Optimal dispatching method, equipment and medium for virtual power plants to participate in the power market

Abstract

The present application provides a method, device and medium for optimizing the scheduling of virtual power plants participating in the electricity market. The method according to the present application includes: obtaining the day-ahead and intraday market revenues of virtual power plants participating in the market, and obtaining the penalty costs of virtual power plants during market settlement; obtaining the resource costs of virtual power plants, and constructing the constraints of the optimization scheduling model for virtual power plants participating in the electricity market; constructing the objective function of the optimization scheduling model for virtual power plants participating in the electricity market with the goal of maximizing the revenue of virtual power plants; solving the optimization scheduling model for virtual power plants participating in the electricity market according to the constraints and objective function of the optimization scheduling model for virtual power plants participating in the electricity market, and obtaining the optimization scheduling plan for virtual power plants participating in the electricity market. The technical solution provided by the present application can obtain the optimization scheduling plan for virtual power plants participating in the electricity market that considers electrochemical energy storage and distributed small hydropower virtual power plant energy storage aggregation units.

Description

Technical Field

[0001] This document relates to the technical field of optimized dispatching of electricity markets, and in particular to an optimized dispatching method, equipment and medium for virtual power plants participating in electricity markets. Background Art

[0002] As a system that integrates and manages distributed energy resources through software and intelligent technology, virtual power plants can participate in power market transactions and power grid operations as "special power plants". Energy storage, as an excellent adjustable resource, has greater flexibility and response speed, and is an indispensable aggregation resource in virtual power plants. On the one hand, as the proportion of new energy installed capacity continues to increase, its intermittent, random, and volatile characteristics make it more difficult to regulate the power system. Through energy storage aggregation units, virtual power plants can more effectively dispatch and manage distributed energy, improve the level of new energy consumption, reduce overall energy costs, and provide auxiliary services such as peak load regulation and frequency regulation for the power grid, achieving "peak shaving and valley filling" and improving the safety and stability of the power system. On the other hand, in extreme weather and peak electricity consumption periods, the difficulty of power supply security increases. Virtual power plant energy storage aggregation units can play an important role at these critical moments, by quickly responding and dispatching energy storage resources, relieving the load pressure of the power grid, and ensuring the stability and reliability of power supply.

[0003] At present, more and more virtual power plants are listing energy storage as an independent aggregation unit, aiming to fully explore its flexible and adjustable capabilities, give full play to its economic efficiency, participate in the day-ahead and intraday transactions of the electricity market, and bring more benefits to virtual power plants. On the other hand, the types of energy storage are gradually enriched. Currently, the energy storage connected to virtual power plants are mostly in the form of electrochemical energy storage and distributed small hydropower. However, current research is mostly focused on electrochemical energy storage, and there is a lack of research on the optimal economic dispatch method for multi-type energy storage aggregation units. Summary of the invention

[0004] The present invention provides an optimization scheduling method, device and medium for a virtual power plant to participate in the electricity market. By establishing an economically optimal model and considering virtual power plant energy storage aggregation units of electrochemical energy storage and distributed small hydropower, the economically optimal scheduling plan for the virtual power plant energy storage aggregation units in intraday and day-ahead transactions in the electricity market is solved.

[0005] An embodiment of the present invention provides an optimization scheduling method for a virtual power plant to participate in an electricity market, comprising:

[0006] S1. Obtain the day-ahead market revenue of virtual power plants, the intraday market revenue of virtual power plants, and the penalty costs of virtual power plants during market settlement;

[0007] S2. Obtaining the resource cost of a virtual power plant, wherein the resource cost of the virtual power plant includes: the electricity cost of electrochemical energy storage and the electricity cost of distributed small hydropower;

[0008] S3. Constructing the constraints of the optimal dispatch model for the virtual power plant to participate in the power market, the constraints include: the day-ahead power balance constraint of the virtual power plant, the day-ahead transaction volume constraint and the purchased power constraint of the virtual power plant, the operation constraint of renewable energy units, the operation constraint of electrochemical energy storage, and the operation constraint of distributed small hydropower;

[0009] S4. Constructing the objective function of the optimal dispatching model of the virtual power plant participating in the electricity market with the goal of maximizing the virtual power plant's revenue; the power plant revenue takes into account the virtual power plant's revenue from participating in the day-ahead market, the virtual power plant's revenue from participating in the intraday market, the penalty cost of the virtual power plant during market settlement, and the resource cost of the virtual power plant;

[0010] S5. Solve the optimal dispatching model for the virtual power plant to participate in the electricity market according to the constraints and objective function of the optimal dispatching model for the virtual power plant to participate in the electricity market, and obtain the optimal dispatching plan for the virtual power plant to participate in the electricity market.

[0011] An embodiment of the present invention provides an electronic device, including:

[0012] processor; and,

[0013] A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the steps of the above-mentioned method for optimizing the scheduling of virtual power plants participating in the electricity market.

[0014] An embodiment of the present invention provides a storage medium for storing computer-executable instructions, which, when executed, implement the steps of the optimization scheduling method for a virtual power plant to participate in an electricity market as described above.

[0015] By adopting the embodiment of the present invention, an economically optimal model is established, and virtual power plant energy storage aggregation units of electrochemical energy storage and distributed small hydropower are considered, and the economically optimal scheduling scheme of virtual power plant energy storage aggregation units in intraday and day-ahead transactions in the power market is solved. The present invention takes into account the participation of virtual power plants with various energy storage types, the scenarios and situations of day-ahead and intraday transactions in the power market, conforms to the basic laws of the power market, and is combined with the actual situation of virtual power plants participating in various trading scenarios, and is typical and extensible. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 A flowchart of an optimization scheduling method for a virtual power plant participating in a power market according to an embodiment of the present invention;

[0018] Figure 2 The present invention is a flowchart of a specific implementation of the optimization scheduling method for a virtual power plant participating in the electricity market according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0020] Method Embodiment

[0021] According to an embodiment of the present invention, a method for optimizing the scheduling of a virtual power plant participating in an electricity market is provided. Figure 1 Flow chart of the optimization dispatching method for virtual power plant participating in the power market according to an embodiment of the present invention. Figure 1 As shown, the optimization scheduling method for a virtual power plant participating in the power market according to an embodiment of the present invention specifically includes:

[0022] S1. Obtain the day-ahead market revenue of virtual power plants, the intraday market revenue of virtual power plants, and the penalty costs of virtual power plants during market settlement;

[0023] The benefits of obtaining virtual power plants from participating in the day-ahead market specifically include:

[0024] The revenue of virtual power plant in the day-ahead market can be obtained by formula 1. Da :

[0025]

[0026] in, represents the day-ahead market electricity price in period t, It represents the number of bids won by virtual power plants in the day-ahead market during period t;

[0027] The cost C of the virtual power plant participating in the day-ahead market is obtained through formula 2 Da ;

[0028]

[0029] in, represents the time-of-use electricity price during period t, Represents the amount of electricity purchased by the virtual power plant during period t.

[0030] Obtain the benefits of participating in the intraday market of virtual power plants, including:

[0031] The power plant revenue R of the virtual power plant during the day of participation is obtained through formula 3 Di :

[0032]

[0033] Among them, ρ ω is the probability of a certain scene appearing, represents the intraday market electricity price in period t, It represents the intraday market trading volume of virtual power plants during period t;

[0034] The intraday market cost C of the virtual power plant participating in the virtual power plant is obtained by formula 4 Di :

[0035]

[0036] in, represents the time-of-use electricity price during period t, Represents the amount of electricity purchased by the virtual power plant during period t.

[0037] The penalty costs for virtual power plants when obtaining market settlement specifically include:

[0038] The penalty cost C of the virtual power plant during the field settlement is obtained through formula 5 punish :

[0039]

[0040] Among them, p punish is the unit penalty price in the electricity market at time t, represents the intraday market trading volume of virtual power plants during period t, ρ ω The probability of a certain scene occurring.

[0041] S2. Obtaining the resource cost of a virtual power plant, wherein the resource cost of the virtual power plant includes: the electricity cost of electrochemical energy storage and the electricity cost of distributed small hydropower;

[0042] The electricity cost of electrochemical energy storage is obtained by formula 6:

[0043]

[0044] Among them, p ESS is the unit charge and discharge cost of electrochemical energy storage, It is the current charge and discharge power of electrochemical energy storage.

[0045] The electricity cost of distributed small hydropower is obtained by formula 7:

[0046]

[0047] Among them, p PS is the unit operating cost of distributed small hydropower, The pumping power of distributed small hydropower in period t, is the power generation of distributed small hydropower in period t.

[0048] S3. Constructing the constraints of the optimal dispatch model for virtual power plants to participate in the power market. The constraints include: virtual power plant day-ahead power balance constraints, virtual power plant day-ahead transaction volume constraints and purchased power constraints, renewable energy unit operation constraints, electrochemical energy storage operation constraints, and distributed small hydropower operation constraints. S3 specifically includes:

[0049] The day-ahead power balance constraint of the virtual power plant is obtained by formula 8:

[0050]

[0051] The day-ahead transaction volume constraint and purchased power constraint of the virtual power plant are obtained through formula 9 and formula 10.

[0052]

[0053] They represent the lower and upper limits of the virtual power plant's participation in the day-ahead market transaction volume;

[0054]

[0055] Represents the lower limit of the virtual power plant's day-ahead market transaction volume and the upper limit of the purchased electricity volume;

[0056] The operation constraints of renewable energy units are obtained through formula 11 and formula 12

[0057]

[0058] Indicates the actual output of wind power and photovoltaic power in time period t under a certain scenario, It represents the predicted output of wind power and photovoltaic power in the period t under a certain scenario;

[0059] The electrochemical energy storage operation constraints are obtained by using formula 13, formula 14, formula 15 and formula 16

[0060]

[0061] E min ≤F t≤E max Formula 15;

[0062] 0≤α1+α2≤1 Formula 16;

[0063] Indicates the upper and lower limits of energy storage charging and discharging probability, E t Represents the remaining capacity of electrochemical energy storage at time t, α1 and α2 are the state variables of the electrochemical energy storage battery charging and discharging, which are 0-1 variables, 1 when in the charging state and 0 when in the discharging state;

[0064] The distributed small hydropower operation constraints are obtained through formula 17 and formula 18:

[0065] E HSmin ≤E HS , t ≤E HSmax Formula 17;

[0066] E HS，1 =E HS，0 Formula 18;

[0067] Among them, E HS，t is the water storage capacity of distributed small hydropower at time t, E HSmin , E HSmax is the minimum and maximum water storage capacity, E HS，1 , E HS，0 The water storage volume at the end and beginning of the operation cycle;

[0068]

[0069] E HS，t-1 is the water storage capacity of distributed small hydropower at time t-1, β p , β g It is the conversion rate of output to flow under pumping and power generation conditions.

[0070] S4. Constructing the objective function of the optimal dispatching model of the virtual power plant participating in the electricity market with the goal of maximizing the virtual power plant's revenue; the power plant revenue takes into account the virtual power plant's revenue from participating in the day-ahead market, the virtual power plant's revenue from participating in the intraday market, the penalty cost of the virtual power plant during market settlement, and the resource cost of the virtual power plant;

[0071] The objective function is obtained by formula 20:

[0072] maxProfit=R Da +R Di -C DA -C Di -C ESS -C PS -C punish Formula 20;

[0073] Among them, R Da is the revenue of the virtual power plant in the day-ahead market, R Di is the daily power plant revenue of the virtual power plant, R load is the electricity fee charged by the virtual power plant to the load, C DA is the cost of virtual power plant participating in the day-ahead market, C Di is the intraday market cost of the virtual power plant, C ESS is the operating cost of electrochemical energy storage in virtual power plants, C PS is the operating cost of small distributed small hydropower in virtual power plants, C punish The penalty cost for virtual power plants during market settlement.

[0074] S5. Solve the optimal dispatching model for the virtual power plant to participate in the electricity market according to the constraints and objective function of the optimal dispatching model for the virtual power plant to participate in the electricity market, and obtain the optimal dispatching plan for the virtual power plant to participate in the electricity market.

[0075] Figure 2 Flow chart of the optimized dispatching method for virtual power plants participating in the power market according to an embodiment of the present invention. Figure 2 It can be seen that the main steps of the optimization scheduling method for a virtual power plant with multiple types of energy storage participating in the power market described in the present invention include: establishing an objective function, determining a virtual power plant participating in the day-ahead and intraday market trading model, determining an electrochemical energy storage and distributed small hydropower model, setting constraints, and solving to obtain the optimization scheduling result, which is as follows:

[0076] Step 1: Establish the objective function to maximize the benefits of the virtual power plant

[0077] The objective function is as follows:

[0078] maxProfit=R Da +R Di -C DA -C Di -C ESS -C PS -C punish ;

[0079] Among them, R Da is the revenue of the virtual power plant in the day-ahead market, R Di is the daily power plant revenue of the virtual power plant, R load is the electricity fee charged by the virtual power plant to the load, C DA is the cost of virtual power plant participating in the day-ahead market, C ESS is the operating cost of electrochemical energy storage in virtual power plants, C PS is the operating cost of small distributed small hydropower in virtual power plants, C punishIt is the penalty cost of virtual power plant in market settlement. In this method, it is assumed that the distributed small hydropower type is a storage type distributed small hydropower station.

[0080] Step 2: Establish a model for virtual power plants to participate in the day-ahead and intraday markets

[0081] 2.1. Virtual power plants participate in the day-ahead market:

[0082]

[0083] in, represents the day-ahead market electricity price in period t, It represents the number of successful bids of virtual power plants participating in the day-ahead market during period t.

[0084]

[0085] in, represents the time-of-use electricity price during period t, Represents the amount of electricity purchased by the virtual power plant during period t.

[0086] 2.2. Virtual power plants participate in the intraday market:

[0087]

[0088] Among them, ρ ω is the probability of a certain scene appearing, represents the intraday market electricity price in period t, It represents the intraday market trading volume of virtual power plants during period t.

[0089]

[0090] Among them, γ represents the penalty coefficient for excess or shortage in the intraday market.

[0091]

[0092] in, Indicates the deviation power in a certain scenario. Indicates the amount of power to be compensated in a certain scenario.

[0093]

[0094] in, Indicates the actual output of wind power and photovoltaic power in time period t under a certain scenario, It indicates the predicted output of wind power and photovoltaic power in the period t under a certain scenario.

[0095]

[0096] in They represent chemical energy storage, the charging and discharging power of distributed small hydropower in a certain scenario within a period of time t, Indicates that other interruptible loads are shedding power.

[0097]

[0098] in, is the electricity price of interruptible load in a certain scenario, It is the total market demand during the day.

[0099]

[0100] in, represents the time-of-use electricity price during period t, Represents the amount of electricity purchased by the virtual power plant during period t.

[0101] 2.3. Penalty cost:

[0102]

[0103] Among them, punish is the penalty price of electricity market unit at time t.

[0104] Step 3: Build a virtual power plant resource model

[0105] 3.1. Electricity cost of electrochemical energy storage:

[0106]

[0107] Among them, p ESS is the unit charge and discharge cost of electrochemical energy storage, is the day-ahead charging and discharging power of electrochemical energy storage. To simplify the model, it is assumed that the virtual power plant contains two energy storage units, one responsible for the day-ahead market and the other responsible for intraday market scheduling.

[0108] 3.2. Electricity cost of distributed small hydropower:

[0109]

[0110] Among them, p PS is the unit operating cost of distributed small hydropower, The pumping power of distributed small hydropower in period t, is the power generation of distributed small hydropower in period t.

[0111] Step 4: Determine the constraints

[0112] 4.1. Day-ahead power balance constraints of virtual power plants

[0113]

[0114] 4.2. Virtual power plant day-ahead transaction volume constraints and external power purchase constraints

[0115]

[0116] They respectively represent the lower and upper limits of the virtual power plant's participation in the day-ahead market trading volume.

[0117]

[0118] Represents the lower limit of the virtual power plant's day-ahead market trading volume and the upper limit of externally purchased electricity.

[0119] 4.3. Operation constraints of renewable energy units

[0120]

[0121] Indicates the actual output of wind power and photovoltaic power in time period t under a certain scenario, It indicates the predicted output of wind power and photovoltaic power in the period t under a certain scenario.

[0122] 4.4. Electrochemical Energy Storage Operation Constraints

[0123]

[0124] E min ≤E t ≤E max ;

[0125] 0≤α1+α2≤1;

[0126] Indicates the upper and lower limits of energy storage charging and discharging probability, E t Represents the remaining capacity of electrochemical energy storage at time t. α1, α2 are the state variables of electrochemical energy storage battery charging and discharging, which are 0-1 variables, 1 when in charging state and 0 when discharging.

[0127] 4.5. Distributed Small Hydropower Operation Constraints

[0128] E HSmin ≤E HS , t ≤E HSmax ;

[0129] E HS , 1 = E HS , 0;

[0130] Among them, E HS，t is the water storage capacity of distributed small hydropower at time t, E HSmin , E HSmaxis the minimum and maximum water storage capacity. HS，1 , E HS，0 It is the water storage volume at the end and beginning of the operation cycle.

[0131]

[0132] E HS，t-1 is the water storage capacity of distributed small hydropower at time t-1, β p , β g It is the conversion rate of output to flow under pumping and power generation conditions.

[0133] Step 5: Solving the optimization problem

[0134] The optimal dispatching model for the virtual power plant containing electrochemical energy storage and distributed small hydropower participating in the day-ahead and intraday balancing market transactions in the electricity market as described in steps one to four is optimized and solved, and an optimal dispatching plan for virtual power plants containing multiple types of energy storage participating in the electricity market is obtained.

[0135] By adopting the embodiment of the present invention, an economically optimal model is established, and virtual power plant energy storage aggregation units of electrochemical energy storage and distributed small hydropower are considered, and the economically optimal scheduling scheme of virtual power plant energy storage aggregation units in intraday and day-ahead transactions in the power market is solved. The present invention takes into account the participation of virtual power plants with various energy storage types, the scenarios and situations of day-ahead and intraday transactions in the power market, conforms to the basic laws of the power market, and is combined with the actual situation of virtual power plants participating in various trading scenarios, and is typical and extensible.

[0136] Device Example 1

[0137] According to an embodiment of the present invention, there is provided an electronic device, including:

[0138] processor; and,

[0139] A memory is arranged to store computer executable instructions, which when executed cause the processor to perform the steps of the above method embodiments.

[0140] Device Example 2

[0141] According to an embodiment of the present invention, a storage medium is provided for storing computer executable instructions, wherein the computer executable instructions implement the steps of the above method embodiment when being executed.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimization dispatching method for a virtual power plant participating in the power market, characterized in that include: S1. Obtain the day-ahead market revenue of virtual power plants, the intraday market revenue of virtual power plants, and the penalty costs of virtual power plants during market settlement; S2. Obtaining the resource cost of a virtual power plant, wherein the resource cost of the virtual power plant includes: the electricity cost of electrochemical energy storage and the electricity cost of distributed small hydropower; S3. Constructing the constraints of the optimal dispatch model for the virtual power plant to participate in the power market, the constraints include: the day-ahead power balance constraint of the virtual power plant, the day-ahead transaction volume constraint and the purchased power constraint of the virtual power plant, the operation constraint of renewable energy units, the operation constraint of electrochemical energy storage, and the operation constraint of distributed small hydropower; S4. Constructing the objective function of the optimal dispatching model of the virtual power plant participating in the electricity market with the goal of maximizing the virtual power plant's revenue; the power plant revenue takes into account the virtual power plant's revenue from participating in the day-ahead market, the virtual power plant's revenue from participating in the intraday market, the penalty cost of the virtual power plant during market settlement, and the resource cost of the virtual power plant; S5. Solve the optimal dispatching model for the virtual power plant to participate in the electricity market according to the constraints and objective function of the optimal dispatching model for the virtual power plant to participate in the electricity market, and obtain the optimal dispatching plan for the virtual power plant to participate in the electricity market.

2. The method according to claim 1, characterized in that The benefits of obtaining virtual power plants from participating in the day-ahead market specifically include: The revenue R of the virtual power plant in the day-ahead market can be obtained by formula 1 Da : in, represents the day-ahead market electricity price in period t, It represents the number of bids won by virtual power plants in the day-ahead market during period t; The cost C of the virtual power plant participating in the day-ahead market is obtained through formula 2 Da ; in, represents the time-of-use electricity price during period t, Represents the amount of electricity purchased by the virtual power plant during period t.

3. The method according to claim 1, characterized in that Obtain the benefits of participating in the intraday market of virtual power plants, including: The power plant revenue R of the virtual power plant during the day of participation is obtained through formula 3 Di : Among them, ρ ω is the probability of a certain scene appearing, represents the intraday market electricity price in period t, It represents the intraday market trading volume of virtual power plants during period t; The intraday market cost C of the virtual power plant participating in the virtual power plant is obtained by formula 4 Di : in, represents the time-of-use electricity price during period t, Represents the amount of electricity purchased by the virtual power plant during period t.

4. The method according to claim 3, characterized in that The penalty costs for virtual power plants when obtaining market settlement specifically include: The penalty cost C of the virtual power plant during the field settlement is obtained through formula 5 punish : Among them, p punish is the unit penalty price in the electricity market at time t, represents the intraday market trading volume of virtual power plants during period t, ρ ω The probability of a certain scene occurring.

5. The method according to claim 1, characterized in that The electricity cost of electrochemical energy storage is obtained by formula 6: Among them, p ESS is the unit charge and discharge cost of electrochemical energy storage, It is the current charge and discharge power of electrochemical energy storage.

6. The method according to claim 1, characterized in that The electricity cost of distributed small hydropower is obtained by formula 7: Among them, p PS is the unit operating cost of distributed small hydropower, The pumping power of distributed small hydropower in period t, is the power generation of distributed small hydropower in period t.

7. The method according to claim 1, characterized in that The S3 specifically includes: The day-ahead power balance constraint of the virtual power plant is obtained by formula 8: The day-ahead transaction volume constraint and purchased power constraint of the virtual power plant are obtained through formula 9 and formula 10. They represent the lower and upper limits of the virtual power plant's participation in the day-ahead market transaction volume; Represents the lower limit of the virtual power plant's day-ahead market transaction volume and the upper limit of the purchased electricity volume; The operation constraints of renewable energy units are obtained through formula 11 and formula 12 Indicates the actual output of wind power and photovoltaic power in time period t under a certain scenario, It represents the predicted output of wind power and photovoltaic power in the period t under a certain scenario; The electrochemical energy storage operation constraints are obtained by using formula 13, formula 14, formula 15 and formula 16 E min ≤E t ≤E max Formula 15: 0≤α1+α2≤1 Formula 16; Indicates the upper and lower limits of energy storage charging and discharging probability, E t Represents the remaining capacity of electrochemical energy storage at time t, α1 and α2 are the state variables of the electrochemical energy storage battery charging and discharging, which are 0-1 variables, 1 when in the charging state and 0 when in the discharging state; The distributed small hydropower operation constraints are obtained through formula 17 and formula 18: E HSmin ≤E HS，t ≤E HSmax Formula 17: E HS，1 =E HS，0 Formula 18; Among them, E HS，t is the water storage capacity of distributed small hydropower at time t, E HSmin , E HSmax is the minimum and maximum water storage capacity, E HS，1 , E HS，0 The water storage volume at the end and beginning of the operation cycle; E HS，t-1 is the water storage capacity of distributed small hydropower at time t-1, β p , β g It is the conversion rate of output to flow under pumping and power generation conditions.

8. The method according to claim 1, characterized in that The objective function is obtained by formula 20: maxProfit=R Da +R Di -C DA -C Di -C ESS -C PS -C punish Formula 20; Among them, R Da is the revenue of the virtual power plant in the day-ahead market, R Di is the daily power plant revenue of the virtual power plant, C DA is the cost of virtual power plant participating in the day-ahead market, C Di is the intraday market cost of the virtual power plant, C ESS is the operating cost of electrochemical energy storage in virtual power plants, C PS is the operating cost of small distributed small hydropower in virtual power plants, C punish The penalty cost for virtual power plants during market settlement.

9. An electronic device, comprising: processor; as well as, A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the steps of the method for optimizing the dispatch of a virtual power plant participating in an electricity market as described in any one of claims 1 to 8.

10. A storage medium for storing computer executable instructions, which, when executed, implement the steps of the method for optimizing the scheduling of a virtual power plant participating in an electricity market as described in any one of claims 1 to 8.

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