CN117010637A - Collaborative optimization method for participation of virtual power plant in electric energy market and auxiliary service market - Google Patents

Collaborative optimization method for participation of virtual power plant in electric energy market and auxiliary service market Download PDF

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CN117010637A
CN117010637A CN202310935697.0A CN202310935697A CN117010637A CN 117010637 A CN117010637 A CN 117010637A CN 202310935697 A CN202310935697 A CN 202310935697A CN 117010637 A CN117010637 A CN 117010637A
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virtual power
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周挺
谭玉东
文明
何智强
刘静
孙晋
张凯
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State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Hunan Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Hunan Electric Power Co Ltd
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Abstract

The application discloses a collaborative optimization method for participation of a virtual power plant in an electric energy market and an auxiliary service market, which comprises the steps of firstly determining a collaborative optimization time period for participation of the virtual power plant in the electric energy market and the auxiliary service market at the same time, then constructing a joint nesting model of participation of the virtual power plant in the electric energy market and the auxiliary service market, comprising a power plant capacity model, an electric energy market output model and an auxiliary service market output model, then solving by a dynamic programming method with the aim of maximizing the total common income of participation of the virtual power plant in the electric energy market and the auxiliary service market, obtaining charge and discharge power, electric energy market output and frequency modulation auxiliary service market output in different time scales in the collaborative optimization time period, and controlling participation of the virtual power plant in the electric energy market and the auxiliary service market according to the collaborative optimization time period. The virtual power plant participates in the cooperative operation of the electric energy market and the auxiliary service market, and the economical efficiency and the utilization efficiency of the virtual power plant are improved.

Description

Collaborative optimization method for participation of virtual power plant in electric energy market and auxiliary service market
Technical Field
The application relates to the field of electric power markets, in particular to a collaborative optimization method for participation of a virtual power plant in an electric energy market and an auxiliary service market.
Background
The participation of the virtual power plant in the electric power market has important significance in the aspects of regulating the electric power supply and demand balance, stabilizing new energy fluctuation and the like, but the operation of the virtual power plant is lack of economy and high efficiency at present. The auxiliary service market in China is characterized by peak shaving auxiliary service, which is a special auxiliary service variety in China, and the spot market in China starts late and cannot achieve the effect of peak shaving and valley filling by means of marketization, so that the electric power enterprises are stimulated to provide peak shaving service by means of price compensation.
The performance of high-quality regulation resources cannot be fully exerted under the condition that the virtual power plant is solely participated in the electric energy market or solely participated in the auxiliary service market, and the economical efficiency and the utilization rate are low. At present, research on dynamic collaborative operation of virtual power plants in the electric energy market and auxiliary service market is rarely performed.
In summary, the application provides a time interval dividing method for the participation of the virtual power plant in the electric energy market and the auxiliary service market, and on the basis, provides a collaborative optimization strategy for the participation of the virtual power plant in the electric energy market and the auxiliary service market, so that the economical efficiency and the utilization efficiency of the virtual power plant are improved.
Disclosure of Invention
The application provides a collaborative optimization method for participation of a virtual power plant in an electric energy market and an auxiliary service market, which aims to solve the problems that the economical efficiency and the utilization rate are low because the performance of high-quality adjustment resources cannot be fully exerted under the condition that the virtual power plant participates in the electric energy market alone or participates in the auxiliary service market alone at present.
In a first aspect, a method for collaborative optimization of participation of a virtual power plant in an electric energy market and an auxiliary service market is provided, including:
s1: determining a collaborative optimization time period for the virtual power plant to participate in the electric energy market and the auxiliary service market simultaneously;
s2: establishing a joint nesting model of a virtual power plant participating in an electric energy market and an auxiliary service market, wherein the joint nesting model comprises a virtual power plant capacity model taking charge and discharge power as a decision variable in a first time scale, an electric energy market output model taking electric energy market output as a decision variable in a second time scale and an auxiliary service market output model taking auxiliary service market output as a decision variable in a third time scale; the charging and discharging power is determined by the electric energy market output and the auxiliary service market output, the first time scale is a positive integer multiple of the second time scale, and the second time scale is a positive integer multiple of the third time scale;
s3: the method comprises the steps of solving and obtaining charge and discharge power, electric energy market output and frequency modulation auxiliary service market output in different time scales in a collaborative optimization time period by a dynamic programming method based on a joint nesting model of the electric energy market and the auxiliary service market of the virtual power plant with the maximum common total income of the electric energy market and the auxiliary service market of the virtual power plant;
s4: and controlling the virtual power plant to participate in the electric energy market and the auxiliary service market according to the charge and discharge power, the electric energy market output and the frequency modulation auxiliary service market output in different time scales within the collaborative optimization time period obtained by solving.
Further, the step S1 includes:
acquiring the electricity price of the electric power spot market for 24 hours;
and determining a charging area and a discharging area of the virtual power plant according to the lowest period and the highest period of the 24-hour electricity price of the electric power spot market, wherein the rest period is used as a collaborative optimization period for the virtual power plant to participate in the electric energy market and the auxiliary service market at the same time.
Further, the virtual power plant capacity model is represented as follows:
the state variables of the virtual power plant are expressed as follows:
in the method, in the process of the application,representing the state of the virtual power plant at the moment t under the virtual power plant capacity model, C t Representing the virtual power plant capacity at time t->The marginal node electricity price of the spot market at the moment t is represented;
the state transfer equation for the virtual power plant capacity is expressed as follows:
in the method, in the process of the application,representing the charge and discharge power of the virtual power plant, < >>Indicating that the virtual power plant is in a state of charge,indicating that the virtual power plant is in a discharge state; η (eta) c And eta d Respectively representing the charging efficiency and the discharging efficiency of the virtual power plant; τ 1 Representing a first time scale, i.e. a first time step;
the constraint conditions are as follows:
wherein P is rated Rated power of virtual power plant, C rated Rated capacity for the virtual power plant;
from time t to t+τ 1 The revenue function of the time virtual power plant participating in the electric energy market is expressed as follows:
in the method, in the process of the application,representing the time t to t+τ 1 The virtual power plant participates in the return of the electric energy market at the moment.
Further, the electric energy market output model is expressed as follows:
the state variables of the virtual power plant are expressed as follows:
in the method, in the process of the application,representing the state of a virtual power plant at the moment t under an electric energy market output model, C t Representing the virtual power plant capacity at time t->The marginal node electricity price of the spot market at the moment t is represented;
the state transfer equation for the virtual power plant capacity is expressed as follows:
in the method, in the process of the application,representing an electrical energy market output; η (eta) c And eta d Respectively representing the charging efficiency and the discharging efficiency of the virtual power plant; τ 2 Representing a second time scale, i.e. a second time step; />From t to t+τ 2 During the moment in time the virtual power plant responds to the charge energy of the frequency modulated signal,/->From t to t+τ 2 The virtual power plant responds to the discharge energy of the frequency modulation signal during the moment;
the constraint conditions are as follows:
wherein K represents the bidding power in the period of T; g τ Representing the tracking precision of the response frequency modulation signal of the virtual power plant; p (P) rated Rated power of virtual power plant, C rated Rated capacity for the virtual power plant;
from time t to t+τ 2 The revenue function of the time virtual power plant participating in the frequency modulation of the electric energy market and the auxiliary service market is expressed as follows:
in the method, in the process of the application,representing the time t to t+τ 2 Time of day virtual power plant participationThe electric energy market and the frequency modulation benefits of auxiliary service market; t (T) 1 Representing a total time period when the virtual power plant participates in the electric energy market; v (V) D Indicating the frequency modulation pricing, representing the time t to t+τ 2 And compensating the frequency modulation performance of the virtual power plant.
Further, the auxiliary service market output model is expressed as follows:
the state variables of the virtual power plant are expressed as follows:
in the method, in the process of the application,representing the state of the virtual power plant at the moment t under the auxiliary service market output model, C t Representing the virtual power plant capacity at time t, D t Represents the frequency modulation signal at the time t, G τ Representing the tracking precision of the response frequency modulation signal of the virtual power plant;
the state transfer equation for the virtual power plant capacity is expressed as follows:
in the method, in the process of the application,representing auxiliary service market output, namely frequency modulation power; />Representing an electrical energy market output; η (eta) c And eta d Respectively representing the charging efficiency and the discharging efficiency of the virtual power plant; τ 3 Representing a third time scale, i.e. a third time step;
the constraint conditions are as follows:
wherein K represents bidding power; g τ Representing the tracking precision of the response frequency modulation signal of the virtual power plant; p (P) rated Rated power of virtual power plant, C rated Rated capacity for the virtual power plant;
from time t to t+τ 3 The revenue function of the time virtual power plant participating in the auxiliary service market frequency modulation is expressed as follows:
in the method, in the process of the application,representing the time t to t+τ 3 The virtual power plant participates in the income of the auxiliary service market frequency modulation at the moment; t (T) 2 Representing a total time period when the virtual power plant participates in the auxiliary service market; k represents bidding power in a period T; v (V) D Indicating the frequency modulation pricing, representing the time t to t+τ 3 And frequency modulation performance compensation benefits of the virtual power plant at the moment.
Further, the objective function of the virtual power plant that maximizes the total revenue of participating in the electric energy market and the auxiliary service market together is represented as follows:
wherein R is T Representing virtual power plants during a period TThe total income of participating in the electric energy market and the auxiliary service market is represented by T, which represents a collaborative optimization time period;the marginal node electricity price of the spot market at the moment t is represented; />Representing the charge and discharge power of the virtual power plant at time t, < >>Indicating that the virtual power plant is in a state of charge +.>Indicating that the virtual power plant is in a discharge state; η (eta) c And eta d Respectively representing the charging efficiency and the discharging efficiency of the virtual power plant; Δt represents the charge-discharge time interval of the virtual power plant in the period T; />Representing the total performance compensation benefit of the virtual power plant participating in the frequency modulation.
Further, a dynamic programming method is adopted to solve and obtain dynamic programming equations of virtual power plant yield functions under a virtual power plant capacity model, an electric energy market output model and an auxiliary service market output model in the process of charge and discharge power, electric energy market output and frequency modulation auxiliary service market output under different time scales in a collaborative optimization time period; firstly, recursively solving to obtain the capacity, charge and discharge power and the benefit of the virtual power plant at each moment under a first time scale; then recursively solving to obtain the tracking accuracy of the virtual power plant capacity, the virtual power plant income, the electric energy market output and the virtual power plant response frequency modulation signal at each moment under the second time scale; and finally, recursively solving to obtain the auxiliary service market output and the virtual power plant income at each moment under the third time scale.
In a second aspect, a collaborative optimization system for participation in an electric energy market and an auxiliary service market by a virtual power plant is provided, comprising:
the collaborative optimization time determining module is used for determining a collaborative optimization time period of the virtual power plant participating in the electric energy market and the auxiliary service market at the same time;
the model construction module is used for constructing a joint nesting model of the virtual power plant participating in the electric energy market and the auxiliary service market, and comprises a virtual power plant capacity model taking charge and discharge power as a decision variable in a first time scale, an electric energy market output model taking electric energy market output as a decision variable in a second time scale and an auxiliary service market output model taking auxiliary service market output as a decision variable in a third time scale; the charging and discharging power is determined by the electric energy market output and the auxiliary service market output, the first time scale is a positive integer multiple of the second time scale, and the second time scale is a positive integer multiple of the third time scale;
the solving module is used for solving and obtaining charge and discharge power, electric energy market output and frequency modulation auxiliary service market output in different time scales in a collaborative optimization time period by adopting a dynamic programming method based on a joint nesting model of the virtual power plant participation electric energy market and the auxiliary service market with the aim of maximum common total income of the virtual power plant participation electric energy market and the auxiliary service market;
and the cooperative operation module is used for controlling the virtual power plant to participate in the electric energy market and the auxiliary service market according to the charge and discharge power, the electric energy market output and the frequency modulation auxiliary service market output of the solved cooperative optimization time period in different time scales.
In a third aspect, an electronic device is provided, comprising:
a memory having a computer program stored thereon;
and the processor is used for loading and executing the computer program to realize the cooperative optimization method of the participation of the virtual power plant in the electric energy market and the auxiliary service market.
In a fourth aspect, a computer readable storage medium is provided, on which a computer program is stored, which when loaded by a processor implements a method for virtual power plant participation in co-optimization of an electric energy market and an auxiliary service market as described above.
The application provides a collaborative optimization method for participation of a virtual power plant in an electric energy market and an auxiliary service market, which comprises the steps of firstly determining a collaborative optimization time period for participation of the virtual power plant in the electric energy market and the auxiliary service market at the same time, then constructing a joint nesting model of participation of the virtual power plant in the electric energy market and the auxiliary service market, comprising a power plant capacity model, an electric energy market output model and an auxiliary service market output model, then solving by a dynamic programming method with the aim of maximizing the total common income of participation of the virtual power plant in the electric energy market and the auxiliary service market, obtaining charge and discharge power, electric energy market output and frequency modulation auxiliary service market output in different time scales in the collaborative optimization time period, and controlling participation of the virtual power plant in the electric energy market and the auxiliary service market according to the collaborative optimization time period. The virtual power plant participates in the cooperative operation of the electric energy market and the auxiliary service market, and the economical efficiency and the utilization efficiency of the virtual power plant are improved.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for collaborative optimization of participation of a virtual power plant in an electric energy market and an auxiliary service market provided by an embodiment of the application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, based on the examples herein, which are within the scope of the application as defined by the claims, will be within the scope of the application as defined by the claims.
Aiming at the problems that the economical efficiency and the utilization rate are low due to the fact that the performance of high-quality regulating resources cannot be fully exerted under the condition that the virtual power plant participates in the electric energy market alone or participates in the auxiliary service market alone at present, the application provides a collaborative optimization method for the virtual power plant to participate in the electric energy market and the auxiliary service market, which is used for improving the independent operation effect of the virtual power plant, and an optimal operation scheme is found by taking the maximum benefit as a target when the virtual power plant participates in the two markets simultaneously through a dynamic programming equation (Bellman equation). Firstly, determining a collaborative optimization time period for a virtual power plant to participate in an electric energy market and an auxiliary service market simultaneously, then constructing a joint nesting model of the virtual power plant to participate in the electric energy market and the auxiliary service market, wherein the joint nesting model comprises a power plant capacity model, an electric energy market output model and an auxiliary service market output model, and then solving by a dynamic programming method with the maximum total common income of the virtual power plant to participate in the electric energy market and the auxiliary service market as a target to obtain charging and discharging power, electric energy market output and frequency modulation auxiliary service market output in different time scales in the collaborative optimization time period, and controlling the virtual power plant to participate in the electric energy market and the auxiliary service market according to the charging and discharging power, the electric energy market output and the frequency modulation auxiliary service market output. The power-assisted virtual power plant is better participated in an electric energy market and an auxiliary service market (with the increasing perfection of the electric spot market in China, peak shaving auxiliary service is finally combined into the electric spot market, so the auxiliary service market disclosed by the application mainly refers to a frequency modulation auxiliary service market). The technical scheme of the application is specifically described below with reference to specific embodiments.
The embodiment of the application provides a collaborative optimization method for participation of a virtual power plant in an electric energy market and an auxiliary service market, which comprises the following steps:
s1: a collaborative optimization time period for the virtual power plant to participate in both the electrical energy market and the auxiliary services market is determined.
Specifically, step S1 includes the steps of:
s11: and obtaining the electricity price of the electric power spot market for 24 hours.
Because of the charge and discharge characteristics of the virtual power plant, the virtual power plant not only purchases and stores electric quantity at a lower price in the low-load period of the power grid according to the identity of a user in the electric energy market, but also performs grid-connected operation on the stored power supply in the high-load period of the power grid according to the identity of a user so as to earn profits of peak-to-valley electricity prices. Most of the electric energy market transactions in China adopt a mode of 'electricity generation side reporting and quoting and electricity utilization side reporting and quoting not', and the 24h electricity price of the spot market is determined according to a mode of node marginal electricity price clearing.
S12: and determining a charging area and a discharging area of the virtual power plant according to the lowest period and the highest period of the 24-hour electricity price of the electric power spot market, wherein the rest period is used as a collaborative optimization period for the virtual power plant to participate in the electric energy market and the auxiliary service market at the same time.
And (3) aiming at maximizing benefits of the virtual power plant, determining a charging area and a discharging area of the virtual power plant according to the lowest period and the highest period of the spot market electricity price obtained in the step (S11), wherein the virtual power plant is independently participated in the electric energy market in the charging and discharging period, and the rest period is taken as a collaborative optimization period of the virtual power plant for simultaneously participating in the electric energy market and the auxiliary service market.
S2: establishing a joint nesting model of a virtual power plant participating in an electric energy market and an auxiliary service market, wherein the joint nesting model comprises a virtual power plant capacity model taking charge and discharge power as a decision variable in a first time scale, an electric energy market output model taking electric energy market output as a decision variable in a second time scale and an auxiliary service market output model taking auxiliary service market output as a decision variable in a third time scale; the charging and discharging power is determined by the electric energy market output and the auxiliary service market output, the first time scale is a positive integer multiple of the second time scale, and the second time scale is a positive integer multiple of the third time scale. In this embodiment, the first time scale, the second time scale, and the third time scale are respectively 1 hour, 15 minutes, and 4 seconds.
S21: virtual power plant capacity model with time scale of 1h
The virtual power plant capacity and the virtual power plant frequency modulation performance are cleared once per hour, firstly, state variables are required to be determined, the state variables are used for representing the natural state where each sub-stage is started, and the state variables of the next sub-stage reflect the ending of the state variables of the previous sub-stage after decision making.
The state variables of the virtual power plant are expressed as follows:
in the method, in the process of the application,representing the state of the virtual power plant at the moment t under the virtual power plant capacity model, C t Representing the virtual power plant capacity at time t->And the marginal node electricity price of the spot market at the moment t is represented.
The state transfer equation for the available virtual power plant capacity is expressed as follows:
in the method, in the process of the application,representing the charge and discharge power of the virtual power plant, < >>Indicating that the virtual power plant is in a state of charge +.>Indicating that the virtual power plant is in a discharge state; η (eta) c And eta d Respectively representing the charging efficiency and the discharging efficiency of the virtual power plant; τ 1 A first time scale, i.e. a first time step, is indicated, in this embodiment 1h.
The present embodiment sets the operating section of the virtual power plant to be0.1 to 0.9. Decision variables for virtual power plantsThe constraint conditions to be satisfied are as follows
Wherein P is rated Rated power of virtual power plant, C rated Rated capacity for the virtual power plant;
from time t to t+τ 1 The revenue function of the time virtual power plant participating in the electric energy market is expressed as follows:
in the method, in the process of the application,representing the time t to t+τ 1 The virtual power plant participates in the return of the electric energy market at the moment.
S22: electric energy market output model with time scale of 15min
In an electric energy market output model with a time scale of 15min, the electric energy market outputIs the only decision variable of the model. The state variables of the virtual power plant are expressed as follows:
in the method, in the process of the application,representing the state of a virtual power plant at the moment t under an electric energy market output model, C t Representing the virtual power plant capacity at time t->And the marginal node electricity price of the spot market at the moment t is represented.
Considering the influence of the frequency modulation auxiliary service on the capacity of the virtual power plant, the change of the capacity of the virtual power plant caused by the participation of the virtual power plant in the frequency modulation (the frequency modulation auxiliary service is carried out for 4s once within 15 min) is equivalent to two random variables, and the definition is thatFrom t to t+τ 2 The virtual power plant responds to the charging energy of the frequency modulated signal during the time of day,from t to t+τ 2 The virtual power plant responds to the discharge energy of the frequency modulated signal during the time of day. Thus, the state transfer equation for the virtual power plant capacity is expressed as follows:
in the method, in the process of the application,representing an electrical energy market output; η (eta) c And eta d Respectively representing the charging efficiency and the discharging efficiency of the virtual power plant; τ 2 Representing a second time scale, i.e. a second time step.
In order to correlate the energy market with the frequency modulated auxiliary service market, a new value G needs to be added τ ∈[0,1]Which represents the tracking precision preset value, G, of the response frequency modulation signal of the virtual power plant in the period from the time t to the time t+tau τ The value from small to large indicates that the accuracy of the virtual power plant tracking frequency modulation signal is from weak to strong.
Thereby, the output of the electric energy market can be calculatedWhen it is required to satisfy constraint conditions such asThe following steps:
wherein K represents T 1 Bidding power in a period; g τ Representing the tracking precision of the response frequency modulation signal of the virtual power plant; p (P) rated Rated power of virtual power plant, C rated Is the rated capacity of the virtual power plant.
Thus, time t reaches t+τ 2 The revenue function of the time virtual power plant participating in the frequency modulation of the electric energy market and the auxiliary service market is expressed as follows:
in the method, in the process of the application,representing the time t to t+τ 2 The virtual power plant participates in the electric energy market and the income of the auxiliary service market frequency modulation at the moment; t (T) 1 Representing the total time period when the virtual power plant participates in the electric energy market (in this embodiment, the electric energy market is 15min as a period, and data within 1h is calculated, so T 1 Taking 1 h); v (V) D Indicating frequency modulation pricing +.>Representing the time t to t+τ 2 And compensating the frequency modulation performance of the virtual power plant.
S22: auxiliary service market output model with time scale of 4s
The auxiliary service market output model decision variable with time scale of 4s is auxiliary service market output, namely frequency modulation power, expressed asOn this time scale, < >>Is the only decision variable of the model, electric energy market output +.>Marginal node electricity price of spot market +.>All remain unchanged. The state variables of the virtual power plant are expressed as follows:
in the method, in the process of the application,representing the state of the virtual power plant at the moment t under the auxiliary service market output model, C t Representing the virtual power plant capacity at time t, D t Represents the frequency modulation signal at the time t, G τ Representing the tracking accuracy of the response of the virtual power plant to the frequency modulated signal.
The state transfer equation for the virtual power plant capacity is expressed as follows:
in the method, in the process of the application,representing an electrical energy market output; η (eta) c And eta d Respectively representing the charging efficiency and the discharging efficiency of the virtual power plant; τ 3 Representing a third time scale, i.e. a third time step;
the constraint conditions are as follows:
wherein K represents T 2 Bidding power in a period; g τ Representing virtual power plant response FM signalsIs used for tracking the precision of the (a); p (P) rated Rated power of virtual power plant, C rated Rated capacity for the virtual power plant;representing the tracking accuracy constraint of the virtual power plant response frequency modulation signal, i.e. the tracking accuracy of the virtual power plant response frequency modulation signal per 15 minutes must not be lower than G τ 。
From time t to t+τ 3 The revenue function of the time virtual power plant participating in the auxiliary service market frequency modulation is expressed as follows:
in the method, in the process of the application,representing the time t to t+τ 3 The virtual power plant participates in the income of the auxiliary service market frequency modulation at the moment; t (T) 2 Representing the total time period (T in this embodiment) when the virtual power plant participates in the auxiliary service market 2 Taking 15 min); k represents bidding power in a period T; v (V) D Indicating frequency modulation pricing +.>Representing the time t to t+τ 3 And frequency modulation performance compensation benefits of the virtual power plant at the moment.
S3: and solving by a dynamic programming method based on a joint nesting model of the virtual power plant participation electric energy market and the auxiliary service market with the aim of maximum total common benefits of the virtual power plant participation electric energy market and the auxiliary service market, so as to obtain the charge and discharge power, the electric energy market output and the frequency modulation auxiliary service market output in different time scales within a collaborative optimization time period.
S31: objective function with maximum total yield
The objective function of the application is established based on the maximization of the total gain of the virtual power plant in the electric energy market and the auxiliary service market. The combined benefits of the virtual power plant consist of two parts, wherein one part of benefits is derived from participation in electric energy market trading and is determined by marginal node electricity prices; the other part is derived from performance compensation participating in the frequency modulation auxiliary service market and is determined by frequency modulation pricing and tracking accuracy. The objective function of the virtual power plant that has the greatest common total benefit of participating in the electric energy market and the auxiliary service market is expressed as follows:
wherein R is T Representing total earnings of the virtual power plant participating in the electric energy market and the auxiliary service market in a period T, wherein T represents a collaborative optimization time period (the total time period of the virtual power plant participating in the electric energy market and the auxiliary service market in one day);the marginal node electricity price of the spot market at the moment t is represented; />Representing the charge and discharge power of the virtual power plant at time t, < >>Indicating that the virtual power plant is in a state of charge +.>Indicating that the virtual power plant is in a discharge state; η (eta) c And eta d Respectively representing the charging efficiency and the discharging efficiency of the virtual power plant; Δt represents the charge-discharge time interval of the virtual power plant in the period T; />Representing the total performance compensation benefit of the virtual power plant participating in the frequency modulation. In the process of solving the common total benefit, recursion is carried out according to a dynamic programming equation of a virtual power plant benefit function under three models
Solving and calculating total performance compensation benefits in the period TWill->Carry over R T Is calculated.
S32: solution of dynamic programming method
And (3) solving by a dynamic programming method to obtain dynamic programming equations of virtual power plant yield functions under the virtual power plant capacity model, the electric energy market output model and the auxiliary service market output model in the processes of charge and discharge power, electric energy market output and frequency modulation auxiliary service market output under different time scales in the collaborative optimization time period.
The dynamic programming equation is a core theory of the dynamic programming algorithm and represents an equation of the relation between adjacent sub-stages in the dynamic programming problem. The problem of the optimal decision in a certain stage is converted into the sub-problem of the optimal decision in the next stage through a dynamic programming equation, so that the optimal decision in the initial state can be solved by the optimal decision problem in the final state in a stepwise iterative manner. The application establishes three layers of nested dynamic programming equations, namely a 4s short time scale dynamic programming equation for determining the auxiliary service market output, a 15min medium time scale dynamic programming equation for determining the electric energy market output and a 1h long time scale dynamic programming equation considering the virtual power plant capacity.
S321: dynamic programming equation for virtual power plant yield function under virtual power plant capacity model
In order to solve the optimal profit solution of the virtual power plant participating in the power grid service at each moment and in the global state, a dynamic programming equation (Bellman equation) is adopted to represent the maximum profit value of the virtual power plant participating in the power grid service from the moment t to the last moment, and the dynamic programming equation of the virtual power plant profit function under the virtual power plant capacity model is represented as follows:
in the method, in the process of the application,representing the benefits of a virtual power plant to participate in grid services (including electric energy market, auxiliary service market) at time t, +.>Representing the benefits of the virtual power plant to participate in the grid service from the moment t to the next moment,/for>Representing the total revenue of the virtual power plant to participate in the grid service at the next time of time t.
The initial state and the final state of the charging area and the discharging area of the virtual power plant corresponding to the lowest electricity price period and the highest electricity price period of the electric power spot market for 24 hours are known, and then the capacity, the charging and discharging power and the benefits of the virtual power plant in each hour can be obtained through recursive solving.
S322: dynamic programming equation of virtual power plant yield function under electric energy market output model
In order to solve the optimal profit solution of the virtual power plant participating in the power grid service at each moment and in the whole situation, a dynamic programming equation is adopted to represent the maximum profit value of the virtual power plant participating in the joint optimization from the moment t to the last moment, and the dynamic programming equation of the virtual power plant profit function under the electric energy market output model is represented as follows:
in the method, in the process of the application,indicating the benefits of the virtual power plant to participate in the grid services at time t,/->Representing the benefits of the virtual power plant to participate in the grid service from the moment t to the next moment,/for>Representing the total revenue of the virtual power plant to participate in the grid service at the next time of time t.
The recursive algorithm finally obtains the maximum value of the virtual power plant income per 15 minutes, thereby determining the electric energy market outputAnd the tracking accuracy G of the response frequency modulation signal of the virtual power plant within 15 minutes each τ 。
S323: dynamic programming equation of virtual power plant yield function under auxiliary service market output model
Similarly, the dynamic programming equation of the virtual power plant profit function under the auxiliary service market output model is expressed as follows:
in the method, in the process of the application,indicating the benefits of the virtual power plant to participate in the grid services at time t,/->Representing the benefits of the virtual power plant to participate in the grid service from the moment t to the next moment,/for>Representing the total revenue of the virtual power plant to participate in the grid service at the next time of time t.
Recursive solution to obtain market output per 4 seconds of auxiliary serviceAnd virtual power plant revenue.
S4: and controlling the virtual power plant to participate in the electric energy market and the auxiliary service market according to the charge and discharge power, the electric energy market output and the frequency modulation auxiliary service market output in different time scales within the collaborative optimization time period obtained by solving.
The embodiment of the application also provides a collaborative optimization system for the participation of the virtual power plant in the electric energy market and the auxiliary service market, which comprises the following steps:
the collaborative optimization time determining module is used for determining a collaborative optimization time period of the virtual power plant participating in the electric energy market and the auxiliary service market at the same time;
the model construction module is used for constructing a joint nesting model of the virtual power plant participating in the electric energy market and the auxiliary service market, and comprises a virtual power plant capacity model taking charge and discharge power as a decision variable in a first time scale, an electric energy market output model taking electric energy market output as a decision variable in a second time scale and an auxiliary service market output model taking auxiliary service market output as a decision variable in a third time scale; the charging and discharging power is determined by the electric energy market output and the auxiliary service market output, the first time scale is a positive integer multiple of the second time scale, and the second time scale is a positive integer multiple of the third time scale;
the solving module is used for solving and obtaining charge and discharge power, electric energy market output and frequency modulation auxiliary service market output in different time scales in a collaborative optimization time period by adopting a dynamic programming method based on a joint nesting model of the virtual power plant participation electric energy market and the auxiliary service market with the aim of maximum common total income of the virtual power plant participation electric energy market and the auxiliary service market;
and the cooperative operation module is used for controlling the virtual power plant to participate in the electric energy market and the auxiliary service market according to the charge and discharge power, the electric energy market output and the frequency modulation auxiliary service market output of the solved cooperative optimization time period in different time scales.
It should be understood that the functional unit modules in the embodiments of the present application may be centralized in one processing unit, or each unit module may exist alone physically, or two or more unit modules may be integrated into one unit module, and may be implemented in hardware or software. The same or similar matters as those in the foregoing embodiments may be referred to in the foregoing description.
The embodiment of the application also provides electronic equipment, which comprises:
a memory having a computer program stored thereon;
and the processor is used for loading and executing the computer program to realize the cooperative optimization method of the participation of the virtual power plant in the electric energy market and the auxiliary service market.
The embodiment of the application also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and the computer program realizes the collaborative optimization method of the participation of the virtual power plant in the electric energy market and the auxiliary service market when being loaded by a processor.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims (10)

1. A method for collaborative optimization of participation of a virtual power plant in an electric energy market and an auxiliary service market is characterized by comprising the following steps:
s1: determining a collaborative optimization time period for the virtual power plant to participate in the electric energy market and the auxiliary service market simultaneously;
s2: establishing a joint nesting model of a virtual power plant participating in an electric energy market and an auxiliary service market, wherein the joint nesting model comprises a virtual power plant capacity model taking charge and discharge power as a decision variable in a first time scale, an electric energy market output model taking electric energy market output as a decision variable in a second time scale and an auxiliary service market output model taking auxiliary service market output as a decision variable in a third time scale; the charging and discharging power is determined by the electric energy market output and the auxiliary service market output, the first time scale is a positive integer multiple of the second time scale, and the second time scale is a positive integer multiple of the third time scale;
s3: the method comprises the steps of solving and obtaining charge and discharge power, electric energy market output and frequency modulation auxiliary service market output in different time scales in a collaborative optimization time period by a dynamic programming method based on a joint nesting model of the electric energy market and the auxiliary service market of the virtual power plant with the maximum common total income of the electric energy market and the auxiliary service market of the virtual power plant;
s4: and controlling the virtual power plant to participate in the electric energy market and the auxiliary service market according to the charge and discharge power, the electric energy market output and the frequency modulation auxiliary service market output in different time scales within the collaborative optimization time period obtained by solving.
2. The method for collaborative optimization of participation in an electric energy market and auxiliary service market in a virtual power plant according to claim 1, wherein step S1 comprises:
acquiring the electricity price of the electric power spot market for 24 hours;
and determining a charging area and a discharging area of the virtual power plant according to the lowest period and the highest period of the 24-hour electricity price of the electric power spot market, wherein the rest period is used as a collaborative optimization period for the virtual power plant to participate in the electric energy market and the auxiliary service market at the same time.
3. The method of collaborative optimization of a virtual power plant participation electric energy market and auxiliary services market according to claim 1, wherein the virtual power plant capacity model is represented as follows:
the state variables of the virtual power plant are expressed as follows:
in the method, in the process of the application,representing deficiencyVirtual power plant state at t moment under quasi-power plant capacity model, C t Representing the capacity of the virtual power plant at time t,the marginal node electricity price of the spot market at the moment t is represented;
the state transfer equation for the virtual power plant capacity is expressed as follows:
in the method, in the process of the application,representing the charge and discharge power of the virtual power plant, < >>Indicating that the virtual power plant is in a state of charge +.>Indicating that the virtual power plant is in a discharge state; η (eta) c And eta d Respectively representing the charging efficiency and the discharging efficiency of the virtual power plant; τ 1 Representing a first time scale, i.e. a first time step;
the constraint conditions are as follows:
wherein P is rated Rated power of virtual power plant, C rated Rated capacity for the virtual power plant;
from time t to t+τ 1 The revenue function of the time virtual power plant participating in the electric energy market is expressed as follows:
in the method, in the process of the application,representing the time t to t+τ 1 The virtual power plant participates in the return of the electric energy market at the moment.
4. The method of collaborative optimization of a virtual power plant participation in an electrical energy market and auxiliary services market according to claim 1, wherein the electrical energy market output model is represented as follows:
the state variables of the virtual power plant are expressed as follows:
in the method, in the process of the application,representing the state of a virtual power plant at the moment t under an electric energy market output model, C t Representing the capacity of the virtual power plant at time t,the marginal node electricity price of the spot market at the moment t is represented;
the state transfer equation for the virtual power plant capacity is expressed as follows:
in the method, in the process of the application,representing an electrical energy market output; η (eta) c And eta d Respectively representing the charging efficiency and the discharging efficiency of the virtual power plant; τ 2 Representing a second time scale, i.e. a second time step; />From t to t+τ 2 During the moment in time the virtual power plant responds to the charge energy of the frequency modulated signal,/->From t to t+τ 2 The virtual power plant responds to the discharge energy of the frequency modulation signal during the moment;
the constraint conditions are as follows:
wherein K represents bidding power; g τ Representing the tracking precision of the response frequency modulation signal of the virtual power plant; p (P) rated Rated power of virtual power plant, C rated Rated capacity for the virtual power plant;
from time t to t+τ 2 The revenue function of the time virtual power plant participating in the frequency modulation of the electric energy market and the auxiliary service market is expressed as follows:
in the method, in the process of the application,representing the time t to t+τ 2 The virtual power plant participates in the electric energy market and the income of the auxiliary service market frequency modulation at the moment; t (T) 1 Representing a total time period when the virtual power plant participates in the electric energy market; v (V) D Indicating the frequency modulation pricing,V D representing the time t to t+τ 2 And compensating the frequency modulation performance of the virtual power plant.
5. The method of collaborative optimization of a virtual power plant participation in an electrical energy market and an auxiliary service market according to claim 1, wherein the auxiliary service market output model is expressed as follows:
the state variables of the virtual power plant are expressed as follows:
in the method, in the process of the application,representing the state of the virtual power plant at the moment t under the auxiliary service market output model, C t Representing the virtual power plant capacity at time t, D t Represents the frequency modulation signal at the time t, G τ Representing the tracking precision of the response frequency modulation signal of the virtual power plant;
the state transfer equation for the virtual power plant capacity is expressed as follows:
in the method, in the process of the application,representing auxiliary service market output, namely frequency modulation power; />Representing an electrical energy market output; η (eta) c And eta d Respectively representing the charging efficiency and the discharging efficiency of the virtual power plant; τ 3 Representing a third time scale, i.e. a third time step;
the constraint conditions are as follows:
wherein K represents bidding power; g τ Representing virtual power plant response tonesTracking accuracy of the frequency signal; p (P) rated Rated power of virtual power plant, C rated Rated capacity for the virtual power plant;
from time t to t+τ 3 The revenue function of the time virtual power plant participating in the auxiliary service market frequency modulation is expressed as follows:
in the method, in the process of the application,representing the time t to t+τ 3 The virtual power plant participates in the income of the auxiliary service market frequency modulation at the moment; t (T) 2 Representing a total time period when the virtual power plant participates in the auxiliary service market; v (V) D Indicating frequency modulation pricing +.> Representing the time t to t+τ 3 And frequency modulation performance compensation benefits of the virtual power plant at the moment.
6. The method for collaborative optimization of a virtual power plant's participation in an electrical energy market and an auxiliary service market according to claim 1, wherein the objective function of the maximum total revenue of the virtual power plant's participation in the electrical energy market and the auxiliary service market is represented as follows:
wherein R is T The total income of the virtual power plant participating in the electric energy market and the auxiliary service market in the period T is represented, and the period T represents a collaborative optimization period;the marginal node electricity price of the spot market at the moment t is represented; />The charge and discharge power of the virtual power plant at the time t is represented,indicating that the virtual power plant is in a state of charge +.>Indicating that the virtual power plant is in a discharge state; η (eta) c And eta d Respectively representing the charging efficiency and the discharging efficiency of the virtual power plant; Δt represents the charge-discharge time interval of the virtual power plant in the period T; />Representing the total performance compensation benefit of the virtual power plant participating in the frequency modulation.
7. The collaborative optimization method for participation in an electric energy market and an auxiliary service market of a virtual power plant according to claim 1, wherein in the process of solving and obtaining charge and discharge power, electric energy market output and frequency modulation auxiliary service market output in different time scales in a collaborative optimization time period by adopting a dynamic programming method, a dynamic programming equation of a virtual power plant gain function under a virtual power plant capacity model, an electric energy market output model and an auxiliary service market output model is respectively established; firstly, recursively solving to obtain the capacity, charge and discharge power and the benefit of the virtual power plant at each moment under a first time scale; then recursively solving to obtain the tracking accuracy of the virtual power plant capacity, the virtual power plant income, the electric energy market output and the virtual power plant response frequency modulation signal at each moment under the second time scale; and finally, recursively solving to obtain the auxiliary service market output and the virtual power plant income at each moment under the third time scale.
8. A virtual power plant participation electric energy market and auxiliary service market collaborative optimization system, comprising:
the collaborative optimization time determining module is used for determining a collaborative optimization time period of the virtual power plant participating in the electric energy market and the auxiliary service market at the same time;
the model construction module is used for constructing a joint nesting model of the virtual power plant participating in the electric energy market and the auxiliary service market, and comprises a virtual power plant capacity model taking charge and discharge power as a decision variable in a first time scale, an electric energy market output model taking electric energy market output as a decision variable in a second time scale and an auxiliary service market output model taking auxiliary service market output as a decision variable in a third time scale; the charging and discharging power is determined by the electric energy market output and the auxiliary service market output, the first time scale is a positive integer multiple of the second time scale, and the second time scale is a positive integer multiple of the third time scale;
the solving module is used for solving and obtaining charge and discharge power, electric energy market output and frequency modulation auxiliary service market output in different time scales in a collaborative optimization time period by adopting a dynamic programming method based on a joint nesting model of the virtual power plant participation electric energy market and the auxiliary service market with the aim of maximum common total income of the virtual power plant participation electric energy market and the auxiliary service market;
and the cooperative operation module is used for controlling the virtual power plant to participate in the electric energy market and the auxiliary service market according to the charge and discharge power, the electric energy market output and the frequency modulation auxiliary service market output of the solved cooperative optimization time period in different time scales.
9. An electronic device, comprising:
a memory having a computer program stored thereon;
processor for loading and executing the computer program for implementing the virtual power plant participation electric energy market and auxiliary service market co-optimization method according to any one of claims 1 to 7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when loaded by a processor, implements the virtual power plant participation electric energy market and auxiliary service market co-optimization method according to any of claims 1 to 7.
CN202310935697.0A 2023-07-28 2023-07-28 Collaborative optimization method for participation of virtual power plant in electric energy market and auxiliary service market Pending CN117010637A (en)

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