Virtual power plant optimization scheduling method, device, electronic device and storage medium

Abstract

The present invention discloses a virtual power plant optimization scheduling method, device, electronic device and storage medium. The method comprises: obtaining the active power of each gas turbine and energy storage device in the virtual power plant when the expected operating cost of the gas turbine and energy storage device in the renewable energy output scenario is minimized based on the coupling constraints of the energy network and information network in a virtual power plant in the power system under the established renewable energy output scenario, the safe operation constraints of the energy network in the power system and the safe operation constraints of the energy equipment; and determining the optimized scheduling scheme of the virtual power plant based on the active power of each gas turbine and energy storage device in the virtual power plant. The method can obtain more accurate and reliable active power of each gas turbine and energy storage device in the virtual power plant, which is conducive to the optimized scheduling of the virtual power plant and is conducive to the safe operation and control of the virtual power plant.

Description

Technical Field

[0001] The present invention relates to the field of smart grid operation and control technology, and in particular to a virtual power plant optimization scheduling method, device, electronic equipment and storage medium. Background Art

[0002] In the context of new power systems, the penetration of distributed resources, including renewable energy generation, electric vehicles, energy storage equipment, and controllable loads, continues to increase. These resources, characterized by randomness and volatility, pose significant challenges to the safe and stable operation of these systems. Virtual power plants, as an effective means of managing distributed resources, closely connect power terminals with the power grid, enabling resource integration and dispatch. They can also gradually participate in the operation of the power market as aggregated entities, improving the safety, economy, and flexibility of power grid operations while reducing investment costs.

[0003] Because distributed resources are scattered across space, virtual power plants (VPPs) require advanced information, metering, and control technologies to schedule and control these resources without changing their grid connection or geographic location. The capacity constraints, connectivity characteristics, and coupling of the information network with the energy grid influence whether distributed resources can effectively respond to VPP control commands. Furthermore, there is a strong coupling between the energy and information networks, and a mutual dependence between information nodes and energy nodes. On the one hand, information nodes require power from corresponding energy nodes, while on the other hand, the response actions of energy node devices must be controlled by the information nodes. However, current VPP optimization scheduling methods primarily focus on the energy flow and topology of the energy network. They fail to consider the impact of the VPP information network on the energy network's optimized scheduling, nor do they account for the uncertainty of renewable energy output. Therefore, the accuracy and reliability of these scheduling methods need to be further improved. Summary of the Invention

[0004] In order to at least partially solve the technical problems existing in the prior art, the inventors have made the present invention, and through specific implementation methods, provide a virtual power plant optimization scheduling method, device, electronic device and storage medium.

[0005] In a first aspect, an embodiment of the present invention provides a virtual power plant optimization scheduling method, comprising the following steps:

[0006] According to the coupling constraints of the energy grid and the information network in a virtual power plant in the power system under the established renewable energy output scenario, the safe operation constraints of the energy grid in the power system and the safe operation constraints of the energy equipment, the active power of each gas turbine and energy storage device in the virtual power plant when the expected operating cost of the gas turbine and energy storage device in the virtual power plant under the renewable energy output scenario is obtained, and the optimal scheduling plan of the virtual power plant is determined according to the active power of each gas turbine and energy storage device in the virtual power plant.

[0007] Optionally, before obtaining the active power of each gas turbine and energy storage device in the virtual power plant when the expected operating cost of the gas turbine and energy storage device in the virtual power plant is minimized under the renewable energy output scenario based on the established energy grid and information network coupling constraints in a virtual power plant in the power system under the renewable energy output scenario, the energy grid safe operation constraints in the power system, and the energy equipment safe operation constraints, the following steps are included:

[0008] Establishing coupling constraints between the energy grid and the information grid in the virtual power plant under the renewable energy output scenario;

[0009] Establishing constraints on the safe operation of the energy grid in the power system under the renewable energy output scenario;

[0010] Establish safe operation constraints for the energy equipment under renewable energy output scenarios.

[0011] Optionally, establishing coupling constraints between the energy network and the information network in the virtual power plant under the renewable energy output scenario includes the following steps:

[0012] Establish the information node operation constraints as follows:

[0013]

[0014] Among them, σ m The power supply validity flag of information node m. When the physical node cannot provide power, information node m cannot work normally and takes 0. m is the set of physical nodes connected to information node m, is the load reduction amount of physical node n, is the load demand of physical node n, γ is a parameter that defines the coupling strength between the information network and the energy network. The range of γ is [0,1]. The larger the γ, the stronger the coupling.

[0015] Establish physical node operation constraints as follows:

[0016]

[0017] Where s is the number of the renewable energy output scenario, Is a binary variable that indicates whether the information node m is faulty. When the information node m is faulty, it is 0, otherwise, it is 1. is the output power of power node m in renewable energy output scenario s, is the maximum power output by power node m in renewable energy output scenario s.

[0018] Optionally, establishing the energy grid safe operation constraint conditions in the power system under the renewable energy output scenario includes the following steps:

[0019] Using the linear power flow equation of the distribution network, the power flow constraints for safe operation of the energy network are established as follows:

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] Among them, P s,mn,t and Q s,mn,t are the active power flow and reactive power flow from node m to node n in the scheduling period t under the renewable energy output scenario s, and are the maximum active power flow and maximum reactive power flow from node m to node n during the scheduling period t, V s,m,t and θ s,m,t are the amplitude and phase angle of the voltage at node m during the dispatch period t under the renewable energy output scenario s, V s,n,t and θ s,n,t are the amplitude and phase angle of the voltage at node n in the dispatch period t under the renewable energy output scenario s, V m and are the minimum and maximum magnitudes of the voltage at node m, θ m and are the minimum and maximum phase angles of the voltage at node m, respectively, mn and r mn are the reactance and resistance of the line between node m and node n respectively;

[0027] The power balance flow constraints are established as follows:

[0028]

[0029]

[0030] Among them, NB is the line set of the power network in the virtual power plant, and are the active power and reactive power of renewable energy connected to node m in the scheduling period t under the renewable energy output scenario s, and are the active power and reactive power of the gas turbine connected to node m in the scheduling period t under the renewable energy output scenario s, and are the generating active power and charging active power of the energy storage device connected to node m in the scheduling period t under the renewable energy output scenario s, and are the active power and reactive power consumed by the load connected to node m during the scheduling period t under the renewable energy output scenario s.

[0031] Optionally, establishing the safe operation constraint conditions of the energy equipment in the renewable energy output scenario includes the following steps:

[0032] The gas turbine power operation constraints are established as follows:

[0033]

[0034]

[0035] Among them, i gt is the number of the gas turbine, and Gas turbine i gt The lower and upper limits of output power, Gas turbines for renewable energy output scenarios gt The output active power in the scheduling period t is: Gas turbines for renewable energy output scenarios gt The output active power in the scheduling period t-1 is: It is a gas turbine gt Climbing range during operation;

[0036] The charging and discharging power operation constraints of the energy storage equipment are established as follows:

[0037]

[0038]

[0039]

[0040]

[0041] Among them, i es is the number of the energy storage device, Energy storage equipment for renewable energy output scenarios es The active power consumed by discharge during the dispatch period t is: Energy storage equipment for renewable energy output scenarios es The active power consumed by charging during the scheduling period t is: Energy storage device i es The maximum active power consumed by discharge, Energy storage device i es The maximum active power consumed by charging, For energy storage equipment i in renewable energy output scenario s es The energy storage capacity in the dispatch period t is: For energy storage equipment i in renewable energy output scenario s es The energy storage capacity in the scheduling period t-1, η is the charging and discharging efficiency, It is an energy storage device es The maximum capacity, It is a binary variable, indicating the energy storage device i under renewable energy output scenario s. es Whether it is in the discharging state during the scheduling period t, that is, when discharging during the scheduling period t, Take 1, otherwise, Take 0;

[0042] The flexible load operation constraints are established as follows:

[0043]

[0044]

[0045] Among them, i load is the load number, The load i under the renewable energy output scenario s load The power consumed in the scheduling period t, The load i under the renewable energy output scenario s load The power of the flexible load in the dispatch period t, is the load i load The power of the non-flexible load in the dispatch period t, The load i under the renewable energy output scenario s load The power of medium-flexible load in the dispatch period t' before load reduction, The load i under the renewable energy output scenario sload The power of the dispatch period t' after the medium flexibility load is reduced, The load i under the renewable energy output scenario s load The power reduction during the scheduling period t is: is the load i load The attenuation factor describes the load rebound process.

[0046] Optionally, obtaining the active power of each gas turbine and energy storage device in the virtual power plant when the expected operating costs of the gas turbine and energy storage device in the virtual power plant are minimized under the renewable energy output scenario based on the established energy grid and information network coupling constraints in a virtual power plant in the power system under the renewable energy output scenario, the energy grid safe operation constraints in the power system, and the energy equipment safe operation constraints, includes the following steps:

[0047] Establish the expected objective function of the operating cost of a virtual power plant gas turbine and energy storage equipment in the power system under the renewable energy output scenario;

[0048] A genetic algorithm is used to determine the active power of each gas turbine and energy storage device in the virtual power plant when the expected objective function value of the operating cost of the gas turbine and energy storage device in the virtual power plant is minimized based on the coupling constraints of the energy grid and information network in a virtual power plant in the power system under the renewable energy output scenario, the safe operation constraints of the energy grid in the power system, and the safe operation constraints of the energy equipment.

[0049] Optionally, establishing an expected objective function for the operating costs of a virtual power plant gas turbine and energy storage equipment in a power system under a renewable energy output scenario includes the following steps:

[0050] An expected function for the operating cost of a gas turbine in a virtual power plant in the power system under the renewable energy output scenario is established. The expected function for the operating cost of the gas turbine is:

[0051]

[0052] Among them, Ω GT For gas turbine assembly, i gt is the number of the gas turbine, For gas turbines gt The operating cost coefficient, Gas turbines for renewable energy output scenarios gt The output active power in the dispatch period t is: The gas turbine set Ω under renewable energy output scenario s GT The operating cost in the scheduling period t;

[0053] An expected function for the operating cost of the energy storage equipment in the virtual power plant in the power system under the renewable energy output scenario is established. The expected function for the operating cost of the energy storage equipment is:

[0054]

[0055] Among them, Ω ES is the energy storage device collection, i es is the number of the energy storage device, Energy storage device i es The operating cost coefficient, Energy storage equipment for renewable energy output scenarios es The active power consumed by charging during the scheduling period t is: Energy storage equipment for renewable energy output scenarios es The active power consumed by discharge during the dispatch period t is: The energy storage device set Ω under the renewable energy output scenario s ES The operating cost in the scheduling period t;

[0056] Based on the gas turbine operating cost expectation function and the energy storage device operating cost expectation function, an expected target function of the operating costs of the virtual power plant gas turbine and energy storage device in the power system under the renewable energy output scenario is established. The expected target function of the operating costs of the virtual power plant gas turbine and energy storage device is:

[0057]

[0058] Where s is the number of the renewable energy output scenario, t is the number of the scheduling period, and λ s is the probability that renewable energy output scenario s may occur, is the operating cost of the gas turbine in the dispatch period t under the renewable energy output scenario s, is the operating cost of the energy storage equipment in the dispatch period t under the renewable energy output scenario s, Ω s is the renewable energy output scenario set, Ω T A collection of scheduled time periods for a day.

[0059] In a second aspect, an embodiment of the present invention provides a virtual power plant optimization scheduling device, comprising:

[0060] an active power determination module for obtaining the active power of each gas turbine and energy storage device in the virtual power plant when the expected operating cost of the gas turbine and energy storage device in the virtual power plant is minimized under the renewable energy output scenario, based on the coupling constraints of the energy grid and the information network in a virtual power plant in the power system under the established renewable energy output scenario, the safe operation constraints of the energy grid in the power system, and the safe operation constraints of the energy equipment;

[0061] The optimized scheduling scheme determination module is used to determine the optimized scheduling scheme of the virtual power plant based on the active power of each gas turbine and energy storage device in the virtual power plant.

[0062] Optionally, also include:

[0063] The constraint establishment module is used to establish the coupling constraints of the energy network and the information network in the virtual power plant under the renewable energy output scenario, establish the safe operation constraints of the energy network in the power system under the renewable energy output scenario, and establish the safe operation constraints of the energy equipment under the renewable energy output scenario.

[0064] Optionally, the constraint condition establishment module includes:

[0065] The energy network and information network coupling constraint establishment unit is used to establish the information node operation constraints as follows:

[0066]

[0067] Among them, σ m The power supply validity flag of information node m. When the physical node cannot provide power, information node m cannot work normally and takes 0. m is the set of physical nodes connected to information node m, is the load reduction amount of physical node n, is the load demand of physical node n, γ is a parameter that defines the coupling strength between the information network and the energy network. The range of γ is [0,1]. The larger the γ, the stronger the coupling.

[0068] Establish physical node operation constraints as follows:

[0069]

[0070] Where s is the number of the renewable energy output scenario, Is a binary variable, indicating whether the information node m is faulty. When the information node m is faulty, it is 0, otherwise, it is 1. is the output power of power node m in renewable energy output scenario s, is the maximum power output by power node m in renewable energy output scenario s;

[0071] The energy network safe operation constraint establishment unit is used to establish the energy network safe operation constraint using the distribution network linear power flow equation as follows:

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] Among them, P s,mn,t and Q s,mn,t are the active power flow and reactive power flow from node m to node n in the scheduling period t under the renewable energy output scenario s, and are the maximum active power flow and maximum reactive power flow from node m to node n during the scheduling period t, V s,m,t and θ s,m,t are the amplitude and phase angle of the voltage at node m during the dispatch period t under the renewable energy output scenario s, V s,n,t and θ s,n,t are the amplitude and phase angle of the voltage at node n in the dispatch period t under the renewable energy output scenario s, V m and are the minimum and maximum magnitudes of the voltage at node m, θ m and are the minimum and maximum phase angles of the voltage at node m, respectively, mn and r mn are the reactance and resistance of the line between node m and node n respectively;

[0079] The power balance flow constraints are established as follows:

[0080]

[0081]

[0082] Among them, NB is the line set of the power network in the virtual power plant, and are the active power and reactive power of renewable energy connected to node m in the scheduling period t under the renewable energy output scenario s, and are the active power and reactive power of the gas turbine connected to node m in the scheduling period t under the renewable energy output scenario s, and are the generating active power and charging active power of the energy storage device connected to node m in the scheduling period t under the renewable energy output scenario s, and are the active power and reactive power consumed by the load connected to node m during the scheduling period t under the renewable energy output scenario s;

[0083] The energy equipment safe operation constraint establishment unit is used to establish the gas turbine power operation constraints as follows:

[0084]

[0085]

[0086] Among them, i gt is the number of the gas turbine, and Gas turbine i gt The lower and upper limits of output power, Gas turbines for renewable energy output scenarios gt The output active power in the scheduling period t is: Gas turbines for renewable energy output scenarios gt The output active power in the scheduling period t-1 is: It is a gas turbine gt Climbing range during operation;

[0087] The charging and discharging power operation constraints of the energy storage equipment are established as follows:

[0088]

[0089]

[0090]

[0091]

[0092] Among them, i es is the number of the energy storage device, Energy storage equipment for renewable energy output scenarios es The active power consumed by discharge during the dispatch period t is: Energy storage equipment for renewable energy output scenarios es The active power consumed by charging during the scheduling period t is: Energy storage device i es The maximum active power consumed by discharge, Energy storage device i es The maximum active power consumed by charging, For energy storage equipment i in renewable energy output scenario s es The energy storage capacity in the dispatch period t is: For energy storage equipment i in renewable energy output scenario ses The energy storage capacity in the scheduling period t-1, η is the charging and discharging efficiency, It is an energy storage device es The maximum capacity, It is a binary variable, indicating the energy storage device i under renewable energy output scenario s. es Whether it is in the discharging state during the scheduling period t, that is, when discharging during the scheduling period t, Take 1, otherwise, Take 0;

[0093] The flexible load operation constraints are established as follows:

[0094]

[0095]

[0096] Among them, i load is the load number, The load i under the renewable energy output scenario s load The power consumed in the scheduling period t, The load i under the renewable energy output scenario s load The power of the flexible load in the dispatch period t, is the load i load The power of the non-flexible load in the dispatch period t, The load i under the renewable energy output scenario s load The power of medium-flexible load in the dispatch period t' before load reduction, The load i under the renewable energy output scenario s load The power of the dispatch period t' after the medium flexibility load is reduced, The load i under the renewable energy output scenario s load The power reduction during the scheduling period t is: is the load i load The attenuation factor describes the load rebound process.

[0097] Optionally, the active power determination module includes:

[0098] The gas turbine operating cost expectation function establishment unit is used to establish an expected operating cost function of a virtual power plant gas turbine in the power system under the renewable energy output scenario. The gas turbine operating cost expectation function is:

[0099]

[0100] Among them, Ω GT For gas turbine assembly, i gt is the number of the gas turbine, For gas turbines gt The operating cost coefficient, Gas turbines for renewable energy output scenarios gt The output active power in the dispatch period t is: The gas turbine set Ω under renewable energy output scenario s GT The operating cost in the scheduling period t;

[0101] The energy storage device operating cost expectation function establishing unit is used to establish the virtual power plant energy storage device operating cost expectation function in the power system under the renewable energy output scenario. The energy storage device operating cost expectation function is:

[0102]

[0103] Among them, Ω ES is the energy storage device collection, i es is the number of the energy storage device, Energy storage device i es The operating cost coefficient, Energy storage equipment for renewable energy output scenarios es The active power consumed by charging during the scheduling period t is: Energy storage equipment for renewable energy output scenarios es The active power consumed by discharge during the dispatch period t is: The energy storage device set Ω under the renewable energy output scenario s ES The operating cost in the scheduling period t;

[0104] An objective function establishing unit is configured to establish an expected objective function of the operating costs of the virtual power plant gas turbine and energy storage device in the power system under the renewable energy output scenario based on the expected operating cost function of the gas turbine and the expected operating cost function of the energy storage device, wherein the expected objective function of the operating costs of the virtual power plant gas turbine and energy storage device is:

[0105]

[0106] Where s is the number of the renewable energy output scenario, t is the number of the scheduling period, and λ s is the probability that renewable energy output scenario s may occur, is the operating cost of the gas turbine in the dispatch period t under the renewable energy output scenario s, is the operating cost of the energy storage equipment in the dispatch period t under the renewable energy output scenario s, Ω s is the renewable energy output scenario set, Ω T A collection of scheduling time periods for a day;

[0107] An active power determination unit is used to use a genetic algorithm to determine the active power of each gas turbine and energy storage device in the virtual power plant when the expected objective function value of the operating cost of the gas turbine and energy storage device in the virtual power plant is minimized based on the coupling constraints of the energy network and the information network in a virtual power plant in the power system under the renewable energy output scenario, the safe operation constraints of the energy network in the power system, and the safe operation constraints of the energy equipment.

[0108] Based on the same inventive concept, an embodiment of the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein the processor implements the aforementioned virtual power plant optimization scheduling method when executing the computer program.

[0109] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, in which computer executable instructions are stored. When the computer executable instructions are executed, the aforementioned virtual power plant optimization scheduling method is implemented.

[0110] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0111] Since the mutual influence between the information network and the energy network in the virtual power plant and the uncertainty of renewable energy output are fully considered in the process of determining the scheduling plan, a more accurate and reliable active power of each gas turbine and energy storage device in the virtual power plant is obtained. Based on the more accurate and reliable active power of each gas turbine and energy storage device in the virtual power plant, it is conducive to the optimal scheduling of the virtual power plant; and compared with the scheduling plan that only considers the energy flow and topology analysis of the energy network, the present invention introduces the constraints of physical nodes and information nodes, considers the impact of the failure of information nodes on the physical nodes of the energy network, and the impact of the physical nodes on the energy supply of information nodes, and can obtain a more practical optimized scheduling plan, which is conducive to the safe operation and control of the virtual power plant.

[0112] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0113] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0115] Figure 1 This is a flow chart of a virtual power plant optimization scheduling method according to an embodiment of the present invention;

[0116] Figure 2 A virtual power plant optimization and scheduling device according to an embodiment of the present invention;

[0117] Figure 3 Schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0118] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0119] In order to solve the problems existing in the prior art, embodiments of the present invention provide a virtual power plant optimization scheduling method, device, electronic device and storage medium.

[0120] Example 1

[0121] The first embodiment of the present invention provides a virtual power plant optimization scheduling method, the process of which is as follows: Figure 1 As shown, the following steps are included:

[0122] Step S101: establishing coupling constraints between the energy network and the information network in the virtual power plant under the renewable energy output scenario;

[0123] Establishing constraints on the safe operation of the energy grid in the power system under the renewable energy output scenario;

[0124] Establish safe operation constraints for the energy equipment under renewable energy output scenarios.

[0125] Optionally, establishing coupling constraints between the energy network and the information network in the virtual power plant in the renewable energy output scenario includes the following steps:

[0126] Establish the information node operation constraints as follows:

[0127]

[0128] Among them, σ m The power supply validity flag of information node m. When the physical node cannot provide power, information node m cannot work normally and takes 0. m is the set of physical nodes connected to information node m, is the load reduction amount of physical node n, is the load demand of physical node n, γ is a parameter that defines the coupling strength between the information network and the energy network. The range of γ is [0,1]. The larger the γ, the stronger the coupling.

[0129] Establish physical node operation constraints as follows:

[0130]

[0131] Where s is the number of the renewable energy output scenario, Is a binary variable, indicating whether the information node m is faulty. When the information node m is faulty, it is 0, otherwise, it is 1. is the output power of power node m in renewable energy output scenario s, is the maximum power output by power node m in renewable energy output scenario s.

[0132] Optionally, establishing energy grid safety operation constraints in the power system under the renewable energy output scenario includes the following steps:

[0133] Using the linear power flow equation of the distribution network, the power flow constraints for safe operation of the energy network are established as follows:

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140] Among them, P s,mn,t and Q s,mn,t are the active power flow and reactive power flow from node m to node n in the scheduling period t under the renewable energy output scenario s, and are the maximum active power flow and maximum reactive power flow from node m to node n during the scheduling period t, V s,m,t and θ s,m,t are the amplitude and phase angle of the voltage at node m during the dispatch period t under the renewable energy output scenario s, V s,n,t and θ s,n,t are the amplitude and phase angle of the voltage at node n in the dispatch period t under the renewable energy output scenario s, V m and are the minimum and maximum magnitudes of the voltage at node m, θ m and are the minimum and maximum phase angles of the voltage at node m, respectively, mn and r mn are the reactance and resistance of the line between node m and node n respectively;

[0141] The power balance flow constraints are established as follows:

[0142]

[0143]

[0144] Among them, NB is the line set of the power network in the virtual power plant, and are the active power and reactive power of renewable energy connected to node m in the scheduling period t under the renewable energy output scenario s, and are the active power and reactive power of the gas turbine connected to node m in the scheduling period t under the renewable energy output scenario s, and are the generating active power and charging active power of the energy storage device connected to node m in the scheduling period t under the renewable energy output scenario s, and are the active power and reactive power consumed by the load connected to node m during the scheduling period t under the renewable energy output scenario s.

[0145] Establishing the safe operation constraints of the energy equipment under the renewable energy output scenario includes the following steps:

[0146] The gas turbine power operation constraints are established as follows:

[0147]

[0148]

[0149] Among them, i gt is the number of the gas turbine, and Gas turbine i gt The lower and upper limits of output power, Gas turbines for renewable energy output scenarios gt The output active power in the scheduling period t is: Gas turbines for renewable energy output scenarios gt The output active power in the scheduling period t-1 is: It is a gas turbinegt Climbing range during operation;

[0150] The charging and discharging power operation constraints of the energy storage equipment are established as follows:

[0151]

[0152]

[0153]

[0154]

[0155] Among them, i es is the number of the energy storage device, Energy storage equipment for renewable energy output scenarios es The active power consumed by discharge during the dispatch period t is: Energy storage equipment for renewable energy output scenarios es The active power consumed by charging during the scheduling period t is: Energy storage device i es The maximum active power consumed by discharge, Energy storage device i es The maximum active power consumed by charging, For energy storage equipment i in renewable energy output scenario s es The energy storage capacity in the dispatch period t is: For energy storage equipment i in renewable energy output scenario s es The energy storage capacity in the scheduling period t-1, η is the charging and discharging efficiency, It is an energy storage device es The maximum capacity, It is a binary variable, indicating the energy storage device i under renewable energy output scenario s. es Whether it is in the discharging state during the scheduling period t, that is, when discharging during the scheduling period t, Take 1, otherwise, Take 0;

[0156] The flexible load operation constraints are established as follows:

[0157]

[0158]

[0159] Among them, i load is the load number, The load i under the renewable energy output scenario s load The power consumed in the scheduling period t, The load i under the renewable energy output scenario s load The power of the flexible load in the dispatch period t, is the load i load The power of the non-flexible load in the dispatch period t, The load i under the renewable energy output scenario s load The power of medium-flexible load in the dispatch period t' before load reduction, The load i under the renewable energy output scenario s load The power of the dispatch period t' after the medium flexibility load is reduced, The load i under the renewable energy output scenario s load The power reduction during the scheduling period t is: is the load i load The attenuation factor describes the load rebound process.

[0160] Step S102: Based on the coupling constraints of the energy grid and the information grid in a virtual power plant in the power system under the established renewable energy output scenario, the safe operation constraints of the energy grid in the power system, and the safe operation constraints of the energy equipment, the active power of each gas turbine and energy storage device in the virtual power plant when the expected operating cost of the gas turbine and energy storage device in the virtual power plant under the renewable energy output scenario is obtained; based on the active power of each gas turbine and energy storage device in the virtual power plant, the optimal scheduling plan of the virtual power plant is determined.

[0161] Optionally, based on the established energy grid and information network coupling constraints in a virtual power plant in the power system under the renewable energy output scenario, the energy grid safe operation constraints in the power system, and the energy equipment safe operation constraints, obtaining the active power of each gas turbine and energy storage device in the virtual power plant when the expected operating costs of the gas turbine and energy storage device in the virtual power plant under the renewable energy output scenario are minimized, includes the following steps:

[0162] Establish the expected objective function of the operating cost of a virtual power plant gas turbine and energy storage equipment in the power system under the renewable energy output scenario;

[0163] A genetic algorithm is used to determine the active power of each gas turbine and energy storage device in the virtual power plant when the expected objective function value of the operating cost of the gas turbine and energy storage device in the virtual power plant is minimized based on the coupling constraints of the energy grid and information network in a virtual power plant in the power system under the renewable energy output scenario, the safe operation constraints of the energy grid in the power system, and the safe operation constraints of the energy equipment.

[0164] Optionally, establishing an expected objective function for the operating costs of a virtual power plant gas turbine and energy storage equipment in a power system under a renewable energy output scenario includes the following steps:

[0165] An expected function for the operating cost of a gas turbine in a virtual power plant in the power system under the renewable energy output scenario is established. The expected function for the operating cost of the gas turbine is:

[0166]

[0167] Among them, Ω GT For gas turbine assembly, i gt is the number of the gas turbine, For gas turbines gt The operating cost coefficient, Gas turbines for renewable energy output scenarios gt The output active power in the dispatch period t is: The gas turbine set Ω under renewable energy output scenario s GT The operating cost in the scheduling period t;

[0168] An expected function for the operating cost of the energy storage equipment in the virtual power plant in the power system under the renewable energy output scenario is established. The expected function for the operating cost of the energy storage equipment is:

[0169]

[0170] Among them, Ω ES is the energy storage device collection, i es is the number of the energy storage device, Energy storage device i es The operating cost coefficient, Energy storage equipment for renewable energy output scenarios es The active power consumed by charging during the scheduling period t is: Energy storage equipment for renewable energy output scenarios es The active power consumed by discharge during the dispatch period t is: The energy storage device set Ω under the renewable energy output scenario s ES The operating cost in the scheduling period t;

[0171] Based on the gas turbine operating cost expectation function and the energy storage device operating cost expectation function, an expected target function of the operating costs of the virtual power plant gas turbine and energy storage device in the power system under the renewable energy output scenario is established. The expected target function of the operating costs of the virtual power plant gas turbine and energy storage device is:

[0172]

[0173] Where s is the number of the renewable energy output scenario, t is the number of the scheduling period, and λ s is the probability that renewable energy output scenario s may occur, is the operating cost of the gas turbine in the dispatch period t under the renewable energy output scenario s, is the operating cost of the energy storage equipment in the dispatch period t under the renewable energy output scenario s, Ω s is the renewable energy output scenario set, Ω T A collection of scheduled time periods for a day.

[0174] For example, in a virtual power plant consisting of a standard 13-node distribution network, the nodes of the power network are connected to energy equipment such as gas turbines, energy storage equipment, loads, and renewable energy. The virtual power plant operator needs to operate and manage the internal equipment and issue scheduling plans to each device to meet operational needs. The response of energy equipment to scheduling instructions is closely related to the effectiveness of information transmission, and the effective transmission of information depends on the effective supply of energy. In this embodiment, the location and connectivity of the information network nodes are consistent with the power grid.

[0175] Virtual power plant operators collect operating parameters for each energy device and energy grid. Gas turbine operating parameters include operating cost coefficient, lower output power limit, upper output power limit, and ramp range. Energy storage operating parameters include the operating cost coefficient, maximum charging power consumption, maximum discharging power consumption, charging and discharging efficiency, and maximum storage capacity. Load parameters include flexible and inflexible load demand, and load recovery attenuation factors. Energy grid parameters include line impedance, reactance, maximum active power flow, maximum reactive power flow, maximum and minimum voltage amplitudes, and maximum and minimum voltage phase angles at each node.

[0176] The virtual power plant operator establishes the objective function of the optimal scheduling model based on expected operating costs, including the operating costs of gas turbines and energy storage, as well as constraints for the safe operation of energy equipment: gas turbine operating constraints, energy storage operating constraints, flexible load operating constraints, power grid safe operation constraints, and operational constraints for information nodes and physical nodes. The optimization model is programmed and executed using Matlab on a computer with an Intel(R) Core(TM) i5-9500 CPU and 8GB of memory. It outputs relevant data for the optimal scheduling plan for the virtual power plant, including the active power of each gas turbine and energy storage device in the virtual power plant, gas turbine output, energy storage device output, flexible load size, voltage amplitude and phase angle at power grid nodes, and valid flags for physical nodes and information nodes. Based on the output of the active power of each gas turbine and energy storage device in the virtual power plant, the optimal scheduling plan for the virtual power plant is determined.

[0177] In the above method of this embodiment, since the mutual influence between the information network and the energy network in the virtual power plant and the uncertainty of the output of renewable energy are fully considered in the process of determining the scheduling plan, a more accurate and reliable active power of each gas turbine and energy storage device in the virtual power plant is obtained. Based on the more accurate and reliable active power of each gas turbine and energy storage device in the virtual power plant, it is beneficial to achieve optimal scheduling of the virtual power plant; and, compared with the scheduling plan that only considers the energy flow and topology analysis of the energy network, the present invention introduces the constraints of physical nodes and information nodes, considers the impact of the failure of information nodes on the physical nodes of the energy network, and the impact of the physical nodes on the energy supply of information nodes, and can obtain a more practical optimized scheduling plan, which is beneficial to the safe operation and control of the virtual power plant.

[0178] Example 2

[0179] The second embodiment of the present invention provides a virtual power plant optimization scheduling device, the structure of which is as follows: Figure 2 As shown, including:

[0180] Active power determination module 101, configured to obtain the active power of each gas turbine and energy storage device in a virtual power plant when the expected operating cost of the gas turbine and energy storage device in the virtual power plant is minimized under the renewable energy output scenario, based on the established energy grid and information network coupling constraints in a virtual power plant in the power system under the renewable energy output scenario, the energy grid safe operation constraints in the power system, and the energy equipment safe operation constraints;

[0181] The optimized scheduling scheme determination module 102 is used to determine the optimized scheduling scheme of the virtual power plant according to the active power of each gas turbine and energy storage device in the virtual power plant.

[0182] Optionally, also include:

[0183] The constraint establishment module 100 is used to establish the coupling constraint conditions between the energy network and the information network in the virtual power plant under the renewable energy output scenario, establish the safe operation constraint conditions of the energy network in the power system under the renewable energy output scenario, and establish the safe operation constraint conditions of the energy equipment under the renewable energy output scenario.

[0184] Optional constraint establishment module, including:

[0185] The energy network and information network coupling constraint establishment unit is used to establish the information node operation constraints as follows:

[0186]

[0187] Among them, σ mThe power supply validity flag of information node m. When the physical node cannot provide power, information node m cannot work normally and takes 0. m is the set of physical nodes connected to information node m, is the load reduction amount of physical node n, is the load demand of physical node n, γ is a parameter that defines the coupling strength between the information network and the energy network. The range of γ is [0,1]. The larger the γ, the stronger the coupling.

[0188] Establish physical node operation constraints as follows:

[0189]

[0190] Where s is the number of the renewable energy output scenario, Is a binary variable, indicating whether the information node m is faulty. When the information node m is faulty, it is 0, otherwise, it is 1. is the output power of power node m in renewable energy output scenario s, is the maximum power output by power node m in renewable energy output scenario s;

[0191] The energy network safe operation constraint establishment unit is used to establish the energy network safe operation constraint using the distribution network linear power flow equation as follows:

[0192]

[0193]

[0194]

[0195]

[0196]

[0197]

[0198] Among them, P s,mn,t and Q s,mn,t are the active power flow and reactive power flow from node m to node n in the scheduling period t under the renewable energy output scenario s, and are the maximum active power flow and maximum reactive power flow from node m to node n during the scheduling period t, V s,m,t and θ s,m,t are the amplitude and phase angle of the voltage at node m during the dispatch period t under the renewable energy output scenario s, V s,n,t and θ s,n,tare the amplitude and phase angle of the voltage at node n in the dispatch period t under the renewable energy output scenario s, V m and are the minimum and maximum magnitudes of the voltage at node m, θ m and are the minimum and maximum phase angles of the voltage at node m, respectively, mn and r mn are the reactance and resistance of the line between node m and node n respectively;

[0199] The power balance flow constraints are established as follows:

[0200]

[0201]

[0202] Among them, NB is the line set of the power network in the virtual power plant, and are the active power and reactive power of renewable energy connected to node m in the scheduling period t under the renewable energy output scenario s, and are the active power and reactive power of the gas turbine connected to node m in the scheduling period t under the renewable energy output scenario s, and are the generating active power and charging active power of the energy storage device connected to node m in the scheduling period t under the renewable energy output scenario s, and are the active power and reactive power consumed by the load connected to node m during the scheduling period t under the renewable energy output scenario s;

[0203] The energy equipment safe operation constraint establishment unit is used to establish the gas turbine power operation constraints as follows:

[0204]

[0205]

[0206] Among them, i gt is the number of the gas turbine, and Gas turbine i gt The lower and upper limits of output power, Gas turbines for renewable energy output scenarios gt The output active power in the scheduling period t is: Gas turbines for renewable energy output scenarios gtThe output active power in the scheduling period t-1 is: It is a gas turbine gt Climbing range during operation;

[0207] The charging and discharging power operation constraints of the energy storage equipment are established as follows:

[0208]

[0209]

[0210]

[0211]

[0212] Among them, i es is the number of the energy storage device, Energy storage equipment for renewable energy output scenarios es The active power consumed by discharge during the dispatch period t is: Energy storage equipment for renewable energy output scenarios es The active power consumed by charging during the scheduling period t is: Energy storage device i es The maximum active power consumed by discharge, Energy storage device i es The maximum active power consumed by charging, For energy storage equipment i in renewable energy output scenario s es The energy storage capacity in the dispatch period t is: For energy storage equipment i in renewable energy output scenario s es The energy storage capacity in the scheduling period t-1, η is the charging and discharging efficiency, It is an energy storage device es The maximum capacity, It is a binary variable, indicating the energy storage device i under renewable energy output scenario s. es Whether it is in the discharging state during the scheduling period t, that is, when discharging during the scheduling period t, Take 1, otherwise, Take 0;

[0213] The flexible load operation constraints are established as follows:

[0214]

[0215]

[0216] Among them, i load is the load number, The load i under the renewable energy output scenario s loadThe power consumed in the scheduling period t, The load i under the renewable energy output scenario s load The power of the flexible load in the dispatch period t, is the load i load The power of the non-flexible load in the dispatch period t, The load i under the renewable energy output scenario s load The power of medium-flexible load in the dispatch period t' before load reduction, The load i under the renewable energy output scenario s load The power of the dispatch period t' after the medium flexibility load is reduced, The load i under the renewable energy output scenario s load The power reduction during the scheduling period t is: is the load i load The attenuation factor describes the load rebound process.

[0217] Optional, active power determination module, including:

[0218] The gas turbine operating cost expectation function establishment unit is used to establish an expected operating cost function of a virtual power plant gas turbine in the power system under the renewable energy output scenario. The gas turbine operating cost expectation function is:

[0219]

[0220] Among them, Ω GT For gas turbine assembly, i gt is the number of the gas turbine, For gas turbines gt The operating cost coefficient, Gas turbines for renewable energy output scenarios gt The output active power in the dispatch period t is: The gas turbine set Ω under renewable energy output scenario s GT The operating cost in the scheduling period t;

[0221] The energy storage device operating cost expectation function establishing unit is used to establish the virtual power plant energy storage device operating cost expectation function in the power system under the renewable energy output scenario. The energy storage device operating cost expectation function is:

[0222]

[0223] Among them, Ω ES is the energy storage device collection, i es is the number of the energy storage device, Energy storage device i es The operating cost coefficient, Energy storage equipment for renewable energy output scenarios es The active power consumed by charging during the scheduling period t is: Energy storage equipment for renewable energy output scenarios es The active power consumed by discharge during the dispatch period t is: The energy storage device set Ω under the renewable energy output scenario s ES The operating cost in the scheduling period t;

[0224] An objective function establishing unit is configured to establish an expected objective function of the operating costs of the virtual power plant gas turbine and energy storage device in the power system under the renewable energy output scenario based on the expected operating cost function of the gas turbine and the expected operating cost function of the energy storage device, wherein the expected objective function of the operating costs of the virtual power plant gas turbine and energy storage device is:

[0225]

[0226] Where s is the number of the renewable energy output scenario, t is the number of the scheduling period, and λ s is the probability that renewable energy output scenario s may occur, is the operating cost of the gas turbine in the dispatch period t under the renewable energy output scenario s, is the operating cost of the energy storage equipment in the dispatch period t under the renewable energy output scenario s, Ω s is the renewable energy output scenario set, Ω T A collection of scheduling time periods for a day;

[0227] An active power determination unit is used to use a genetic algorithm to determine the active power of each gas turbine and energy storage device in the virtual power plant when the expected objective function value of the operating cost of the gas turbine and energy storage device in the virtual power plant is minimized based on the coupling constraints of the energy network and the information network in a virtual power plant in the power system under the renewable energy output scenario, the safe operation constraints of the energy network in the power system, and the safe operation constraints of the energy equipment.

[0228] For example, in a virtual power plant consisting of a standard 13-node distribution network, the nodes of the power network are connected to energy equipment such as gas turbines, energy storage equipment, loads, and renewable energy. The virtual power plant operator needs to operate and manage the internal equipment and issue scheduling plans to each device to meet operational needs. The response of energy equipment to scheduling instructions is closely related to the effectiveness of information transmission, and the effective transmission of information depends on the effective supply of energy. In this embodiment, the location and connectivity of the information network nodes are consistent with the power grid.

[0229] Virtual power plant operators collect operating parameters for each energy device and energy grid. Gas turbine operating parameters include operating cost coefficient, lower output power limit, upper output power limit, and ramp range. Energy storage operating parameters include the operating cost coefficient, maximum charging power consumption, maximum discharging power consumption, charging and discharging efficiency, and maximum storage capacity. Load parameters include flexible and inflexible load demand, and load recovery attenuation factors. Energy grid parameters include line impedance, reactance, maximum active power flow, maximum reactive power flow, maximum and minimum voltage amplitudes, and maximum and minimum voltage phase angles at each node.

[0230] In the virtual power plant optimization scheduling device, the virtual power plant operator establishes an objective function for optimal scheduling based on expected operating costs, including the operating costs of gas turbines and energy storage. Constraints include energy grid security constraints, gas turbine operation constraints, energy storage operation constraints, flexible load operation constraints, power grid security constraints, and information and physical node operation constraints. The optimization scheduling device includes a Matlab experimental program running on a computer with an Intel(R) Core(TM) i5-9500 CPU and 8GB of memory. Based on the aforementioned constraints and objective function, a corresponding Matlab program is configured to output relevant data for the virtual power plant's optimal scheduling plan, including the active power, gas turbine output, energy storage output, flexible load size, power grid node voltage amplitude and phase angle, and valid flags for physical and information nodes for each gas turbine and energy storage device in the virtual power plant. The optimized scheduling plan for the virtual power plant is determined based on the output active power of each gas turbine and energy storage device in the virtual power plant.

[0231] In the above-mentioned device of this embodiment, since the mutual influence between the information network and the energy network in the virtual power plant and the uncertainty of the output of renewable energy are fully considered in the process of determining the scheduling plan, a more accurate and reliable active power of each gas turbine and energy storage device in the virtual power plant is obtained. Based on the more accurate and reliable active power of each gas turbine and energy storage device in the virtual power plant, it is beneficial to achieve optimal scheduling of the virtual power plant; and, compared with the scheduling plan that only considers the energy flow and topology analysis of the energy network, the present invention introduces the constraints of physical nodes and information nodes, considers the impact of the failure of information nodes on the physical nodes of the energy network, and the impact of the physical nodes on the energy supply of information nodes, and can obtain a more practical optimized scheduling plan, which is beneficial to the safe operation and control of the virtual power plant.

[0232] Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, the structure of which is as follows: Figure 3As shown, it includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the aforementioned virtual power plant optimization scheduling method is implemented.

[0233] Based on the same inventive concept, an embodiment of the present invention further provides a computer storage medium, in which computer executable instructions are stored. When the computer executable instructions are executed, the aforementioned virtual power plant optimization scheduling method is implemented.

[0234] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

Claims

1. A virtual power plant optimization scheduling method, characterized in that: The following steps are involved: According to the coupling constraints of the energy grid and the information grid in a virtual power plant in the power system under the established renewable energy output scenario, the safe operation constraints of the energy grid in the power system, and the safe operation constraints of the energy equipment, the active power of each gas turbine and energy storage device in the virtual power plant when the expected operating costs of the gas turbine and energy storage device in the virtual power plant under the renewable energy output scenario are obtained, and according to the active power of each gas turbine and energy storage device in the virtual power plant, an optimal scheduling plan for the virtual power plant is determined; The method of establishing coupling constraints between the energy network and the information network in a virtual power plant in a power system under a renewable energy output scenario includes the following steps: Establish the information node operation constraints as follows: in, Information Node The energy supply validity flag bit, when the physical node cannot provide power, the information node Cannot work properly, take 0, To connect to an information node The set of physical nodes, Scheduling period t physical nodes n Load reduction, Scheduling period t physical nodes n The load demand, To define the parameters of the coupling strength between information network and energy network, The range is [0,1], The larger it is, the stronger the coupling; Establish physical node operation constraints as follows: in, s is the number of the renewable energy output scenario, Is a binary variable representing an information node Is it a fault? When the information node When a fault occurs, it is 0, otherwise it is 1. is the scheduling period under renewable energy output scenario s t Power Node The output power, is the scheduling period under renewable energy output scenario s t Power Node Maximum output power.

2. The method according to claim 1, wherein Before obtaining the active power of each gas turbine and energy storage device in the virtual power plant when the expected operating cost of the gas turbine and energy storage device in the virtual power plant is minimized under the renewable energy output scenario based on the established energy grid and information grid coupling constraints in a virtual power plant in the power system under the renewable energy output scenario, the safe operation constraints of the energy grid in the power system, and the safe operation constraints of the energy equipment, the following steps are included: Establishing coupling constraints between the energy grid and the information grid in the virtual power plant under the renewable energy output scenario; Establishing constraints on the safe operation of the energy grid in the power system under the renewable energy output scenario; Establish safe operation constraints for the energy equipment under renewable energy output scenarios.

3. The method according to claim 2, wherein The establishment of energy grid safety operation constraints in the power system under the renewable energy output scenario includes the following steps: Using the linear power flow equation of the distribution network, the power flow constraints for safe operation of the energy network are established as follows: in, and In the renewable energy output scenario s Next scheduling period t Slave nodes To Node The active power flow and reactive power flow of and During the scheduling period t Slave nodes To Node The maximum active power flow and maximum reactive power flow, and In the renewable energy output scenario s Node t in the next scheduling period The magnitude and phase angle of the voltage, and They are respectively the nodes in the scheduling period t under the renewable energy output scenario s The magnitude and phase angle of the voltage, and Node Minimum and maximum voltage amplitudes, and Node Minimum and maximum phase angles of voltage, and Node and nodes reactance and resistance of the lines between them; The power balance flow constraints are established as follows: in, NB is the line set of the power network in the virtual power plant, and They are respectively connected to the node under the renewable energy output scenario s of renewable energy during the dispatch period t The active power and reactive power, and Renewable energy output scenarios s Downlink Node of gas turbines during the dispatch period t The active power and reactive power, and In the renewable energy output scenario s Downlink Node Energy storage equipment during the dispatch period t The generating active power and charging active power, and In the renewable energy output scenario s Downlink Node The load during the dispatch period t Active and reactive power consumed.

4. The method according to claim 2, wherein The establishment of the energy equipment safe operation constraint conditions in the renewable energy output scenario includes the following steps: The gas turbine power operation constraints are established as follows: in, is the number of the gas turbine, and Gas turbine The lower and upper limits of output power, Scenario for renewable energy output s Lower gas turbine During the scheduling period t The output active power, Scenario for renewable energy output s Lower gas turbine During the scheduling period t -1 output active power, It is a gas turbine Climbing range during operation; The charging and discharging power operation constraints of the energy storage equipment are established as follows: in, is the number of the energy storage device, Scenario for renewable energy output s Energy storage equipment During the scheduling period t The active power consumed by discharge, Scenario for renewable energy output s Energy storage equipment During the scheduling period t Active power consumed by charging, For energy storage equipment The maximum active power consumed by discharge, For energy storage equipment The maximum active power consumed by charging, For renewable energy output scenarios s Energy storage equipment During the scheduling period t The energy storage capacity, For renewable energy output scenarios s Energy storage equipment During the scheduling period t -1 energy storage capacity, is the charge and discharge efficiency, It is an energy storage device The maximum capacity, Is a binary variable representing the renewable energy output scenario s Energy storage equipment During the scheduling period t Is it in the discharge state, that is, when in the scheduling period t When discharging, Take 1, otherwise, Take 0; The flexible load operation constraints are established as follows: in, is the load number, Scenario for renewable energy output s Underload During the scheduling period t Power consumed, Scenario for renewable energy output s Underload Medium flexibility load during the dispatch period t Power, For load Central and African flexible load during the dispatch period t The power, Scenario for renewable energy output s Underload Medium flexibility load is dispatched during the period before load reduction Power, Scenario for renewable energy output s Underload Dispatch period after medium flexibility load reduction Power, Scenario for renewable energy output s Underload During the scheduling period t The reduction in power, is load The attenuation factor describes the load rebound process.

5. The method according to claim 1, wherein The method of obtaining the active power of each gas turbine and energy storage device in the virtual power plant when the expected operating costs of the gas turbine and energy storage device in the virtual power plant are minimized under the renewable energy output scenario based on the established energy grid and information grid coupling constraints in a virtual power plant in the power system under the renewable energy output scenario, the energy grid safe operation constraints in the power system, and the energy equipment safe operation constraints, includes the following steps: Establish the expected objective function of the operating cost of a virtual power plant gas turbine and energy storage equipment in the power system under the renewable energy output scenario; A genetic algorithm is used to determine the active power of each gas turbine and energy storage device in the virtual power plant when the expected objective function value of the operating cost of the gas turbine and energy storage device in the virtual power plant is minimized based on the coupling constraints of the energy grid and information network in a virtual power plant in the power system under the renewable energy output scenario, the safe operation constraints of the energy grid in the power system, and the safe operation constraints of the energy equipment.

6. The method according to claim 5, wherein The method of establishing an expected objective function for the operating costs of a virtual power plant gas turbine and energy storage equipment in a power system under a renewable energy output scenario includes the following steps: An expected function for the operating cost of a gas turbine in a virtual power plant in the power system under the renewable energy output scenario is established. The expected function for the operating cost of the gas turbine is: in, For gas turbine assembly, is the number of the gas turbine, For gas turbines The operating cost coefficient, Scenario for renewable energy output s Lower gas turbine The output active power in the dispatch period t is: Scenario for renewable energy output s Lower gas turbine assembly During the scheduling period t operating costs; An expected function for the operating cost of the energy storage equipment in the virtual power plant in the power system under the renewable energy output scenario is established. The expected function for the operating cost of the energy storage equipment is: in, A collection of energy storage devices. is the number of the energy storage device, For energy storage equipment The operating cost coefficient, Scenario for renewable energy output s Energy storage equipment The active power consumed by charging during the scheduling period t is: Scenario for renewable energy output s Energy storage equipment The active power consumed by discharge during the dispatch period t is: Scenario for renewable energy output s Energy storage equipment collection The operating cost in the scheduling period t; Based on the gas turbine operating cost expectation function and the energy storage device operating cost expectation function, an expected target function of the operating costs of the virtual power plant gas turbine and energy storage device in the power system under the renewable energy output scenario is established. The expected target function of the operating costs of the virtual power plant gas turbine and energy storage device is: in, s is the number of the renewable energy output scenario, t is the number of the scheduling period, Scenario for renewable energy output s The probability of it happening, Scenario for renewable energy output s The operating cost of the gas turbine in the scheduling period t is: Scenario for renewable energy output s The operating cost of the energy storage equipment in the dispatch period t is: is the renewable energy output scenario ensemble, A collection of scheduled time periods for a day.

7. A virtual power plant optimization scheduling device, characterized in that: include: an active power determination module for obtaining the active power of each gas turbine and energy storage device in the virtual power plant when the expected operating cost of the gas turbine and energy storage device in the virtual power plant is minimized under the renewable energy output scenario, based on the coupling constraints of the energy grid and the information network in a virtual power plant in the power system under the established renewable energy output scenario, the safe operation constraints of the energy grid in the power system, and the safe operation constraints of the energy equipment; an optimized scheduling scheme determining module, configured to determine an optimized scheduling scheme for the virtual power plant based on the active power of each gas turbine and energy storage device in the virtual power plant; The constraint condition establishment module includes: The energy network and information network coupling constraint establishment unit is used to establish the information node operation constraints as follows: in, Information Node The energy supply validity flag bit, when the physical node cannot provide power, the information node Cannot work properly, take 0, To connect to an information node The set of physical nodes, Scheduling period t physical nodes n Load reduction, Scheduling period t physical nodes n The load demand, To define the parameters of the coupling strength between information network and energy network, The range is [0,1], The larger it is, the stronger the coupling; Establish physical node operation constraints as follows: in, s is the number of the renewable energy output scenario, Is a binary variable representing an information node Is it a fault? When the information node When a fault occurs, it is 0, otherwise it is 1. is the scheduling period under renewable energy output scenario s t Power Node The output power, is the scheduling period under renewable energy output scenario s t Power Node Maximum output power.

8. The device according to claim 7, wherein Also includes: The constraint establishment module is used to establish the coupling constraints of the energy network and the information network in the virtual power plant under the renewable energy output scenario, establish the safe operation constraints of the energy network in the power system under the renewable energy output scenario, and establish the safe operation constraints of the energy equipment under the renewable energy output scenario.

9. The device according to claim 8, wherein The constraint condition establishment module further includes: The energy network safe operation constraint establishment unit is used to establish the energy network safe operation constraint using the distribution network linear power flow equation as follows: in, and They are the scheduling periods under renewable energy output scenario s. t Slave nodes To Node The active power flow and reactive power flow of and During the scheduling period t Slave nodes To Node The maximum active power flow and maximum reactive power flow, and In the renewable energy output scenario s Next scheduling period t node The magnitude and phase angle of the voltage, and In the renewable energy output scenario s Next scheduling period t node The magnitude and phase angle of the voltage, and Node Minimum and maximum voltage amplitudes, and Node Minimum and maximum phase angles of voltage, and Node and nodes reactance and resistance of the lines between them; The power balance flow constraints are established as follows: in, NB is the line set of the power network in the virtual power plant, and In the renewable energy output scenario s Downlink Node of renewable energy during the dispatch period t The active power and reactive power, and Renewable energy output scenarios s Downlink Node The active power and reactive power of the gas turbine in the scheduling period t, and In the renewable energy output scenario s Downlink Node Energy storage equipment during the dispatch period t The generating active power and charging active power, and In the renewable energy output scenario s Downlink Node The load during the dispatch period t Active and reactive power consumed; The energy equipment safe operation constraint establishment unit is used to establish the gas turbine power operation constraints as follows: in, is the number of the gas turbine, and Gas turbine The lower and upper limits of output power, Scenario for renewable energy output s Lower gas turbine The output active power in the scheduling period t is: Scenario for renewable energy output s Lower gas turbine During the scheduling period t -1 output active power, It is a gas turbine Climbing range during operation; The charging and discharging power operation constraints of the energy storage equipment are established as follows: in, is the number of the energy storage device, Scenario for renewable energy output s Energy storage equipment During the scheduling period t The active power consumed by discharge, Scenario for renewable energy output s Energy storage equipment During the scheduling period t Active power consumed by charging, For energy storage equipment The maximum active power consumed by discharge, For energy storage equipment The maximum active power consumed by charging, For renewable energy output scenarios s Energy storage equipment During the scheduling period t The energy storage capacity, For renewable energy output scenarios s Energy storage equipment During the scheduling period t -1 energy storage capacity, is the charge and discharge efficiency, It is an energy storage device The maximum capacity, Is a binary variable representing the renewable energy output scenario s Energy storage equipment During the scheduling period t Is it in the discharge state, that is, when in the scheduling period t When discharging, Take 1, otherwise, Take 0; The flexible load operation constraints are established as follows: in, is the load number, Scenario for renewable energy output s Underload During the scheduling period t Power consumed, Scenario for renewable energy output s Underload Medium flexibility load during the dispatch period t Power, For load Central and African flexible load during the dispatch period t Power, Scenario for renewable energy output s Underload Medium flexibility load is dispatched during the period before load reduction The power, Scenario for renewable energy output s Underload Dispatch period after medium flexibility load reduction The power, Scenario for renewable energy output s Underload During the scheduling period t The reduction in power, is load The attenuation factor describes the load rebound process.

10. The device according to claim 7, wherein The active power determination module includes: The gas turbine operating cost expectation function establishment unit is used to establish an expected operating cost function of a virtual power plant gas turbine in the power system under the renewable energy output scenario. The gas turbine operating cost expectation function is: in, For gas turbine assembly, is the number of the gas turbine, For gas turbines The operating cost coefficient, Scenario for renewable energy output s Lower gas turbine During the scheduling period t The output active power, Scenario for renewable energy output s Lower gas turbine assembly During the scheduling period t operating costs; The energy storage device operating cost expectation function establishing unit is used to establish the virtual power plant energy storage device operating cost expectation function in the power system under the renewable energy output scenario. The energy storage device operating cost expectation function is: in, A collection of energy storage devices. is the number of the energy storage device, For energy storage equipment The operating cost coefficient, Scenario for renewable energy output s Energy storage equipment During the scheduling period t Active power consumed by charging, Scenario for renewable energy output s Energy storage equipment During the scheduling period t The active power consumed by discharge, Scenario for renewable energy output s Energy storage equipment collection During the scheduling period t operating costs; An objective function establishing unit is configured to establish an expected objective function of the operating costs of the virtual power plant gas turbine and energy storage device in the power system under the renewable energy output scenario based on the expected operating cost function of the gas turbine and the expected operating cost function of the energy storage device, wherein the expected objective function of the operating costs of the virtual power plant gas turbine and energy storage device is: in, s is the number of the renewable energy output scenario, t is the number of the scheduling period, Scenario for renewable energy output s The probability of it happening, Scenario for renewable energy output s The gas turbine is in the dispatch period t operating costs, Scenario for renewable energy output s The energy storage equipment is in the dispatch period t operating costs, is the renewable energy output scenario ensemble, A collection of scheduling time periods for a day; An active power determination unit is used to use a genetic algorithm to determine the active power of each gas turbine and energy storage device in the virtual power plant when the expected objective function value of the operating cost of the gas turbine and energy storage device in the virtual power plant is minimized based on the coupling constraints of the energy network and the information network in a virtual power plant in the power system under the renewable energy output scenario, the safe operation constraints of the energy network in the power system, and the safe operation constraints of the energy equipment.

11. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the virtual power plant optimization scheduling method according to any one of claims 1 to 6 when executing the computer program.

12. A computer storage medium, characterized in that The computer storage medium stores computer executable instructions, which, when executed, implement the virtual power plant optimization scheduling method described in any one of claims 1-6.

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