Virtual power plant with price maker participation in energy and reserve market regulation

Abstract

Virtual power plant is a method of regulating the price of manufacturers participating in the energy and reserve market, including the following steps: Step 1, build a database, prepare data parameters for units in virtual power plants and collect sample data sets; Step 2, establish a model, express the uncertainty changes of renewable energy production, upper and lower reserve request coefficients and upper and lower reserve energy prices in virtual power plants as a stochastic double-layer model; Step 3, introduce uncertainty scenarios; Step 4, test the effect of optimization method. A stochastic double-layer model is constructed for expression. Compared with existing research, this strategy improves the capacity price of low reserve market, helps to increase its capacity income in the electricity market, and at the same time reduces the electricity capacity transaction of the market, which helps to reduce the energy cost related to low and low reserve market.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of distributed energy market regulation, and particularly relates to a method for a virtual power plant to participate in energy and reserve market regulation as a price maker. BACKGROUND

[0002] Due to concerns about the future of renewable energy, different countries have taken policies to promote the installation of renewable energy power generation. The global renewable energy power generation capacity continues to grow, and the amount of solar power generation has exceeded that of other renewable energies [1] . However, distributed energy has small single-machine capacity and is scattered, and cannot flexibly participate in active power distribution network scheduling, which may result in resource waste due to insufficient system consumption capacity. With the increase of renewable energy and distributed energy penetration, the wholesale energy market price decreases, and the demand for auxiliary services increases, resulting in high balancing costs [2] .

[0003] Integration in the power grid is a complex problem that needs to be solved hierarchically. Possible solutions include designing alternative power market structures, introducing demand response schemes, integrating large-scale storage, and aggregating multiple distributed energies to achieve effective control [3] . Virtual power plant (VPP) is a multi-dimensional heterogeneous entity that aggregates various distributed energies. Driven by the aggregation scale benefit, through advanced information collection, control, and communication technology, controllable distributed resources across regions and multiple types, including air conditioners, washing machines, and other controllable loads, as well as distributed gas turbines, batteries, and other controllable power sources, are integrated into a coordinated and consistent same power plant to meet the entire load demand [4]Because the behavior of renewable energy depends on the randomness of primary energy, the reliable supply of load demand becomes particularly important in VPP operation. In different time stages, VPP can use flexible market mechanisms to adjust the generation and consumption behavior of each participant, aggregate controllable distributed resources, and fully utilize the flexibility and complementarity of resources. However, for VPP operators who bear their own profits and losses, it is necessary to improve the aggregation management capability of internal resources and obtain the aggregation characteristics in order to better participate in market transactions as an independent market subject. In existing research, there are mainly two strategies for traditional VPP to aggregate user-side resources: one is to directly control the load; the other is to adjust the energy demand by predicting the load and unilaterally setting the price to guide the user. However, the former cannot respect the user's energy preference, and the latter cannot accurately predict the user's behavior, and the price signal issued by VPP cannot accurately guide the user's behavior. Moreover, the above strategies only involve the user's energy demand, and rarely consider the user's generation capacity as a producer and consumer. Therefore, in order to balance the interests of internal producers and consumers and VPP, the present scheme proposes that VPP acts as a price maker in the energy and reserve market to participate in cooperative modeling, and decides its issuance decision one day in advance. By replacing the equilibrium solution of market clearing problem with equivalent first-order optimality conditions, the stochastic two-stage model is converted into a mathematical program with equilibrium constraints, which can increase the profit of the price maker in the energy and reserve market.

[0004] [1] Ma M, Liu Y. Stochastic optimization dispatch of wind power integrated system considering residential hybrid energy demand response[J]. Solar Energy, 2022, 43(01): 11-20.

[0005] [2] Xu LX, Shen ZJ, Liu MB, Lu WT. A bi-level mixed integer optimization model for market bidding of distribution network security checking considering distributed energy aggregators[J]. Power System Technology, 2021, 45(11): 4395-4406.

[0006] [3] Wang X, He YG, Ma HR, Wang ZY. Distributed optimization control method of virtual energy storage power plant for grid ancillary service[J]. Power System Automation, 2022, 1-10.

[0007] [4] Yuan HR. Community energy transaction mode and optimal operation strategy based on Nash bargaining game[D]. Guangxi University, 2021, 001159. SUMMARY

[0008] The technical problem solved by the present application is to provide a method for regulating the participation of a virtual power plant in the energy and reserve market as a price maker, aiming to promote renewable energy consumption by the VPP participating in the energy and reserve power market, and providing a VPP as a price maker to decide its issuance strategy in the market one day in advance.

[0009] To solve the above technical problems, the technical solution adopted by the present application is:

[0010] The method for regulating the participation of a virtual power plant in the energy and reserve market as a price maker comprises the following steps:

[0011] Step 1, build a database, prepare data parameters of units in the virtual power plant and collect sample data sets;

[0012] Step 2, establish a model, express the uncertain changes of renewable energy production, up and down reserve request coefficients and up and down reserve energy prices in the virtual power plant as a stochastic bi-level model, the upper model represents the operation optimization and operation aiming at maximizing the expected profit while considering the technical constraints of each unit in the virtual power plant, and the lower model takes maximizing the market profit of the energy and reserve market as the target, takes the power balance constraint and capacity constraint as the constraint condition, and represents the market clearing of the energy and reserve market;

[0013] The obtained stochastic bi-level model is converted into a first-order optimal condition with equilibrium constraints to replace the market clearing problem;

[0014] Step 3, introduce uncertainty scenarios, the virtual power plant publishes its price optimization strategy one day in advance, which is affected by the uncertainty of renewable energy production level, up and down reserve request coefficients for providing planned up and down reserve capacity, and energy price, and these uncertainties are represented by a set of scenarios indexed by ω;

[0015] Step 4, test the effect of the optimization method.

[0016] The specific steps of the above Step 1 are:

[0017] Step 1.1, prepare parameters, the virtual power plant includes conventional generating units, renewable energy units and energy storage systems, prepare unit capacity, fixed cost, variable cost, start-up cost coefficient, and energy storage system capacity, minimum energy level, initial energy level, charge and discharge capacity and charge and discharge efficiency data;

[0018] Step 1.2, collect samples

[0019] Step1.2.1, Latin hypercube sampling is used to generate random independent noise with mean 0 and standard deviation of fitting residual, so that the sampling samples can uniformly cover the entire sample space without changing the original density function, then in order to balance the calculation accuracy and efficiency, Cholesky decomposition is used to reduce the correlation of different scenes, and then the sample scene set is obtained to simulate the random distribution of wind speed prediction variables, according to the correlation coefficient between scenes, an approximately orthogonal matrix is constructed, and the sampling element position is reordered to reduce its correlation;

[0020] Step1.2.2, the autoregressive moving average model is used to predict the wind speed time series by minimizing the variance, and the autocorrelation time series is generated by fitting the model;

[0021] Step1.2.3, the obtained time series is transformed back to Weibull distribution: using wind speed data generation method, using maximum likelihood method to fit the Weibull distribution of each hour wind speed time series, then using normal transformation method to transform the time series, making the edge distribution as a standard normal random variable, fitting an autoregressive average model to the transformed time series, making the residual incoherent in time, and finally calculating the standard value of the residual.

[0022] In the above Step2, the random double-layer model considers the flexible demand, conventional generator, renewable energy generator and energy storage unit in the virtual power plant, and uses the pre-set scene set to give the uncertainty change of renewable energy production, up and down reserve request coefficient and up and down reserve energy price in the virtual power plant;

[0023] The objective function expression of the upper model is:

[0024]

[0025] In the formula: f VPP represents the expected profit of VPP; t represents the period; represents the set of period t, which is 24h; represents the scene; W represents the set of scene ω; represents the energy market price in period t, represents the capacity price of upper reserve market in period t, represents the capacity price of lower reserve market in period t; represents the power dispatch of VPP in the energy market in period t; represents the power generation of VPP in the upper reserve market transaction period t; represents the power generation of VPP in the lower reserve market transaction period t; represents the upper reserve energy price in period t and scene ω; represents the upper reserve energy price in period t and scene ω; Represents the upper reserve request coefficient for time period t and scenario ω; This represents the reserve request coefficient for time period t and scenario ω; This represents the total production cost of a VPP conventional generator set over time period t and scenario ω.

[0026] The power balance constraints of the upper-level model are specifically expressed as follows:

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] In the formula: This represents the power dispatch of a VPP in the energy market during time period t; This represents the amount of electricity generated by the VPP during the storage market trading period t. This represents the amount of electricity generated by the VPP during the next reserve market trading period t. This represents the power generation of the VPP conventional generator set under time period t and scenario ω; This represents the power output of the VPP renewable generator set during time period t and scenario ω; This represents the discharge power of the energy storage system under time period t and scenario ω; This represents the charging power of the energy storage system under time period t and scenario ω; This represents the power loss of VPP during time period t and scene ω; This indicates the minimum production level for VPP conventional generator sets; This represents a binary variable, where p is 1 if the conventional generators of VPP generate electricity during time period t, and 0 otherwise; -C，VPP Indicates the capacity of the VPP conventional generator set; This represents the stochastic renewable production level of VPP during time period t; p represents a binary variable used to prevent the VPP energy storage system from charging and discharging simultaneously during time period t and scenario ω; -SD,VPP Indicates the discharge capacity of the VPP energy storage system; Indicates the charging capacity of the VPP energy storage system; This represents the minimum consumption of VPP elasticity demand within time period t; represents the maximum consumption of VPP flexibility demand in the t time period.

[0034] In Step 2, the objective function expression of the lower model is as follows:

[0035]

[0036] In the formula: f Market represents the social profit of the energy and reserve market; t represents the time period; represents the set of time periods t, which is 24h; z represents the index of consumers participating in the energy market; Z represents the set of consumer participation indexes in the energy market; h represents the index of producers participating in the energy market; H represents the set of producer participation indexes in the energy market; m represents the index of reserve market participants; M represents the set of reserve market participant indexes; U dt represents the bidding price of consumer d in the energy market in the t time period; represents the power consumption of energy market user d in the t time period; represents the time interval; C gt represents the offer of producer g in the energy market in the t time period; represents the power generation of producer g in the energy market in the t time period; represents the offer of VPP in the energy market in the t time period; represents the power scheduling of VPP in the t time period in the energy market; represents the offer of participant r in the upper reserve market in the t time period; represents the power capacity of participant r in the upper reserve market in the t time period; represents the offer of VPP in the upper reserve market in the t time period; represents the power generation of VPP in the upper reserve market in the t time period; represents the offer of participant r in the lower reserve market in the t time period; represents the power capacity of participant r in the lower reserve market in the t time period; represents the offer of VPP in the lower reserve market in the t time period; represents the power generation of VPP in the lower reserve market in the t time period;

[0037] The constraint expression of the lower model is as follows:

[0038] Power balance constraint:

[0039]

[0040]

[0041]

[0042] where h represents the index of producers participating in the energy market; H represents the set of indices of producers participating in the energy market; m represents the index of reserve market participants; M represents the set of indices of reserve market participants; represents the power generation of producer g in the energy market at time period t; represents the power dispatch of VPP in the energy market at time period t; represents the power consumption of energy market user d at time period t; represents the power capacity of participant r in the upper reserve market at time period t; represents the power generation of VPP in the upper reserve market at time period t; represents the reserve capacity required by the upper reserve market at time period t; represents the power capacity of participant r in the lower reserve market at time period t; represents the power generation of VPP in the lower reserve market at time period t; represents the reserve capacity required by the lower reserve market at time period t;

[0043] Capacity constraints:

[0044]

[0045] where: represents the power capacity of participant r in the upper reserve market at time period t; represents the capacity provided by participant r in the upper reserve market at time period t; represents the power generation of VPP in the upper reserve market at time period t; represents the capacity of VPP in the upper reserve market at time period t; represents the power capacity of participant r in the lower reserve market at time period t; represents the power capacity of participant r in the lower reserve market at time period t; represents the power generation of VPP in the lower reserve market at time period t; represents the capacity of VPP in the lower reserve market at time period t.

[0046] In Step 2 above, the random bi-level model established is responsible for managing the price strategy in the virtual power plant by the energy management system, which receives the forecast and demand of units in the virtual power plant and solves the profit maximization problem of the feasible operating region limit of units in the virtual power plant. The output of this problem includes the decisions provided by the virtual power plant, which are conveyed to the market operator. The market operator, together with the bids from other market participants, clears the energy and reserve markets, and delivers the market output to the virtual power plant and other participating units.

[0047] In Step 3 above, the price optimization strategy sequence represented by the ω index scenario is:

[0048] Step3.1, the virtual power plant decides its supply strategy in the day-ahead energy and reserve market, and the commitment state of the conventional generating units, the market operator solves the clearing problem of the energy and reserve power market, and provides a schedule and price corresponding to the market equilibrium;

[0049] Step3.2, a scenario of omega is realized, the virtual power plant gets the actual value of renewable energy production level, which provides the energy reserve amount and the upper and lower fluctuation values of the energy reserve price;

[0050] Step3.3, the virtual power plant decides its actual dispatching according to the scenario omega.

[0051] The virtual power plant provided by the application is a method for a price manufacturer to participate in the regulation of the energy and reserve market, which is expressed by constructing a random double-layer model. Compared with the existing research, this strategy improves the capacity price of the low reserve market, helps to increase its capacity income in the power market, and at the same time reduces the power capacity transaction of the market, which helps to reduce the energy cost related to the low reserve market. BRIEF DESCRIPTION OF DRAWINGS

[0052] The application will be further described below in combination with the drawings and examples:

[0053] Figure 1 It is a diagram of the interaction between the virtual power plant and the energy and reserve market;

[0054] Figure 2 It is a trend diagram of the maximum consumption of elastic demand per hour in the example;

[0055] Figure 3 It is the influence of the virtual power plant in different roles in the energy market;

[0056] Figure 4 It is a schematic diagram of the power capacity of the virtual power plant in the uplink reserve market transaction;

[0057] Figure 5 It is a schematic diagram of the power capacity of the virtual power plant in the low reserve market transaction. DETAILED DESCRIPTION

[0058] The technical solutions of the application will be described in detail below in combination with the drawings and examples.

[0059] The method for a price manufacturer to participate in the regulation of the energy and reserve market by the virtual power plant comprises the following steps:

[0060] Step1, construct a database, prepare the data parameters of the units in the virtual power plant and collect sample data sets;

[0061] Step2, model establishment, the uncertainty of renewable energy production, up and down reserve request coefficient and up and down reserve energy price in virtual power plant is expressed as a stochastic bi-level model, the upper model represents the operation optimization and operation with the maximum expected profit as the target while considering the technical constraints of each unit in virtual power plant, the lower model takes the market profit of energy and reserve market as the target, with power balance constraint and capacity constraint as the constraint condition, representing the market clearing of energy and reserve market;

[0062] The obtained stochastic bi-level model is converted into a first-order optimal condition with equilibrium constraint to replace the market clearing problem;

[0063] Step3, introduce uncertainty scenario, virtual power plant publishes its price optimization strategy one day in advance, which is affected by the uncertainty of renewable energy production level, up and down reserve request coefficient and energy price, which is represented by a set of scenarios indexed by ω;

[0064] Step4, test the effect of optimization method.

[0065] The specific steps of Step1 above are:

[0066] Step1.1, prepare parameters, virtual power plant includes conventional generator unit, renewable energy unit and energy storage system, prepare unit capacity, fixed cost, variable cost, start-up cost coefficient, and energy storage system capacity, minimum energy level, initial energy level, charge and discharge capacity and charge and discharge efficiency data;

[0067] Step1.2, sample collection

[0068] Step1.2.1, Latin hypercube sampling is used to generate random independent noise with mean value of 0 and standard deviation of fitting residual, which can uniformly cover the entire sample space without changing the original density function, then Cholesky decomposition is used to reduce the correlation of different scenarios to obtain sample scenario set to simulate the random distribution of wind speed prediction variable, according to the correlation coefficient between scenarios, an approximate orthogonal matrix is constructed, and the sampling element position is reordered to reduce its correlation;

[0069] Step1.2.2, use Auto-regressive Moving Average, ARMA model to predict wind speed time series by minimizing variance, and generate autocorrelation time series by fitting the model;

[0070] Step 1.2.3. Transform the obtained time series back to Weibull distribution: using the wind speed data generation method, the maximum likelihood method is used to fit the hourly wind speed time series to the Weibull distribution, and then the normal transformation method is used to transform the time series, so that the marginal distribution is a standard normal random variable. An autoregressive mean model is fitted to the transformed time series, so that the residual is not correlated in time. Finally, the standard value of the residual is calculated.

[0071] In Step 2 above, a stochastic bilevel model is used to solve the price strategy, which considers a price manufacturer VPP that decides its issuance strategy in the energy and reserve market one day in advance. The model considers flexible demand, conventional generating units, renewable energy generating units and storage units in the VPP, and uses a pre-set scenario set to give the uncertainty of renewable energy production, up and down reserve request coefficients and up and down reserve energy price in the VPP;

[0072] The upper model represents the decision-making and operation of different units in the VPP while considering the technical constraints of each unit in the VPP, with the goal of maximizing expected profit. At the same time, the lower model represents the market clearing of the energy and reserve market. The stochastic bilevel model is converted into a mathematical program with equilibrium constraints (MPEC), and the equivalent first-order optimality condition is replaced by the market clearing problem. The resulting MPEC model is a mixed integer nonlinear programming problem, which is redefined as a mixed integer linear programming problem.

[0073] Then the corresponding detection model is constructed, and the energy management system is responsible for managing the VPP to decide the provision strategy. The interaction between the VPP and the energy and reserve market is shown in Figure 1 The energy management system receives the forecast and demand of the units in the VPP and solves the profit maximization problem subject to the feasible operating region of the units in the VPP. The output of this problem includes the decisions made by the VPP, which are communicated to the market operator. The market operator, together with the bids from other market participants, clears the energy and reserve market, and the market output is passed to the VPP and other participating units.

[0074] The objective function expression of the upper model is:

[0075]

[0076] In the formula: f VPP represents the expected profit of the VPP; t represents the time period; Γ represents the set of time periods t, which is 24h; ω represents the scenario; W represents the set of scenarios ω; represents the energy market price in period t, represents the capacity price of the reserve market in period t, represents the capacity price of the reserve market in period t, PVPtωdenotes the power schedule of the VPP in the energy market at time period t and scenario ω; PVPtωupdenotes the power generation of the VPP at the up reserve market trading period t and scenario ω; PVPtωdowndenotes the power generation of the VPP at the down reserve market trading period t and scenario ω; PVPtωupdenotes the up reserve energy price at time period t and scenario ω; PVPtωdowndenotes the down reserve energy price at time period t and scenario ω; PVPtωupdenotes the up reserve request coefficient at time period t and scenario ω; PVPtωdowndenotes the down reserve request coefficient at time period t and scenario ω; PVPtωdenotes the total production cost of the VPP conventional generator units at time period t and scenario ω;

[0077] The power balance constraint condition of the upper model is specifically represented as:

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] wherein: PVPtωdenotes the power schedule of the VPP in the energy market at time period t and scenario ω; PVPtωupdenotes the power generation of the VPP at the up reserve market trading period t and scenario ω; PVPtωdowndenotes the power generation of the VPP at the down reserve market trading period t and scenario ω; PVPtωdenotes the power generation of the VPP conventional generator units at time period t and scenario ω; PVPtωdenotes the power output of the VPP renewable generator units at time period t and scenario ω; PVPtωdenotes the discharging power of the energy storage system at time period t and scenario ω; PVPtωdenotes the charging power of the energy storage system at time period t and scenario ω; PVPtωdenotes the power loss of the VPP at time period t and scenario ω; PVPtωdenotes the minimum production level of the VPP conventional generator units; PVPtωdenotes a binary variable, which is 1 if the VPP conventional generator units generate power at time period t, otherwise 0; p -C，VPP PVPtωdenotes the capacity of the VPP conventional generator units; PVPtωdenotes the random renewable production level of the VPP at time period t; denotes a binary variable for avoiding VPP energy storage system charging and discharging simultaneously at time period t and scenario ω; p -SD,VPP denotes the discharging capacity of VPP energy storage system; denotes the charging capacity of VPP energy storage system; denotes the minimum consumption of VPP flexible demand at time period t; denotes the maximum consumption of VPP flexible demand at time period t.

[0085] In Step 2 above, the objective function expression of the lower model is as follows:

[0086]

[0087] In the formula: f Market denotes the social profit of energy and reserve market; t denotes time period; Γ denotes the set of time period t, which is 24h; z denotes the index of consumer participating in energy market; Z denotes the set of consumer participating in energy market index; h denotes the index of producer participating in energy market; H denotes the set of producer participating in energy market index; m denotes the index of reserve market participant; M denotes the set of reserve market participant index; U dt denotes the bidding price of consumer d in energy market at time period t; denotes the electricity consumption of energy market user d at time period t; Δt denotes time interval; C gt denotes the offer of producer g in energy market at time period t; denotes the power generation of producer g in energy market at time period t; denotes the offer of VPP in energy market at time period t; denotes the power scheduling of VPP in energy market at time period t; denotes the offer of participant r in upper reserve market at time period t; denotes the power capacity of participant r in upper reserve market at time period t; denotes the offer of VPP in upper reserve market at time period t; denotes the power generation of VPP in upper reserve market at time period t; denotes the offer of participant r in lower reserve market at time period t; denotes the power capacity of participant r in lower reserve market at time period t; denotes the offer of VPP in lower reserve market at time period t; denotes the power generation of VPP in lower reserve market at time period t;

[0088] The constraint condition expression of the lower model is as follows:

[0089] Power balance constraint:

[0090]

[0091]

[0092]

[0093] where h represents the index of producers participating in the energy market; H represents the set of indices of producers participating in the energy market; m represents the index of reserve market participants; M represents the set of indices of reserve market participants; represents the power generation of producer g in the energy market at time period t; represents the power dispatch of VPP in the energy market at time period t; represents the power consumption of energy market user d at time period t; represents the power capacity of participant r in the upper reserve market at time period t; represents the power generation of VPP in the upper reserve market at time period t; represents the reserve capacity required by the upper reserve market at time period t; represents the power capacity of participant r in the lower reserve market at time period t; represents the power generation of VPP in the lower reserve market at time period t; represents the reserve capacity required by the lower reserve market at time period t;

[0094] Capacity constraints:

[0095]

[0096]

[0097]

[0098]

[0099] where: represents the power capacity of participant r in the upper reserve market at time period t; represents the capacity provided by participant r in the upper reserve market at time period t; represents the power generation of VPP in the upper reserve market at time period t; represents the capacity of VPP in the upper reserve market at time period t; represents the power capacity of participant r in the lower reserve market at time period t; represents the power capacity of participant r in the lower reserve market at time period t; represents the power generation of VPP in the lower reserve market at time period t; represents the capacity of VPP in the lower reserve market at time period t.

[0100] In Step 2 above, the established stochastic double-layer model is responsible for managing the price strategy in the virtual power plant by the energy management system, which receives the forecast and demand of units in the virtual power plant and solves the profit maximization problem of the feasible operating region limit of units in the virtual power plant. The output of this problem includes the decisions provided by the virtual power plant, which are conveyed to the market operator. The market operator, together with the bids from other market participants, clears the energy and reserve market and delivers the market output to the virtual power plant and other participating units.

[0101] In Step 3 above, the ω index scenario is generated according to the wind power forecast data using the Latin hypercube sampling method without changing the original density function, so that the sampling samples can uniformly cover the entire sample space. The wind power scenario is reduced to ω scenarios using the synchronous back substitution elimination method to reduce the computational amount of optimal scheduling.

[0102] The specific steps of the synchronous back substitution elimination method are as follows:

[0103] Step one, initialization, import the generated N initial scenarios, set the number of iterations, and equalize the probability of any scenario, i.e.

[0104] p i = 1 / N (7)

[0105] In the formula, p i represents the probability of any scenario; N represents the number of initial scenarios.

[0106] Step two, for any scenario ω i , calculate the other scenarios with the shortest distance.

[0107] D i,min = minζ j d(ω i ,ω j ), j = 1, 2,..., N, j ≠ i (8)

[0108] In the formula, D i,min represents the other scenario with the shortest distance; ζ j represents the probability of scenario ω j ; d(ω i ,ω j ) represents the Euclidean distance between scenario ω i and scenario ω j .

[0109] Step three, determine the scenario ω i to be deleted.

[0110] D min = minζ i D i,minj = 1,2,...,N (9)

[0111] Step four, update the number of scenes N = N-1, add the probability of the deleted scene to the nearest scene in the Euclidean distance, and ensure that the sum of the probabilities of all scenes is 1. Repeat the above steps until the scene number reaches the set value of the reduced scene.

[0112] In the above Step3, the issuance strategy is determined one day in advance, therefore, some uncertainties affect the decision-making problem, including renewable energy production level, rising and falling reserve request coefficients that require to provide planned rising and falling reserve capacity, and energy price, these uncertainties are represented by a set of scenes indexed by ω, each scene has a related probability, the decision-making sequence is described as follows:

[0113] Step3.1, the virtual power plant decides its supply strategy in the day-ahead energy and reserve market, and the commitment state of the conventional generator unit, the market operator solves the clearing problem of the energy and reserve power market, and provides a schedule and price corresponding to the market equilibrium; these decisions do not depend on the realization of the scene, because they are made before the actual realization of the uncertain parameters, therefore, these decisions are called here-and-now decisions;

[0114] Step3.2, a scene ω is realized, the virtual power plant gets the actual value of renewable energy production, which provides the energy reserve amount and the up and down fluctuation values of energy reserve price;

[0115] Step3.3, the VPP decides the actual dispatch of its units, these decisions depend on the scene ω, because they are made after knowing the actual realization of the uncertain parameters, therefore, they are called wait-and-see decisions with respect to the scene ω.

[0116] The essence of Latin hypercube sampling used in this strategy is inverse function conversion stratified sampling, unlike other sampling probability distribution discretization processing, Latin hypercube sampling can make the sampling sample uniformly cover the entire sample space without changing the original density function. The accuracy of the scene is related to the sampling value and the correlation of different sampling values, generally, the smaller the correlation, the higher the accuracy.

[0117] In the above Step4, during testing, the data of the conventional generator unit and the energy storage system are set as shown in Table 1 and Table 2, the maximum hourly consumption of the flexible demand is shown in Table 3, the minimum hourly energy consumption is set to 0, the minimum daily energy consumption is set to 700 MWh, and the installed capacity of the wind turbine is 300 megawatts; Figure 2

[0118]

[0119] Table 1​

[0120]

[0121] Table 2

[0122] The market clearing problem of the energy and reserve power markets is solved by ignoring the participation of VPPs, which provides the prices of the energy market and the capacity prices of the up and down reserve markets, which are the prices used in the problem of the provision of price maker VPPs in Step 3;

[0123] The problem of the provision of price maker VPPs is solved by considering the prices obtained in Step 2, which limits the power levels traded in the up and down reserve markets to the reserve capacity required by the corresponding market, which provides the price maker VPPs with the power to be sold in the energy market, the up reserve market and the down reserve market;

[0124] The market clearing problem of the energy and reserve power markets is solved by considering the participation of VPPs, which provides the capacity of the VPPs in each market corresponding to the values obtained in Step 3, which guarantees that these offers can be accepted. The prices of the energy market and the capacity prices of the up and down reserve markets are given considering the participation of price maker VPPs;

[0125] The expected profit of price maker VPPs is calculated considering the prices obtained in Step 3, according to the power to be sold in Step 2;

[0126] The proposed strategy will compare the results of the model before and after the strategy, by Figure 3 , Figure 4 and Figure 5 The numerical results allow us to conclude that the proposed method allows VPPs to exercise market power, resulting in higher energy and reserve market prices, so modeling VPPs as price makers allows to increase the profit according to the strategy that VPPs cannot influence the market prices, in addition, the proposed strategy can solve real short-term problems in a reasonable time.

[0127] Combining Figures 3-5 , considering the proposal of price maker VPPs and the participation of price maker VPPs, the profits obtained by this model are mostly greater than or equal to the profits obtained without considering that VPPs can exercise market power, meaning that the expected profit of VPPs will be higher. In the low reserve market, VPPs increase their capacity prices, which helps to increase the capacity income of VPPs in this market, while reducing the power capacity transactions in this market, which helps to reduce the energy costs related to the low reserve market.

Claims

1. A method for regulating the energy and storage markets by virtual power plants as price manufacturers, characterized in that: Includes the following steps: Step 1: Build a database, prepare data parameters for the units in the virtual power plant, and collect sample datasets; Step 2: Establish a model to describe the uncertainties in the output of renewable energy, the upper and lower reserve request coefficients, and the prices of upper and lower reserve energy in the virtual power plant as a stochastic two-level model. The upper-level model represents the operation optimization and operation with the goal of maximizing expected profits while considering the technical constraints of each unit in the virtual power plant. The lower-level model aims to maximize the market profits of the energy and storage market, with power balance constraints and capacity constraints as constraints, representing the market liquidation of the energy and storage market. The obtained stochastic two-level model is transformed into a first-order optimal conditional replacement market clearing problem with equilibrium constraints. The stochastic two-level model considers flexible demand, conventional generator sets, renewable energy generator sets and energy storage units in a virtual power plant. Using a pre-defined set of scenarios, it provides the uncertainty changes in renewable energy output, upper and lower reserve request coefficients and upper and lower reserve energy prices in the virtual power plant. The objective function expression of the upper-level model is: ； （1） In the formula: This indicates the expected profit of the VPP; Indicates a time period; Indicates time period t The set is 24 hours; Indicates a scene; Representing a scene A set; express t Energy market prices during the period express t Reserve market capacity and price over time period express t Reserve market capacity and price during a given period; This indicates that VPPs in the energy market... t Power scheduling during specific time periods; This indicates that VPP is trading during the upper reserve market session. t The amount of electricity generated; This indicates that VPP is trading during the lower reserve market session. t The amount of electricity generated; Indicates time period t And the price of stored energy in scenario ω; Indicates time period t And the price of stored energy in scenario ω; Indicates time period t The upper reserve request coefficient of scenario ω; Indicates time period t And the reserve request coefficient under scenario ω; This indicates the VPP conventional generator set during the time period t The total production cost of scenario ω; The power balance constraints of the upper-level model are specifically expressed as follows: ；（2） ；（3） In the formula: This indicates that VPPs in the energy market... t Power scheduling during specific time periods; This indicates that VPP is trading during the upper reserve market session. t The amount of electricity generated; This indicates that VPP is trading during the lower reserve market session. t The amount of electricity generated; Indicates the VPP conventional generator set during the time period t And the amount of electricity generated under scenario ω; This indicates that the VPP renewable generator set is in the time period t and the power output of scene ω; Indicates the energy storage system during the time period t and discharge power under scenario ω; Indicates the energy storage system during the time period t and charging power under scenario ω; Indicates VPP during the time period t And the power loss of scene ω; This indicates the minimum production level for VPP conventional generator sets; Representing a binary variable, if the conventional generator set of VPP is in t The value is 1 if power generation occurs during a specific time period, and 0 otherwise. Indicates the capacity of the VPP conventional generator set; Indicates VPP in t The level of stochastic renewable production over a given period; This indicates that it is used to avoid VPP energy storage systems during certain time periods. t A binary variable that charges and discharges simultaneously with the scene ω; Indicates the discharge capacity of the VPP energy storage system; Indicates the charging capacity of the VPP energy storage system; Indicates in t Minimum consumption of VPP elasticity demand within a time period; Indicates in t The maximum consumption of VPP elasticity demand within a given time period; The objective function expression for the lower-level model is as follows: ；（4） In the formula: This represents the social profit of the energy and reserves market; Indicates a time period; Indicates time period t The set is 24 hours; Indicators representing consumer participation in the energy market; A set of indicators representing consumer participation in the energy market; Indicators representing producer participation in the energy market; A set of indicators representing producer participation in the energy market; Indicators representing participants in the reserve market; A set of indicators representing participants in the reserve market; express t The bid price of consumer d in the energy market during the specified time period; express t Electricity consumption of user d in the energy market during a given time period; Indicates a time interval; express t The price quoted by producer g in the energy market during a given period; express t The amount of electricity generated by producer g in the energy market during a given period; express t The price quoted by VPPs in the energy market during a given period; This indicates that VPPs in the energy market... t Power scheduling during specific time periods; express t Time period participants r Additional bids in the upper reserve market; express t Time period participants r Power capacity in the upper-level reserve market; express t VPP's price quote in the upper reserve market during the specified period; express t The amount of electricity generated by VPP in the upper storage market during the specified period; express t Time period participants r Additional bids in the lower reserve market; express t Time period participants r Power capacity in the underlying reserve market; express t VPP's price quote in the lower reserve market during the period; express t Power generation of VPP in the lower reserve market during the period; The constraint expression for the lower-level model is as follows: Power balance constraints: ；（5） In the formula: Indicators representing producer participation in the energy market; A set of indicators representing producer participation in the energy market; Indicators representing participants in the reserve market; A set of indicators representing participants in the reserve market; express t The amount of electricity generated by producer g in the energy market during a given period; This indicates that VPPs in the energy market... t Power scheduling during specific time periods; express t Electricity consumption of user d in the energy market during a given time period; express t Time period participants r Power capacity in the upper-level reserve market; express t The amount of electricity generated by VPP in the upper storage market during the specified period; express t The required reserve capacity for the reserve market over a given period; express t Time period participants r Power capacity in the underlying reserve market; express t Power generation of VPP in the lower reserve market during the period; express t The required reserve capacity of the reserve market during a given period; Capacity constraints: ； (6) In the formula: express t Time period participants r Power capacity in the upper-level reserve market; express t Time period participants r The capacity provided by the upper reserve market; express t The amount of electricity generated by VPP in the upper storage market during the specified period; express t VPPs are building up market capacity during this period; express t Time period participants r Power capacity in the underlying reserve market; express t Time period participants r Power capacity in the underlying reserve market; express t Power generation of VPP in the lower reserve market during the period; express t VPP's capacity in the downstream reserve market during the period; Step 3: Introduce uncertainty scenarios. The virtual power plant announces its price optimization strategy one day in advance. This strategy is affected by the uncertainty of renewable energy production levels, the rising and falling reserve request coefficients for planned rising and falling reserve capacity, and energy prices. These uncertainties are represented by a set of scenarios indexed by ω. The ω-indexed scenarios are generated based on wind power forecast data using the Latin hypercube sampling method without changing the original density function. This ensures that the sampling samples can uniformly cover the entire sample space. The synchronous back-substitution elimination method is then used to reduce the number of wind power scenarios to ω to reduce the computational load of optimization scheduling. Step 4: Test the effectiveness of the optimization method.

2. The method for regulating the energy and storage markets using virtual power plants as price manufacturers, as described in claim 1, is characterized in that... The specific steps of Step 1 are as follows: Step 1.1: Prepare parameters. The virtual power plant includes conventional generator sets, renewable energy generator sets, and energy storage systems. Prepare the generator set capacity, fixed cost, variable cost, start-up cost coefficient, and energy storage system capacity, minimum energy level, initial energy level, charge and discharge capacity, and charge and discharge efficiency data. Step 1.2, Sample Collection Step 1.2.1: Latin hypercube sampling is used to generate random independent noise with a mean of 0 and a fitting residual standard deviation. Without changing the original density function, the sampled samples can uniformly cover the entire sample space. Then, to balance the calculation accuracy and efficiency, Cholesky decomposition is used to reduce the correlation between different scenarios, thereby obtaining a set of sample scenarios to simulate the random distribution of wind speed prediction variables. Based on the correlation coefficient between scenarios, an approximately orthogonal matrix is ​​constructed, and the positions of the sampled elements are reordered to reduce their correlation. Step 1.2.2: Use the autoregressive moving average model to predict the wind speed time series by minimizing the variance, and fit the model to generate the autocorrelation time series; Step 1.2.3: Transform the obtained time series back to the Weibull distribution: Using the wind speed data generation method, fit the hourly wind speed time series to the Weibull distribution using the maximum likelihood method. Then, use the normal transformation method to transform the time series so that the marginal distribution is a standard normal random variable. Fit an autoregressive average model to the transformed time series so that the residuals are incoherent in time. Finally, calculate the standard value of the residuals.

3. The method for regulating the energy and storage markets using virtual power plants as price manufacturers, as described in claim 2, is characterized in that... In Step 2, the established stochastic two-layer model is managed by the energy management system, which is responsible for managing the pricing strategy in the virtual power plant. The energy management system receives forecasts and demand from the units in the virtual power plant and solves the profit maximization problem under the constraints of the feasible operating area of ​​the units in the virtual power plant. The output of this problem includes the decisions provided by the V virtual power plant, which are communicated to the market operator. The market operator, together with bids from other market participants, clears the energy and storage market and transmits the market output to the virtual power plant and other participating units.

4. The method for regulating the energy and storage markets using virtual power plants as price manufacturers, as described in claim 3, is characterized in that... In Step 3, the order of price optimization strategies represented by the ω index scenario is as follows: Step 3.1: The virtual power plant determines its supply strategy in the day-ahead energy and storage market, as well as the commitment status of conventional generating units. Market operators resolve the clearing issues in the energy and storage electricity market and provide a timetable and price corresponding to market equilibrium. Step 3.2: Implement a scenario for ω, where the virtual power plant obtains the actual value of renewable energy production levels, providing energy reserves and the fluctuation values ​​of energy reserve prices. Step 3.3: The virtual power plant determines its actual scheduling based on scenario ω.

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