Optimal bidding control method for multiple wind farms based on demand response resource reserve

Abstract

The present disclosure provides a multi-wind farm optimal bidding control method based on demand response resource reserve, comprising the following steps: obtaining the day-ahead bid amount and actual power generation of the wind farm, using the demand response resource as a reserve resource, calculating the deviation between the day-ahead bid amount and the actual power generation, and performing balancing adjustment on the deviation that cannot be compensated by the demand response resource in the balancing market; constructing an optimal bidding control model for interactive matching of multi-wind farms and demand response considering uncertainty, combining the actual operating characteristics of the wind farm and the actual operating conditions of the power market; inputting the wind turbine output scenario into the optimal bidding control model, iteratively solving the optimal bidding control model for the wind farm under the uncertainty scenario, and obtaining the optimal bidding solution.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of power system optimization control, and in particular relates to a multi-wind farm optimal bidding control method based on demand response resource reserve. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art.

[0003] Wind power, as a renewable energy source, is increasingly accounting for a larger share of the power system. This is closely related to the continued efforts of countries to promote renewable energy generation to improve the sustainability of their power systems. Due to the intermittent nature of wind power and the potential for unpredictable short-term fluctuations in power output, most wind farms impose penalties for deviations between the previous day's bid and actual output, and implement appropriate measures to adjust for these discrepancies. Therefore, achieving interactive operational matching between bid control schemes and demand response resources is a key issue facing renewable energy power generation companies competing in the market.

[0004] The intermittent and uncontrollable nature of wind power makes bidding for wind power significantly more complex than traditional thermal power. The uncertainty of wind power output can lead to price fluctuations in the electricity market, creating risks for wind farms' profitability. Wind farms submit bids in the day-ahead market and are settled according to the day-ahead market clearing price. However, there can be discrepancies between the actual power output of wind farms and the bids submitted. The use of backup resources can help stabilize wind power output, compensate for these discrepancies, and reduce volatility, thereby meeting the requirements of the electricity spot market and power dispatch. Currently, energy storage is the most common backup resource, with batteries being the primary storage device of choice. Batteries offer high charge and discharge efficiency and fast power response. They can also provide voltage and frequency regulation, improve power quality, and maintain power within the system, significantly enhancing the power system's ability to accommodate intermittent renewable energy. However, energy storage costs are prohibitive. Using energy storage as a resource to balance these discrepancies will directly impact wind power producers' enthusiasm for participating in the electricity market. Compared to the high cost of energy storage, reasonably priced demand response resources are more practical.

[0005] Therefore, demand response resources are being introduced into wind power bidding in the electricity market, acting as a backup resource to balance the discrepancy between day-ahead bids and actual output. Key to wind power's competitiveness in the electricity market lies in rationally designing an interactive matching mechanism between renewable energy and demand response, and developing a bidding control method that bundles both into the electricity market. Summary of the Invention

[0006] In order to solve the above problems, the present disclosure proposes a multi-wind farm optimal bidding control method based on demand response resource reserve, optimizes the bidding control of each wind farm, realizes the optimal bidding control of each wind power product participating in the power market competition, and obtains the optimal bidding control scheme.

[0007] According to some embodiments, the solution of the present disclosure provides a multi-wind farm optimal bidding control method based on demand response resource reserve, which adopts the following technical solutions:

[0008] The optimal bidding control method for multiple wind farms based on demand response resource reserve includes the following steps:

[0009] Step S01: Obtain the day-ahead bid amount and actual power generation of the wind farm, use demand response resources as backup resources, calculate the deviation between the day-ahead bid amount and the actual power generation, and perform balancing adjustments in the balancing market for the deviation that cannot be compensated by the demand response resources;

[0010] Step S02: Based on the actual operating characteristics of the wind farm and the actual operating conditions of the power market, an optimal bidding control model for interactive matching of multiple wind farms and demand response considering uncertainty is constructed;

[0011] Step S03: Inputting the wind turbine output scenario into the optimal bidding control model, performing iterative solution of the optimal bidding control model of the wind farm under the uncertainty scenario, and obtaining the optimal bidding solution.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] Based on the operational characteristics of wind farms participating in the electricity market, this paper constructs a wind farm profit-risk trade-off model. This model optimizes bidding control for each wind farm, minimizing wind curtailment and output shortfalls while maximizing profits and minimizing risks. This approach achieves optimal bidding control for each wind farm participating in the electricity market, ultimately yielding the optimal bidding control solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings, which constitute a part of the present disclosure, are used to provide a further understanding of the present disclosure. The exemplary embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation to the present disclosure.

[0015] Figure 1 is a flow chart of a multi-wind farm optimal bidding control method based on demand response resource reserve in an embodiment of the present disclosure;

[0016] FIG2( a ) is a graph showing the output power of the first wind farm according to an embodiment of the present disclosure;

[0017] FIG2( b ) is a graph showing the output power of the second wind farm according to an embodiment of the present disclosure;

[0018] FIG2( c ) is a graph showing the output power of the third wind farm according to an embodiment of the present disclosure;

[0019] Figure 3 is a price package information issued by the power grid and a curve chart of the actual day-ahead market clearing price in the embodiment of the present disclosure;

[0020] FIG4( a ) is a bar graph of positive demand responses and negative demand responses of the first wind farm contract demand response in an embodiment of the present disclosure;

[0021] FIG4( b ) is a bar graph of positive demand responses and negative demand responses of the second wind farm contract demand response in an embodiment of the present disclosure;

[0022] FIG4( c ) is a bar chart of positive demand responses and negative demand responses of the third wind farm contract demand response in an embodiment of the present disclosure;

[0023] Figure 5 is a bidding curve diagram of each wind farm after executing the optimal bidding control method in the embodiment of the present disclosure;

[0024] Figure 6 Schematic diagram of the profits of each wind farm after the optimal bidding control method is implemented in the embodiment of the present disclosure. DETAILED DESCRIPTION

[0025] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0026] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs.

[0027] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0028] In the absence of conflict, the embodiments of the present disclosure and the features thereof may be combined with each other.

[0029] This embodiment introduces a multi-wind farm optimal bidding control method based on demand response resource reserve.

[0030] like Figure 1 The multi-wind farm optimal bidding control method based on demand response resource reserve shown includes the following steps:

[0031] (1) Obtain environmental information and operation information of the wind farm, obtain wind turbine output scenarios, and generate uncertain output power implementation scenarios during wind turbine operation;

[0032] (2) Generate the day-ahead market clearing price based on the total electricity demand information and price package information published by the power market in which the wind farm participates;

[0033] (3) Obtain the day-ahead bid amount and actual power generation of the wind farm, use demand response resources as backup resources, calculate the deviation between the day-ahead bid amount and actual power generation, and make balancing adjustments in the balancing market for the deviation that cannot be compensated by demand response resources;

[0034] (4) Combining the actual operating characteristics of wind farms and the actual operating conditions of the power market, an optimal bidding control model that considers the interaction between multiple wind farms and demand response and takes into account uncertainty is constructed;

[0035] (5) Inputting the wind turbine output scenario into the optimal bidding control model, iteratively solving the optimal bidding control model of the wind farm under the uncertainty scenario, and obtaining the optimal bidding solution.

[0036] As one or more implementation methods, in step (1), a wind turbine output scenario consisting of a wind turbine output model, a random wind speed model, and a scenario reduction method is generated based on environmental information and operation information of the wind farm.

[0037] The output power of the wind turbine is described using a scenario generation method. In this embodiment, three wind farms are used. The specific parameters required for the wind turbines in the wind farms are shown in Table 1 below.

[0038] Table 1 Wind turbine parameters of the three simulated wind farms

[0039]

[0040] The simulated wind turbine output powers of the three wind farms obtained by the scenario generation method are shown in FIG2( a ), FIG2( b ), and FIG2( c ). In this embodiment, ten scenarios are considered for the output power of each wind farm.

[0041] The distribution of wind speed obeys the positive skew distribution. The two-parameter curve of Weibull distribution is considered to be the most suitable one to describe the probability density function of wind speed, that is, the probability density function of wind speed is

[0042]

[0043] Where v represents the wind speed; τ and k represent the two parameters of the Weibull distribution, k represents the shape parameter, and τ represents the scale parameter.

[0044] The output power of the fan is closely related to the change of wind speed. According to the specific fan, the wind speed is divided into cut-in wind speed, rated wind speed, and cut-out wind speed. Therefore, the actual output power of the fan is calculated in sections through different wind speed intervals, further forming the output model of the fan, that is,

[0045]

[0046] Among them, v in 、v rated and v out are the cut-in wind speed, rated wind speed and cut-out wind speed respectively; P and P rated are the actual and rated output power of the wind turbine respectively.

[0047] Combined with formula (1), the wind speed scenario is generated. According to formula (3), the wind turbine output scenario is obtained and the wind speed is sampled. Then, the wind turbine output scenario is reduced. In this embodiment, the backward reduction method (also known as the synchronous back substitution elimination method) is used to reduce the wind turbine output scenario. The specific process is as follows:

[0048] a) Calculate the Euclidean distance between different scenes, that is

[0049]

[0050] in, They are defined as wind turbine output scenarios, P t i 、P t j Respectively represent the output values ​​of the i-th scenario and the j-th scenario at time T;

[0051] b) The optimal case of scene reduction is when the probability distance between the scene set before scene reduction and the scene subset finally retained after reduction is minimized, that is,

[0052] min∑ρ ω ·d(ω i ,ω j ) (4)

[0053] Among them, ρ ω For the scene i Probability of occurrence.

[0054] As one or more implementation methods, in step (2), based on the total electricity demand information and price package information released by the power market, a pricing function based on the Cournot game is used to simulate the process of forming a clearing price in the day-ahead market to generate a day-ahead market clearing price.

[0055] Cournot game theory is commonly used to describe competitive behavior among oligopolistic firms. This example considers the significant initial infrastructure investment required for wind farms, which is unaffordable for small and medium-sized enterprises. Large power generation companies typically invest in these facilities, similar to oligopolistic firms. Therefore, in the day-ahead market, the non-cooperative game relationship between wind farms is described using Cournot game theory, generating a market-clearing price.

[0056] The pricing function formula based on Cournot game is as follows:

[0057]

[0058] in, and are the total demand and electricity price announced in advance by the power grid; and q n,t are the day-ahead market clearing price and the bid quantity of wind farm n respectively.

[0059] Figure 3 The figure shows the package electricity price announced by the power grid and the actual clearing price curve of the market on the day before. The bidding price announced by the power grid during dispatch is often higher, but in the actual operation of the market, this price will change with the bidding behavior of market participants, and eventually reach a stable, reasonable and market clearing price recognized by all parties.

[0060] As one or more implementation methods, in step (3), demand response resources are introduced into the bidding method as compensation reserve resources in the bidding process, and the bidding deviation of the wind farm is penalized in the balancing market to balance the deviation between the actual power generation and the day-ahead bid amount, so as to achieve accuracy and rationality of the bidding.

[0061] Balancing adjustments include upward and downward adjustments; upward adjustments mean that when the actual wind power output is less than the day-ahead bid amount, the market price is higher than the day-ahead market clearing price; downward adjustments mean that when the actual wind power output is greater than the day-ahead bid amount, the market price is lower than the day-ahead market clearing price; that is,

[0062]

[0063]

[0064] in, and are the positive adjustment price and negative adjustment price for balancing the market; S pos and S neg are the adjustment parameters for the positive imbalance price and the negative imbalance price, respectively. pos and S neg Take 1.1 and 0.9 respectively.

[0065] Demand response resources include contract demand response and activated demand response; contract demand response is the contract quantity signed between the wind farm and the demand response provider based on historical conditions, including positive contract demand response and negative contract demand response; activated demand response is the demand response used by the wind farm to compensate for the deviation between the day-ahead bid quantity and the actual power generation during actual operation, and its maximum value does not exceed the contract demand response.

[0066] The balancing market is used to balance the deviation between the actual power generation of a wind farm and the day-ahead bid. Demand response resources are used as backup resources to compensate for the deviation, which needs to be taken into account when calculating the deviation. The deviation in this embodiment is the deviation that exists after the wind farm uses demand response resources to compensate for the wind farm's output shortfall and wind curtailment. The formula for calculating positive and negative power deviation is as follows:

[0067]

[0068]

[0069] in, and They are positive power deviation and negative power deviation respectively; and They are activation of positive demand response value and activation of negative demand response value; and are the contract positive demand response value and the contract negative demand response value respectively; P n,t,ω is the wind turbine output described in the scenario.

[0070] To sum up, the profit formula for a balanced market can be expressed as:

[0071]

[0072] Demand response resources are used to compensate for output deviations from wind farms. This is divided into two parts: contracted demand response and activated demand response. Activated demand response is considered when calculating imbalance deviations. Furthermore, the costs of contracted and activated demand response must also be factored into the calculation of each wind farm's profitability.

[0073] The demand response resource cost formula is as follows:

[0074]

[0075] in, and They are activating positive demand response prices and activating negative demand response prices; is the contracted demand response price. Typically, the activated positive demand response price, the activated negative demand response price, and the contracted demand response price are all constant values. In this embodiment, the activated positive demand response price and the activated negative demand response price are 0.00077965 and 0.77965, respectively, and the positive contracted demand response price and the negative contracted demand response price are 0.0007 and 0.8, respectively.

[0076] As can be seen from formulas (8) and (9), positive demand response is used when the wind turbine output is too high, that is, when there is wind curtailment; while negative demand response is used when the wind turbine output is insufficient. Therefore, it can be seen that the negative demand response purchased by the wind farm is theoretically equivalent to purchasing power generation. To prevent the problem that when the price of the negative demand response during operation is much lower than the market clearing price, the wind farm does not use its actual power generation to participate in the bidding, but instead uses the purchased demand response at a lower price to participate in the bidding, in this embodiment, the price of the negative demand response (whether it is contract demand response or activated demand response) is set to be basically consistent with the market clearing price.

[0077] Figures 4(a), 4(b), and 4(c) illustrate the contracted positive and negative demand responses required by the three wind farms after implementing the optimal bidding control method in this embodiment. As can be seen from the figures, the three wind farms purchased more positive contracted demand responses than negative contracted demand responses. This indicates a conservative attitude during the bidding process, resulting in more wind curtailment than under-processing. This is also a requirement that the wind farms must implement in the model of this embodiment. Risk considerations must be taken into account, and the compensation bias of demand response must be comprehensively utilized to maximize benefits. Furthermore, the figures show that the negative contracted demand response during the early morning hours is essentially zero. This is because user demand is low during the early morning hours, and therefore the wind farms' bids are lower, resulting in a significant amount of wind curtailment.

[0078] As one or more implementation methods, in step (4), the optimal bidding control model takes profit maximization and risk minimization of the wind farm as the goal, and constructs an objective function consisting of three parts: expected profit, risk cost and weight coefficient.

[0079] Based on the operational characteristics of wind farms and the actual market structure, and to meet the needs of maximizing profits and minimizing risks for each wind farm, an optimal bidding control model that considers uncertainty and interactively matches multiple wind farms with demand response is established:

[0080]

[0081] in, is the decision variable; ρ ω is the probability of the wind turbine output scenario.

[0082] The optimal bidding control model consists of three parts:

[0083] The first part is the expected profit of each wind farm, which is calculated as follows:

[0084]

[0085] Among them, q n,t is the bidding amount of the nth wind farm; the first item is the revenue of the day-ahead market; the second and the third It is the income and cost of the balanced market due to the existence of positive and negative power imbalance; the fourth item and the fifth The sixth item is the cost of purchasing activated positive and negative demand response resources; It is the cost required to purchase the contracted volume determined in advance when the wind power producer signs a contract with the demand response provider.

[0086] The second part is the risk cost. This embodiment uses conditional value at risk (CVaR) to measure the risk value of each wind farm, which will become a useful tool for wind farm risk mitigation. CVaR is defined as the expected value of the profit that is less than the value at risk (VaR) under a given confidence level. Its calculation formula is as follows:

[0087]

[0088] Among them, the auxiliary variable ξ i is the VaR value under the confidence level α; is the probability density function of profit. To meet the actual calculation simulation, the continuous CVaR is discretized:

[0089]

[0090]

[0091] η ω ≤0 (17)

[0092] Among them, the auxiliary variable η ω Used to describe the relationship between wind farm profitability and VaR.

[0093] The last part is the weight coefficient γ; the weight coefficient controls the balance between expected profit and risk value, provides a basis for wind power producers to judge bidding risks, and reflects the wind power producers' attitude towards risk.

[0094] The constraints of the optimal bidding control model for interactive matching of multiple wind farms and demand response considering uncertainty are as follows:

[0095] 1) Wind farm bidding quantity constraints:

[0096]

[0097] in, The maximum output of the fan; Negative demand response value proposed for the contract.

[0098] 2) CVaR constraints:

[0099]

[0100] η ω ≤0 (20)

[0101] 3) Contract demand response constraints:

[0102]

[0103]

[0104] in, They are respectively the positive and negative demand responses proposed in the contract.

[0105] 4) Activate demand response constraints:

[0106]

[0107]

[0108] The activated demand response is the amount of demand response used during the actual bidding run, so it is capped at the contracted demand response.

[0109] 5) Power balance constraints:

[0110]

[0111]

[0112] Among them, the output deviation of the wind farm can be compensated by the activated demand response resources.

[0113] As one or more implementation methods, in step (5), the wind turbine output scenario generated by the random scenario is input into the model solving method, and the optimal control model of the wind farm under the uncertainty scenario is iteratively solved until the termination condition is met and the loop is exited to obtain the optimal bidding solution. In the actual model solving process, this embodiment allows all deviation values ​​to be compensated by demand response, so the balancing market can be ignored. In addition, when using the pricing function based on the Cournot game to calculate the market clearing price, because the Cournot game theory is a non-cooperative game, the information between competitors is confidential, so it is necessary to use the bid amount generated in the previous iteration to participate in the solution of this model. After solving the model, the bid amount curves of the three wind farms are obtained as shown below. Figure 5 As shown in the figure, we can see that the environmental factors in the area where the third wind farm is located cause its output power to be generally higher than that of the first and second wind farms, and so does the bidding volume. However, the overall analysis shows that the bidding volume is generally lower in the early morning hours and higher during peak hours. Figure 6 The figure shows the profit-making process for each wind farm provided by this embodiment. The figure illustrates that the bidding strategy proposed in this embodiment maximizes participation in market bidding and attracts wind power while ensuring profitability for each wind farm. Demand response resources are also used as backup resources, bundled with wind farms for market bidding competition, compensating for bidding deviations as much as possible, smoothing the bidding curve, and maximizing wind farm profits.

[0114] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Those skilled in the art should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A multi-wind farm optimal bidding control method based on demand response resource reserve, characterized in that: The following steps are involved: Step S01: Obtain the day-ahead bid amount and actual power generation of the wind farm, use demand response resources as a backup resource, calculate the deviation between the day-ahead bid amount and the actual power generation, and balance the deviation that cannot be compensated by the demand response resources in the balancing market. In this case, the demand response resources are introduced into the bidding method as a compensatory backup resource in the bidding process, and the bidding deviation of the wind farm is penalized in the balancing market to balance the deviation between the actual power generation and the day-ahead bid amount. Generate a wind turbine output scenario consisting of a wind turbine output model, a random wind speed model, and a scenario reduction method based on the environmental information and operation information of the wind farm; The output power of a wind turbine is closely related to changes in wind speed. Depending on the specific wind turbine, the wind speed is divided into cut-in wind speed, rated wind speed, and cut-out wind speed. The actual output power of the wind turbine is calculated in sections across different wind speed intervals to form a wind turbine output model. Step S02: Based on the actual operating characteristics of the wind farm and the actual operating conditions of the power market, an optimal bidding control model for interactive matching of multiple wind farms and demand response is constructed, taking into account uncertainty. The optimal bidding control model aims to maximize the profit and minimize the risk of the wind farm, and constructs an objective function consisting of three parts: expected profit, risk cost, and weight coefficient. The risk cost hedges the risk of the wind farm by measuring the conditional risk value of the wind farm; Step S03: inputting the wind turbine output scenario generated by the random scenario into the optimal bidding control model, performing iterative solution of the optimal bidding control model of the wind farm under the uncertainty scenario, until the final condition is met and the loop is exited to obtain the optimal bidding solution; The balancing market is used to balance the difference between actual wind farm power generation and day-ahead bids. Demand response resources are used as backup resources to compensate for this difference, and this difference must be taken into account when calculating the difference. The difference is the difference that exists after a wind farm uses demand response resources to compensate for insufficient output and curtailed wind power. Positive demand response is used when wind turbine output is too high and there is wind curtailment; while negative demand response is used when wind turbine output is insufficient. The objective function of the optimal bidding control model for interactive matching of multiple wind farms and demand response considering uncertainty is: in, is the decision variable; ρ ω is the probability of the wind turbine output scenario; For the expected profit of each wind farm, q n,t is the bidding amount of the nth wind farm; is the revenue of the day-ahead market; and It is the revenue and cost of balancing the market due to the existence of positive and negative power imbalance; and is the cost of purchasing activated positive and negative demand response resources; It is the cost required to purchase the contracted volume determined in advance when the wind power producer signs a contract with the demand response provider; is the risk cost; Auxiliary variable ξ i is the risk value under the confidence level α; is the probability density function of profit; γ is the weight coefficient used to control the balance between expected profit and risk value; The constraints of the optimal bidding control model for interactive matching of multiple wind farms and demand response considering uncertainty include wind farm bidding quantity constraints, power balance constraints and demand response activation constraints; the wind farm bidding quantity constraints are in, The maximum output of the fan; The negative demand response value proposed for the contract; the activation demand response constraint condition is The activated demand response is the demand response amount used in the actual bidding operation process, and the upper limit is the contract demand response; the power balance constraint is Among them, the output deviation of the wind farm can be compensated by the activated demand response resources.

2. The multi-wind farm optimal bidding control method based on demand response resource reserve as claimed in claim 1, characterized in that: Before step S01 , a day-ahead market clearing price is generated based on the acquired total electricity demand information and price package information published by the power market in which the wind farm participates.

3. The optimal bidding control method for multiple wind farms based on demand response resource reserve as claimed in claim 2, characterized in that: According to the total electricity demand information and price package information released by the power market, the pricing function based on Cournot game is used to simulate the process of forming the clearing price in the day-ahead market and generate the day-ahead market clearing price.

4. The multi-wind farm optimal bidding control method based on demand response resource reserve as claimed in claim 1, characterized in that: In step S01, the balancing adjustment includes upward and downward adjustments; the upward adjustment means that when the actual wind power output is less than the day-ahead bid amount, the market price is higher than the day-ahead market clearing price; the downward adjustment means that when the actual wind power output is greater than the day-ahead bid amount, the market price is lower than the day-ahead market clearing price.

5. The multi-wind farm optimal bidding control method based on demand response resource reserve as claimed in claim 1, characterized in that: In step S01, the demand response resources include contract demand response and activation demand response; the contract demand response is the contract quantity signed between the wind farm and the demand response provider based on historical conditions, including positive contract demand response and negative contract demand response; the activation demand response is the demand response used by the wind farm to compensate for the deviation between the day-ahead bid quantity and the actual power generation during actual operation, and its maximum value does not exceed the contract demand response.

6. The multi-wind farm optimal bidding control method based on demand response resource reserve as claimed in claim 1, characterized in that: In step S02 , the constraints of the optimal bidding control model include a wind farm day-ahead bidding quantity constraint, a conditional value-at-risk constraint, a contract demand response constraint, an activated demand response constraint, and a power balance constraint.

7. The multi-wind farm optimal bidding control method based on demand response resource reserve as claimed in claim 1, characterized in that: In step S03, during the iterative solution process, a non-cooperative game method is adopted, and the day-ahead bid amount of the market participants is used as the last bid result, and the day-ahead bid amount generated in the last iteration is combined to participate in the solution of the model.

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