A decision method and system for power capacity market demand based on scenario method

Abstract

The application discloses a kind of decision method and system of power capacity market demand response based on scene method, determine the basic assumption of the participation capacity market of demand side response supplier, based on basic assumption, determine the uncertainty factor of the demand side response supplier decision model to be established;Determine the probability distribution of uncertainty factor, extract the different scene of the demand side response supplier decision model to be established;Determine the number of scenes that need to be removed, remove scene based on preset rule;Based on the scene after removal, establish demand side response supplier decision model;Establishing deterministic decision model, random decision model as contrast model;To demand side response supplier decision model, deterministic decision model and random decision model are solved, obtain the participation capacity of demand side response supplier, income and loss;Calculate the probability of violation, based on the probability of violation and the income and loss of demand side response supplier, determine the optimal number of removed scenes.

Description

Technical Field

[0001] This invention relates to the field of electricity market technology, and more specifically, to a decision-making method and system for electricity capacity market demand based on a scenario-based approach. Background Technology

[0002] The capacity market is designed to ensure sufficient power generation resources to guarantee the reliability of power operation. It assesses the value of reliable resources and uses price signals to enable power generation, grid, and demand-side resources to participate and earn revenue by providing capacity. The capacity market is an incentive market mechanism that encourages resource suppliers to invest in corresponding supply resources, optimizing site selection and cost to meet power system reliability requirements. Demand-side response resources, as important resources, can participate in both the energy market and the capacity market. In significant capacity markets (such as the US PJM market), demand-side resources participate in the capacity market and receive revenue based on the clearing price. Demand-side resources can participate in the capacity market by pre-emptively reducing load and may receive revenue or penalties based on their response. As participants in the capacity market, demand-side response resource providers continuously optimize their bidding strategies to maximize their revenue.

[0003] As a long-term market, uncertainty is a core issue that all market participants, including demand-side response resource providers, need to consider in the capacity market. Two main approaches to address uncertainty are robust optimization and stochastic optimization. Robust optimization solutions require satisfying all possible scenarios; however, the resulting costs are often significant. The fact that many low-probability extreme scenarios are considered equally with other normal scenarios often leads to conservative results in robust optimization. Stochastic optimization can consider the probabilities of different scenarios; however, the probability distribution of random variables is sometimes difficult to obtain, making it challenging to provide accurate quantitative assessments of the optimization results.

[0004] Therefore, a technology is needed to enable demand-side response supplier decisions based on a scenario-based approach. Summary of the Invention

[0005] The present invention provides a decision-making method and system for electricity capacity market demand response based on a scenario-based approach, in order to solve the problem of how to make decisions on electricity capacity market demand response based on a scenario-based approach.

[0006] To address the aforementioned problems, this invention provides a scenario-based decision-making method for electricity capacity market demand response, the method comprising:

[0007] The basic assumptions of the participation capacity market for demand-side response suppliers are determined, and based on these basic assumptions, the uncertainties of the demand-side response supplier decision-making model to be established are identified.

[0008] Determine the probability distribution of the uncertainties and extract different scenarios for the demand-side response supplier decision-making model to be established;

[0009] Determine the number of scenes that need to be removed, and remove the scenes based on preset rules;

[0010] Based on the removed scenario, establish a demand-side response supplier decision-making model;

[0011] Establish deterministic decision-making models and stochastic decision-making models as comparative models;

[0012] Solve the demand-side response supplier decision model, the deterministic decision model, and the stochastic decision model to obtain the participation capacity, benefits, and losses of the demand-side response supplier;

[0013] Calculate the violation probability, and based on the violation probability and the gains and losses of the demand-side response supplier, determine the optimal number of scenarios to be removed.

[0014] Preferably, the uncertainty factors in determining the demand-side response supplier decision-making model to be established include: the clearing price of the capacity market and the activation time of demand resources.

[0015] Preferably, determining the probability distribution of the uncertainty factors and extracting different scenarios for the demand-side response supplier decision-making model to be established includes:

[0016] Based on the clearing price of the target capacity market and the activation time of the demand resources, different scenarios are determined.

[0017] Preferably, the establishment of the demand-side response supplier decision model includes:

[0018] The objective of the demand-side response supplier decision-making model is to maximize revenue and minimize cost under the worst-case scenario. The model is constructed by introducing an auxiliary variable h as follows:

[0019] min h

[0020]

[0021] 0≤P Cap ≤P Capmax

[0022] In the formula, P Cap P represents the supplier's market share. Capmax C represents the maximum capacity that the supplier can provide.Capi t represents the market clearing price in the i-th scenario. uhi denoted by , where represents the number of hours the demand-side response service is activated in the i-th scenario, 'a' represents the cost parameter, 'k' represents the number of scenarios removed, and 'h' represents an auxiliary variable used to transform the model into a linear programming problem.

[0023] Preferably, the establishment of a deterministic decision-making model and a stochastic decision-making model as a comparison model includes:

[0024] The formula for establishing a deterministic decision-making model is:

[0025]

[0026] Subject to: 0≤P Cap ≤P Capmax

[0027] Among them, C Cap The market clearing price, t uh P represents the number of hours the demand-side response service is active. Capmax This represents the maximum market capacity in which the supplier participates.

[0028] The formula for establishing a stochastic decision model is:

[0029]

[0030] Subject to: 0≤P Cap ≤P Capmax

[0031] Among them, prob i This represents the probability of scenario i occurring.

[0032] Based on another aspect of the present invention, the present invention provides a decision system for electricity capacity market demand response based on a scenario-based approach, the system comprising:

[0033] An initial unit is used to determine the basic assumptions of the participation capacity market for demand-side response suppliers, and based on the basic assumptions, to determine the uncertainty factors of the demand-side response supplier decision-making model to be established.

[0034] The extraction unit is used to determine the probability distribution of the uncertainty factors and extract different scenarios of the demand-side response supplier decision-making model to be established.

[0035] The removal unit is used to determine the number of scenes to be removed and removes scenes based on preset rules;

[0036] Establish a unit to build a demand-side response supplier decision-making model based on the removed scenario; establish a deterministic decision-making model and a stochastic decision-making model as comparison models;

[0037] The result unit is used to solve the demand-side response supplier decision model, the deterministic decision model, and the stochastic decision model to obtain the participation capacity, benefits, and losses of the demand-side response supplier; calculate the violation probability; and determine the optimal number of scenarios to be removed based on the violation probability and the benefits and losses of the demand-side response supplier.

[0038] Preferably, the uncertainty factors in determining the demand-side response supplier decision-making model to be established include: the clearing price of the capacity market and the activation time of demand resources.

[0039] Preferably, determining the probability distribution of the uncertainty factors and extracting different scenarios for the demand-side response supplier decision-making model to be established includes:

[0040] Based on the clearing price of the target capacity market and the activation time of the demand resources, different scenarios are determined.

[0041] Preferably, the establishment of the demand-side response supplier decision model includes:

[0042] The objective of the demand-side response supplier decision-making model is to maximize revenue and minimize cost under the worst-case scenario. The model is constructed by introducing an auxiliary variable h as follows:

[0043] min h

[0044]

[0045] 0≤P Cap ≤P Capmax

[0046] In the formula, P Cap P represents the supplier's market share. Capmax C represents the maximum capacity that the supplier can provide. Capi t represents the market clearing price in the i-th scenario. uhi denoted by , where represents the number of hours the demand-side response service is activated in the i-th scenario, 'a' represents the cost parameter, 'k' represents the number of scenarios removed, and 'h' represents an auxiliary variable used to transform the model into a linear programming problem.

[0047] Preferably, the establishment of a deterministic decision-making model and a stochastic decision-making model as a comparison model includes:

[0048] The formula for establishing a deterministic decision-making model is:

[0049]

[0050] Subject to: 0≤P Cap ≤PCapmax

[0051] Among them, C Cap The market clearing price, t uh P represents the number of hours the demand-side response service is active. Capmax This represents the maximum market capacity in which the supplier participates.

[0052] The formula for establishing a stochastic decision model is:

[0053]

[0054] Subject to: 0≤P Cap ≤P Capmax

[0055] Among them, prob i This represents the probability of scenario i occurring.

[0056] This invention provides a scenario-based decision-making method and system for demand response in the electricity capacity market. The method includes: determining the basic assumptions for demand-side response (DSR) suppliers' participation in the capacity market; based on these assumptions, identifying the uncertainties in the DSR supplier decision-making model to be established; determining the probability distribution of the uncertainties and extracting different scenarios for the DSR supplier decision-making model; determining the number of scenarios to be removed and removing scenarios based on preset rules; establishing a DSR supplier decision-making model based on the removed scenarios; establishing a deterministic decision-making model and a stochastic decision-making model as comparative models; solving the DSR supplier decision-making model, the deterministic decision-making model, and the stochastic decision-making model to obtain the DSR supplier's participation capacity, revenue, and losses; calculating the violation probability; and determining the optimal number of scenarios to be removed based on the violation probability and the DSR supplier's revenue and losses. This invention provides a method for participants in a long-term, uncertain capacity market, such as DSR suppliers, to make decisions that consider uncertainty and can dynamically adjust the scenario set. Decision-makers make decisions based on the requirements of maximizing the violation probability and profit. This invention proposes a scenario-based demand-side response supplier decision-making model, which has the ability to balance decision robustness and performance, and can improve the decision-making level of market participants. Attached Figure Description

[0057] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0058] Figure 1 A flowchart illustrating a scenario-based decision-making method for electricity capacity market demand response according to a preferred embodiment of the present invention;

[0059] Figure 2 This is a schematic diagram of the sampling distribution of capacity market clearing price uncertainty parameters according to a preferred embodiment of the present invention;

[0060] Figure 3 This is a schematic diagram of the sampling distribution of the uncertainty parameter of the actual activation utilization hours of demand response resources according to a preferred embodiment of the present invention;

[0061] Figure 4 This is a schematic diagram illustrating the actual benefits of different methods according to preferred embodiments of the present invention in all scenarios;

[0062] Figure 5 A flowchart illustrating a scenario-based decision-making method for demand-side response suppliers according to a preferred embodiment of the present invention; and

[0063] Figure 6 This is a structural diagram of a decision system for electricity capacity market demand response based on a scenario-based approach according to a preferred embodiment of the present invention. Detailed Implementation

[0064] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0065] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0066] Figure 1 This is a flowchart illustrating a scenario-based decision-making method for electricity capacity market demand response according to a preferred embodiment of the present invention. A scenario-based approach addresses optimization problems with uncertainty. This method can be seen as an improvement on robust optimization, its most significant feature being its ability to theoretically define the relationship between scenario selection and risk (violation probability), allowing decision-makers to balance robustness and performance. Furthermore, the scenario-based approach does not require knowledge of the distribution of uncertain boundary conditions.

[0067] The scenario approach is explained below. Imagine a convex programming optimization problem with uncertainty, whose mathematical optimization model is as follows:

[0068]

[0069]

[0070] In the formula, UCP represents a convex programming problem with uncertainty. δ∈Δ represents the parameters of the uncertain scenario. Δ represents the set of scenarios. This is a robust optimization problem; however, due to the constraints of extreme scenarios that need to be satisfied, the optimization results may not be economical or efficient.

[0071] The convex programming problem for the scenario is shown below:

[0072]

[0073] Subject to: f(x,δ) (i) )≤0,i=1,...,Nk (2)

[0074] In the formula, SCP represents a convex programming problem considering the scenario. N represents a set of scenarios randomly selected from an uncertain set Δ, where k scenarios can be deleted according to any specific rule. In other words, this method provides the possibility of arbitrarily removing extreme scenarios or selecting the most suitable scenario removal algorithm. If we let ε be the violation probability variable and β∈(0,1) be the confidence parameter, the scenario method gives a theoretical formula, that is, the probability that the violation probability variable is less than or equal to ε is greater than or equal to 1-β.

[0075]

[0076] In the formula, d represents the number of decision variables.

[0077] Compared to stochastic optimization, scenario-based methods do not require an uncertain probability distribution of parameters; ε provides an upper bound on the probability of violation. Compared to robust optimization, scenario-based methods offer a way to find a balance between robustness and economy, allowing for improved decision-making effectiveness by removing some extreme scenarios from the set of uncertain scenarios.

[0078] This invention provides a method for decision-making by demand-side response suppliers and other participants in capacity markets with long-term uncertainty. This method considers uncertainty and allows for dynamic adjustment of the scenario set, enabling decision-makers to make decisions based on the requirements of maximizing both the probability of violation and profit. This invention proposes a scenario-based decision-making model for demand-side response suppliers. This model possesses the ability to balance robustness and performance in decision-making, thereby improving the decision-making level of market participants.

[0079] like Figure 1 As shown, this invention provides a decision-making method for electricity capacity market demand response based on a scenario-based approach. The method includes:

[0080] Step 101: Determine the basic assumptions of the participation capacity market for demand-side response suppliers, and based on the basic assumptions, determine the uncertainties in the demand-side response supplier decision-making model to be established;

[0081] Preferably, the uncertainties in the demand-side response supplier decision-making model to be established are determined, including: the clearing price of the capacity market and the activation time of demand resources.

[0082] In step 101, this invention establishes the basic assumptions for demand-side response (DSR) suppliers participating in the capacity market and determines the uncertainties considered in the model. These basic assumptions include that participating suppliers have relatively small market caps and will not affect the market clearing price; suppliers are considered price takers. The goal of DSR suppliers is to optimize their costs and benefits while considering uncertainties. The model established in this invention considers two main uncertainties: the market clearing price and the activation time of demand resources.

[0083] Step 102: Determine the probability distribution of uncertainty factors and extract different scenarios for the demand-side response supplier decision-making model to be established;

[0084] Preferably, the probability distribution of the uncertainty factors is determined, and different scenarios for the demand-side response supplier decision-making model to be established are extracted, including:

[0085] Based on the clearing price of the target capacity market and the activation time of the demand resources, different scenarios are determined.

[0086] In step 102, this invention determines the probability distribution of uncertainty factors and extracts samples to construct different scenarios. In this invention, based on historical data of clearing prices and demand resource activation hours in a certain capacity market, relevant scenarios are sampled according to the data distribution. Specific sampling results are as follows... Figure 2 and Figure 3 As shown.

[0087] Step 103: Determine the number of scenes to be removed, and remove the scenes based on preset rules;

[0088] In step 103, the present invention determines the number of scenes to be removed and uses specific rules to remove the corresponding scenes.

[0089] Step 104: Based on the removed scenario, establish a demand-side response supplier decision-making model;

[0090] Preferably, a demand-side response supplier decision-making model is established, including:

[0091] The objective of the demand-side response supplier decision-making model is to maximize revenue and minimize cost under the worst-case scenario. The model is constructed by introducing an auxiliary variable h as follows:

[0092] min h

[0093]

[0094] 0≤P Cap ≤P Capmax

[0095] In the formula, P Cap P represents the supplier's market share. Capmax C represents the maximum capacity that the supplier can provide. Capi t represents the market clearing price in the i-th scenario. uhi denoted by , where represents the number of hours the demand-side response service is activated in the i-th scenario, 'a' represents the cost parameter, 'k' represents the number of scenarios removed, and 'h' represents an auxiliary variable used to transform the model into a linear programming problem.

[0096] In step 104, the present invention establishes a demand-side response supplier decision model based on a scenario-based approach.

[0097] The demand-side response supplier decision-making model based on the scenario approach is as follows. Its goal is to maximize revenue and minimize cost under the worst-case scenario. The model is constructed by introducing an auxiliary variable h as follows:

[0098] min h (4)

[0099]

[0100] 0≤P Cap ≤P Capmax (6)

[0101] In the formula, P Cap P represents the capacity of the supplier to participate in the market. Capmax C represents the maximum capacity that the supplier can provide. Capi t represents the market clearing price in the i-th scenario. uhi 'a' represents the number of hours the demand-side response service is activated in the i-th scenario, and 'a' represents the cost parameter. The first term in constraint (5) reflects the revenue of the supplier participating in the capacity market, and the second term reflects the costs incurred by the supplier in actual operation.

[0102] Step 105: Establish deterministic decision-making models and stochastic decision-making models as comparison models;

[0103] Preferably, a deterministic decision-making model and a stochastic decision-making model are established as comparison models, including:

[0104] The formula for establishing a deterministic decision-making model is:

[0105]

[0106] Subject to: 0≤P Cap ≤P Capmax

[0107] Among them, C Cap The market clearing price, t uh P represents the number of hours the demand-side response service is active. Capmax This represents the maximum market capacity in which the supplier participates.

[0108] The formula for establishing a stochastic decision model is:

[0109]

[0110] Subject to: 0≤P Cap ≤P Capmax

[0111] Among them, prob i This represents the probability of scenario i occurring.

[0112] In step 105 of this invention, deterministic decision-making and stochastic decision-making models are established as comparison models.

[0113] In contrast, a deterministic decision-making model was established:

[0114]

[0115] Subject to: 0≤P Cap ≤P Capmax (8)

[0116] Stochastic decision-making models:

[0117]

[0118] Subject to: 0≤P Cap ≤P Capmax (10)

[0119] Step 106: Solve the demand-side response supplier decision model, deterministic decision model, and stochastic decision model to obtain the participation capacity, benefits, and losses of the demand-side response supplier;

[0120] In step 106, the present invention solves the model to obtain the participation capacity and benefits of suppliers participating in the submission.

[0121] Step 107: Calculate the violation probability. Based on the violation probability and the gains and losses of the demand-side response supplier, determine the optimal number of scenarios to be removed.

[0122] In step 107, this invention calculates the violation probability, analyzes the supplier's gains and losses in actual scenarios, and selects the optimal number of removal scenarios.

[0123] This invention proposes a novel decision-making framework model for demand-side response suppliers participating in capacity markets. This model fully considers the uncertainties of clearing prices and real-time market demand response activation times in capacity markets, and takes the maximization of supplier interests as the objective function. It fully reflects the profit demands of suppliers in capacity markets and provides suppliers with optimization decision-making techniques.

[0124] This invention applies a scenario-based approach to the demand-side response supplier decision-making model, enabling a full consideration of the trade-off between cost and robustness in decision-making, and providing suppliers with a more reasonable and economical way to participate in the capacity market.

[0125] This invention verifies the effectiveness and advancement of its proposed scenario-based decision-making method by comparing it with deterministic, stochastic, and robust decision-making methods. Calculations and analysis show that the deterministic method exhibits the most aggressive performance because it considers only one deterministic scenario, resulting in poor robustness and potential capacity violations during actual implementation. The stochastic optimization method yields the highest expected return and also presents a relatively aggressive result. The robust optimization method provides the most conservative result, with suppliers only bidding for 1.715 MW of capacity to participate in the capacity market. The table shows that the scenario-based method offers a trade-off between robustness and model performance; as more scenarios are removed, suppliers bid for more capacity, representing a more aggressive decision-making approach.

[0126] Table 1. Demand-side response capacity of suppliers participating in the capacity market under different decision-making models.

[0127]

[0128]

[0129] To address the uncertainties surrounding capacity market clearing prices and the number of activation hours for demand-side response resources, a total of over 10,000 scenarios were used as the validation set. Figure 4The performance of different methods in real-world scenarios is presented. It can be seen that in ideal scenarios, deterministic and stochastic methods exhibit good performance and returns. However, in extreme scenarios, these two methods generate significant negative returns, meaning that using deterministic and stochastic methods for decision-making in these situations may result in substantial losses. Comparing these two methods, while the scenario-based approach performs slightly worse in ideal scenarios, its overall performance is more stable, ensuring the interests of demand response suppliers. Furthermore, in most scenarios, its returns are significantly higher than conservative robust optimization decision-making methods. As more extreme scenarios are removed, the model's performance in most scenarios continuously improves, demonstrating that the scenario-based approach offers a trade-off between robustness and model performance.

[0130] Table 2 shows the comparison between the performance and violation probability of the robust optimization method and the scenario optimization decision method. In this invention, the expected value of the benefit is used to express the performance of the model. As can be seen from the table, the robust optimization method has the most conservative performance. When the scenario method is optimized based on all sampled scenarios, the model performance is slightly improved while the violation probability ε is very small. As extreme scenarios are removed according to specific rules, the model performance continues to improve while the violation probability ε also increases. This means that the improvement in benefits is obtained by taking risks. It can also be seen that as the number of scenarios removed increases, the trend of performance improvement gradually slows down.

[0131] Table 2 Expected Returns and Probabilities of Violation under Different Methods

[0132]

[0133]

[0134] Figure 6 This is a structural diagram of a decision-making system for electricity capacity market demand response based on a scenario-based approach according to a preferred embodiment of the present invention. Figure 6 As shown, this invention provides a decision-making system for electricity capacity market demand response based on a scenario-based approach. The system includes:

[0135] The initial unit 601 is used to determine the basic assumptions of the participation capacity market of demand-side response suppliers, and based on the basic assumptions, to determine the uncertainty factors of the demand-side response supplier decision-making model to be established; preferably, the uncertainty factors of the demand-side response supplier decision-making model to be established include: the clearing price of the capacity market and the activation time of demand resources.

[0136] Extraction unit 602 is used to determine the probability distribution of uncertainty factors and extract different scenarios of the demand-side response supplier decision-making model to be established;

[0137] Preferably, the probability distribution of the uncertainty factors is determined, and different scenarios for the demand-side response supplier decision-making model to be established are extracted, including:

[0138] Based on the clearing price of the target capacity market and the activation time of the demand resources, different scenarios are determined.

[0139] The removal unit 603 is used to determine the number of scenes to be removed and remove scenes based on preset rules;

[0140] Unit 604 is established to build a demand-side response supplier decision-making model based on the removed scenario; a deterministic decision-making model and a stochastic decision-making model are established as comparison models.

[0141] Preferably, a demand-side response supplier decision-making model is established, including:

[0142] The objective of the demand-side response supplier decision-making model is to maximize revenue and minimize cost under the worst-case scenario. The model is constructed by introducing an auxiliary variable h as follows:

[0143] min h

[0144]

[0145] 0≤P Cap ≤P Capmax

[0146] In the formula, P Cap P represents the supplier's market share. Capmax C represents the maximum capacity that the supplier can provide. Capi t represents the market clearing price in the i-th scenario. uhi denoted by , where represents the number of hours the demand-side response service is activated in the i-th scenario, 'a' represents the cost parameter, 'k' represents the number of scenarios removed, and 'h' represents an auxiliary variable used to transform the model into a linear programming problem.

[0147] Preferably, a deterministic decision-making model and a stochastic decision-making model are established as comparison models, including:

[0148] The formula for establishing a deterministic decision-making model is:

[0149]

[0150] Subject to: 0≤P Cap ≤P Capmax

[0151] Among them, C Cap The market clearing price, t uh P represents the number of hours the demand-side response service is active. CapmaxThis represents the maximum market capacity in which the supplier participates.

[0152] The formula for establishing a stochastic decision model is:

[0153]

[0154] Subject to: 0≤P Cap ≤P Capmax

[0155] Among them, prob i This represents the probability of scenario i occurring.

[0156] The result unit 605 is used to solve the demand-side response supplier decision model, deterministic decision model and stochastic decision model to obtain the participation capacity, benefits and losses of the demand-side response supplier; calculate the violation probability, and determine the optimal number of scenarios to be removed based on the violation probability and the benefits and losses of the demand-side response supplier.

[0157] The preferred embodiment of the present invention provides a decision system 600 for electricity capacity market demand response based on a scenario-based approach, which corresponds to another preferred embodiment of the present invention provides a decision method 100 for electricity capacity market demand response based on a scenario-based approach. These will not be described in detail here.

[0158] The invention has been described with reference to a few embodiments. However, as will be known to those skilled in the art, and as defined in the appended claims, other embodiments besides those disclosed above fall equivalently within the scope of the invention.

[0159] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed unless explicitly stated otherwise.

Claims

1. A scenario-based decision-making method for electricity capacity market demand response, the method comprising: The basic assumptions for the participation of demand-side response suppliers in the capacity market are determined. Based on these assumptions, the uncertainties in the decision-making model of the demand-side response suppliers to be established are determined, including: the clearing price of the capacity market and the activation time of demand resources. Determine the probability distribution of the uncertainties and extract different scenarios for the demand-side response supplier decision-making model to be established, including: Based on the clearing price of the target capacity market and the activation time of the demand resources, different scenarios are determined; The number of scenarios to be removed is determined, and the scenarios are removed based on preset rules. The preset rules are as follows: for scenarios generated by two types of uncertain factors, namely the clearing price of the capacity market and the activation time of demand resources, the scenarios are sorted from high to low according to their severity. Extremely severe scenarios that cause a significant drop in supplier revenue or even serious losses are removed, while typical scenarios that are close to the actual operating state of the market are retained. Based on the removed scenario, a demand-side response supplier decision-making model is established, including: The objective of the demand-side response supplier decision-making model is to maximize revenue and minimize cost under the worst-case scenario, by introducing auxiliary variables. h The model is constructed as follows: Subject to: In the formula, This represents the supplier's market capacity. This represents the maximum capacity that the supplier can provide. This represents the market clearing price in the i-th scenario. This represents the number of hours the demand-side response service has been activated in the i-th scenario. Represents cost parameters, k This represents the number of scenes removed. h These represent auxiliary variables used to transform the model into a linear programming problem; Establish deterministic decision-making models and stochastic decision-making models as comparative models; Solve the demand-side response supplier decision model, the deterministic decision model, and the stochastic decision model to obtain the participation capacity, benefits, and losses of the demand-side response supplier; Calculate the violation probability, and based on the violation probability and the gains and losses of the demand-side response supplier, determine the optimal number of scenarios to be removed.

2. The method according to claim 1, wherein establishing a deterministic decision model and a stochastic decision model as a comparison model includes: The formula for establishing a deterministic decision-making model is: Subject to: in, The market clearing price. This represents the number of hours the demand-side response service has been active. This represents the maximum market capacity in which the supplier participates. The formula for establishing a stochastic decision model is: Subject to: in, This represents the probability of scenario i occurring.

3. A decision-making system for electricity capacity market demand response based on a scenario-based approach, the system comprising: The initial unit is used to determine the basic assumptions of the participation capacity market of demand-side response suppliers. Based on the basic assumptions, the uncertainties of the demand-side response supplier decision-making model to be established are determined, including: the clearing price of the capacity market and the activation time of demand resources. The extraction unit is used to determine the probability distribution of the uncertainties and extract different scenarios for the demand-side response supplier decision-making model to be established, including: Based on the clearing price of the target capacity market and the activation time of the demand resources, different scenarios are determined; The removal unit is used to determine the number of scenarios to be removed and remove scenarios based on preset rules. The preset rules are as follows: for scenarios generated by two types of uncertain factors, namely the clearing price of the capacity market and the activation time of demand resources, the scenarios are sorted from high to low according to their severity. Extremely severe scenarios that cause a significant drop in supplier revenue or even serious losses are removed, while typical scenarios that are close to the actual operating state of the market are retained. Establishment unit, used to build a demand-side response supplier decision model based on the removed scenario, including: The objective of the demand-side response supplier decision-making model is to maximize revenue and minimize cost under the worst-case scenario, by introducing auxiliary variables. h The model is constructed as follows: Subject to: In the formula, This represents the supplier's market capacity. This represents the maximum capacity that the supplier can provide. This represents the market clearing price in the i-th scenario. This represents the number of hours the demand-side response service has been activated in the i-th scenario. Represents cost parameters, k This represents the number of scenes removed. h Auxiliary variables are used to transform the model into a linear programming problem; deterministic decision-making models and stochastic decision-making models are established as comparison models; The result unit is used to solve the demand-side response supplier decision model, the deterministic decision model, and the stochastic decision model to obtain the participation capacity, benefits, and losses of the demand-side response supplier; calculate the violation probability; and determine the optimal number of scenarios to be removed based on the violation probability and the benefits and losses of the demand-side response supplier.

4. The system according to claim 3, wherein establishing a deterministic decision model and a stochastic decision model as a comparison model includes: The formula for establishing a deterministic decision-making model is: Subject to: in, The market clearing price. This represents the number of hours the demand-side response service has been active. This represents the maximum market capacity in which the supplier participates. The formula for establishing a stochastic decision model is: Subject to: in, This represents the probability of scenario i occurring.

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