Method and system for controlling participation of virtual power plant in electricity market

Abstract

The invention relates to the technical field of virtual power plant energy scheduling, and discloses a control method and system for a virtual power plant to participate in an electricity market, and the method comprises the steps: constructing a multi-element flexible load response model, and determining the operation boundary and cost income relation of various flexible loads in different scenes; the various flexible loads comprise a reducible load, a transferable load and a transferable load; based on the multi-element flexible load response model, constructing a joint transaction strategy model of the virtual power plant participating in the power market, and determining a bidding strategy of the virtual power plant in the power market; the electric power market comprises an electric energy market and a peak regulation market. According to the method, a transaction framework that the virtual power plant participates in the energy and peak regulation market is constructed, multi-element flexible load participates in demand response, the virtual power plant participates in joint transaction of the main and auxiliary markets, and uncertainty of market price, load, new energy unit output and the like is processed.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plant energy dispatching, and particularly to a control method and system for a virtual power plant to participate in the power market. Background Technique

[0002] As an aggregation of distributed energy resources (DERs), a virtual power plant (VPP) can achieve coordinated complementarity of internal resources and optimize the allocation and utilization of energy. With the development of the power market, VPPs can participate in the electricity energy market and ancillary service market transactions to make profits.

[0003] Currently, numerous studies have been carried out on VPPs' participation in market transactions. In this process, demand response (DR), as a key means to improve the operating efficiency of VPPs and the stability of the power grid, has become increasingly important. However, most studies have many deficiencies in constructing the demand response mechanism. For example, in dealing with the uncertainties of market prices, loads, and the output of new energy units in VPPs, the Monte Carlo simulation sampling method adopted by existing studies has low efficiency and is prone to missing key scenarios. The heuristic synchronous back substitution algorithm reduces scenarios depending on subjective thresholds, and the scenario representativeness is insufficient. In terms of constructing the flexible load model, only curtailable and shiftable loads are considered, ignoring types such as shiftable loads, resulting in insufficient excavation of load response potential. In the joint trading strategy of VPPs participating in the main and ancillary markets, the existing models do not clarify the details of the joint bidding strategy between the electricity energy market and the peak shaving market, lacking practicality.

[0004] For example, the literature "Research on the Day-ahead Market Trading Strategy of Virtual Power Plants Considering Demand Response" (Electric Power Economy) proposes: First, construct a trading framework for virtual power plants to participate in the energy and peak shaving markets. Then, construct an energy-peak shaving response model for flexible loads, and propose a stepped compensation demand response mechanism considering the peak-valley transfer deviation for shiftable loads in flexible loads, aiming to guide shiftable loads to participate in demand response while increasing the revenue of virtual power plants in the peak shaving market. Finally, construct a master-slave game model between virtual power plants and flexible loads, and use a method combining the particle swarm algorithm and Gurobi to solve the Nash equilibrium of the profit of virtual power plants and the electricity consumption cost of flexible loads. However, this method has the following limitations: 1. In terms of uncertainty handling. This method uses Monte Carlo simulation to generate a set of scenarios. When generating samples, it conducts random sampling, which means a large number of samples are required to better cover the entire value range of variables. Otherwise, it is easy to miss some key scenarios, resulting in low sampling efficiency. In the virtual power plant market trading scenario, the changes in variables such as market price, load, and new energy unit output are complex. If key scenarios are missed, it will lead to inaccurate estimation of the market situation by the virtual power plant, and then unreasonable trading decisions will be made, affecting its economic benefits. This method uses a heuristic synchronous back substitution algorithm that relies on subjective thresholds when reducing scenarios. Different researchers may set different subjective thresholds, which makes the results of scenario reduction lack objectivity and consistency. It is difficult to ensure that the remaining scenarios can accurately represent the actual situation, and some important information may be lost, unable to provide a reliable decision-making basis for the virtual power plant.

[0005] 2. In terms of multiple flexible loads participating in demand response. The flexible load model of this method is single, only considering curtailable and shiftable loads, and ignoring types such as shiftable loads. Shiftable loads widely exist in reality, such as some industrial production equipment, adjustable charging equipment, etc., which have the characteristic of flexibly adjusting the operation time. Ignoring such loads will lead to insufficient exploration of the load response potential, unable to achieve refined scheduling of the load by the virtual power plant, reducing the economic benefits of the virtual power plant in demand response and its contribution to grid stability.

[0006] 3. In terms of VPP participating in the main and auxiliary markets. This method establishes a master-slave game model in which VPP acts as the leader and flexible loads act as the followers to participate in the energy and peaking markets, without clarifying the details of the joint bidding strategy for the electricity energy market and the peaking market (such as the correlation between the energy storage charge-discharge time sequence and market declaration), making it difficult to guide actual transactions. The market trading strategy lacks practicality and cannot effectively guide the virtual power plant to reasonably arrange resources and participate in bidding in a complex power market environment, affecting the comprehensive benefits of the virtual power plant in the electricity energy market and the peaking market.

[0007] It can be seen that the above problems seriously restrict the development of virtual power plants in the power market and need to be solved urgently. Summary of the Invention

[0008] To solve the above problems, the present invention proposes a control method and system for a virtual power plant to participate in the power market, constructs a trading framework for the virtual power plant to participate in the energy and peaking markets, considers multiple flexible loads to participate in demand response, enables the virtual power plant to participate in joint trading in the main and auxiliary markets, and handles uncertainties such as market price, load, and new energy unit output.

[0009] The technical solutions adopted by the present invention are as follows: A control method for a virtual power plant to participate in the power market, including: Construct a multi - variable flexible load response model to determine the operation boundaries and cost - benefit relationships of various types of flexible loads under different scenarios; the various types of flexible loads include curtailable loads, shiftable loads, and translatable loads; Based on the multi - variable flexible load response model, construct a joint trading strategy model for a virtual power plant to participate in the electricity market, and determine the bidding strategy of the virtual power plant in the electricity market; the electricity market includes the electric energy market and the peaking market.

[0010] Furthermore, the construction of the joint trading strategy model for a virtual power plant to participate in the electricity market based on the multi - variable flexible load response model includes: Regarding the uncertainties of market price, load, and the output of new - energy units in the virtual power plant, generate a set of scenarios for wind - solar output, market electricity price, and load based on Latin hypercube sampling; Reduce the set of scenarios using the scenario reduction technique based on the Kantorovich distance to obtain typical scenarios and their occurrence probabilities .

[0011] Furthermore, the generation of the set of scenarios for wind - solar output, market electricity price, and load based on Latin hypercube sampling includes: Divide the vertical axis of the cumulative probability distribution function into equal parts without overlap in each interval; For any th interval, randomly generate a random number within the range of [0, 1] , and then use the random number to find the corresponding cumulative probability function value of the interval : :

[0012] Substitute the cumulative probability function value into the inverse function of the cumulative probability distribution function to obtain the th sampling value : .

[0013] Furthermore, the reduction of the set of scenarios using the scenario reduction technique based on the Kantorovich distance includes: Initialize the probability values of each scenario , at this time, the probabilities of each scenario are equal, that is, there is ; For scenarios, calculate any two scenarios , Kantorovich distance:

[0014] For a scenario , select the scenario with the smallest distance from it , and then calculate the product of the Kantorovich distance and the scenario probability ; repeat this step for each scenario, and select the scenario with the minimum value and delete it; keep looping until the required number of target scenarios is met.

[0015] Furthermore, in the multi - flexible load response model, the total electricity cost of shiftable loads participating in demand response includes:

[0016] Where is the total electricity cost of shiftable loads participating in demand response; is the market price, is the incentive compensation price set by the virtual power plant for shiftable loads; is the power of shiftable loads at time after shifting, is the transferred - out electricity of shiftable loads; T is the total number of time segments for power market scheduling or trading; and are dissatisfaction cost coefficients; the Boolean variable indicates whether the shiftable load is shifted in a certain time period, indicates that the load has been shifted to a certain time period, indicates that the load has not been shifted yet.

[0017] Furthermore, in the multi - flexible load response model, the total electricity cost of curtailable loads participating in demand response includes:

[0018] Where is the total electricity cost of curtailable loads participating in demand response; is the market price, is the incentive compensation price set by the virtual power plant for curtailable loads under scenario ; is the electricity consumption after participating in demand response, is the curtailment amount; T is the total number of time segments for power market scheduling or trading; and are dissatisfaction cost coefficients; is the curtailment time To minimize the reduction time; To maximize the reduction time; is the reduction status at time, with a value of 1 indicating reduction and a value of 0 indicating no reduction; is the maximum number of reduction times.

[0019] Furthermore, in the multi - flexible load response model, the electricity consumption of the shiftable load after participating in demand response includes:

[0020] Among them, is the electricity consumption of the shiftable load after participating in demand response, is the initial electricity consumption, is the transferred - in electricity quantity, is the transferred - out electricity quantity; and is a Boolean variable; is the minimum value of the transferred - in electricity quantity, is the maximum value of the transferred - in electricity quantity; is the minimum value of the transferred - out electricity quantity, is the maximum value of the transferred - out electricity quantity.

[0021] Furthermore, in the multi - flexible load response model, the total electricity cost of the shiftable load after participating in demand response includes:

[0022] Among them, is the total electricity cost of the shiftable load after participating in demand response; is the market price, is the incentive compensation price set by the virtual power plant for the shiftable load; is the electricity consumption of the shiftable load after participating in demand response, is the scenario and time period under which the power transfer amount of the shiftable load, is the transferred - out electricity quantity of the shiftable load; T is the total number of time segments for power market scheduling or trading; and is the dissatisfaction cost coefficient; is a Boolean variable.

[0023] Furthermore, in the joint trading strategy model, the total profit objective function of the virtual power plant in market trading includes:

[0024] Among them, WVPP is the total profit of the virtual power plant in the market transaction, is the scenario the revenue of the virtual power plant in the energy market under the scenario, is the scenario the revenue of the virtual power plant in the peaking market under the scenario, is the revenue from supplying power to flexible loads, is the scenario the operating cost of the virtual power plant under the scenario; N ω is the total number of typical scenarios, η ω is the typical scenario the occurrence probability of; is the electricity price in the energy market; and is a Boolean variable, a value of 1 indicates that the virtual power plant sells electricity in the energy market, a value of 1 indicates that the virtual power plant purchases electricity in the energy market; represents the electricity purchase volume of the virtual power plant in the energy market, represents the electricity sales volume of the virtual power plant in the energy market, T is the total number of time segments for power market dispatching or trading; represents the compensation price obtained by the virtual power plant for participating in peak shaving, represents the compensation price obtained by the virtual power plant for participating in valley filling; is the peak shaving volume declared by the virtual power plant, is the valley filling volume declared by the virtual power plant; is the output of the gas turbine in the peaking market; is the charging of the energy storage in the peaking market, is the discharging of the energy storage in the peaking market; is the peak shaving volume of the curtailable load in the peaking market; is the transferred-out volume of the shiftable load during the peak period in the peaking market, is the transferred-in volume of the shiftable load during the valley period in the peaking market; is the electricity consumption of the shiftable load after participating in demand response, is the electricity consumption of the curtailable load and the shiftable load after participating in demand response, is after translation the power of the shiftable load that can be shifted at the moment; is the scenario the operating cost of the virtual power plant under the scenario, including the demand response cost and the energy storage operating cost and the operating cost of the gas turbine .

[0025] A control system for a virtual power plant to participate in the electricity market, comprising: A multi-flexible load response model construction module, configured to construct a multi-flexible load response model, and determine the operating boundaries and cost-benefit relationships of various flexible loads under different scenarios; the various flexible loads include curtailable loads, shiftable loads, and shiftable loads; A joint trading strategy model construction module, configured to construct a joint trading strategy model for the virtual power plant to participate in the electricity market based on the multi-flexible load response model, and determine the bidding strategy of the virtual power plant in the electricity market; the electricity market includes the electric energy market and the peaking market.

[0026] The beneficial effects of the present invention are as follows: 1. When dealing with uncertainty, the present invention uses Latin hypercube sampling to generate a scenario set, uses scenario reduction technology based on Kantorovich distance to reduce the scenario set, and through reasonable partitioning of the cumulative probability distribution function, the sampling can more evenly cover the variable value range, and the key information can be more accurately retained during the scenario reduction process, reducing the calculation amount while improving the scenario representativeness, enabling the VPP to make more scientific and reasonable decisions when facing uncertainty, effectively enhancing the risk response ability of the VPP in market transactions, and improving economic benefits.

[0027] 2. In terms of multi-flexible load participation in demand response. The present invention constructs a more comprehensive multi-flexible load response model, clarifies the operating boundaries and cost-benefit relationships of various flexible loads under different scenarios, covering curtailable, shiftable, and shiftable loads, etc., fully excavates the flexible load response potential, gives greater flexibility to the economic dispatch of the virtual power plant and electricity market transactions, helps to achieve more refined demand response management, improves the dispatching efficiency of the virtual power plant for loads, and enhances the stability of the power grid.

[0028] 3. In terms of the VPP's participation in the main and auxiliary markets. The present invention constructs a joint trading strategy model for the virtual power plant with multi-flexible loads to participate in the main and auxiliary markets, clarifies the specific bidding strategies of the VPP in the electric energy market and the peaking market, provides a practical operation plan for the VPP to participate in market transactions, enables it to reasonably arrange energy storage charging and discharging according to market prices, load demands, and its own resource conditions, and accurately declare the purchase and sale electricity quantities and peaking quantities, providing a more practical guiding plan for the VPP to participate in market transactions.

[0029] In summary, the present invention constructs a multi - element flexible load response model, covering load that can be curtailed, transferred, and shifted, etc., fully exploiting the potential of flexible load response, and endowing flexibility to the economic dispatch of virtual power plants and electricity market transactions; builds a joint trading strategy model for virtual power plants to participate in the main and auxiliary markets, aggregates various energy resources, and formulates bidding strategies for electricity energy and peaking markets based on market information to enhance the profit of VPP in the day - ahead market; in view of the uncertainties such as market price, load, and output of new - energy units in the virtual power plant, uses Latin hypercube sampling and scenario reduction techniques to handle, taking into account reducing the electricity - using cost of flexible loads and achieving a win - win situation for VPP and flexible loads, providing a powerful strategy for virtual power plants to participate in electricity market transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flowchart of a control method for a virtual power plant to participate in the electricity market in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] In order to have a clearer understanding of the technical features, objectives, and effects of the present invention, the specific embodiments of the present invention are now described. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0032] Embodiment 1 As Figure 1 shown, this embodiment provides a control method for a virtual power plant to participate in the electricity market, including: Construct a multi - element flexible load response model to determine the operation boundaries and cost - benefit relationships of various flexible loads under different scenarios; the various flexible loads include load that can be curtailed, load that can be transferred, and load that can be shifted; Based on the multi - element flexible load response model, construct a joint trading strategy model for the virtual power plant to participate in the electricity market to determine the bidding strategy of the virtual power plant in the electricity market; the electricity market includes the electricity energy market and the peaking market.

[0033] Preferably, constructing a joint trading strategy model for the virtual power plant to participate in the electricity market based on the multi - element flexible load response model includes: In view of the uncertainties of market price, load, and output of new - energy units in the virtual power plant, generate a scenario set of wind and light output, market electricity price, and load based on Latin hypercube sampling; Reduce the scenario set based on the scenario reduction technique based on Kantorovich distance to obtain typical scenarios and their occurrence probabilities .

[0034] Specifically, the method of this embodiment can be implemented by the following steps: (1) Establish a multi - flexible load response model As a schedulable resource on the "load side" of a virtual power plant, flexible load can also act as a "source - side" resource, providing flexibility for the economic dispatch of the virtual power plant and its participation in electricity market transactions.

[0035] (1 - 1) Dissatisfaction cost model: Model the dissatisfaction generated by flexible load participating in demand response as dissatisfaction cost, defined as a quadratic function, and the expression is as follows:

[0036] Where is the dissatisfaction cost, is the response volume of the load participating in demand response, and are the dissatisfaction cost coefficients.

[0037] (1 - 2) Reducible Load (RL) response model: The way RL participates in demand response is to reduce the load during a specific period to obtain economic compensation, and the total electricity cost expression is as follows:

[0038] Where is the total electricity cost after the reducible load participates in demand response; is the market price, is the scenario The incentive compensation price set by the virtual power plant for the reducible load; is the electricity consumption after participating in demand response, is the reduction amount; T is the total number of time segments for power market dispatch or trading; and are the dissatisfaction cost coefficients; is the reduction time, is the minimum value of the reduction time; is the maximum value of the reduction time; is The reduction state at time, taking the value of 1 means reduction, and taking the value of 0 means no reduction; is the maximum number of reduction times.

[0039] (1 - 3) Transferable Load (TL) response model: The way TL participates in demand response is usually to transfer the load in a certain period to another period, and the electricity consumption expression after its participation in demand response is as follows:

[0040] Among them, is the electricity consumption after the shiftable load participates in demand response, is the initial electricity consumption, is the transferred-in electricity quantity, is the transferred-out electricity quantity; and are Boolean variables; is the minimum value of the transferred-in electricity quantity, is the maximum value of the transferred-in electricity quantity; is the minimum value of the transferred-out electricity quantity, is the maximum value of the transferred-out electricity quantity.

[0041] The total electricity cost expression after TL participates in demand response is as follows:

[0042] Among them, is the total electricity cost after the shiftable load participates in demand response; is the market price, is the incentive compensation price set by the virtual power plant for the shiftable load; is the electricity consumption after the shiftable load participates in demand response, is the scenario and time period under which the power transfer amount of the shiftable load, is the transferred-out electricity quantity of the shiftable load; T is the total number of time segments for power market scheduling or trading; and are dissatisfaction cost coefficients; is a Boolean variable.

[0043] (1-4) Shiftable Load (SL) response model: SL participates in demand response and adjusts its operation time within a certain time range through certain technical means and control strategies to obtain economic compensation. The total electricity cost expression is as follows:

[0044] Among them, is the total electricity cost after the shiftable load participates in demand response; is the market price, is the incentive compensation price set by the virtual power plant for the shiftable load; is the power of the shiftable load at the moment after translation, is the transferred-out electricity quantity of the shiftable load; T is the total number of time segments for power market scheduling or trading; and is the dissatisfaction cost coefficient; the Boolean variable indicates whether the shiftable load has shifted during a certain period, indicates that the load has been shifted to a certain period, indicates that the load has not shifted yet.

[0045] (2) Construct a joint trading strategy model for virtual power plants participating in the main and auxiliary markets The VPP model constructed in this embodiment aggregates wind power, photovoltaic power, gas turbines, and energy storage, and is uniformly coordinated and controlled by the VPP operator. In addition, the VPP also agents diversified flexible loads and participates in the main and auxiliary market transactions together with internal resources. As a price taker, the VPP formulates bidding strategies in the electricity energy market and the peak shaving market according to market information. In the electricity energy market, the VPP declares the electricity purchase and sale volume according to the internal resource output and load demand, and schedules the energy storage to "charge low and discharge high" to make a profit; in the peak shaving market, the VPP implements demand response and declares the peak shaving volume and valley filling volume by combining the remaining capacity of the energy storage and gas turbine units to obtain peak shaving benefits.

[0046] (2-1) Uncertainty processing: In view of the uncertainty of market prices, loads, and the output of new energy units within the VPP, this embodiment uses Latin hypercube sampling (LHS) to generate a set of scenarios for wind and solar power output, market electricity prices, and loads, and then uses scenario reduction technology based on Kantorovich distance to reduce the obtained scenario set to a scenario set containing only a small number of representative scenarios to obtain typical scenarios and the occurrence probability of each scenario is .

[0047] (2-1-1) Scenario generation based on LHS Divide the vertical axis of the cumulative probability distribution function into equal parts, and there is no overlap in each interval. For any th interval, randomly generate a number within the range of [0,1], and then use this random number to find the cumulative probability function value corresponding to the interval , and the expression is as follows:

[0048] Substitute into the inverse function of , to obtain the th sampling value , and the expression is as follows:

[0049] Scenario reduction (2-1-2) Initialize the probability values of each scenario , at this time, the probabilities of all scenarios are equal, that is . For scenarios, calculate the Kantorovich distance between any two scenarios , . Its expression is as follows

[0050] For scenario , select the scenario with the smallest distance from it , and then calculate the product of the Kantorovich distance and the scenario probability . Repeat this step for each scenario, and select the scenario with the smallest value and delete it. Repeat the above steps until the required number of target scenarios is met

[0051] (2-2) Objective function: The expression of the total profit objective function of VPP in the market transaction is as follows

[0052] Where and are the revenues of VPP in the energy market and the peak regulation market under scenario respectively, is the revenue from supplying power to flexible loads is the operating cost of VPP under scenario , including the DR cost , the energy storage operating cost (considering the battery loss cost) and the gas turbine operating cost ; is the electricity price in the energy market and are Boolean variables, with a value of 1 indicating that VPP sells electricity in the energy market with a value of 1 indicating that VPP purchases electricity in the energy market and represent the electricity purchase and sale volumes of VPP in the energy market and are the peak shaving volume and valley filling volume declared by VPP and represent the compensation prices obtained by VPP for participating in peak shaving and valley filling is the output of the gas turbine in the peak regulation market and are the charge and discharge of the energy storage in the peak regulation market respectively is the peak shaving volume of RL in the peak shaving market; and are respectively the transfer-out volume during the peak period and the transfer-in volume during the valley period of TL in the peak shaving market; and are respectively the electricity consumption of RL and TL after participating in DR; is the response compensation cost of VPP to users; , are respectively the operating costs of energy storage and gas turbine units; is the battery loss coefficient per unit charge and discharge; and represent the charge and discharge power of energy storage; and are respectively the charge and discharge of energy storage in the energy market; and are respectively the charge and discharge of energy storage in the peak shaving market; Boolean variable , , is the state of whether the gas turbine is working, starting, or stopping at time is the fixed cost of the gas turbine; is the slope of the power generation cost of the th segment of the gas turbine unit after linearizing the quadratic function formula of the operating cost; is the output of the gas turbine unit in the th segment during the time period; is the total output of the unit during the and respectively represent the output of the gas turbine in the energy market and the peak shaving market; and are respectively the start-stop costs of the unit; N T is the number of gas turbines in the virtual power plant, n j is the number of segments of the gas turbine cost model.

[0053] (2 - 3) Constraint conditions: Include market transaction constraints, energy storage constraints (charge and discharge constraints and capacity constraints), gas turbine constraints, and power balance constraints, as follows: (2 - 3 - 1) Market transaction constraints

[0054] Among them, and are respectively the maximum values of electricity sales and purchases in the energy market.

[0055] (2-3-2) Energy storage constraints (charge-discharge constraints and capacity constraints) (2-3-2-1) Charge-discharge constraints

[0056] Among them, and are the state variables of energy storage charge and discharge respectively; when has a value of 1, it means that the energy storage is in the charging state; has a value of 1, it means that the energy storage is in the discharging state; and represent the maximum values of energy storage charge and discharge power.

[0057] (2-3-2-2) Energy storage capacity constraint

[0058] Among them, and are the charge-discharge efficiencies of the energy storage; and are the upper and lower limits of the energy storage capacity.

[0059] (2-3-3) Gas turbine constraints

[0060] Among them, and are the minimum and maximum outputs of the gas turbine unit; and are the upward and downward ramp rates of the unit.

[0061] (2-3-4) Power balance constraint

[0062] Among them, and are Boolean variables, with a value of 1 indicating that the VPP sells electricity in the energy market, with a value of 1 indicating that the VPP purchases electricity in the energy market.

[0063] In summary, the method of this embodiment constructs a multi - flexible load response model, covering load that can be curtailed, transferred, and shifted, etc., fully exploiting the potential of flexible load response, and endowing flexibility for the economic dispatch of virtual power plants and electricity market transactions; builds a joint trading strategy model for virtual power plants to participate in the main and auxiliary markets, aggregates various energy resources, and formulates bidding strategies for electricity energy and peaking markets based on market information to increase the profit of VPP in the day - ahead market; in response to uncertainties such as market prices, loads, and new - energy unit outputs, uses Latin - hypercube sampling and scenario reduction techniques to handle, taking into account reducing the electricity - using costs of flexible loads, achieving a win - win situation for VPP and flexible loads, and providing a powerful strategy for virtual power plants to participate in electricity market transactions.

[0064] Embodiment 2 This embodiment is based on Embodiment 1: This embodiment provides a control system for a virtual power plant to participate in the electricity market, including: A multi - flexible load response model construction module, configured to construct a multi - flexible load response model and determine the operating boundaries and cost - benefit relationships of various flexible loads under different scenarios; the various flexible loads include load that can be curtailed, load that can be transferred, and load that can be shifted. A joint trading strategy model construction module, configured to construct a joint trading strategy model for a virtual power plant to participate in the electricity market based on the multi - flexible load response model and determine the bidding strategy of the virtual power plant in the electricity market; the electricity market includes the electricity - energy market and the peaking market.

[0065] Embodiment 3 This embodiment is based on Embodiment 1: This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the control method of a virtual power plant participating in the electricity market in Embodiment 1. Among them, the computer program can be in the form of source code, object code, executable file, or some intermediate form, etc.

[0066] Embodiment 4 This embodiment is based on Embodiment 1: This embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the control method of a virtual power plant participating in the electricity market in Embodiment 1. Among them, the computer program can be in the form of source code, object code, executable file or some intermediate forms, etc. The storage medium includes: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the storage medium does not include electrical carrier signals and telecommunication signals.

[0067] It should be noted that for the foregoing method embodiments, for the sake of simplicity of description, they are expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

Claims

1. A control method for a virtual power plant to participate in the electricity market, characterized in that, Including: Construct a multi - flexible load response model to determine the operating boundaries and cost - benefit relationships of various flexible loads under different scenarios; The various flexible loads include curtailable loads, shiftable loads, and shift - able loads; Based on the multi - flexible load response model, construct a joint trading strategy model for a virtual power plant to participate in the electricity market and determine the bidding strategy of the virtual power plant in the electricity market; the electricity market includes the energy market and the peaking market.

2. The control method for a virtual power plant to participate in the electricity market according to claim 1, characterized in that, The constructing of the joint trading strategy model for a virtual power plant to participate in the electricity market based on the multi - flexible load response model includes: Aiming at the uncertainties of market price, load, and the output of new - energy units in the virtual power plant, generate a scenario set of wind - solar output, market electricity price, and load based on Latin - hypercube sampling; The scenario reduction technique based on the Kantorovich distance reduces the scenario set to obtain typical scenarios and their occurrence probabilities .

3. The control method for a virtual power plant to participate in the electricity market according to claim 2, characterized in that, The generating of the scenario set of wind - solar output, market electricity price, and load based on Latin - hypercube sampling includes: Divide the vertical axis of the curve of the cumulative probability distribution function into equal parts without overlap in each interval; For any interval, randomly generate a random number within the range of [0, 1] , and then use the random number to find the cumulative probability function value corresponding to the interval : ​ Substitute the cumulative probability function value into the inverse function of the cumulative probability distribution function , to obtain the th sampling value : 。 4. The control method for a virtual power plant to participate in the electricity market according to claim 2, characterized in that, The scenario reduction technology based on the Kantorovich distance reduces the scenario set, including: Initialize the probability values of each scenario , at this time, the probabilities of each scenario are equal, that is, there is ; For scenarios, calculate the Kantorovich distance between any two of them , : For the scenario , select the scenario with the smallest distance from it , and then calculate the product of the Kantorovich distance and the scenario probability ; repeat this step for each scenario, and select the scenario with the smallest value and delete it; keep looping until the required number of target scenarios is met.

5. The control method for a virtual power plant to participate in the electricity market according to claim 1, characterized in that, In the multi - flexible load response model, the total electricity cost after the shift - able load participates in the demand response includes: Among them, is the total electricity cost after the shiftable load participates in demand response; is the market price, is the incentive compensation price set by the virtual power plant for the shiftable load; After translation is the power of the shiftable load at time is the transferred-out electricity of the shiftable load; T is the total number of time segments for power market dispatching or trading; and is the dissatisfaction cost coefficient; the Boolean variable indicates whether the shiftable load is shifted in a certain time period, indicates that the load has been shifted to a certain time period, indicates that the load has not been shifted yet.

6. The control method for a virtual power plant to participate in the electricity market according to claim 1, wherein, In the multi - flexible load response model, the total electricity cost after the curtailable load participates in the demand response includes: Among them, is the total electricity cost after the flexible load participates in demand response; is the market price, is the scenario The incentive compensation price set by the virtual power plant for the flexible load; is the electricity consumption after participating in demand response, is the reduction amount; T is the total number of time segments for power market dispatching or trading; and is the dissatisfaction cost coefficient; is the reduction time, is the minimum value of the reduction time; is the maximum value of the reduction time; is The reduction status at time, taking the value of 1 means reduction, and the value of 0 means no reduction; is the maximum number of reduction times.

7. A control method for a virtual power plant to participate in the electricity market according to claim 1, characterized in that, In the multi - flexible load response model, the electricity consumption after the shiftable load participates in the demand response includes: Among them, is the electricity consumption after the transferable load participates in demand response, is the initial electricity consumption, is the transferred-in electricity quantity, is the transferred-out electricity quantity; and is a Boolean variable; is the minimum value of the transferred-in electricity quantity, is the maximum value of the transferred-in electricity quantity; is the minimum value of the transferred-out electricity quantity, is the maximum value of the transferred-out electricity quantity.

8. A control method for a virtual power plant to participate in the electricity market according to claim 7, characterized in that, In the multi - flexible load response model, the total electricity cost after the shiftable load participates in the demand response includes: Among them, is the total electricity cost after the transferable load participates in demand response; is the market price, is the incentive compensation price set by the virtual power plant for the transferable load; is the electricity consumption after the transferable load participates in demand response, is the scenario and time period is the power transfer amount of the transferable load under the condition, is the transferred-out electricity of the transferable load; T is the total number of time segments for power market dispatching or trading; and is the dissatisfaction cost coefficient; is a Boolean variable.

9. The control method for a virtual power plant to participate in the electricity market according to claim 1, characterized in that, In the joint trading strategy model, the total profit objective function of the virtual power plant in the market transaction includes: Among them, W VPP is the total profit of the virtual power plant in the market transaction, is the scenario under which the virtual power plant's revenue in the energy market, is the scenario under which the virtual power plant's revenue in the peaking market, is the revenue from supplying power to flexible loads, is the scenario under which the operating cost of the virtual power plant; N ω is the total number of typical scenarios, η ω is the typical scenario of the occurrence probability; is the electricity price in the energy market; and is a Boolean variable, with a value of 1 indicating that the virtual power plant sells electricity in the energy market, and a value of 1 indicating that the virtual power plant purchases electricity in the energy market; represents the electricity purchase volume of the virtual power plant in the energy market, and represents the electricity sales volume of the virtual power plant in the energy market, T is the total number of time segments for power market scheduling or trading; represents the compensation price obtained by the virtual power plant for participating in peak shaving, represents the compensation price obtained by the virtual power plant for participating in valley filling; is the peak shaving volume declared by the virtual power plant, is the valley filling volume declared by the virtual power plant; is the output of the gas turbine in the peak shaving market; is the charging of the energy storage in the peak shaving market, is the discharging of the energy storage in the peak shaving market; is the peak shaving amount of the curtailable load in the peak shaving market; is the transferred-out amount of the shiftable load during the peak period in the peak shaving market, is the transferred-in amount of the shiftable load during the valley period in the peak shaving market; is the electricity consumption after the transferable load participates in demand response, is the electricity consumption after the curtailable load and the transferable load participate in demand response, after translation is the power of the shiftable load at the For the scenario the operating cost of the virtual power plant, including the demand response cost , the energy storage operating cost and the gas turbine operating cost .

10. A control system for a virtual power plant to participate in the electricity market, characterized in that, Including: A multi - flexible load response model construction module, configured to construct a multi - flexible load response model to determine the operating boundaries and cost - benefit relationships of various flexible loads under different scenarios; the various flexible loads include curtailable loads, shiftable loads, and shift - able loads; A joint trading strategy model construction module, configured to construct a joint trading strategy model for a virtual power plant to participate in the electricity market based on the multi - flexible load response model and determine the bidding strategy of the virtual power plant in the electricity market; the electricity market includes the energy market and the peaking market.

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