Dynamic demand response method based on VPP participating in auxiliary service in market environment

Abstract

The invention discloses a dynamic demand response method based on VPP participating in auxiliary service in a market environment, and relates to the technical field of virtual power plants. Comprising the following steps: 1, modeling a dynamic demand response problem of a virtual power plant participating in auxiliary service, and defining variables and parameters; 2, modeling is carried out on the uncertainty; 3, constructing a double-layer optimization model between the VPP income and the user electricity purchase cost; step 4, formulating constraint conditions, including constraints in the aspects of power market rules, coordinated operation requirements of VPP internal distributed energy and power purchase demands of users; 5, solving a double-layer optimization problem by using an optimization algorithm; and step 6, designing a dynamic demand response strategy, so that the VPP can perform flexible adjustment according to the market change, the user demand and the system condition real-time information. According to the method, the problem of dynamic demand response when the virtual power plant VPP participates in the auxiliary service in the market environment is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and particularly to a dynamic demand response method for VPP to participate in ancillary services under a market environment. Background Art

[0003] Distributed power sources such as photovoltaic and wind turbines improve the economy and environmental protection of the power system, but the technology has randomness, volatility, and intermittency. The grid connection of a high proportion of distributed power sources leads to diversified grid operation modes and probabilistic power balance, making the real-time power balance of the power system more complex. As a new type of distributed power source coordination control and energy management technology, a virtual power plant (VPP) effectively aggregates various distributed power sources to participate in power market transactions. Compared with traditional controllable units, the VPP has poor power regulation ability, slow response in a complex power market, and needs to pay penalty fees. To adapt to power market transactions and enhance the response ability of the VPP, it is necessary to control the coordinated operation of distributed energy within the VPP. Summary of the Invention

[0004] The present invention aims to solve the dynamic demand response problem faced by virtual power plants when participating in ancillary services under a market environment. Specifically, the present invention focuses on the volatility of photovoltaic power, the randomness of user demand, and the coordinated operation of various distributed energy devices within the virtual power plant. By optimizing the dispatching and dynamic adjustment strategies, the economy and environmental protection of the VPP in the power system are realized. To solve the above problems, the present invention provides a dynamic demand response method for VPP to participate in ancillary services under a market environment.

[0005] To achieve the above object, the technical solutions adopted by the present invention are as follows:

[0006] A dynamic demand response method for VPP to participate in ancillary services under a market environment includes the following steps:

[0007] Step 1: Considering the volatility of photovoltaic power, the randomness of load demand, and the factor of user electricity purchase cost, model the dynamic demand response of the virtual power plant VPP to participate in ancillary services, and define variables and parameters;

[0008] Step 2: Model the randomness of photovoltaic power and user load demand;

[0009] Step 3: Construct a two-layer optimization model between the VPP revenue and the user electricity purchase cost. In the upper layer, with the goal of maximizing the VPP revenue, considering the market transaction and ancillary service revenue items. In the lower layer, with the goal of minimizing the load electricity purchase cost, considering the user electricity purchase cost;

[0010] Step 4: Formulate constraint conditions, including the power balance of distributed energy within the VPP, user electricity purchase demand, equipment operation limitations, market regulations, and constraints of electricity market rules;

[0011] Step 5: Solve the bi-level optimization model based on the above constraints to obtain the optimal VPP operation strategy;

[0012] Step 6: Design a VPP dynamic adjustment strategy based on the prediction of the VPP photovoltaic output power and the real-time user electricity purchase demand, update the parameters in Step 1, repeat Steps 1-5, and update the optimal VPP operation strategy.

[0013] Preferably, Step 1 includes:

[0014] Define variables and parameters:

[0015] P PV : Photovoltaic output power;

[0016] D: User load demand;

[0017] P VPP : Total VPP power;

[0018] C narket : Market electricity price;

[0019] C user : User electricity purchase cost;

[0020] R VPP : VPP revenue;

[0021] The photovoltaic output power model is represented by random variables:

[0022]

[0023] where μ PV is the average photovoltaic output power, and σ PV is the standard deviation of the photovoltaic output power;

[0024] The user load demand is modeled by a probability distribution:

[0025] D ~ D(θ D )

[0026] where θ D is the distribution parameter of the user load demand;

[0027] The total VPP power consists of photovoltaic and other distributed energy sources and is expressed as:

[0028] P VPP = P PV + P other

[0029] where P other is the power of other distributed energy sources;

[0030] The VPP revenue is related to market transactions and the user's electricity purchase cost, and is expressed as:

[0031] R VpP = MarketIncome - UserCost

[0032] Wherein, MarketIncome = (C market - C user ) × P VPP 、UserCost = C user × D.

[0033] Preferably, the step 2 includes:

[0034] Modeling the randomness of photovoltaic power, defining a random variable P PV representing the photovoltaic output power, and its probability density function is:

[0035]

[0036] Wherein, μ PV is the average photovoltaic output power, and σ PV is the standard deviation of the photovoltaic output power;

[0037] Modeling the randomness of user load demand, and its probability density function is:

[0038]

[0039] Wherein, μ D and σ D are respectively the mean and standard deviation of the user load demand.

[0040] Preferably, the step 3 includes:

[0041] Upper-layer optimization model, defining the upper-layer objective function as:

[0042] max P VPP R VPP

[0043] Wherein, R VPP is the VPP revenue, and is expressed as:

[0044] R VPP = MarketIncome - UserCost

[0045] Wherein, MarketIncome = (C market - C user ) × P VPP 、UserCost = C user × D;

[0046] Lower - layer optimization model. Define the lower - layer objective function as follows:

[0047] min D UserCost

[0048] where UserCost = C user ×D;

[0049] Mutually constrain the upper - layer and lower - layer optimization problems to ensure the consistency of the solutions. The constraint conditions include power - balance constraints and equipment - operation limitations.

[0050] Preferably, step 4 includes:

[0051] Ensure that the total power of the VPP is equal to the sum of the photovoltaic output power and other distributed energy:

[0052] P VPP = P PV + P other

[0053] where P other is the power of other distributed energy;

[0054] The user's electricity - purchase demand meets the constraint:

[0055]

[0056] where D i is the load demand of various users;

[0057] Equipment - operation limitation constraints:

[0058] 0 ≤ P PV ≤ Capacity PV

[0059] 0 ≤ P storage ≤ Capacity storage

[0060] where Capacity PV is the capacity of the photovoltaic equipment inside the VPP, P storage is the power of the energy - storage equipment inside the VPP, Capacity storage is the capacity of the energy - storage equipment inside the virtual power plant;

[0061] Market regulations and electricity - market - rule constraints:

[0062] C min ≤ C market ≤ C max .

[0063] where C minis the minimum market electricity price specified by the electricity market regulations, C max is the maximum market electricity price specified by the electricity market regulations.

[0064] Preferably, the step 5 includes:

[0065] Based on the established bi-level optimization model, transform it into the MILP form, modify the original objective function, and introduce binary variables to represent the integer nature of the decision variables:

[0066]

[0067] Transform the original constraint conditions into the MILP constraint form, considering the limitations of integer variables:

[0068] P PV = z × P PV,max

[0069] 0 ≤ z ≥ 1

[0070] Solve the established MILP model to obtain the optimal VPP operation strategy, while satisfying all constraint conditions.

[0071] Preferably, the step 6 includes:

[0072] Use the ARIMA model to predict the photovoltaic output:

[0073]

[0074] Considering the dynamic nature of user demand, design a dynamic demand response strategy to adjust user demand according to real-time information:

[0075] D(t + 1) = g(D(t), C market (t - 1),...)

[0076] Based on the predicted photovoltaic output and real-time user electricity purchase demand, design a VPP dynamic adjustment strategy:

[0077]

[0078] where P storage is the power of the energy storage device inside the VPP;

[0079] Make the dynamic demand response strategy work in coordination with the bi-level optimization model to ensure that the overall economy and environmental protection can still be maintained during real-time adjustment.

[0080] Preferably, establish a real-time monitoring system to monitor the system status, market changes, and user demand conditions, and feed back to the VPP in real time according to the monitoring results to adjust the strategy and update the ARIMA model.

[0081] Compared with the prior art, the technical progress achieved by the present invention lies in:

[0082] The present invention constructs a two-layer optimization model for solution considering the volatility of photovoltaic power and the randomness of load demand, etc., and obtains an optimal VPP operation strategy. Also, through the prediction of the photovoltaic output power of the VPP and the real-time electricity purchase demand of users, a VPP dynamic adjustment strategy is designed, enabling the optimal VPP operation strategy to be updated following the predicted and real-time electricity purchase demand of users. Thus, it is realized that the virtual power plant can execute the optimal VPP operation strategy following the predicted and real-time electricity purchase demand of users, achieving the economy and environmental friendliness of the VPP in the power system.

[0083] Compared with the traditional method, the present invention combines the dynamic demand response strategy and the optimization model to work together, comprehensively considering the variability of the market environment and the real-time dynamic changes of user demands, so as to better adapt to the actual operation conditions of the power system.

[0084] The present invention predicts the photovoltaic output using prediction models such as ARIMA. The method can effectively reduce the impact of photovoltaic volatility on the system, improve the predictability of photovoltaic energy, and thus reduce the uncertainty in the market. The present invention adopts an elastic demand model, and the user demand adjustment strategy can be flexibly adjusted according to the real-time changes in market electricity prices. This improves the response speed of users to market signals, reduces the electricity purchase cost, and also improves the overall economic efficiency of the VPP. The present invention introduces a real-time monitoring system. By monitoring the system status, market changes, and user demands, it is fed back to the VPP for adjustment in real time. Such a mechanism enables the system to promptly perceive and respond to various emergencies, improving the robustness and stability of the system. The present invention realizes the overall economy and environmental friendliness of the VPP in a changing market environment by combining the dynamic demand response strategy and the optimization model. This collaborative optimization can make the operation of the VPP more intelligent and efficient on different time scales.

[0085] The advantages of the present invention lie in comprehensively considering the uncertainty of distributed energy, the diversity of the market environment, and the dynamics of user demands. Through collaborative optimization and real-time adjustment, the virtual power plant can participate in ancillary services more flexibly and intelligently, improving the economy and environmental friendliness of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention.

[0087] In the drawings:

[0088] Figure 1 is the flowchart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0089] The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below with reference to the accompanying drawings.

[0090] Based on the market environment, specifically considering the volatility of photovoltaic power and the randomness of load demand, the general steps of the dynamic demand response of virtual power plant (VPP) participating in ancillary services are as follows:

[0091] 1. Problem modeling and demand analysis

[0092] First, model the dynamic demand response problem of VPP participating in ancillary services. Considering factors such as the volatility of photovoltaic power, the randomness of load demand, and the electricity purchase cost of users, establish a mathematical model, define relevant variables and parameters, such as photovoltaic output power, user load demand, market electricity price, etc.

[0093] 2. Uncertainty modeling

[0094] Considering the volatility of photovoltaic power and the randomness of multiple types of load demands, use probability distributions or stochastic processes to model the uncertainty. For example, use random variables to represent the fluctuations of photovoltaic power and the probability distributions of user load demands.

[0095] 3. Establishment of a two-layer optimization model

[0096] Construct a two-layer optimization model between the VPP revenue and the user's electricity purchase cost. In the upper layer, with the goal of maximizing the VPP's own revenue, consider revenue items such as market transactions and ancillary services. In the lower layer, with the goal of minimizing the load's electricity purchase cost, consider the user's electricity purchase cost and other relevant factors.

[0097] 4. Formulation of constraint conditions

[0098] Formulate relevant constraint conditions, including constraints in aspects such as electricity market rules, coordinated operation requirements of distributed energy within the VPP, and user electricity purchase demands. These constraint conditions can include power balance, equipment operation limits, market regulations, etc.

[0099] 5. Selection and implementation of optimization algorithms

[0100] Select appropriate optimization algorithms, such as linear programming, mixed integer programming, or methods based on heuristic algorithms, to solve the two-layer optimization problem.

[0101] 6. Design of dynamic demand response strategies

[0102] Design dynamic demand response strategies so that the VPP can make flexible adjustments according to real-time information such as market changes, user demands, and system conditions.

[0103] As shown Figure 1 in the figure, the method process of this embodiment specifically includes the following steps:

[0104] Step 1: Considering the volatility of photovoltaic power, the randomness of load demand, and the factor of user electricity purchase cost, model the dynamic demand response of the virtual power plant (VPP) participating in ancillary services, and define variables and parameters;

[0105] Step 2: Model the randomness of photovoltaic power and user load demand;

[0106] Step 3: Construct a two-layer optimization model between the VPP revenue and the user electricity purchase cost. In the upper layer, with the goal of maximizing the VPP revenue, considering the market trading and ancillary service revenue items. In the lower layer, with the goal of minimizing the load electricity purchase cost, considering the user electricity purchase cost;

[0107] Step 4: Formulate constraint conditions, including the power balance of distributed energy within the VPP, user electricity purchase demand, equipment operation limitations, market regulations, and constraints of electricity market rules;

[0108] Step 5: Based on the above constraint conditions, solve the two-layer optimization model to obtain the optimal VPP operation strategy;

[0109] Step 6: Based on the prediction of the VPP photovoltaic output power and the real-time user electricity purchase demand, design a VPP dynamic adjustment strategy, update the parameters in Step 1, repeat Steps 1-5, and update the optimal VPP operation strategy.

[0110] Specifically, Step 1 includes:

[0111] In this step, a mathematical model of the dynamic demand response of the virtual power plant participating in ancillary services will be established, considering the volatility of photovoltaic power, the randomness of load demand, and the user electricity purchase cost.

[0112] 1.1 Define relevant variables and parameters:

[0113] P PV : Photovoltaic output power;

[0114] D: User load demand;

[0115] P VPP : Total power of the virtual power plant;

[0116] C market : Market electricity price;

[0117] C user : User electricity purchase cost;

[0118] R VPP : Virtual power plant revenue.

[0119] 1.2 Establish a stochastic model for photovoltaic and user load:

[0120] The photovoltaic output power model can be represented using random variables:

[0121]

[0122] where, μ PV is the average photovoltaic output power, and σ PV is the standard deviation of the photovoltaic output power.

[0123] The user load demand can be modeled through a probability distribution:

[0124] D ∼ D(θ D )

[0125] where, θ D is the distribution parameter of the user load demand.

[0126] 1.3 Determine the total power model of the virtual power plant:

[0127] The total power of the virtual power plant consists of photovoltaic and other distributed energy sources and can be expressed as:

[0128] P VPP = P PV + P 0ther

[0129] where, P other is the power of other distributed energy sources.

[0130] 1.4 Define the revenue model of the virtual power plant:

[0131] The revenue of the virtual power plant is related to market transactions and user electricity purchase costs and can be expressed as:

[0132] R VPP = MarketIncome - UserCost

[0133] where, MarketIncome = (C market - C user ) × P VPP

[0134] UserCost = C user × D

[0135] In summary, a dynamic demand response mathematical model of the virtual power plant considering photovoltaic volatility, load demand randomness, and user electricity purchase costs is established. Next, this bilevel optimization problem will be solved through optimization methods to maximize the revenue of the virtual power plant and minimize the user electricity purchase cost.

[0136] Specifically, Step 2 includes:

[0137] 2.1 Stochastic Modeling of Photovoltaic Power:

[0138] The stochastic nature of photovoltaic power can be represented by using random variables and probability distributions. The normal distribution (Gaussian distribution) is used to describe the fluctuations of photovoltaic power, and a random variable P is defined PV to represent the photovoltaic output power, and its probability density function is:

[0139]

[0140] where μ PV is the average photovoltaic output power, and σ PV is the standard deviation of the photovoltaic output power.

[0141] 2.2 Stochastic Modeling of User Load Demand:

[0142] The stochastic nature of user load demand can be modeled by using probability distributions, and its probability density function is:

[0143]

[0144] where μ D and σ D are the mean and standard deviation of the user load demand, respectively.

[0145] Through the above steps, the stochastic nature of photovoltaic power and user load demand can be modeled, and these models will be used in subsequent steps to solve the optimization problem to achieve the dynamic demand response of the virtual power plant.

[0146] Specifically, step 3 includes:

[0147] 3.1 Upper - layer Optimization Model (Maximizing the Profit of VPP Itself):

[0148] The upper - layer optimization problem aims to maximize the profit of the VPP. Considering revenue items such as market trading and ancillary services, the upper - layer objective function is defined as:

[0149]

[0150] where R VPP is the profit of the virtual power plant, expressed as:

[0151] R VPP = MarketIncome - UserCost

[0152] where MarketIncome=(C market - C user )×P VPP

[0153] UserCost = C user × D

[0154] 3.2 Lower - layer optimization model (minimizing user's electricity purchase cost):

[0155] The lower - layer optimization problem aims to minimize the user's electricity purchase cost, considering the user's electricity purchase cost and other relevant factors. Define the lower - layer objective function as:

[0156] min D UserCost

[0157] where UserCost = C user × D

[0158] 3.3 Determine the constraint conditions:

[0159] Mutually constrain the upper - layer and lower - layer optimization problems to ensure the consistency of the solutions. The constraint conditions include power balance constraints, equipment operation limits, etc.

[0160] In summary, a two - layer optimization model between the VPP revenue and the user's electricity purchase cost is established. In the subsequent steps, an appropriate optimization algorithm will be used to solve this two - layer problem to obtain the optimal virtual power plant operation strategy.

[0161] Specifically, step 4 includes:

[0162] 4.1 Power balance constraint:

[0163] Ensure that the total power of the virtual power plant is equal to the sum of the photovoltaic output power and other distributed energy:

[0164] P VPP = P PV + P other

[0165] 4.2 User's electricity purchase demand satisfaction constraint:

[0166] The user's electricity purchase demand must meet its actual demand, that is:

[0167]

[0168] where D i is the load demand of various users.

[0169] 4.3 Equipment operation limit constraint:

[0170] Consider the operation limits of distributed energy equipment within the VPP, such as photovoltaic capacity, energy storage equipment capacity, etc.:

[0171] 0 ≤ P PV ≤ Capacity PV

[0172] 0 ≤ P storage ≤ Capacity storage

[0173] 4.4 Market regulations and power market rule constraints:

[0174] Consider the regulations of the power market, such as the minimum and maximum market electricity prices, and the relevant rules for ancillary services:

[0175] C min ≤ C market ≤ C max

[0176] Integrate the above constraints into the bi-level optimization model to ensure that the constraints in terms of market regulations, equipment operation limits, user demands, etc. are satisfied during the optimization process. These constraints will be considered in the optimization algorithm to ensure that the obtained solution is reasonable within the actual operation range.

[0177] Specifically, step 5 includes:

[0178] 5.1 MILP model establishment:

[0179] Based on the previously established bi-level optimization model, transform it into the MILP form. Introduce binary variables to represent the integer nature of the decision variables. For example, introduce z to represent whether the photovoltaic power is enabled:

[0180]

[0181] 5.2 MILP objective function:

[0182] Modify the original objective function, considering the introduced binary variables. For example:

[0183]

[0184] 5.3 MILP constraint conditions:

[0185] Transform the original constraint conditions into the MILP constraint form, considering the limitations of integer variables:

[0186] P PV = z × P PV,max

[0187] 0 ≤ z ≥ 1

[0188] 5.4 Select a solver and solve:

[0189] Select an appropriate MILP solver, such as Gurobi, CPLEX, etc., to solve the established MILP model, and the optimal operation strategy of the virtual power plant will be obtained, while satisfying all constraint conditions.

[0190] Analyze the solution results, including key indicators such as the maximum revenue of the virtual power plant and the electricity purchase cost of users, and optimize and adjust according to the results to ensure the effectiveness of the model in practical applications.

[0191] Specifically, step 6 includes:

[0192] 6.1 Photovoltaic output prediction:

[0193] Use the ARIMA model to predict the photovoltaic output, considering the volatility of photovoltaic power:

[0194]

[0195] 6.2 Dynamic adjustment of user demand:

[0196] Considering the dynamics of user demand, design strategies to adjust user demand according to real-time information, including the elastic demand of user load, strategies to respond to market price changes, etc. For example, adjust user demand according to the real-time change of market electricity price:

[0197] D(t + 1) = g(D(t), C market (t - 1),...)

[0198] 6.3 VPP dynamic adjustment strategy:

[0199] Based on the predicted photovoltaic output and real-time user electricity purchase demand, design the dynamic adjustment strategy of VPP, which involves the coordinated operation and power adjustment of various distributed energy devices inside the virtual power plant. For example, adjust the photovoltaic output and the discharge / charge strategy of energy storage devices:

[0200]

[0201] 6.4 Real-time monitoring and feedback:

[0202] Establish a real-time monitoring system to monitor the system status, market changes and user demand conditions, and feedback the monitoring results to VPP in real time to adjust the strategy and update the prediction model.

[0203] 6.5 Model collaborative optimization:

[0204] Make the dynamic demand response strategy work in coordination with the optimization model to ensure that the overall economy and environmental protection can still be maintained during real-time adjustment. Through model collaborative optimization, VPP can better adapt to the changing market and system environment.

[0205] Through the above steps, the designed dynamic demand response strategy can enable VPP to adjust the photovoltaic output and user demand in real time to optimize the operation and adapt to the changing market and system conditions.

[0206] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A dynamic demand response method for VPP to participate in ancillary services based on the market environment, characterized in that It includes the following steps: Step 1: Considering the volatility of photovoltaic power, the randomness of load demand, and the factors of user electricity purchase cost, model the dynamic demand response of the virtual power plant (VPP) participating in ancillary services, and define variables and parameters; Step 2: Model the randomness of photovoltaic power and user load demand; Step 3: Construct a two-layer optimization model between the VPP revenue and the user electricity purchase cost. In the upper layer, with the goal of maximizing the VPP revenue, considering the market trading and ancillary service revenue items, and in the lower layer, with the goal of minimizing the load electricity purchase cost, considering the user electricity purchase cost; Step 4: Formulate constraint conditions, including the power balance of distributed energy within the VPP, user electricity purchase demand, equipment operation limitations, market regulations, and constraints of electricity market rules; Step 5: Based on the above constraint conditions, solve the two-layer optimization model to obtain the optimal VPP operation strategy; Step 6: Based on the prediction of the VPP photovoltaic output power and the real-time user electricity purchase demand, design a VPP dynamic adjustment strategy, update the parameters in Step 1, repeat Steps 1 - 5, and update the optimal VPP operation strategy.

2. The dynamic demand response method for VPP to participate in ancillary services based on the market environment according to claim 1, characterized in that The said Step 1 includes: Define variables and parameters: P PV : Photovoltaic output power; D: User load demand; P VPP : Total VPP power; C market : Market electricity price; C user : User's electricity purchase cost; R VPP : VPP revenue; The photovoltaic output power model is represented by random variables: where μ PV is the average photovoltaic output power, and σ PV is the standard deviation of the photovoltaic output power; The user load demand is modeled through probability distribution: D to D(θ D ) where θ D is the distribution parameter of the user load demand; The total power of the VPP consists of photovoltaic and other distributed energy, expressed as: P VPP = P PV + P other Among them, P other is the power of other distributed energy sources; The VPP revenue is related to market trading and user electricity purchase cost, expressed as: R VPP = MarketIncome - UserCost Among them, MarketIncome = (C market - C user ) × P VPP , UserCost = C user × D.

3. The dynamic demand response method for VPP to participate in ancillary services based on the market environment according to claim 2, wherein The said Step 2 includes: Modeling the randomness of photovoltaic power, define a random variable P PV which represents the photovoltaic output power, and its probability density function is as follows: where μ PV is the average output power of the photovoltaic, and σ PV is the standard deviation of the photovoltaic output power; Model the randomness of user load demand, and its probability density function is: where μ D and σ D are the mean and standard deviation of the user load demand, respectively.

4. The dynamic demand response method for VPP to participate in ancillary services based on the market environment according to claim 3, characterized in that, The said Step 3 includes: Upper-layer optimization model, define the upper-layer objective function as: Among them, R VPP is the VPP revenue, expressed as: R VPP = Market Income - User Cost where MarketIncome = (C market - C user ) × P VPP , UserCost = C user × D; Lower-layer optimization model, define the lower-layer objective function as: min D UserCost where UserCost = C user × D; Mutually constrain the upper and lower-layer optimization problems to ensure the consistency of the solutions. The constraint conditions include power balance constraints and equipment operation limitations.

5. The dynamic demand response method for VPP to participate in ancillary services based on the market environment according to claim 4, characterized in that The said Step 4 includes: Ensure that the total power of the VPP is equal to the sum of the photovoltaic output power and other distributed energy: P VPP = P PV + P other Among them, P other is the power of other distributed energy sources; The user electricity purchase demand satisfies the constraint: Among them, D i is the load demand of various users; Equipment operation limitation constraint: 0 ≤ P PV ≤ Capacity PV 0 ≤ P storage ≤ Capacity storage Among them, Capacity PV represents the capacity of the photovoltaic equipment inside the VPP, and P storage represents the power of the energy storage equipment inside the VPP, and Capacity storage represents the capacity of the energy storage equipment inside the virtual power plant; Market regulations and electricity market rule constraints: C min ≤C market ≤C max 。 Among them, C min represents the minimum market electricity price stipulated by the electricity market, and C max represents the maximum market electricity price stipulated by the electricity market.

6. The dynamic demand response method for VPP to participate in ancillary services based on the market environment according to claim 5, characterized in that, The said Step 5 includes: Based on the established two-layer optimization model, transform it into the MILP form, modify the original objective function, and introduce binary variables to represent the integer nature of decision variables: Transform the original constraint conditions into the MILP constraint form, considering the limitations of integer variables: P PV = z × P PV,max 0≤z≥1 Solve the established MILP model to obtain the optimal VPP operation strategy while satisfying all constraint conditions.

7. The dynamic demand response method based on the participation of VPP in ancillary services under the market environment according to claim 6, characterized in that The said Step 6 includes: Use the ARIMA model to predict the photovoltaic output: Considering the dynamic nature of user demand, design a dynamic demand response strategy to adjust user demand according to real-time information: D(t + 1) = g(D(t), C market (t - 1), …) Based on the predicted photovoltaic output and real-time user electricity purchase demand, design a VPP dynamic adjustment strategy: Among them, P storage represents the power of the energy storage device inside the VPP; Make the dynamic demand response strategy work in coordination with the two-layer optimization model to ensure the overall economy and environmental protection can still be maintained during real-time adjustment.

8. The dynamic demand response method for VPP to participate in ancillary services based on the market environment according to claim 7, characterized in that: Establish a real-time monitoring system to monitor the system status, market changes, and user demand conditions, and feedback the monitoring results to the VPP in real time to adjust the strategy and update the ARIMA model.

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