Multi-virtual power plant coordinated dispatch optimization method based on partition autonomy

Abstract

The present application relates to a multi-virtual power plant coordination scheduling optimization method based on partition autonomy, and belongs to the technical field of power system scheduling. The method analyzes the adjustable resources in the region, obtains the types and scheduling potential of each virtual power plant, and increases the objective functions of maximizing new energy consumption of the power supply type virtual power plant and maximizing the benefits of the load type virtual power plant in the upper layer optimization scheduling model. Meanwhile, the method optimizes the power generation cost of the power supply type virtual power plant and the load type virtual power plant as the optimization objective in the lower layer optimization scheduling model. The method can finely manage the virtual power plants participating in the electricity market transaction, fully utilize new energy generation, and improve the virtual power plant benefits.

Description

Technical Field

[0001] This invention belongs to the field of power system dispatching technology, and in particular relates to an optimization method for coordinated dispatching of multiple virtual power plants based on regional autonomy. Background Technology

[0002] A virtual power plant (VPP) differs from a traditional power plant with a physical presence. It not only breaks down the physical boundaries between power plants but also the physical boundaries between the generation and consumption sides in a traditional power system. VPPs utilize advanced control and communication technologies to aggregate different types of distributed power sources, such as photovoltaics, wind power, controllable loads, energy storage systems, and electric vehicles. By aggregating these resources, the virtual power plant participates in the electricity market as an organic whole. However, the purpose of a VPP's participation in the electricity market is to generate profits, fully utilize renewable energy, and reduce generation costs.

[0003] However, considering the impact of weather conditions on the output of distributed power sources such as photovoltaics and wind power, as well as the randomness of electricity consumption, these uncertainties will bring difficulties and challenges to the safe operation and economic dispatch of the system. Generally, virtual power plants are mainly divided into two categories: load-side virtual power plants and power-side virtual power plants. Load-side virtual power plants aggregate market-based electricity users with load regulation capabilities, including electric vehicles, adjustable loads, and interruptible loads, to form a unified virtual power plant, providing flexible load-side response. Power-side virtual power plants are built on the distributed power generation side, aggregating resource-based power generation groups such as large-scale distributed photovoltaics, wind power, micro gas turbines, and energy storage. While there is a wealth of literature on the participation of virtual power plants in power system optimization dispatch, there is no literature reporting research on the classification and dispatch management of virtual power plants with different characteristics. Existing technologies suffer from problems such as insufficiently refined management methods and the inability to fully utilize new energy power generation. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a multi-virtual power plant coordinated scheduling optimization method based on regional autonomy. This method enables refined management of virtual power plants participating in electricity market transactions, fully utilizes new energy power generation, and improves the revenue of virtual power plants.

[0005] According to one aspect of the present invention, a multi-virtual power plant coordinated scheduling optimization method based on regional autonomy is provided, which constructs a regional power grid dispatching system including multiple virtual power plants, and divides the dispatching system into: a regional power grid dispatching layer and a virtual power plant layer; the coordinated scheduling optimization method includes:

[0006] S1, by analyzing the adjustable resources within the region, obtains the type and scheduling potential of each virtual power plant;

[0007] S2. Construct an upper-level scheduling model with the goal of regional power grid scheduling. The upper-level scheduling model includes two stages. The first stage is to optimize with the goal of minimizing scheduling deviation. Based on the optimization in the first stage, the second stage is to perform multi-objective optimization with the goal of minimizing the operating cost of the regional power grid, maximizing the absorption of new energy by power source virtual power plants, and maximizing the revenue of load-type virtual power plants.

[0008] S3, the regional power grid dispatch center decomposes and sends the dispatch plan to each virtual power plant based on the dispatch potential of each virtual power plant and the power generation capacity of conventional units;

[0009] S4. Construct a lower-level scheduling model with each virtual power plant as the scheduling target. Each virtual power plant optimizes according to the received power generation plan, with the lowest power generation cost as the optimization target for both power source virtual power plants and load virtual power plants.

[0010] Preferably, in step S1, load data of each virtual power plant is collected to obtain the regulation potential of the virtual power plant in each time period, and virtual power plants with similar electricity load characteristics are classified by cluster analysis.

[0011] Preferably, the objective function for optimization in step S2, which aims to minimize scheduling deviation, is specifically:

[0012]

[0013] Where T is the number of time periods, K is the total number of virtual power plants, and P g For conventional generating units, P k,vpp Let k be the power generation capacity of the virtual power plant. Let T be the average load power within the system over a period of time.

[0014] Preferably, the objective function for multi-objective optimization in step S2, which aims to minimize the regional power grid operating cost, maximize the renewable energy consumption of power source-type virtual power plants, and maximize the revenue of load-type virtual power plants, is specifically as follows:

[0015]

[0016]

[0017]

[0018] Where T is the number of time periods, K is the total number of virtual power plants, M is the total number of power source type virtual power plants, and N is the total number of load type virtual power plants, where M + N = K; C g For the power generation cost of conventional generating units, C m,vpp Let C be the electricity purchase cost from the m-th power source virtual power plant by the regional power grid. n,vppC represents the cost of electricity purchased by the regional power grid from the nth load-type virtual power plant. loss I represents the total cost of power dispatch in the regional power grid; J represents the total number of wind power stations within the power source virtual power plant; P represents the total number of photovoltaic power stations within the power source virtual power plant. m,i,wind Let P be the power generation capacity of the i-th wind power station within the power source virtual power plant. m,j,pv Let j be the power generation capacity of the j-th photovoltaic power station within the power source virtual power plant. The effective power generation time within a given period; P n,dr The adjustable power of the nth load-type virtual power plant. This refers to the electricity price during the specified time period.

[0019] Preferably, the objective functions for optimizing power source virtual power plants and load virtual power plants with the goal of minimizing power generation costs are specifically as follows:

[0020]

[0021]

[0022] Where T is the number of time periods, I is the total number of wind power stations within the power generation virtual power plant, J is the total number of photovoltaic power stations within the power generation virtual power plant, and P... dg P represents the power output of a power-generating virtual power plant to the regional power grid. dr This refers to the power supplied by a load-type virtual power plant to the regional power grid. Electricity price during the specified time period; C i,wind Let C be the power generation cost of the i-th wind power station within the power source virtual power plant. j,pv Let C be the power generation cost of the j-th photovoltaic power station within the power source virtual power plant. st C represents the operating cost of the s-th energy storage power station within the power source-type virtual power plant. CL C TL C IL This includes load reduction costs, load transfer costs, and load interruption costs within a load-type virtual power plant.

[0023] The specific constraints are as follows:

[0024] Power balance constraints:

[0025] P vpp +P g =P loss +P load

[0026] P vpp Injecting power into the virtual power plant, P g For conventional generating units, P loss For the regional power grid load loss, P load For regional power grid load

[0027] Tie line power constraints:

[0028] P w,min < P w < P w,max

[0029] P w,min and P w,max For the upper and lower limits of the power of the wth virtual power plant and the regional power grid transmission branch;

[0030] Distributed power generation output constraints:

[0031]

[0032]

[0033] These are the upper and lower limits of the photovoltaic unit's output, respectively. These are the upper and lower limits of wind turbine output;

[0034] Output constraints of conventional units:

[0035]

[0036] Output constraints of battery energy storage power stations:

[0037]

[0038]

[0039]

[0040]

[0041] These represent the battery SOC values ​​at time t and time t-1, respectively. These represent the battery's charging capacity and discharging capacity at time t, respectively. Let represent the battery charging efficiency and discharging efficiency at time t, respectively. These represent the minimum and maximum SOC values ​​of the battery, respectively. This represents the minimum and maximum discharge values ​​of the battery at time t; This represents the minimum and maximum charging values ​​of the battery at time t;

[0042] Users can reduce load constraints:

[0043]

[0044] User-interruptible load constraints:

[0045]

[0046] User-transferable load constraints:

[0047]

[0048] These represent the load reduction value, the percentage of load reduction, and the maximum load reduction value at time t, respectively. These represent the interruptible load value, the interruptible load percentage, and the maximum interruptible load value at time t, respectively. These represent the transferable load value, transferable load percentage, and maximum transferable load at time t, respectively.

[0049] This invention has the following technical advantages: It analyzes adjustable resources within a region, considering the different characteristics of load-side and power-side virtual power plants, to obtain the type and scheduling potential of each virtual power plant. By constructing an upper-level scheduling model targeting regional power grid scheduling and a lower-level scheduling model targeting each virtual power plant, the upper-level optimization scheduling model adds objective functions to maximize the renewable energy absorption of power-side virtual power plants and the revenue of load-side virtual power plants. Simultaneously, the lower-level optimization scheduling model optimizes both power-side and load-side virtual power plants with the lowest generation cost as the optimization objective. This method enables refined management of virtual power plants participating in the electricity market, fully utilizes renewable energy generation, and improves the revenue of virtual power plants. Attached Figure Description

[0050] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0051] Figure 1 This is a flowchart of a multi-virtual power plant coordination and scheduling optimization method based on partitioned autonomy, provided by an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0053] One aspect of this invention provides a multi-virtual power plant coordinated scheduling optimization method based on regional autonomy, constructing a regional power grid dispatching system including multiple virtual power plants, and dividing the dispatching system into: a regional power grid dispatching layer and a virtual power plant layer; see appendix. Figure 1 The coordination and scheduling optimization method includes:

[0054] S1, by analyzing the adjustable resources within the region, obtains the type and scheduling potential of each virtual power plant;

[0055] S2. Construct an upper-level scheduling model with the goal of regional power grid scheduling. The upper-level scheduling model includes two stages. The first stage is to optimize with the goal of minimizing scheduling deviation. Based on the optimization in the first stage, the second stage is to perform multi-objective optimization with the goal of minimizing the operating cost of the regional power grid, maximizing the absorption of new energy by power source virtual power plants, and maximizing the revenue of load-type virtual power plants.

[0056] S3, the regional power grid dispatch center decomposes and sends the dispatch plan to each virtual power plant based on the dispatch potential of each virtual power plant and the power generation capacity of conventional units;

[0057] S4. Construct a lower-level scheduling model with each virtual power plant as the scheduling target. Each virtual power plant optimizes according to the received power generation plan, with the lowest power generation cost as the optimization target for both power source virtual power plants and load virtual power plants.

[0058] Preferably, in step S1, load data of each virtual power plant is collected to obtain the regulation potential of the virtual power plant in each time period, and virtual power plants with similar electricity load characteristics are classified by cluster analysis.

[0059] Preferably, the objective function for optimization in step S2, which aims to minimize scheduling deviation, is specifically:

[0060]

[0061] Where T is the number of time periods, K is the total number of virtual power plants, and P g For conventional generating units, P k,vpp Let k be the power generation capacity of the virtual power plant. Let T be the average load power within the system over a period of time.

[0062] Preferably, the objective function for multi-objective optimization in step S2, which aims to minimize the regional power grid operating cost, maximize the renewable energy consumption of power source-type virtual power plants, and maximize the revenue of load-type virtual power plants, is specifically as follows:

[0063]

[0064]

[0065]

[0066] Where T is the number of time periods, K is the total number of virtual power plants, M is the total number of power source type virtual power plants, and N is the total number of load type virtual power plants, where M + N = K; C g For the power generation cost of conventional generating units, C m,vpp Let C be the electricity purchase cost from the m-th power source virtual power plant by the regional power grid. n,vpp C represents the cost of electricity purchased by the regional power grid from the nth load-type virtual power plant. loss I represents the total cost of power dispatch in the regional power grid; J represents the total number of wind power stations within the power source virtual power plant; P represents the total number of photovoltaic power stations within the power source virtual power plant. m,i,wind Let P be the power generation capacity of the i-th wind power station within the power source virtual power plant. m,j,pv Let j be the power generation capacity of the j-th photovoltaic power station within the power source virtual power plant. The effective power generation time within a given period; P n,dr The adjustable power of the nth load-type virtual power plant. This refers to the electricity price during the specified time period.

[0067] In simple terms, conventional generating units mainly refer to gas turbines. Power-type virtual power plants primarily utilize intermittent renewable energy sources, including distributed wind farms, distributed photovoltaic power stations, and corresponding energy storage power stations. Load-type virtual power plants mainly guide flexible loads on the user side to change their operating modes through compensation fees, thereby participating in the optimized dispatch of the virtual power plant. Flexible loads include loads that can be reduced, transferred, and interrupted; all of them can participate in DR (Distribution and Reduction) dispatch, being reduced, transferred, or interrupted according to supply and demand, offering a high degree of controllability.

[0068] Preferably, the objective functions for optimizing power source virtual power plants and load virtual power plants with the goal of minimizing power generation costs are specifically as follows:

[0069]

[0070]

[0071] Where T is the number of time periods, I is the total number of wind power stations within the power generation virtual power plant, J is the total number of photovoltaic power stations within the power generation virtual power plant, and P... dg P represents the power output of a power-generating virtual power plant to the regional power grid. dr This refers to the power supplied by a load-type virtual power plant to the regional power grid. Electricity price during the specified time period; C i,wind Let C be the power generation cost of the i-th wind power station within the power source virtual power plant. j,pv Let C be the power generation cost of the j-th photovoltaic power station within the power source virtual power plant. stC represents the operating cost of the s-th energy storage power station within the power source-type virtual power plant. CL C TL C IL This includes load reduction costs, load transfer costs, and load interruption costs within a load-type virtual power plant.

[0072] In essence, optimization of a two-level model can be achieved using the objective cascade analysis method. This method distributes the optimization objective from the upper-level system to the lower-level subsystem, while responses at each level are continuously fed back from the bottom up. The problems of the upper-level and lower-level systems are solved independently, with overlapping optimizations, until convergence conditions are met. The upper-level system passes the optimized decision variables to the lower-level system, and this value becomes the lower-level system's objective. The lower-level system, while satisfying its own operational constraints, optimizes its own problem and introduces a penalty term into the objective function to bring the optimized value closer to the objective. The penalty term represents the consistency constraint of coupled variables during the decomposition of complex systems.

[0073] The system needs to meet constraints including power balance constraints, tie-line power constraints, distributed power output constraints, conventional unit output constraints, and battery energy storage station output constraints.

[0074] Power balance constraints:

[0075] P vpp +P g =P loss +P load

[0076] P vpp Injecting power into the virtual power plant, P g For conventional generating units, P loss For the regional power grid load loss, P load For regional power grid load

[0077] Tie line power constraints:

[0078] P w,min < P w < P w,max

[0079] P w,min and P w,max For the upper and lower limits of the power of the wth virtual power plant and the regional power grid transmission branch;

[0080] Distributed power generation output constraints:

[0081]

[0082]

[0083] These are the upper and lower limits of the photovoltaic unit's output, respectively. These are the upper and lower limits of wind turbine output;

[0084] Output constraints of conventional units:

[0085]

[0086] Output constraints of battery energy storage power stations:

[0087]

[0088]

[0089]

[0090]

[0091] These represent the battery SOC values ​​at time t and time t-1, respectively. These represent the battery's charging capacity and discharging capacity at time t, respectively. Let represent the battery charging efficiency and discharging efficiency at time t, respectively. These represent the minimum and maximum SOC values ​​of the battery, respectively. This represents the minimum and maximum discharge values ​​of the battery at time t; This represents the minimum and maximum charging values ​​of the battery at time t;

[0092] Users can reduce load constraints:

[0093]

[0094] User-interruptible load constraints:

[0095]

[0096] User-transferable load constraints:

[0097]

[0098] These represent the load reduction value, the percentage of load reduction, and the maximum load reduction value at time t, respectively. These represent the interruptible load value, the interruptible load percentage, and the maximum interruptible load value at time t, respectively. These represent the transferable load value, transferable load percentage, and maximum transferable load at time t, respectively.

[0099] Through the above technical solutions, this invention achieves refined management of virtual power plants participating in electricity market transactions, makes full use of new energy power generation, and improves the revenue of virtual power plants.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A multi-virtual power plant coordinated scheduling optimization method based on regional autonomy, comprising constructing a regional power grid dispatching system including multiple virtual power plants, and dividing the dispatching system into: a regional power grid dispatching layer and a virtual power plant layer; characterized in that, The coordination and scheduling optimization method includes: S1, by analyzing the adjustable resources within the region, obtains the type and scheduling potential of each virtual power plant; S2. Construct an upper-level scheduling model with the goal of regional power grid scheduling. The upper-level scheduling model includes two stages. The first stage is to optimize with the goal of minimizing scheduling deviation. Based on the optimization in the first stage, the second stage is to perform multi-objective optimization with the goal of minimizing the operating cost of the regional power grid, maximizing the absorption of new energy by power source virtual power plants, and maximizing the revenue of load-type virtual power plants. S3, the regional power grid dispatch center decomposes and sends the dispatch plan to each virtual power plant based on the dispatch potential of each virtual power plant and the power generation capacity of conventional units; S4. Construct a lower-level scheduling model with each virtual power plant as the scheduling target. Each virtual power plant optimizes according to the received power generation plan, with the lowest power generation cost as the optimization target for both power source virtual power plants and load virtual power plants. The objective function for optimization in step S2, which aims to minimize scheduling deviation, is specifically: ； Where T is the number of time periods, K is the total number of virtual power plants, and P g For conventional generating units, P k,vpp Let k be the power generation capacity of the virtual power plant. The average load power within the system over time T; The objective function for multi-objective optimization in step S2, which aims to minimize the regional power grid operating cost, maximize the renewable energy consumption of power source-type virtual power plants, and maximize the revenue of load-type virtual power plants, is specifically as follows: ； ； ； Where T is the number of time periods, K is the total number of virtual power plants, M is the total number of power source type virtual power plants, and N is the total number of load type virtual power plants, where M + N = K; C g For the power generation cost of conventional generating units, C m,vpp Let C be the electricity purchase cost from the m-th power source virtual power plant by the regional power grid. n,vpp C represents the cost of electricity purchased by the regional power grid from the nth load-type virtual power plant. loss I represents the total cost of power dispatch in the regional power grid; J represents the total number of wind power stations within the power source virtual power plant; P represents the total number of photovoltaic power stations within the power source virtual power plant. m,i,wind Let P be the power generation capacity of the i-th wind power station within the power source virtual power plant. m,j,pv Let j be the power generation capacity of the j-th photovoltaic power station within the power source virtual power plant. The effective power generation time within a given period; P n,dr The adjustable power of the nth load-type virtual power plant. This refers to the electricity price during the specified time period.

2. The multi-virtual power plant coordinated scheduling optimization method based on partitioned autonomy according to claim 1, characterized in that: In step S1, load data of each virtual power plant is collected to obtain the regulation potential of the virtual power plant in each time period, and virtual power plants with similar electricity load characteristics are classified by cluster analysis.

3. The multi-virtual power plant coordinated scheduling optimization method based on partitioned autonomy according to claim 2, characterized in that: The objective functions for optimizing power-generating virtual power plants and load-generating virtual power plants with the goal of minimizing power generation costs are as follows: ； ； Where T is the number of time periods, I is the total number of wind power stations within the power generation virtual power plant, J is the total number of photovoltaic power stations within the power generation virtual power plant, and P... dg P represents the power output of a power-generating virtual power plant to the regional power grid. dr This refers to the power supplied by a load-type virtual power plant to the regional power grid. Electricity price during the specified time period; C i,wind Let C be the power generation cost of the i-th wind power station within the power source virtual power plant. j,pv Let C be the power generation cost of the j-th photovoltaic power station within the power source virtual power plant. st C represents the operating cost of the s-th energy storage power station within the power source-type virtual power plant. CL C TL C IL This includes load reduction costs, load transfer costs, and load interruption costs within a load-type virtual power plant.

4. The multi-virtual power plant coordinated scheduling optimization method based on partitioned autonomy according to claim 3, characterized in that: The specific constraints are as follows: Power balance constraints: P vpp +P g =P loss +P load ; P vpp Injecting power into the virtual power plant, P g For conventional generating units, P loss For the regional power grid load loss, P load For regional power grid load; Tie line power constraints: P w,min < P w < P w,max ; P w,min and P w,max For the upper and lower limits of the power of the wth virtual power plant and the regional power grid transmission branch; Distributed power generation output constraints: ； ； These are the upper and lower limits of the photovoltaic unit's output, respectively. These are the upper and lower limits of wind turbine output; Output constraints of conventional units: ； Output constraints of battery energy storage power stations: ； ； ； ； These represent the battery SOC values ​​at time t and time t-1, respectively. These represent the battery's charging capacity and discharging capacity at time t, respectively. Let represent the battery charging efficiency and discharging efficiency at time t, respectively. These represent the minimum and maximum SOC values ​​of the battery, respectively. This represents the minimum and maximum discharge values ​​of the battery at time t; This represents the minimum and maximum charging values ​​of the battery at time t; Users can reduce load constraints: ； User-interruptible load constraints: ； User-transferable load constraints: ； These represent the load reduction value, the percentage of load reduction, and the maximum load reduction value at time t, respectively. These represent the interruptible load value, the interruptible load percentage, and the maximum interruptible load value at time t, respectively. These represent the transferable load value, transferable load percentage, and maximum transferable load at time t, respectively.

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