Virtual power plant coupling multi-market operation optimization and risk evaluation method

Abstract

The invention discloses a virtual power plant coupling multi-market operation optimization and risk evaluation method, and relates to the technical field of virtual power plants. Comprising the steps of 1, establishing a resource model in a virtual power plant; 2, establishing corresponding market models for different participated markets, and establishing an interface model of the virtual power plant and the markets; 3, designing a virtual power plant overall operation optimization model; 4, establishing a risk evaluation model, and considering various risks possibly faced by the virtual power plant participating in the market; 5, coupling the overall operation optimization model and the risk evaluation model to form a comprehensive model; 6, a real-time scheduling system is established, and market changes and resource states are monitored; and 7, performing system simulation verification by using actual data, adjusting model parameters, and optimizing an algorithm. According to the invention, the overall operation problem of the virtual power plant in a complex market environment is solved, including the technical problems in the aspects of resource scheduling, market transaction and risk management.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and particularly to a method for optimizing the operation and risk assessment of a virtual power plant coupled with multiple markets. Background Art

[0002] A virtual power plant (VPP) is a power coordination management system that realizes the aggregation and coordinated optimization of distributed energy resources (DERs) such as distributed generators (DGs), energy storage systems, controllable loads, and electric vehicles through advanced information and communication technologies and software systems, and participates in the power market and grid operation as a special power plant.

[0003] The virtual power plant is located on the distribution network side by integrating distributed resources, and coordinates their operation into a controllable whole, thus effectively promoting the safe and stable operation of the power system. As an independent and equal market participant, the virtual power plant shows potential advantages in market transactions. Nevertheless, the risks it faces in participating in the market cannot be ignored, and the research on the optimization risk scheduling model of virtual power plants is relatively insufficient. Therefore, it is very urgent to conduct in-depth quantitative analysis on the specific contributions of various resources within the virtual power plant to the overall revenue and risk, so as to help achieve the balance between revenue and risk in the resource allocation process of the virtual power plant in practice. Summary of the Invention

[0004] In view of the above deficiencies, the present invention provides a method for optimizing the operation and risk assessment of a virtual power plant coupled with multiple markets to help achieve the balance between revenue and risk in the resource allocation process of the virtual power plant in practice.

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

[0006] A method for optimizing the operation and risk assessment of a virtual power plant coupled with multiple markets includes the following steps:

[0007] Step 1: Inside the virtual power plant, conduct a detailed analysis of the technical characteristics, response speed, and controllability of each distributed resource, and establish a resource model;

[0008] Step 2: For different markets participated by the virtual power plant, establish corresponding market models, and establish an interface model between the virtual power plant and the market;

[0009] Step 3: Establish an overall operation optimization model of the virtual power plant;

[0010] Step 4: Establish a risk assessment model to identify the main risk factors that the virtual power plant may face in participating in the market, including price fluctuations, technical failures, and supply uncertainties, and obtain the overall risk indicators;

[0011] Step 5: Couple the overall operation optimization model and the risk assessment model to form a comprehensive model; use the overall risk indicator as a constraint condition of the comprehensive model to obtain the Pareto optimal solution set;

[0012] Step 6: Establish a real-time monitoring system to obtain real-time market changes and the status of distributed resources in real time; compare the real-time monitoring data with the prediction results of the comprehensive model, and adjust the parameters of the comprehensive model in a timely manner through a feedback mechanism according to the comparison results to improve the prediction accuracy of the comprehensive model;

[0013] Step 7: Collect actual market data and distributed resource operation data, establish a system simulation model and conduct system simulation verification; based on the simulation results, adjust and optimize the parameters of the system simulation model; conduct multiple rounds of simulations to gradually approach the optimal system simulation model, and adjust and optimize the corresponding parameters in Steps 1 - 5 based on the optimal system simulation model.

[0014] Preferably, the said Step 1 includes:

[0015] Collect the technical parameters of each distributed resource, including power generation capacity, energy storage capacity, and response speed, and obtain the historical data of the resources, including the power generation curve and response time distribution;

[0016] Conduct probability distribution modeling on the resource capabilities:

[0017]

[0018] where μ is the mean, σ is the standard deviation, and G is the power generation;

[0019] Conduct probability distribution modeling on the response speed:

[0020] P(RT) = λe -λRT

[0021] where λ is the rate parameter and RT represents the response time;

[0022] Establish a controllability parameter to represent the adjustable range of the resource:

[0023] C(Control) = [Min_Control, Max_Control]

[0024] where Min_Control and Max_Control respectively represent the minimum and maximum values of the resource controllability parameter;

[0025] Preferably, step 2 includes:

[0026] Analyze the rules of each market, including the price formation mechanism, trading cycle, and market participants;

[0027] Use game theory to construct a game model between the virtual power plant and the market, and define the revenue function U of the virtual power plant i , where i represents the participating market:

[0028]

[0029] where t1 and t2 represent the time periods considered for power trading and production, P i represents the price that can be obtained by selling one unit of electricity in the market, Q i represents the amount of electricity sold in the market, C i represents the cost required to produce one unit of electricity;

[0030] Establish a market price prediction model. Assume that the market price P i is related to the output G of the virtual power plant, the output G o of other market participants, and the external factor E, and is expressed as:

[0031] P i = f(G, G o , E)+ε

[0032] where ∈ represents the random fluctuation term of the price;

[0033] Establish an interaction interface model between the virtual power plant and the market, define the clearing strategy of the virtual power plant, and Q i represents the amount of electricity that the virtual power plant plans to clear in market i:

[0034]

[0035] Constraint conditions:

[0036]

[0037] where t1 and t2 represent the time periods considered for power trading and production, P i represents the price that can be obtained by selling one unit of electricity in the market, C i represents the cost required to produce one unit of electricity.

[0038] Preferably, step 3 includes:

[0039] Establish the objective function of the overall operation optimization model, including: maximizing the total revenue, minimizing the cost, and maximizing the safety margin of the power system. Represent the objective function as f, and it is expressed as:

[0040] f = α·Profit - β·Cost + r·Safety_Margin

[0041] Where α, β, and γ are coefficients for weighing each objective;

[0042] Model the overall operation optimization problem as a multi-objective optimization problem, define decision variables, including the clearing strategies for each market and the resource scheduling plan, and set the objective function to maximize f, that is:

[0043] Maximize f

[0044] The resource scheduling plan is formulated by the virtual power plant based on the resource model to maximize the overall revenue and consider the contribution of resources in the risk assessment; the virtual power plant uses the information provided by the market model to formulate the best operation strategy to maximize the revenue; and ensures the interaction between the virtual power plant and the market through the interface model;

[0045] Introduce constraint conditions to ensure the feasibility and stability of the system, including power balance constraints, market clearing constraints, and resource availability constraints. Let h represent the constraint conditions, expressed as:

[0046] h(Q, G,...) = 0

[0047] Where Q represents the clearing strategy and G represents the resource scheduling plan;

[0048] Solve using a multi-objective optimizer to obtain a series of Pareto optimal solutions and form the trade-off coefficients for weighing different objectives.

[0049] Preferably, step 4 includes:

[0050] Identify the main risk factors that the virtual power plant may face when participating in the market, including price fluctuations, technical failures, and supply uncertainties;

[0051] Establish a corresponding probability distribution model for each risk factor, expressed as:

[0052]

[0053] μ represents the mean value and σ represents the standard deviation;

[0054] Define a corresponding risk index for each risk factor, expressed as:

[0055] Risk Price = Var(ΔP)

[0056] Integrate the indicators of each risk factor to form an overall risk index, and obtain the overall risk index:

[0057]

[0058] Among them, w i represents the weight of the i-th risk item.

[0059] Preferably, step 5 includes:

[0060] Combining the objective function of the overall operation optimization model with the risk indicators of the risk assessment model to form a comprehensive objective function:

[0061] f = α·Profit - β·Cost + γ·Safety_Margin - δ·Total_Risk

[0062] Among them, α, β, and γ are coefficients for weighing each objective, and δ is the risk weighing coefficient;

[0063] Redefine the multi-objective optimization problem, consider the new comprehensive objective function, and add the overall risk indicator of the risk assessment as a constraint condition to form:

[0064] Maximize f

[0065] Subject to Total_Risk ≤ Risk_Threshold

[0066] Among them, Risk_Threshold is a pre-set risk threshold;

[0067] Use a collaborative optimizer for collaborative optimization, and find the Pareto optimal solution set under different said weighing coefficients.

[0068] Preferably, step 6 includes:

[0069] Establish a real-time monitoring system to obtain market changes and distributed resource status in real time;

[0070] Design a model parameter adaptive algorithm to adjust the parameters in the model based on real-time monitoring data:

[0071] θ new = θ old + η·ΔJ(θ old )

[0072] Among them, θ new is the new parameter, θ old is the old parameter, η is the learning rate, is the gradient of the objective function J with respect to the parameter;

[0073] Introduce a feedback mechanism to compare the real-time monitoring data with the prediction results of the comprehensive model; if there is a large deviation between the prediction results of the comprehensive model and the actual situation, adjust the model parameters in a timely manner through the feedback mechanism to improve the prediction accuracy of the model;

[0074] Consider the robustness of the system during the parameter adjustment process to avoid overfitting the real-time data.

[0075] Preferably, the step 7 includes:

[0076] Collect actual market data and distributed resource operation data, including market prices, electricity demand, and distributed resource generation data;

[0077] Establish a system simulation model based on the collected actual data, including a market trading model, a distributed resource model, an overall operation optimization model, and a risk assessment model;

[0078] Run the system simulation model to simulate the operation of the virtual power plant in the actual market environment, compare it with the actual market data, and verify the accuracy and practicality of the model;

[0079] Based on the simulation results, adjust and optimize the parameters of the model;

[0080] Conduct multiple rounds of simulation verification and optimization to gradually approach the optimal system simulation model;

[0081] Based on the optimal system simulation model, adjust and optimize the corresponding parameters in steps 1 - step 5.

[0082] Compared with the prior art, the technical progress achieved by the present invention lies in:

[0083] Through multi-objective optimization, the present invention simultaneously considers multiple objectives such as maximizing revenue, minimizing cost, safety and stability, and risk management, which is more comprehensive than traditional methods.

[0084] The present invention introduces a real-time parameter adjustment and feedback mechanism, which can adjust the model parameters in real time according to the market and resource status, improving the system robustness and adaptability.

[0085] The present invention embeds risk assessment in the overall operation optimization, and considers the balance between revenue and risk through collaborative optimization, which is more in line with the actual needs than a single optimization objective.

[0086] The present invention uses actual data to establish a system simulation model, and through multiple rounds of simulation verification and optimization, improves the accuracy and practicality of the model.

[0087] The present invention considers technical characteristics such as power generation capacity and response speed in the distributed resource model, making the model closer to the actual operation situation. Description of the Drawings

[0088] The accompanying drawings are used to provide a further understanding of the present invention and form 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.

[0089] In the accompanying drawings:

[0090] Figure 1 is a flow chart of the present invention. Detailed implementation manners

[0091] The following specific embodiments can be combined with each other. For the same or similar concepts or processes, they may not be repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0092] The general steps of the present invention regarding the operation optimization and risk assessment of a virtual power plant coupled with multiple markets are as follows:

[0093] The present invention discloses a method for the operation optimization and risk assessment of a virtual power plant coupled with multiple markets, including the following steps:

[0094] 1. Resource characteristic analysis

[0095] Inside the virtual power plant, the technical characteristics, response speed, controllability, etc. of each distributed resource are analyzed in detail, and a resource model is established, including parameters such as the capabilities, costs, and efficiencies of the resources.

[0096] 2. Market trading mechanism modeling

[0097] For different participating markets (such as the electricity market, capacity market, etc.), corresponding market models are established, considering factors such as market rules, price fluctuations, trading mechanisms, etc., and an interface model between the virtual power plant and the market is established.

[0098] 3. Overall operation optimization model of the virtual power plant

[0099] Design an overall operation optimization model of the virtual power plant, considering maximizing the overall revenue in different markets while ensuring the safe and stable operation of the power system, including maximizing profits, minimizing risks, etc.

[0100] 4. Establishment of a risk assessment model

[0101] Establish a risk assessment model, considering various risks that the virtual power plant may face when participating in the market, such as price fluctuations, technical failures, supply uncertainties, etc.

[0102] 5. Coupling of optimization and risk assessment

[0103] Couple the overall operation optimization model and the risk assessment model to form a comprehensive model that can consider risks while optimizing operation decisions to balance the relationship between benefits and risks.

[0104] 6. Parameter Adjustment and Real-time Optimization

[0105] Establish a real-time scheduling system to monitor market changes and resource status. Through a feedback mechanism, adjust the parameters in the model in real time to adapt to changes in different market conditions and resource characteristics, and ensure the robustness of the system.

[0106] 7. Simulation Verification and Optimization

[0107] Use actual data for system simulation verification, adjust model parameters, and optimize algorithms. Through multiple rounds of simulation verification, continuously improve the operation efficiency and risk management level of the virtual power plant.

[0108] As Figure 1 shown, the steps of the embodiment of the present invention are:

[0109] Step 1: Inside the virtual power plant, conduct a detailed analysis of the technical characteristics, response speed, and controllability of each distributed resource, and establish a resource model;

[0110] Step 2: For different markets participated by the virtual power plant, establish corresponding market models and establish an interface model between the virtual power plant and the market;

[0111] Step 3: Establish an overall operation optimization model for the virtual power plant;

[0112] Step 4: Establish a risk assessment model to identify the main risk factors that the virtual power plant may face in participating in the market, including price fluctuations, technical failures, and supply uncertainties, and obtain the overall risk index;

[0113] Step 5: Couple the overall operation optimization model and the risk assessment model to form a comprehensive model; use the overall risk index as a constraint condition of the comprehensive model to obtain the Pareto optimal solution set;

[0114] Step 6: Establish a real-time monitoring system to obtain market changes and distributed resource status in real time; compare the real-time monitoring data with the prediction results of the comprehensive model, and adjust the parameters of the comprehensive model in a timely manner through a feedback mechanism according to the comparison results to improve the prediction accuracy of the comprehensive model;

[0115] Step 7: Collect actual market data and distributed resource operation data, establish a system simulation model and conduct system simulation verification; based on the simulation results, adjust and optimize the parameters of the system simulation model; conduct multiple rounds of simulations to gradually approach the optimal system simulation model, and adjust and optimize the corresponding parameters in Steps 1 - 5 based on the optimal system simulation model.

[0116] Specifically, step 1 includes:

[0117] Resource feature collection:

[0118] Collect the technical parameters of each distributed resource, such as power generation capacity, energy storage capacity, response speed, etc., and obtain the historical data of the resources, including power generation curves, response time distributions, etc.

[0119] Probability distribution modeling:

[0120] Perform probability distribution modeling on the resource capabilities, taking into account the influence of factors such as weather and seasons:

[0121]

[0122] Among them, μ is the mean and σ is the standard deviation.

[0123] Perform probability distribution modeling on the response speed, expressed as:

[0124] P(RT) = λe -λRT

[0125] Among them, λ is the rate parameter.

[0126] Controllability parameter modeling:

[0127] Establish controllability parameters, representing the schedulable range of the resources, and define controllability C (Control) as an interval, representing the schedulable range of the resources within a certain period of time.

[0128] Resource model integration:

[0129] Integrate the above probability distribution and controllability parameters to obtain the overall resource model. For example, the power generation and response time of the resources can be obtained through random sampling, while the controllability parameters limit the actual scheduling range.

[0130] The resource model established in this way can provide an accurate description of the resource characteristics of the virtual power plant in subsequent optimization and risk assessment.

[0131] Specifically, step 2 includes:

[0132] Market rule analysis:

[0133] Analyze the rules of each market, including price formation mechanisms, trading cycles, market participants, etc. Taking the electricity market as an example, consider the clearing price and supply-demand relationship of each time period.

[0134] Game theory modeling:

[0135] Use game theory to construct a game model between the virtual power plant and the market. Define the revenue function U of the virtual power planti , where \(i\) represents the participating market, considering the competition and cooperation relationships between the virtual power plant and other market participants:

[0136]

[0137] Price prediction model:

[0138] Establish a market price prediction model, considering external factors such as weather and demand. Assume the market price \(P\) i is related to the output \(G\) of the virtual power plant, the output \(G\) o of other market participants,

[0139] \(P\) i \(= f(G, G\) o , E)+\(\epsilon\)

[0140] Transaction mechanism interface modeling:

[0141] Establish an interaction interface model between the virtual power plant and the market. Define the clearing strategy \(Q\) i of the virtual power plant, representing the amount of electricity that the virtual power plant plans to clear in market \(i\). Considering the changes in market prices, the clearing strategy needs to find a balance between maximizing revenue and minimizing risk.

[0142] Clearing strategy optimization:

[0143]

[0144] Constraint conditions:

[0145]

[0146] Specifically, step 3 includes:

[0147] Objective function definition:

[0148] Design the objective function of the overall operation optimization model, covering multiple objectives such as maximizing the total revenue, minimizing the cost, and maximizing the security margin of the power system. Let \(f\) represent the objective function, which can be expressed as:

[0149] \(f = \alpha\cdot Profit - \beta\cdot Cost+\gamma\cdot Safety\_Margin\)

[0150] where \(\alpha\), \(\beta\), and \(\gamma\) are coefficients for weighing each objective.

[0151] Multi-objective optimization problem:

[0152] Model the overall operation optimization problem as a multi-objective optimization problem, define the decision variables, including the clearing strategies of each market, the scheduling plans of resources, etc., and set the objective function to maximize \(f\), that is:

[0153] Maximize f

[0154] The scheduling plan of the said resources is formulated by the virtual power plant based on the resource model to maximize the overall revenue, and the contribution of the resources is considered in the risk assessment; the virtual power plant uses the information provided by the market model to formulate the best operation strategy to maximize the revenue; and through the interface model, the interaction between the virtual power plant and the market is ensured.

[0155] Constraint conditions:

[0156] Introduce various constraint conditions to ensure the feasibility and stability of the system, including power balance constraint, market clearing constraint, resource availability constraint, etc. Representing the constraint conditions by h, it can be expressed as:

[0157] h(Q, G,...) = 0

[0158] Among them, Q represents the clearing strategy, and G represents the scheduling plan of the resources.

[0159] Application of multi-objective optimizer:

[0160] Use a multi-objective optimizer (such as NSGA-II, MOEA / D, etc.) to solve the above problems, and obtain a series of Pareto optimal solutions to form a solution set that weighs different objectives.

[0161] Through this multi-objective optimization model, the virtual power plant can find a set of balanced solutions in different markets while ensuring the safe and stable operation of the power system.

[0162] Specifically, step 4 includes:

[0163] Risk factor identification:

[0164] Identify the main risk factors that the virtual power plant may face when participating in the market, including but not limited to price fluctuations, technical failures, supply uncertainties, etc.

[0165] Probability distribution modeling:

[0166] Establish a corresponding probability distribution model for each risk factor. Taking price fluctuation as an example, assume that the price change conforms to a normal distribution, which is expressed as:

[0167]

[0168] μ is the mean value, and σ is the standard deviation.

[0169] Risk index definition:

[0170] Define corresponding risk indices for each risk factor. Taking price fluctuation as an example, the variance of price fluctuation can be defined as the risk index, which is expressed as:

[0171] Risk Price = Var(ΔP)

[0172] Comprehensive risk indicator:

[0173] Integrate the indicators of each risk factor to form an overall risk indicator. Methods such as weighted average can be used to obtain the overall risk indicator:

[0174]

[0175] Through this risk assessment model, the virtual power plant can quantify the impacts of different risk factors and consider risk management factors in overall operation.

[0176] Specifically, step 5 includes:

[0177] Comprehensive objective function:

[0178] Combine the objective function of the overall operation optimization model with the risk indicator of the risk assessment model to form a comprehensive objective function:

[0179] f = α·Profit - β·Cost + γ·Safety_Margin - δ·total_Risk

[0180] Where δ is the risk trade-off coefficient.

[0181] Redefinition of the multi-objective optimization problem:

[0182] Redefine the multi-objective optimization problem, considering the new comprehensive objective function. Incorporate the overall risk indicator of the risk assessment as a constraint to form:

[0183] Maximize f

[0184] Subject to Total_Risk ≤ Risk_Threshold

[0185] Where Risk_Threshold is the pre-set risk threshold.

[0186] Application of the collaborative optimizer:

[0187] Use a collaborative optimizer (such as the Weighted Aggregated Sum Approach) for collaborative optimization. The optimizer can find the Pareto optimal solution set under different trade-off coefficients, which balances the relationship between overall profit and risk.

[0188] Through this step, the virtual power plant can flexibly balance profit and risk in overall operation decisions and adjust the risk tolerance according to the actual situation.

[0189] Specifically, step 6 includes:

[0190] Real-time monitoring system:

[0191] Establish a real-time monitoring system to obtain market changes and distributed resource status in real time. Through data collection, sensors and other means, continuously update the system status information.

[0192] Model parameter adaptation:

[0193] Design a model parameter adaptation algorithm to adjust the parameters in the model based on real-time monitoring data. Consider using machine learning algorithms or adaptive control methods in control theory, for example:

[0194] θ new = θ old + η·ΔJ(θ old )

[0195] where θ new is the new parameter, θ old is the old parameter, η is the learning rate, is the gradient of the objective function J with respect to the parameter.

[0196] Feedback mechanism:

[0197] Introduce a feedback mechanism to compare the real-time monitoring data with the prediction results of the model. If the deviation between the model prediction results and the actual situation is large, adjust the model parameters in a timely manner through the feedback mechanism to improve the prediction accuracy of the model.

[0198] Robustness consideration:

[0199] Consider the robustness of the system during the parameter adjustment process to avoid overfitting the real-time data. A regularization term or a limit on the amplitude of parameter adjustment can be introduced to prevent the model from being too sensitive.

[0200] Through this step, the virtual power plant can achieve real-time perception of the market and resource status, and through the adaptive adjustment of model parameters, ensure that the prediction and optimization of the model remain accurate in a changing environment.

[0201] Specifically, step 7 includes:

[0202] Data preparation:

[0203] Collect actual market data and distributed resource operation data, including market prices, electricity demand, distributed resource generation data, etc., to ensure the quality and timeliness of the data.

[0204] System simulation model establishment:

[0205] Based on the actual data collected, a system simulation model is established, which needs to include a market trading model, a distributed resource model, an overall operation optimization model, and a risk assessment model. Specifically:

[0206] a Establishment of the market trading model:

[0207] Define the basic rules of market trading, considering the market price formation mechanism, trading cycle, etc. Taking the electricity market as an example, a market clearing model can be established, and the relationship between the market price P and the cleared electricity quantity Q of the virtual power plant can be set as:

[0208] P = f(Q) + ∈

[0209] Where f(Q) is the market clearing function and ∈ is the random fluctuation term of the price.

[0210] b Establishment of the distributed resource model:

[0211] Establish a detailed model for each distributed resource, considering its power generation capacity, response speed, etc. Taking solar power generation as an example, the power generation model can be used:

[0212] G = α·Solar_Radiation - β·Temperature + γ·Other_Factors - η·∈

[0213] c Establishment of the overall operation optimization model:

[0214] Integrate the market trading model and the distributed resource model into the overall operation optimization model, considering maximizing the overall revenue in different markets while ensuring the safe and stable operation of the power system. The objective function can be expressed as:

[0215] f = α·Profit - β·Cost + γ·Safety_Margin

[0216] d Establishment of the risk assessment model:

[0217] Model the risk factors, considering factors such as price fluctuations and technical failures. Taking price fluctuations as an example, the risk index can be expressed as the variance of the price:

[0218] Risk Price = Var(∈)

[0219] Obtain the overall risk index by synthesizing various risk factors:

[0220]

[0221] Through this system simulation model, the operation of the virtual power plant in the real market environment can be simulated, considering the characteristics of distributed resources and the complexity of market trading.

[0222] Simulation operation and verification:

[0223] Run the system simulation model to simulate the operation of the virtual power plant in the actual market environment, compare it with the actual market data, verify the accuracy and practicability of the model, and consider evaluation indicators such as total revenue, risk level, and system security.

[0224] Model parameter adjustment and optimization:

[0225] Based on the simulation results, adjust and optimize the parameters of the model. Optimization algorithms such as genetic algorithms and particle swarm algorithms can be used to continuously adjust the model parameters according to the simulation results to make the model better fit the actual situation.

[0226] Multiple rounds of iteration:

[0227] Conduct multiple rounds of simulation verification and optimization to gradually improve the operation efficiency and risk management level of the virtual power plant. Through multiple iterations of the model and algorithm, gradually approach the optimal solution, that is, the optimal system simulation model.

[0228] Adjustment and optimization:

[0229] Based on the optimal system simulation model, adjust and optimize the corresponding parameters in steps 1 - 5.

[0230] Finally, it should be noted that the above are only the 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 method for optimizing the coupled multi-market operation and risk assessment of a virtual power plant, characterized in that, It includes the following steps: Step 1: Inside the virtual power plant, conduct a detailed analysis of the technical characteristics, response speed, and controllability of each distributed resource, and establish a resource model; Step 2: For different markets in which the virtual power plant participates, establish corresponding market models and establish an interface model between the virtual power plant and the market; Step 3: Establish an overall operation optimization model for the virtual power plant; Step 4: Establish a risk assessment model, identify the main risk factors that the virtual power plant may face when participating in the market, including price fluctuations, technical failures, and supply uncertainties, and obtain the overall risk index; Step 5: Couple the overall operation optimization model and the risk assessment model to form a comprehensive model; use the overall risk index as a constraint condition of the comprehensive model to obtain the Pareto optimal solution set; Step 6: Establish a real-time monitoring system to obtain market changes and the status of distributed resources in real time; compare the real-time monitoring data with the prediction results of the comprehensive model, and adjust the parameters of the comprehensive model in a timely manner through a feedback mechanism according to the comparison results to improve the prediction accuracy of the comprehensive model; Step 7: Collect actual market data and distributed resource operation data, establish a system simulation model and conduct system simulation verification; based on the simulation results, adjust and optimize the parameters of the system simulation model; Conduct multiple rounds of simulation to gradually approach the optimal system simulation model, and adjust and optimize the corresponding parameters in Steps 1 - 5 based on the optimal system simulation model.

2. The method for optimizing the operation and risk assessment of a virtual power plant coupled with multiple markets according to claim 1, wherein The said Step 1 includes: Collect the technical parameters of each distributed resource, including power generation capacity, energy storage capacity, and response speed, and obtain the historical data of the resource, including the power generation curve and response time distribution; Conduct probability distribution modeling of the resource capacity: where μ is the mean, σ is the standard deviation, and G is the power generation; Conduct probability distribution modeling of the response speed: P(RT) = λe -λRT where λ is the rate parameter and RT represents the response time; Establish a controllability parameter to represent the adjustable range of the resource: C(Control) = [Min_Control, Max_Control] where Min_Control and Max_Control respectively represent the minimum and maximum values of the resource controllability parameter.

3. The method for optimizing the operation and risk assessment of a virtual power plant coupled with multiple markets according to claim 2, wherein The said Step 2 includes: Analyze the rules of each market, including the price formation mechanism, trading cycle, and market participants; Use game theory to construct a game model between the virtual power plant and the market, and define the revenue function U of the virtual power plant i , where i represents the participating market: where, t1 and t2 represent the time periods considered for power trading and production, P i represents the price that can be obtained for selling one unit of electricity in the market, Q i represents the amount of electricity sold in the market, C i represents the cost required to produce one unit of electricity; Build a market price prediction model, assuming the market price P i is related to the output G of the virtual power plant and the output G o of other market participants, as well as the external factor E, expressed as: P i = f(G, G o , E) + ε where ∈ represents the random fluctuation term of the price; Establish an interaction interface model between the virtual power plant and the market, define the clearing strategy of the virtual power plant, Q i represents the electricity quantity that the virtual power plant plans to clear in market i: Constraint conditions: Among them, t1 and t2 represent the time periods considered for power trading and production, and P i represents the price that can be obtained for selling one unit of electricity in the market, and C i represents the cost required to produce one unit of electricity.

4. The method for optimizing the coupling of a virtual power plant with multi-market operation and risk assessment according to claim 3, wherein The said Step 3 includes: Establish the objective function of the overall operation optimization model, including: maximizing the overall revenue, minimizing the cost, and maximizing the safety margin of the power system. Represent the objective function with f, which is expressed as: f = α·Profit - β·Cost + γ·Safety_Margin where α, β, and γ are coefficients for weighing each objective; Model the overall operation optimization problem as a multi-objective optimization problem, define decision variables, including the clearing strategy of each market and the scheduling plan of the resources, and set the objective function to maximize f, that is: Maximize f The scheduling plan of the resources is formulated by the virtual power plant based on the resource model to maximize the overall revenue, and the contribution of the resources is considered in the risk assessment; the virtual power plant uses the information provided by the market model to formulate the best operation strategy to maximize the revenue; and ensures the interaction between the virtual power plant and the market through the interface model; Introduce constraint conditions to ensure the feasibility and stability of the system, including power balance constraints, market clearing constraints, and resource availability constraints. h represents the constraint conditions, expressed as: h(Q,G,...) = 0 where Q represents the clearing strategy and G represents the scheduling plan of the resources; Use a multi-objective optimizer to solve and obtain a series of Pareto optimal solutions to form a trade-off coefficient for different objectives.

5. The method for optimizing the operation and risk assessment of a virtual power plant coupled with multiple markets according to claim 4, wherein The step 4 includes: Identify the main risk factors that the virtual power plant may face when participating in the market, including price fluctuations, technical failures, and supply uncertainties; Establish a corresponding probability distribution model for each risk factor, expressed as: μ represents the mean value and σ represents the standard deviation; Define a corresponding risk index for each risk factor, expressed as: Risk Price = Var(ΔP) Integrate the indexes of each risk factor to form an overall risk index, and obtain the overall risk index: Total_Risk = ∑ i w i ·Risk i where, w i represents the weight of the i-th risk item.

6. The method for optimizing the operation and risk assessment of a virtual power plant coupled with multiple markets according to claim 5, wherein The step 5 includes: Combine the objective function of the overall operation optimization model with the risk index of the risk assessment model to form a comprehensive objective function: f = α·Profit - β·Cost + γ·Safety_Margin - δ·Total_Bisk where α, β, and γ are coefficients for weighing each objective, and δ is the risk trade-off coefficient; Redefine the multi-objective optimization problem, consider the new comprehensive objective function, and add the overall risk index of the risk assessment as a constraint condition to form: Maximze f Subject to Total_Risk ≤ Risk_Threshold where Risk_Threshold is a pre-set risk threshold; Use a collaborative optimizer for collaborative optimization to find the Pareto optimal solution set under different trade-off coefficients.

7. The method for optimizing the coupling of a virtual power plant in multi-market operation and risk assessment according to claim 6, characterized in that, The step 6 includes: Establish a real-time monitoring system to obtain market changes and distributed resource status in real time; Design a model parameter adaptive algorithm to adjust the parameters in the model based on real-time monitoring data: θ new = θ old + η·ΔJ(θ old ) where θ new is the new parameter, θ old is the old parameter, η is the learning rate, is the gradient of the objective function J with respect to the parameter; Introduce a feedback mechanism to compare the real-time monitoring data with the prediction results of the comprehensive model; if the prediction results of the comprehensive model deviate greatly from the actual situation, adjust the model parameters in time through the feedback mechanism to improve the prediction accuracy of the model; Consider the robustness of the system during the parameter adjustment process to avoid overfitting the real-time data.

8. The method for optimizing the operation and risk assessment of a virtual power plant coupled with multiple markets according to claim 1, wherein The step 7 includes: Collect actual market data and distributed resource operation data, including market prices, electricity demands, and distributed resource generation data; Based on the collected actual data, establish a system simulation model, including a market trading model, a distributed resource model, an overall operation optimization model, and a risk assessment model; Run the system simulation model to simulate the operation of the virtual power plant in the actual market environment, and compare it with the actual market data to verify the accuracy and practicability of the model; Based on the simulation results, adjust and optimize the parameters of the model; Carry out multiple rounds of simulation verification and optimization to gradually approach the optimal system simulation model; Based on the optimal system simulation model, adjust and optimize the corresponding parameters in Steps 1 - 5.

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