Operation method of shared mobile energy storage in virtual power plant cluster

Abstract

The invention relates to an operation method of shared mobile energy storage in a virtual power plant cluster. The method comprises the following steps: (1) establishing a shared operation architecture of the mobile energy storage in the virtual power plant cluster; (2) mobile energy storage supply and demand side benefits are analyzed, a shared mobile energy storage pricing and regulation and control method based on the master-slave game is established, and multi-agent benefit balance is achieved; (3) considering the electric power-traffic coupling operation characteristics of the shared mobile energy storage, and establishing a regulation and control environment model of a shared mobile energy storage operator; (4) in the virtual power plant cluster, establishing a regulation and control environment model of the virtual power plant cluster by considering source load characteristics of each main body, a grid structure and operation safety constraints; and (5) providing a solution method based on decomposition iteration for the decision-making problem of the shared mobile energy storage operation model. Compared with the prior art, the method fully considers the influence of the power-traffic operation environment and the virtual power plant cluster operation demand on the shared mobile energy storage operation strategy, and achieves the balanced optimization of the mobile energy storage operation benefit and the virtual power plant cluster operation cost. And guidance is provided for popularization and application of shared mobile energy storage and an energy storage configuration strategy of the virtual power plant cluster.

Description

Technical Field

[0001] The present invention relates to a shared energy storage operation method, and in particular to an operation method of shared mobile energy storage in a virtual power plant cluster. Background Art

[0002] Energy storage technology is one of the key technologies to promote the high proportion of clean energy grid connection and enhance the flexible regulation capability of urban power grids. With the development of advanced energy storage manufacturing technology with small volume and large capacity, mobile energy storage has received extensive attention and engineering applications in power systems in recent years. In urban power grids, mobile energy storage relies on a highly developed road system and can provide users with charging and discharging services at different times and spaces. It has strong configuration flexibility and scheduling flexibility, and plays multiple roles such as peak shaving, intelligent charging and selling, and emergency rescue.

[0003] Clean energy units need to be equipped with energy storage according to a certain installed capacity ratio. At present, most virtual power plants independently configure energy storage according to their own operating needs, which requires high investment and operation and maintenance costs. Due to the long investment payback period of energy storage equipment, it lacks appeal to other investors. Considering this bottleneck problem faced by the promotion and application of energy storage, shared energy storage technology can effectively integrate users' energy storage configuration needs, improve energy storage utilization efficiency, and lower the capital threshold for energy storage configuration. The spatiotemporal operation characteristics of mobile energy storage make it naturally have shared operation characteristics. By establishing a reasonable shared operation framework and formulating pricing and regulation strategies for mobile energy storage shared operation, it can provide energy storage leasing services for virtual power plant clusters in cities, improve the regulation flexibility, operational economy, and clean electricity of virtual power plant clusters.

[0004] At present, there have been several studies on the sharing model and pricing mechanism of energy storage, but they usually only focus on the power regulation ability and trading model of energy storage participating in the power system users, ignoring the impact of the power system operating environment, grid structure and safety constraints on the operating income of energy storage. At the same time, the existing research and application of shared energy storage are still mainly static energy storage power stations, and few involve the shared operation model and pricing mechanism of mobile energy storage. Compared with the shared pricing and regulation decision-making methods of static energy storage, the integer variables regulated by mobile energy storage in the transportation network will bring difficulties to the model solution. Therefore, considering the power-transportation coupled operating environment, a pricing and scheduling strategy for shared mobile energy storage in the application of virtual power plant clusters is proposed, which is of great value for improving the operating efficiency and promotion of mobile energy storage, as well as improving the regulation capability of virtual power plants. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide an operating method of shared mobile energy storage in a virtual power plant cluster.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A method for operating shared mobile energy storage in a virtual power plant cluster, the method comprising the following steps:

[0008] (1) Based on the spatiotemporal operation characteristics of mobile energy storage and the energy storage configuration requirements of the virtual power plant cluster, a shared operation architecture is established in which mobile energy storage provides energy storage services for the virtual power plant cluster;

[0009] (2) Analyze the operational efficiency goals of mobile energy storage investors and virtual power plant clusters, and establish a shared mobile energy storage pricing and regulation model based on master-slave game. The operator optimizes the rental pricing and spatiotemporal operation strategy of shared mobile energy storage in the upper model, and the virtual power plant cluster optimizes the configuration strategy of shared mobile energy storage in the lower model. Their decision results influence each other.

[0010] (3) Considering the power-transportation coupled operation characteristics of shared mobile energy storage, a regulatory environment model for shared mobile energy storage operators is established;

[0011] (4) In the virtual power plant cluster, the control environment model of the virtual power plant cluster is established by considering the source and load characteristics of each entity, the grid structure and the operational safety constraints;

[0012] (5) For the established shared mobile energy storage time-sharing leasing pricing and scheduling model, the lower-level model contains integer decision variables, and the two-level mixed integer programming problem is difficult to solve. A solution method based on decomposition and iteration is proposed.

[0013] Step (1) is specifically as follows: shared mobile energy storage is independently operated by a third-party operator, does not provide exclusive services for a certain virtual power plant, and provides shared energy storage services for virtual power plant clusters. Under the shared operation framework, mobile energy storage operators make profits by leasing energy storage services to virtual power plants and selling electricity, thereby improving the investment and operation benefits of mobile energy storage. At the same time, relying on the energy storage capacity and shared operation model, there is also a sharing relationship between the power of each virtual power plant. During operation, shared mobile energy storage provides shared energy storage services for the virtual power plant cluster, improves the operating efficiency of each virtual power plant in the cluster, and enhances the economy, reliability, and low carbon nature of its participation in power services. This shared operation framework achieves a win-win situation for shared mobile energy storage operators and virtual power plant clusters.

[0014] Step (2) is as follows: the shared mobile energy storage operator leases the mobile energy storage it has invested in as a shared resource to the virtual power plant cluster, and optimizes the leasing pricing and scheduling plan of the shared mobile energy storage to maximize the operating benefits of the mobile energy storage. The virtual power plant cluster leases mobile energy storage services to improve its operating costs and optimize its operating status, and settles the energy storage service leasing fees between the operator and each virtual power plant according to the usage period.

[0015] Therefore, there is a master-slave game relationship between the shared mobile energy storage operator and the virtual power plant cluster. The shared mobile energy storage operator publishes the time-sharing leasing pricing of shared mobile energy storage and accepts the feedback on the mobile energy storage leasing demand of the virtual power plant. After reaching a transaction with each virtual power plant under the conditions of satisfying the mobile energy storage transportation-power coupling operation constraints, the day-ahead scheduling decision of the shared mobile energy storage is completed. The virtual power plant makes decisions on the leasing and charging and discharging plans of the shared mobile energy storage in each time period based on its own operating economy, safety requirements and the time-sharing leasing price of shared mobile energy storage, and feeds back to the shared mobile energy storage operator. The time-sharing leasing pricing of mobile energy storage published by the operator and the plans of each virtual power plant to lease energy storage influence each other until equilibrium is reached among all entities.

[0016] Step (3) is as follows: the operator makes an optimization decision on the pricing and scheduling plan of shared mobile energy storage time-sharing leasing, which needs to consider the optimization goal of shared mobile energy storage operation efficiency and the regulation constraints under the power-transportation coupling operation environment.

[0017] (301) The operator's mobile energy storage pricing and scheduling decisions are aimed at maximizing the operating benefits of mobile energy storage. Its operating benefits are related to the energy storage rental demand and charging and discharging demand of the virtual power plant, which can be specifically described as follows:

[0018]

[0019] Among them, c r,t is the time-sharing leasing price coefficient of shared mobile energy storage, which is decided by the operator based on the energy storage leasing demand and charging and discharging demand of the virtual power plant cluster; i,t is the leasing status of virtual power plant i for shared mobile energy storage, which is decided by each virtual power plant; Node is the collection of charging and discharging stations within the service range of shared mobile energy storage.

[0020] (302) As a shared control resource among multiple virtual power plants, mobile energy storage can be connected to any charging and discharging station in the virtual power plant cluster to provide energy storage services for the economic operation of the connected virtual power plant. The control environment of shared mobile energy storage includes the power grid and the transportation network. Therefore, it is necessary to consider both its operation model in the transportation network and the power control model of the virtual power plant.

[0021] Considering the spatiotemporal variation characteristics of traffic network conditions, a weighted directed graph of the spatiotemporal traffic network is established to describe the operation environment of the traffic network. The travel cost matrix T between the charging and discharging sites of each virtual power plant at time t is:

[0022]

[0023] In the space-time transportation network, the driving state of mobile energy storage must meet the uniqueness, continuity, and starting station location constraints, which can be described as follows:

[0024]

[0025]

[0026]

[0027] Among them, t0 and τ0 represent the starting time of the t period and the τ period respectively; (o, d) represents the starting point and the end point of the mobile energy storage driving path; A is the set of all possible moving states of the mobile energy storage; 0 / 1 variable γ (o,d),t0 Indicates the mobile state (o, d) of the mobile energy storage, whether t0 is realized. When the mobile energy storage moves from site o to site d at time t0, γ (o,d),t0 =1, otherwise γ (o,d),t0 =0.

[0028] Mobile energy storage does not charge or discharge during driving, does not participate in the economic dispatch of any user in the virtual power plant cluster, and consumes electricity; when it stops at a charging station, its battery can be connected to the virtual power plant to participate in the dispatch. Therefore, the power state of the mobile energy storage battery is related to the driving state and the charging and discharging state in the virtual power plant, which can be described as follows:

[0029]

[0030]

[0031] in, They represent the charging active power, discharging active power and reactive power of mobile energy storage after it is connected to the virtual power plant; S ess ,C ess Respectively represent the rated power and capacity of the battery; C t is the battery power in period t; Node represents the location set of charging and discharging stations; p travel SOC is the battery energy consumption per unit time during mobile energy storage driving; max and SOC min C is the upper and lower limits of the battery state of charge. r The default value for the start and end time of the run.

[0032] Since operators are responsible for the investment and maintenance of mobile energy storage equipment, they also need to consider the upper and lower limits of mobile energy storage time-of-use pricing in their decision-making process to ensure that economic benefits can be obtained through leasing energy storage, but avoid extremely high pricing when energy storage services are in short supply.

[0033] (303) The operator’s shared mobile energy storage pricing and dispatch model is described as:

[0034] maxf O (x,y i )

[0035] stg O (x,y i )≤0,h O (x,y i )=0

[0036] y i ∈argminf VPP,i (y i )

[0037] Among them, x is the decision variable of the upper-level shared mobile energy storage operator, y i is the decision variable of each virtual power plant in the lower layer. O ≤0 includes pricing range constraints, mobile energy storage power safety constraints, h O = 0 includes the traffic network driving constraints of mobile energy storage.

[0038] Step (4) is as follows: each virtual power plant in the cluster coordinates and optimizes the leasing and charging and discharging of shared mobile energy storage, as well as the regulation of other types of flexible resources, which requires considering the total cost of leasing and operating shared mobile energy storage for each virtual power plant, as well as the constraints of coordinated regulation of multiple types of resources considering grid structure and safety constraints. (401) The total operating cost of the virtual power plant includes the leasing cost of mobile energy storage, the cost of purchasing electricity from multiple types of units, and the cost of flexible load scheduling. The operating cost model of the i-th virtual power plant is specifically described as:

[0039]

[0040] Among them, c G,t , c L,t is the cost coefficient vector of power purchase and flexible load regulation of the unit, P G,i,t , P L,i,t are the unit power generation and flexible load regulation power vector, c r,t The time-sharing leasing pricing for shared mobile energy storage, λ i,t is the shared mobile energy storage leasing decision of virtual power plant i at time t, λ i,t ≤γ (i,i),t .

[0041] (402) The line flow and node voltage security constraints considering the grid structure in the virtual power plant are described as follows:

[0042]

[0043]

[0044]

[0045]

[0046] Among them, P ji,t ,Q ji,t is the active and reactive power flow on line ji, U i,t is the node voltage amplitude, r ji ,x ji is the line resistance and reactance, S ji,max ,S ji,min ,U i,max ,U i,min are the upper and lower limits of the line power flow and the upper and lower limits of the node voltage respectively. The injected power p at node i i,t ,q i,t It is related to the power of multiple types of power sources, flexible loads, and energy storage. The active and reactive power regulation of distributed thermal power units, distributed new energy units, flexible loads and other source and load equipment in the virtual power plant is carried out within the rated range. During the period when the virtual power plant rents mobile energy storage services, the mobile energy storage is connected to the charging and discharging station in the virtual power plant and participates in the optimization and scheduling of the virtual power plant within its power safety range. The model is described as follows:

[0047]

[0048]

[0049]

[0050] in, It is the active and reactive power input by the power supply to the system. is the power factor angle, P G,i,min ,P G,i,max P is the upper and lower limits of the active output of the power supply. L,i,min ,P L,i,max It is the upper and lower limits for flexible load power adjustment.

[0051] (403) The optimal operation model of each virtual power plant is described as follows:

[0052] minf vpp,i (y i )

[0053] stg vpp,i (y i )≤0

[0054] Among them, g vpp,i ≤0 includes the operating safety constraints of the virtual power plant and the power control range constraints of each internal unit, load, and energy storage.

[0055] In step (5), for the optimization pricing and scheduling problem of the shared mobile energy storage, it is difficult for the lower-level mixed integer programming model to directly use the KKT conditions to convert the double-layer problem into a single-layer optimization problem for solution. The decomposition solution method based on the C&CG algorithm design model can achieve the optimal solution of the double-layer mixed integer linear programming model in a finite number of iterations.

[0056] (501) In the upper-level optimization problem, the replication variables and replication constraints of the lower-level optimization problem are introduced to construct the main problem;

[0057] (502) Solve the main problem, obtain the optimal values ​​of the upper-layer operator's pricing and scheduling decision variables and the replicated lower-layer virtual power plant mobile energy storage leasing plan decision variables, and update the upper bound of the upper-layer operator's optimal revenue solution;

[0058] (503) Substituting the optimal solution of the upper-layer operator's pricing and scheduling decision variables into the lower-layer virtual power plant optimization scheduling problem to solve it, and obtaining the optimal value of the operating cost of each virtual power plant;

[0059] (504) Feedback the optimal value of the operating cost of each virtual power plant and the mobile energy storage leasing plan to the upper-level operator, solve the upper-level problem again, make corrections to the scheduling plan of the mobile energy storage, and update the lower bound of the optimal solution of the operator's revenue;

[0060] (505) Determine whether the optimal solution for the operator's revenue converges. If so, the iteration terminates. Otherwise, under the virtual power plant mobile energy storage leasing plan obtained in the current iteration step, construct the KKT conditions of the lower-level problem, add them to the main problem constructed in (501), and return to (502) for iterative solution.

[0061] The shared energy storage model provides a flexible energy storage configuration solution for virtual power plant clusters. The existing shared energy storage operation model usually ignores the network constraints and security constraints on the power side, making it difficult to accurately characterize the charging and discharging needs of virtual power plants, affecting the effect of pricing and regulation decision-making results on improving the efficiency of energy storage operations and the operating costs of virtual power plants. Compared with the prior art, the present invention has the following advantages: based on the operation architecture of shared mobile energy storage, mobile energy storage equipment with flexible time and space characteristics is introduced in the optimized operation of virtual power plants, reducing the energy storage configuration cost of virtual power plants, and improving the flexibility of virtual power plant cluster regulation, operational economy, and low-carbon electricity; considering the operating network and security constraints of mobile energy storage on the power side and the transportation side, a pricing and regulation model and solution algorithm for shared mobile energy storage in virtual power plant clusters are proposed, achieving a win-win situation for mobile energy storage operators and virtual power plant clusters, and providing technical support for the promotion and application of shared mobile energy storage in virtual power plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1A flowchart of the pricing and regulation method of shared mobile energy storage in a virtual power plant cluster in the present invention;

[0063] Figure 2 The results of the time-sharing leasing pricing of shared mobile energy storage within a day and the location of shared energy storage connected to the virtual power plant cluster at different times in the test scenario;

[0064] Figure 3 In the test scenario, the charging and discharging power and battery charge changes of shared mobile energy storage in different virtual power plants at different times of the day. DETAILED DESCRIPTION

[0065] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0066] Example

[0067] like Figure 1 As shown, a pricing and regulation method for shared mobile energy storage in a virtual power plant cluster includes the following steps:

[0068] Step 1: Build a shared operation architecture for mobile energy storage in the virtual power plant cluster, specify that shared mobile energy storage is invested, operated, and maintained by a third-party independent operator, and provide mobile energy storage time-sharing leasing services to users of shared mobile energy storage, namely the virtual power plant cluster.

[0069] Step 2: Analyze the profit needs of shared mobile energy storage operators and the operating costs of virtual power plant cluster users, study the impact of shared mobile energy storage time-sharing leasing pricing on the benefits of operators and virtual power plant cluster users, and establish a shared mobile energy storage time-sharing leasing pricing and scheduling decision-making method based on the master-slave game model to achieve multi-agent benefit balance.

[0070] Step 3: Clarify the decision variables of the shared mobile energy storage operator in the upper problem of the master-slave game model, including the time-sharing rental pricing of shared mobile energy storage c r,t , the location of the virtual power plant cluster connected in each period γ (i,i),t , and the charging and discharging capacity provided to each virtual power plant Under the conditions of meeting the power side and traffic side operation constraints of shared mobile energy storage, as well as the pricing range constraints, the optimization goal is to maximize the overall rental income of shared mobile energy storage. The operator's shared mobile energy storage pricing and scheduling model can be simplified as follows:

[0071] maxf O (x,y i )

[0072] stg O (x,y i )≤0,h O (x,y i)=0

[0073] y i ∈argminf VPP,i (y i )

[0074] Among them, x is the decision variable of the upper-level shared mobile energy storage operator, and y is the decision variable of each virtual power plant in the lower level. O ≤0 includes pricing range constraints, mobile energy storage quantity constraints and safety constraints, h O = 0 includes the traffic network driving constraints of mobile energy storage.

[0075] Step 4: The operating revenue objective function of the shared mobile energy storage operator is related to the energy storage rental demand of the virtual power plant, which is specifically described as:

[0076]

[0077] Among them, c r,t is the time-sharing leasing price coefficient of shared mobile energy storage, which is decided by the operator based on the energy storage leasing demand and charging and discharging demand of the virtual power plant cluster; i,t is the leasing status of virtual power plant i for shared mobile energy storage, which is decided by each virtual power plant; Node is the collection of charging and discharging stations within the service range of shared mobile energy storage.

[0078] Step 5: Mobile energy storage is a shared control resource among multiple virtual power plants. It can be connected to any power station in the virtual power plant cluster for charging and discharging, providing energy storage services for the economic operation of the connected virtual power plant. The control environment of shared mobile energy storage includes the power grid and the transportation network, so it is necessary to consider its operation model in the transportation network and the power control constraints of the virtual power plant operation.

[0079] Considering the spatiotemporal variation characteristics of traffic network conditions, a weighted directed graph of the spatiotemporal traffic network is established to describe the operation environment of the traffic network. The travel cost matrix T between the charging and discharging sites of each virtual power plant at time t is:

[0080]

[0081] In the space-time transportation network, the driving state of mobile energy storage must meet the uniqueness, continuity, and starting station location constraints, which can be described as follows:

[0082]

[0083]

[0084]

[0085] Among them, t0 and τ0 represent the starting time of the t period and the τ period respectively; (o, d) represents the starting point and the end point of the mobile energy storage driving path; A is the set of all possible moving states of the mobile energy storage; 0 / 1 variable γ (o,d),t0 Indicates the mobile state (o, d) of the mobile energy storage, whether t0 is realized. When the mobile energy storage moves from site o to site d at time t0, γ (o,d),t0 =1, otherwise γ (o,d),t0 =0.

[0086] Mobile energy storage does not charge or discharge during driving, does not participate in the economic dispatch of any user in the virtual power plant cluster, and consumes electricity; when it stops at a charging station, its battery can be connected to the virtual power plant to participate in the dispatch. Therefore, the power state of the mobile energy storage battery is related to the driving state and the charging and discharging state in the virtual power plant, which can be described as follows:

[0087]

[0088]

[0089] Among them, p ch ,p di ,q ess They represent the charging active power, discharging active power and reactive power of mobile energy storage after it is connected to the virtual power plant; S ess ,C ess Respectively represent the rated power and capacity of the battery; C t is the battery power in period t; Node represents the location set of charging and discharging stations; p travel It is the battery energy consumption per unit time during mobile energy storage driving; SOC is the battery charge state, and usually sets the upper and lower limits during operation and the default values ​​for the start and end times of operation.

[0090] Since operators are responsible for the investment and maintenance of mobile energy storage equipment, they also need to consider the upper and lower limits of mobile energy storage time-of-use pricing in their decision-making process to ensure that economic benefits can be obtained through leasing energy storage, but avoid extremely high pricing when energy storage services are in short supply. The model is specifically described as follows:

[0091]

[0092]

[0093] In the formula, M is the upper limit of pricing, C I Represents the average daily investment and maintenance cost of shared energy storage.

[0094] Step 6: Clarify the decision variables of the virtual power plant cluster in the lower-level problem of the master-slave game model, including the time-sharing leasing plan λ of the shared mobile energy storage r,t, active and reactive power generation of distributed power sources, and flexible load regulation power. Under the conditions of satisfying grid structure and safety constraints, the optimization goal is to minimize the total operating cost of the virtual power plant. The optimization operation model of the virtual power plant i in the cluster can be simplified as:

[0095] minf vpp,i (y)

[0096] stg vpp,i (y)≤0

[0097] Among them, g vpp,i ≤0 includes the operating safety constraints of the virtual power plant and the power control range constraints of each internal unit, load, and energy storage.

[0098] Step 7: The total operating cost of the virtual power plant includes the rental fee of mobile energy storage, the cost of purchasing electricity from multiple types of units, and the cost of flexible load dispatching. The specific description of the operating cost model of the i-th virtual power plant is:

[0099]

[0100] Among them, c G,t , c L,t is the cost coefficient vector of power purchase and flexible load regulation of the unit, P G,i,t , P L,i,t are the unit power generation and flexible load regulation power vector, c r,t The time-sharing leasing pricing for shared mobile energy storage, λ i,t is the shared mobile energy storage leasing decision of virtual power plant i at time t, λ i,t ≤γ (i,i),t .

[0101] Step 8: The line flow and node voltage security constraints considering the grid structure in the virtual power plant are described as follows:

[0102]

[0103]

[0104]

[0105]

[0106] Among them, P ji,t ,Q ji,t is the active and reactive power flow on line ji, U i,t is the node voltage amplitude, r ji ,x ji is the line resistance and reactance, S ji,max ,S ji,min ,Ui,max ,U i,min are the upper and lower limits of the line power flow and the upper and lower limits of the node voltage respectively. The injected power p at node i i,t ,q i,t Related to multiple types of power sources, flexible loads, and energy storage, the specific model is:

[0107]

[0108]

[0109] The active and reactive power control of distributed thermal power units, distributed new energy units, flexible loads and other source and load equipment in the virtual power plant is carried out within the rated range. During the period when the virtual power plant rents mobile energy storage services, the mobile energy storage is connected to the charging and discharging station in the virtual power plant and participates in the optimization dispatch of the virtual power plant within its power safety range. The model is described as follows:

[0110]

[0111]

[0112]

[0113] in, It is the active and reactive power input by the power supply to the system. is the power factor angle, P G,i,min ,P G,i,max P is the upper and lower limits of the active output of the power supply. L,i,min ,P L,i,max It is the upper and lower limits for flexible load power adjustment.

[0114] Step 9: For the two-level optimization problem of time-of-use pricing and scheduling of the shared mobile energy storage, it is difficult for the lower-level mixed integer programming model to directly use the KKT condition to convert the two-level problem into a single-level optimization problem for solution. The decomposition solution method based on the C&CG algorithm design model is used to obtain the optimal solution of the two-level mixed integer linear programming model in a finite number of iterations.

[0115] (501) In the upper-level optimization problem, the replication variables and replication constraints of the lower-level optimization problem are introduced to construct the main problem;

[0116] (502) Solve the main problem, obtain the optimal values ​​of the upper-layer operator's pricing and scheduling decision variables and the replicated lower-layer virtual power plant mobile energy storage leasing plan decision variables, and update the upper bound of the upper-layer operator's optimal revenue solution;

[0117] (503) Substituting the optimal solution of the upper-layer operator's pricing and scheduling decision variables into the lower-layer virtual power plant optimization scheduling problem to solve it, and obtaining the optimal value of the operating cost of each virtual power plant;

[0118] (504) Feedback the optimal value of the operating cost of each virtual power plant and the mobile energy storage leasing plan to the upper-level operator, solve the upper-level problem again, make corrections to the scheduling plan of the mobile energy storage, and update the lower bound of the optimal solution of the operator's revenue;

[0119] (505) Determine whether the optimal solution for the operator's revenue converges. If so, the iteration terminates. Otherwise, under the virtual power plant mobile energy storage leasing plan obtained in the current iteration step, construct the KKT conditions of the lower-level problem, add them to the main problem constructed in (501), and return to (502) for iterative solution.

[0120] This embodiment uses a virtual power plant cluster system constructed based on the local area of ​​the Sioux Falls road network and a 4*IEEE-33 node standard test system as an example to illustrate the time-sharing pricing and regulation results of shared mobile energy storage. The shared mobile energy storage of the virtual power plant cluster has a rated capacity of 10MWh, a rated power of 2MW, a charging and discharging efficiency of 95%, an initial power of 50%, and an hourly driving power consumption of 0.05MW. The four virtual power plants are all equipped with distributed new energy units, distributed gas turbines, and flexible loads. The traffic conditions, wind power, photovoltaics, and load power data used in the analysis are all based on actual measured data in a certain place. Based on the recent forecast information on traffic conditions and source-load power, the method of the present invention can optimize the time-sharing leasing pricing of shared mobile energy storage in the virtual power plant cluster and the location of access to the virtual power plant cluster in each time period, such as Figure 2 At the same time, the method of the present invention optimizes the charging and discharging power of shared mobile energy storage in different virtual power plants in each time period to ensure that the battery power is within a safe range, such as Figure 3 shown.

[0121] The application scenarios of this embodiment include the following: providing technical support for the investment and operation model of shared mobile energy storage, improving the operating income of mobile energy storage, and promoting the promotion of mobile energy storage technology; providing a new energy storage configuration model for virtual power plant clusters, integrating the energy storage configuration needs of virtual power plant clusters, promoting the sharing of power in virtual power plant clusters and the coordinated consumption of clean electricity, and supporting the economic and low-carbon operation of virtual power plant clusters.

Claims

1. A pricing and regulation method for shared mobile energy storage in a virtual power plant cluster, characterized in that: The method comprises the following steps: (1) Based on the spatiotemporal operation characteristics of mobile energy storage and the energy storage configuration requirements of the virtual power plant cluster, a shared operation architecture is established in which mobile energy storage provides energy storage services for the virtual power plant cluster; (2) Analyze the operational efficiency goals of mobile energy storage investors and virtual power plant clusters, and establish a shared mobile energy storage operation model based on master-slave game. The operator optimizes the rental pricing and spatiotemporal operation strategy of shared mobile energy storage in the upper model, and the virtual power plant cluster optimizes the configuration strategy of shared mobile energy storage in the lower model. Their decision results influence each other. (3) Considering the power-transportation coupled operation characteristics of shared mobile energy storage, a regulatory environment model for shared mobile energy storage operators is established; (4) In the virtual power plant cluster, the control environment model of the virtual power plant cluster is established by considering the source and load characteristics of each entity, the grid structure and the operational safety constraints; (5) For the established shared mobile energy storage time-sharing leasing pricing and scheduling model, the lower-level model contains integer decision variables, and the two-level mixed integer programming problem is difficult to solve. A solution method based on decomposition and iteration is proposed.

2. The method for operating a shared mobile energy storage in a virtual power plant cluster according to claim 1, characterized in that: In step (1): shared mobile energy storage is independently operated by a third-party operator, and does not provide exclusive services for a certain virtual power plant, but provides shared energy storage services for virtual power plant clusters. Under the shared operation framework, mobile energy storage operators make profits by leasing energy storage services to virtual power plants and selling electricity, thereby improving the investment and operation benefits of mobile energy storage. At the same time, relying on the energy storage power and shared operation model, there is also a sharing relationship between the power of each virtual power plant.

3. The method for operating a shared mobile energy storage in a virtual power plant cluster according to claim 1, characterized in that: In step (2), there is a master-slave game relationship between the operator of shared mobile energy storage and the virtual power plant cluster. The shared mobile energy storage operator publishes the time-sharing leasing pricing of shared mobile energy storage and accepts the feedback on the mobile energy storage leasing demand of the virtual power plant. After reaching a transaction with each virtual power plant under the conditions of satisfying the mobile energy storage transportation-power coupling operation constraints, the day-ahead scheduling decision of the shared mobile energy storage is completed. The virtual power plant makes decisions on the leasing and charging and discharging plans of the shared mobile energy storage in each time period based on its own operating economy, safety requirements and the time-sharing leasing price of shared mobile energy storage, and feeds back to the shared mobile energy storage operator. The time-sharing leasing pricing of mobile energy storage published by the operator and the leasing plans of each virtual power plant for energy storage influence each other until equilibrium is reached among all entities.

4. The method for operating a shared mobile energy storage in a virtual power plant cluster according to claim 1, characterized in that: In step (3), the operator makes an optimization decision on the pricing and scheduling plan of shared mobile energy storage time-sharing leasing, which needs to take into account the optimization goal of shared mobile energy storage operation efficiency and the regulation constraints under the power-transportation coupled operation environment. (301) The operator's mobile energy storage pricing and scheduling decisions are aimed at maximizing the operating benefits of mobile energy storage. Its operating benefits are related to the energy storage rental demand and charging and discharging demand of the virtual power plant, which can be specifically described as follows: Among them, c r,t is the time-sharing leasing price coefficient of shared mobile energy storage, which is decided by the operator based on the energy storage leasing demand and charging and discharging demand of the virtual power plant cluster; i,t is the leasing status of virtual power plant i for shared mobile energy storage, which is decided by each virtual power plant; Node is the collection of charging and discharging stations within the service range of shared mobile energy storage. (302) As a shared control resource among multiple virtual power plants, mobile energy storage can be connected to any charging and discharging station in the virtual power plant cluster to provide energy storage services for the economic operation of the connected virtual power plant. The control environment of shared mobile energy storage includes the power grid and the transportation network. Therefore, it is necessary to consider both its operation model in the transportation network and the power control model of the virtual power plant. Considering the spatiotemporal variation characteristics of traffic network conditions, a weighted directed graph of the spatiotemporal traffic network is established to describe the traffic network operating environment. In the spatiotemporal traffic network, the driving state of mobile energy storage must meet the constraints of uniqueness, continuity, and the location of the starting station. Mobile energy storage does not charge or discharge during driving, does not participate in the economic dispatch of any user in the virtual power plant cluster, and consumes electricity; when it stays at a charging station, its battery can be connected to the virtual power plant to participate in the dispatch. Therefore, the power state of the mobile energy storage battery is related to the driving state and the charging and discharging state in the virtual power plant, which can be described specifically as: in, They represent the charging active power, discharging active power and reactive power of mobile energy storage after it is connected to the virtual power plant, which are decided by each virtual power plant within a safe range; S ess ,C ess Respectively represent the rated power and capacity of the battery; C t is the battery power in period t; Node represents the location set of charging and discharging stations; when the shared mobile energy storage stops at station i at time t, the 0 / 1 variable γ (i,i),t =1, otherwise 0; p travel SOC is the battery energy consumption per unit time during mobile energy storage driving; max and SOC min The upper and lower limits of the battery state of charge. Since operators are responsible for the investment and maintenance of mobile energy storage equipment, they also need to consider the upper and lower limits of mobile energy storage time-of-use pricing in their decision-making process to ensure that economic benefits can be obtained through leasing energy storage, but avoid extremely high pricing when energy storage services are in short supply. (303) The operator’s shared mobile energy storage pricing and dispatch model is described as: max f O (x,y i ) s.t.g O (x,y i )≤0,h O (x,y i )=0 y i ∈arg min f VPP,i (y i ) Among them, x is the decision variable of the upper-level shared mobile energy storage operator, y i is the decision variable of the ith virtual power plant in the lower layer. O ≤0 includes pricing range constraints, mobile energy storage power safety constraints, h O = 0 includes the traffic network driving constraints of mobile energy storage.

5. The method for operating shared mobile energy storage in a virtual power plant cluster according to claim 1, characterized in that: In step (4), each virtual power plant in the cluster collaboratively optimizes the leasing and charging and discharging of shared mobile energy storage, as well as the regulation of other multiple types of flexible resources, which requires considering the total cost of leasing and operating shared mobile energy storage for each virtual power plant, as well as the collaborative regulation constraints of multiple types of resources taking into account grid structure and safety constraints. (401) The total operating cost of a virtual power plant includes the rental cost of mobile energy storage, the cost of purchasing electricity from multiple types of units, and the cost of flexible load dispatching. The operating cost model of the i-th virtual power plant is specifically described as: Among them, c G,t , c L,t is the cost coefficient vector of power purchase and flexible load regulation of the unit, P G,i,t , P L,i,t are the unit power generation and flexible load regulation power vector, c r,t The time-sharing leasing pricing published by the shared mobile energy storage operator, λ i,t is the shared mobile energy storage leasing decision of virtual power plant i at time t, λ i,t ≤γ (i,i),t . (402) The optimal operation model of each virtual power plant is described as follows: min f vpp,i (y i ) s.t.g vpp,i (y i )≤0 Among them, g vpp,i ≤0 includes the operational safety constraints of the virtual power plant taking into account the grid structure, as well as the power control range constraints of each internal distributed thermal power unit, distributed new energy unit, flexible load, and leased shared mobile energy storage.

6. The method for operating shared mobile energy storage in a virtual power plant cluster according to claim 1, characterized in that: In step (5), for the optimization pricing and scheduling problem of the shared mobile energy storage, it is difficult for the lower-level mixed integer programming model to directly use the KKT conditions to convert the double-layer problem into a single-layer optimization problem for solution. The decomposition solution method based on the C&CG algorithm design model can achieve the optimal solution of the double-layer mixed integer linear programming model in a finite number of iterations. (501) In the upper-level optimization problem, the replication variables and replication constraints of the lower-level optimization problem are introduced to construct the main problem; (502) Solve the main problem, obtain the optimal values ​​of the upper-layer operator's pricing and scheduling decision variables and the replicated lower-layer virtual power plant mobile energy storage leasing plan decision variables, and update the upper bound of the upper-layer operator's optimal revenue solution; (503) Substituting the optimal solution of the upper-layer operator's pricing and scheduling decision variables into the lower-layer virtual power plant optimization scheduling problem to solve it, and obtaining the optimal value of the operating cost of each virtual power plant; (504) Feedback the optimal value of the operating cost of each virtual power plant and the mobile energy storage leasing plan to the upper-level operator, solve the upper-level problem again, make corrections to the scheduling plan of the mobile energy storage, and update the lower bound of the optimal solution of the operator's revenue; (505) Determine whether the optimal solution for the operator's revenue converges. If so, the iteration terminates. Otherwise, under the virtual power plant mobile energy storage leasing plan obtained in the current iteration step, construct the KKT conditions of the lower-level problem, add them to the main problem constructed in (501), and return to (502) for iterative solution.

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