includes the VPP operator’s aggregated costs and constraints; The aggregated costs of virtual power plant operators include fixed user access costs, adjustable capacity payment costs, and power deviation penalty costs; Constraints include adjustable power constraints, user selection constraints, and call price constraints.

6. The virtual power plant customized aggregation optimization method considering master-slave game according to claim 5 is characterized in that: The aggregate cost expression of the virtual power plant operator in period t is as follows: C vpp =C1+C2+C3 Where C VPP is the aggregation cost; C1 is the user's fixed access cost; C2 is the adjustable capacity payment cost; C3 is the power deviation penalty cost; The user fixed access cost C1 is expressed as follows: Where I is the user set; C i is the user's fixed access cost coefficient; x i ∈{0,1} is the user selection flag, indicating whether to aggregate the corresponding user; The expression of capacity adjustment payment cost C2 is as follows: Where, are the upward and downward adjustable capacity prices in the adjustable capacity price paid by the virtual power plant operator to user i in period t respectively; are the adjustable power and adjustable power reported by user i to the virtual power plant operator during period t, respectively; The power deviation penalty cost C3 is expressed as follows: Where ρ is the power deviation penalty coefficient of the virtual power plant; U t With D t Adjustable power targets and adjustable power targets customized for virtual power plant operators respectively; Substitute C1, C2 and C3 into C VPP In the expression of , we get: The expression of the adjustable power constraint is as follows: Where M is the adjustable power deviation range allowed by the virtual power plant operator, but the power deviation will cause a power deviation penalty cost C3; The number of users in the aggregation is less than or equal to the total number of users. The expression of the user selection constraint is as follows: Where N I is the total number of users; The adjustable capacity price negotiated between the virtual power plant operator and each user has upper and lower limits, so the call price constraint expression is: Where, and The minimum and maximum adjustable capacity prices paid by the virtual power plant operator to user i in period t are respectively are the minimum and maximum downward adjustable capacity prices paid by the virtual power plant operator to user i in period t.

7. The virtual power plant customized aggregation optimization method considering master-slave game according to claim 6 is characterized in that: The expression of user expected benefit B is as follows: Where S is the uncertainty scenario set of wind and solar power unit output and user demand for distributed energy resource load; π s is the probability of scenario s; E is the user's adjustable capability benefit; C DG 、C ES 、C AC and C EV They are respectively the operating cost of wind and solar units, the operating cost of energy storage equipment, the operating cost of air conditioning and the operating cost of vehicle-pile units; C grid The cost of purchasing and selling electricity from the upper network to the user; C cut The cost of curtailing wind and solar power generation; Among them, E, C grid 、C DG 、C ES 、C AC 、C EV and C cut The expression is as follows: Where, and are the upward and downward adjustable capacity prices in the adjustable capacity prices paid by the virtual power plant to users during period t respectively; Where, are the electricity purchase and sales prices in period t under scenario s; are the power purchased and sold to the superior network by the user in time period t under scenario s; Where r i dg is the operation and maintenance cost coefficient of the wind and solar power unit; Where r i es is the depreciation cost coefficient of the energy storage equipment; in the optimization results, or Any one of them is zero; Where r i ac 、r i ev are the operation and maintenance cost coefficients of air conditioning and vehicle-pile unit respectively; Where r i cut is the cost coefficient of wind and solar power curtailment of wind and solar power generation units.

8. The virtual power plant customized aggregation optimization method considering master-slave game according to claim 7 is characterized in that: Based on the game aggregation framework, combined with the virtual power plant operator benefit model and user benefit model, a master-slave game two-layer optimization model is established. Through dynamic iterative adjustment, customized aggregation optimization of the virtual power plant is achieved. The specific steps include: The upper-level model is constructed with the minimum aggregate cost of the virtual power plant operator as the goal. The minimum aggregate cost expression is as follows: The lower-level model is constructed with the goal of maximizing the user's expected benefit. The expression for maximizing the expected benefit is as follows: The upper model and the lower model constitute a master-slave game two-layer optimization model; Based on the master-slave game two-layer optimization model, the virtual power plant operator, as the leader, takes the lead in automatically adjusting capacity prices and user selection results; Followers optimize the performance parameters of their own distributed energy resources based on the adjustable capacity price and upload the performance parameters to the virtual power plant operator; The virtual power plant operator adjusts the price of adjustable capacity based on user responses; Virtual power plant operators and users make dynamic iterative adjustments to ultimately achieve the optimal aggregation state and maximize benefits for both parties.

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