Energy storage power station day-ahead electric energy market bidding curve decision-making method and system considering primary frequency modulation obligation

Abstract

The invention provides an energy storage power station day-ahead electric energy market bidding curve decision-making method and system considering primary frequency modulation obligation, and the method comprises the steps: generating and reducing a wind power output and market electricity price scene through obtaining the next-day wind power output prediction, primary frequency modulation amplitude limiting and market clearing price prediction; and establishing a wind storage power station bidding decision model considering primary frequency modulation obligation constraint, solving to obtain a bidding curve, and calculating a power station profit expectation value. According to the bidding decision model, the primary frequency modulation obligation constraint borne by the wind power storage power station is considered, the primary frequency modulation capacity distribution of wind power and energy storage is taken into the constraint condition, meanwhile, the deviation assessment penalty and the energy storage operation cost are taken into account in the target function, and the benefit maximization of the wind power storage power station can be realized while the frequency modulation requirement is met.

Description

Technical Field

[0001] The present invention relates to the technical field of power market, and in particular to a bidding curve decision method taking into account a primary frequency regulation obligation when a new energy distribution and storage power station participates in a day-ahead power energy market. Background Art

[0002] As the proportion of installed capacity of new energy in my country grows rapidly, new energy consumption is gradually adopting a market-oriented consumption approach. In the early stage of the current spot market construction, new energy power stations usually adopt a bidding method of "reporting quantity but not price" in the day-ahead market, providing the market with a bidding curve for bidding.

[0003] However, due to the inherent characteristics of new energy, such as randomness, uncertainty, and volatility, new energy is at a disadvantage in market competition. In addition, in many domestic regions, such as Central China and East China, grid-connected new energy stations are required to have primary frequency regulation capabilities and bear certain primary frequency regulation obligations. Providing primary frequency regulation capabilities for new energy stations will bring high opportunity costs, further reducing their market competitiveness.

[0004] Energy storage, with its advantages of fast response, flexible regulation and precise control, can reshape the power curve of new energy stations and provide primary frequency regulation capabilities. Therefore, new energy stations can cooperate with energy storage to participate in market competition, thereby improving the market competitiveness of new energy.

[0005] However, there is still a lack of a bidding curve decision method for the day-ahead electricity market of wind-storage power stations that takes into account the primary frequency regulation obligation. Summary of the invention

[0006] The purpose of the present invention is to address the problems in the background technology and propose a method and system for making a decision on a bidding curve for a day-ahead electric energy market of an energy storage power station, so as to provide a basis for operational decisions of the energy storage power station.

[0007] The technical solution of the present invention is:

[0008] On the one hand, the present invention provides a method for determining a bidding curve of a day-ahead electric energy market for an energy storage power station taking into account a primary frequency regulation obligation, comprising:

[0009] S1. Obtain the forecast curve of wind power station output for the next day, the upper and lower limits of primary frequency regulation of wind power station and the forecast curve of clearing price of the day-ahead electricity market;

[0010] S2. Based on the wind power output forecast curve and the day-ahead electric energy market clearing price forecast curve, generate a wind power output scenario set and a day-ahead electric energy market clearing price scenario set, and reduce the generated scenario set to obtain a reduced wind power output scenario set, a day-ahead electric energy market clearing price scenario set and their corresponding probabilities;

[0011] S3. Establish a decision model for the bidding curve of the day-ahead electricity market for wind and storage power stations taking into account the primary frequency regulation obligation. The model includes an objective function and constraints for maximizing the expected total revenue. The objective function considers the day-ahead electricity market revenue, deviation assessment penalty fees, and energy storage operation costs. The constraints include primary frequency regulation capacity allocation constraints, wind and storage power station output constraints, wind power output constraints, and energy storage operation constraints.

[0012] S4. Solve the decision model to obtain the bidding curve submitted by the wind power station with storage power plant in the day-ahead electric energy market; calculate the expected profit of the wind power station with storage power plant in the day-ahead electric energy market according to the obtained bidding curve.

[0013] Furthermore, the acquisition of the next-day wind power station output forecast curve, the wind power station primary frequency regulation upper and lower limits, and the day-ahead electric energy market clearing price forecast curve described in S1 includes:

[0014] Obtain the next day's wind power station output forecast curve, which represents the maximum wind power output forecast value in each period;

[0015] Obtain the upper and lower limits of the primary frequency regulation of the wind power station;

[0016] Get the day-ahead electricity energy market forecast clearing price curve, which represents the day-ahead electricity energy market clearing price forecast value for each period.

[0017] Furthermore, the reduction of the generated scene set in S2 includes:

[0018] The Monte Carlo sampling method is used to generate wind power output scenarios and day-ahead electricity market clearing price scenarios. The probability distance between scenarios is obtained by multiplying the scenario's own probability by its distance from other scenarios. The synchronous back-substitution method based on probability distance is used to iteratively delete scenarios with similar distances until the stop condition for scenario reduction is reached. The reduced wind power output scenario set, day-ahead electricity market clearing price scenario set and their corresponding probabilities are obtained.

[0019] Furthermore, the decision model for the bidding curve of the day-ahead electricity market of the wind power station considering the primary frequency regulation obligation as described in S3 includes:

[0020] Establish a decision-making model with the maximum expected total revenue as the objective function. The objective function includes the day-ahead electricity market revenue, deviation assessment penalty fees, and energy storage operation costs.

[0021] A primary frequency regulation capacity allocation constraint equation is established in the model, indicating that the primary frequency regulation obligation of the power station is jointly undertaken by wind power and energy storage;

[0022] The output constraint equation of the wind-storage power station is established in the model to limit the total output range of the power station, and the deviation assessment power is expressed as the difference between the actual output of the power station and the bid power.

[0023] Furthermore, the establishment of a decision model for the bidding curve of the day-ahead electricity market of the wind power station taking into account the primary frequency regulation obligation described in S3 also includes:

[0024] A wind power output constraint equation is established in the model, indicating that the wind power output does not exceed the maximum wind power output;

[0025] The energy storage operation constraint equations are established in the model, including that the energy storage energy change is caused by the charging and discharging power and primary frequency modulation, the energy storage charge state is the same at the beginning and end, the energy storage cannot be in the charging and discharging state at the same time, and the energy storage charging and discharging power and capacity limitations.

[0026] Further, solving the decision model in S4 includes:

[0027] The decision model is solved by using an optimization algorithm; the bid power value of the wind power station in each time period is obtained to form a bid curve submitted by the wind power station in the day-ahead electric energy market.

[0028] Furthermore, the expected profit of the wind power station in the day-ahead electricity market is calculated according to the obtained bidding curve in S4, including:

[0029] Iterate over all reduced scenes;

[0030] For each scenario, calculate the day-ahead electricity market revenue, deviation assessment penalty fees, and energy storage operating costs of the wind power plant in that scenario;

[0031] Calculate the profit of wind power plant in each scenario;

[0032] The profit of the wind-storage power station in each scenario is multiplied by the corresponding scenario occurrence probability and then summed to obtain the expected profit of the wind-storage power station in the day-ahead electricity energy market.

[0033] On the other hand, the present invention provides a bidding curve decision system for a day-ahead electric energy market of an energy storage power station taking into account a primary frequency regulation obligation, comprising:

[0034] The data acquisition module is used to obtain the forecast curve of wind power station output for the next day, the upper and lower limits of the primary frequency regulation of the wind power station, and the forecast curve of the clearing price of the day-ahead electric energy market;

[0035] A scenario generation module is used to generate a wind power output scenario set and a day-ahead electric energy market clearing price scenario set based on a wind power output forecast curve and a day-ahead electric energy market clearing price forecast curve, and to reduce the generated scenario set to obtain a reduced wind power output scenario set, a day-ahead electric energy market clearing price scenario set and their corresponding probabilities;

[0036] The model building module is used to establish a bidding curve decision model for the day-ahead electricity market of wind and storage power stations taking into account the primary frequency regulation obligation. The model contains the objective function and constraints for maximizing the expected total revenue. The objective function considers the day-ahead electricity market revenue, deviation assessment penalty fees and energy storage operation costs. The constraints include primary frequency regulation capacity allocation constraints, wind and storage power station output constraints, wind power output constraints and energy storage operation constraints.

[0037] The solution calculation module is used to solve the decision model to obtain the bidding curve submitted by the wind power station in the day-ahead electric energy market; based on the obtained bidding curve, the expected profit of the wind power station in the day-ahead electric energy market is calculated.

[0038] Beneficial effects of the present invention:

[0039] The present invention discloses a method and system for making a decision on a bidding curve for a day-ahead electric energy market of an energy storage power station. First, the next day's wind power output forecast curve, the upper and lower limits of primary frequency regulation, and the day-ahead market clearing price forecast curve are obtained, and the scenario set is reduced using a synchronous back-substitution method based on probability distance. Then, a decision model for the day-ahead market bidding curve of a wind storage power station considering the primary frequency regulation obligation is established, and the objective function is to maximize the total expected benefit, including market revenue, deviation penalty, and energy storage cost. The model sets constraints such as primary frequency regulation capacity allocation, total output of the wind storage power station, wind power characteristics, and energy storage operation. By solving the decision model, the optimal bidding curve of the wind storage power station in the day-ahead market is obtained, and the corresponding expected profit is calculated.

[0040] The present invention effectively solves the problem of how energy storage power stations can balance primary frequency regulation obligations and economic benefits when participating in the day-ahead electricity energy market. This method can help wind energy storage power stations formulate reasonable market bidding strategies, maximize profits while ensuring system frequency regulation needs, and is of great significance to promoting the consumption of new energy and the construction of the electricity market.

[0041] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0043] Figure 1 A flow chart of the present invention is shown.

[0044] Figure 2 The following is a wind power station output prediction curve for the next day according to an embodiment of the present invention;

[0045] Figure 3The day-ahead electricity energy market forecast clearing price curve according to one embodiment of the present invention is shown;

[0046] Figure 4 shows a wind power output scenario generated according to an embodiment of the present invention;

[0047] Figure 5 A scenario of a day-ahead electricity energy market clearing price generated according to an embodiment of the present invention is shown;

[0048] Figure 6 A set of wind power output scenarios after reduction according to an embodiment of the present invention is shown;

[0049] Figure 7 The probability of wind power output scenario after reduction according to an embodiment of the present invention is shown;

[0050] Figure 8 A scenario of a day-ahead electricity energy market clearing price after curtailment according to an embodiment of the present invention is shown;

[0051] Fig. 9 The probability of a scenario of a day-ahead electricity energy market clearing price after curtailment according to an embodiment of the present invention is shown;

[0052] Fig.10 The figure shows a bidding curve submitted by a wind power plant in the day-ahead electricity energy market according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0054] Example 1

[0055] Figure 1 A flow chart of the present invention is shown.

[0056] like Figure 1 As shown, the present invention provides a method for deciding the bidding curve of the day-ahead electric energy market of a wind power station with energy storage considering the primary frequency regulation obligation. Taking a wind power station with 250MW wind power installed capacity and 50MW / 100MWh energy storage as an example, the method includes the following steps:

[0057] S1. Obtain the forecast curve of wind power station output for the next day, obtain the primary frequency regulation limit of wind power station, and obtain the forecast curve of the clearing price of the day-ahead electricity market.

[0058] S11. Obtain the wind power station output forecast curve for the next day like Figure 2 As shown; It is the predicted value of the maximum wind power output at time period t.

[0059] S12. Obtain the upper limit of the primary frequency regulation of the wind power station and lower limit

[0060] According to the National Standard of the People's Republic of China "Technical Regulations and Test Guidelines for Primary Frequency Regulation of Grid-connected Power Sources" (GB / T40595-2021), the upper limit of primary frequency regulation of wind storage power stations is 6%; the lower limit is 10%.

[0061] S13. Obtain the forecast clearing price curve of the day-ahead electricity energy market like Figure 3 As shown; It is the forecast value of the day-ahead electricity market clearing price at time period t.

[0062] S2. Generate and reduce scenarios based on the forecast results of wind power output and market electricity prices.

[0063] S21. Generate scenarios based on the forecast results of wind power output and market electricity prices.

[0064] The Monte Carlo sampling method is used to generate scenarios based on the forecast results of wind power output and market electricity prices. is the maximum wind power output at time period t in scenario s; is the day-ahead electricity market clearing price at time t in scenario s; Composing the generated scene set Among them, the generated wind power output scenario is as follows Figure 4 As shown in the figure, the generated day-ahead electricity market clearing price scenario is as follows Figure 5 shown.

[0065] S22: Reduce the generated scene.

[0066] The generated scene set Through the simultaneous back-substitution method based on probability distance, we get λ DA , P s . is the maximum wind power output at time period t in scenario s, λ DA (s,t) is the day-ahead electricity market clearing price at time t in scenario s; P s (s) is the probability of scene s appearing. λ DA Constitute the reduced scene set Ω s ,like Figure 6 The probability of wind power output after reduction is shown in Figure 7 The scenario of the day-ahead electricity market clearing price after reduction is as follows: Figure 8The probability of the clearing price scenario of the day-ahead electricity market after the reduction is as follows: Fig. 9 shown.

[0067] S3. Calculate using the bidding curve decision model of the day-ahead electricity energy market for wind power stations that takes into account the primary frequency regulation obligation to obtain the bidding curve submitted by the wind power stations in the day-ahead electricity energy market.

[0068] S31. Establish a decision model for the bidding curve of the day-ahead electricity market for wind-storage power stations taking into account the primary frequency regulation obligation; the objective function for maximizing the total expected revenue of the wind-storage power station, which includes the day-ahead electricity market revenue and the energy storage life cost, is specifically:

[0069]

[0070] Among them, Income total is the expected total income of the wind power station; DA (s) is the revenue of the wind power storage station in the day-ahead electricity energy market in scenario s; dev (s) is the penalty fee for deviation assessment in scenario s; Cost ESS (s) is the operating cost of energy storage in scenario s.

[0071] Income DA (s) The specific calculation method is:

[0072]

[0073] Where T is the total number of time periods; is the total power of the wind power station at time period t reported to the market; Δt is the time interval per unit time period.

[0074] Punish dev (s) The specific calculation method is:

[0075]

[0076] in, is the deviation assessment coefficient; P dev (s,t) is the deviation power in time period t in scene s.

[0077] The primary frequency regulation capacity allocation constraint equation is as follows:

[0078]

[0079] Among them, P STA (s, t) is the power station output at time period t in scenario s; They are respectively the primary frequency regulation up and down power capacities that the wind-storage power station needs to bear at time period t in scenario s; They are respectively the primary frequency regulation up and down power capacities undertaken by wind power at time period t in scenario s; They are respectively the primary frequency regulation up and down power capacities undertaken by the energy storage at time period t in scenario s.

[0080] Constraint equations Equations (5) and (6) indicate that the primary frequency regulation obligation of the power station is shared by wind power and energy storage.

[0081] The constraint equation of the wind power station is as follows:

[0082]

[0083] Among them, P W (s, t) is the wind power output at time period t in scenario s; are respectively the energy storage charging and discharging power at time period t in scene s.

[0084] Constraint equations Equations (8) and (9) indicate that the deviation assessment power is the difference between the actual power output of the power station and the bid power.

[0085] The wind power constraint equation is as follows:

[0086]

[0087] The energy storage constraint equation is specifically:

[0088]

[0089]

[0090] Among them, S ESS (s, t) is the energy storage charge state at time period t in scene s; E ESS (s, t) is the energy stored in the energy storage at time period t in scene s; C ESS is the available energy capacity of the energy storage; are the energy storage charging and discharging efficiency; ΔE PFR,ESS (s, t) is the energy change of energy storage due to primary frequency modulation in time period t in scenario s; They are the energy changes caused by the unit up and down frequency regulation capacity of energy storage in unit time; They are the maximum and minimum energy allowed to be stored in the energy storage, respectively; is the initial charge state of energy storage; are the maximum charging and discharging power of energy storage respectively; They are the energy storage charge and discharge flags at time period t in scene s, 1 represents that the energy storage is in the charge or discharge state, and 0 represents that the energy storage is in the non-charge or non-discharge state; is the energy storage investment cost; N c is the rated number of energy storage cycles.

[0091] Constraint equations (12) and (13) indicate that the energy change of the energy storage is caused by the charging and discharging power and the primary frequency modulation. Constraint equation (15) indicates that the energy storage has the same charge state at the beginning and end. Constraint equation (16) indicates that the energy storage cannot be in the charging and discharging state at the same time.

[0092] S4. Solve the decision model of the day-ahead electricity energy market operation curve of the wind-storage power station taking into account the primary frequency regulation obligation, and obtain the bidding curve submitted by the wind-storage power station in the day-ahead electricity energy market.

[0093] The information of the wind power station participating in the day-ahead electricity energy market is shown in Table 1.

[0094] The total number of market time periods is 96; the deviation assessment coefficient is 2.0; the installed capacity of wind power is 250MW; the energy storage specification is 50MW / 100MW·h; the investment cost of energy storage is 692,000 yuan; the maximum number of cycles that can be performed by energy storage at rated discharge depth is 3000; the maximum charging and discharging power of energy storage is 50MW; the energy storage discharge efficiency is 0.9, and the energy storage charging efficiency is 0.9; the energy storage energy capacity is 100MWh; the minimum state of charge allowed for energy storage is 0.05, and the maximum state of charge allowed is 0.9; the initial state of charge of energy storage is 0.5; the energy change coefficient brought by the unit frequency regulation capacity of energy storage is 0.12, and the energy change coefficient brought by the unit frequency regulation capacity is 0.12.

[0095] Table 1 Information on the participation of wind power stations in the day-ahead electricity market

[0096]

[0097]

[0098] The bidding curve submitted by wind power station in the day-ahead electricity market is as follows: Fig.10 shown.

[0099] According to the obtained bidding curve, the expected profit of the wind power plant in the day-ahead electricity market is calculated;

[0100] The calculation formula for the expected value of power station profit is shown in formula (19).

[0101]

[0102] Among them, Income is the expected profit of the power station.

[0103] The expected profit of the wind power station in the day-ahead electricity market is 699,900 yuan.

[0104] Example 2

[0105] The present invention provides a day-ahead electric energy market bidding curve decision system for an energy storage power station taking into account a primary frequency regulation obligation, comprising:

[0106] The data acquisition module is used to obtain the forecast curve of wind power station output for the next day, the upper and lower limits of the primary frequency regulation of the wind power station, and the forecast curve of the clearing price of the day-ahead electric energy market;

[0107] A scenario generation module is used to generate a wind power output scenario set and a day-ahead electric energy market clearing price scenario set based on a wind power output forecast curve and a day-ahead electric energy market clearing price forecast curve, and to reduce the generated scenario set to obtain a reduced wind power output scenario set, a day-ahead electric energy market clearing price scenario set and their corresponding probabilities;

[0108] The model building module is used to establish a bidding curve decision model for the day-ahead electricity market of wind and storage power stations taking into account the primary frequency regulation obligation. The model contains the objective function and constraints for maximizing the expected total revenue. The objective function considers the day-ahead electricity market revenue, deviation assessment penalty fees and energy storage operation costs. The constraints include primary frequency regulation capacity allocation constraints, wind and storage power station output constraints, wind power output constraints and energy storage operation constraints.

[0109] The solution calculation module is used to solve the decision model to obtain the bidding curve submitted by the wind power station in the day-ahead electric energy market; based on the obtained bidding curve, the expected profit of the wind power station in the day-ahead electric energy market is calculated.

[0110] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for making a decision on the bidding curve of the day-ahead electricity market of an energy storage power station considering the primary frequency regulation obligation, characterized in that: include: S1. Obtain the forecast curve of wind power station output for the next day, the upper and lower limits of primary frequency regulation of wind power station and the forecast curve of clearing price of the day-ahead electricity market; S2. Based on the wind power output forecast curve and the day-ahead electric energy market clearing price forecast curve, generate a wind power output scenario set and a day-ahead electric energy market clearing price scenario set, and reduce the generated scenario set to obtain a reduced wind power output scenario set, a day-ahead electric energy market clearing price scenario set and their corresponding probabilities; S3. Establish a decision model for the bidding curve of the day-ahead electricity market for wind and storage power stations taking into account the primary frequency regulation obligation. The model includes an objective function and constraints for maximizing the expected total revenue. The objective function considers the day-ahead electricity market revenue, deviation assessment penalty fees, and energy storage operation costs. The constraints include primary frequency regulation capacity allocation constraints, wind and storage power station output constraints, wind power output constraints, and energy storage operation constraints. S4. Solve the decision model to obtain the bidding curve submitted by the wind power station with storage power plant in the day-ahead electric energy market; calculate the expected profit of the wind power station with storage power plant in the day-ahead electric energy market according to the obtained bidding curve.

2. The bidding curve decision method for the day-ahead electricity market of the energy storage power station considering the primary frequency regulation obligation according to claim 1 is characterized in that In S1, the acquisition of the next day's wind power station output forecast curve, the wind power station's primary frequency regulation upper and lower limits, and the day-ahead electricity energy market clearing price forecast curve includes: Obtain the next day's wind power station output forecast curve, which represents the maximum wind power output forecast value in each period; Obtain the upper and lower limits of the primary frequency regulation of the wind power station; Get the day-ahead electricity energy market forecast clearing price curve, which represents the day-ahead electricity energy market clearing price forecast value for each period.

3. The bidding curve decision method for the day-ahead electricity market of the energy storage power station considering the primary frequency regulation obligation according to claim 1 is characterized in that In S2, the generated scene set is reduced, including: Monte Carlo sampling method is used to generate wind power output scenarios and day-ahead electricity market clearing price scenarios; Multiply the probability of the scene itself by its distance to other scenes to get the probability distance between the scenes; The synchronous back-substitution method based on probability distance is used to iteratively delete scenes with similar distances until the stopping condition of scene reduction is reached; Obtain the reduced wind power output scenario set, the day-ahead electricity market clearing price scenario set and their corresponding probabilities.

4. The bidding curve decision method for the day-ahead electricity market of the energy storage power station considering the primary frequency regulation obligation according to claim 1 is characterized in that In S3, the establishment of a decision model for the day-ahead electricity market bidding curve of a wind power station taking into account the primary frequency regulation obligation includes: Establish a decision-making model with the maximum expected total revenue as the objective function. The objective function includes the day-ahead electricity market revenue, deviation assessment penalty fees, and energy storage operation costs. A primary frequency regulation capacity allocation constraint equation is established in the model, indicating that the primary frequency regulation obligation of the power station is jointly undertaken by wind power and energy storage; The output constraint equation of the wind-storage power station is established in the model to limit the total output range of the power station, and the deviation assessment power is expressed as the difference between the actual output of the power station and the bid power.

5. The wind power station day-ahead electricity market bidding strategy method as claimed in claim 4 is characterized in that In S3, the establishment of a decision model for the bidding curve of the day-ahead electricity market of the wind power station taking into account the primary frequency regulation obligation also includes: A wind power output constraint equation is established in the model, indicating that the wind power output does not exceed the maximum wind power output; The energy storage operation constraint equations are established in the model, including that the energy storage energy change is caused by the charging and discharging power and primary frequency modulation, the energy storage charge state is the same at the beginning and end, the energy storage cannot be in the charging and discharging state at the same time, and the energy storage charging and discharging power and capacity limitations.

6. The wind power station day-ahead electricity market bidding strategy method according to claim 1 is characterized in that In S4, solving the decision model includes: Using an optimization algorithm to solve the decision model; The bid power values ​​of the wind power station in each period are obtained to form the bid curve submitted by the wind power station in the day-ahead electricity energy market.

7. The wind power station day-ahead electricity market bidding strategy method according to claim 1 is characterized in that In S4, the expected profit of the wind power station in the day-ahead electricity market is calculated based on the obtained bidding curve, including: Iterate over all reduced scenes; For each scenario, calculate the day-ahead electricity market revenue, deviation assessment penalty fees, and energy storage operating costs of the wind power plant in that scenario; Calculate the profit of wind power plant in each scenario; The profit of the wind-storage power station in each scenario is multiplied by the corresponding scenario occurrence probability and then summed to obtain the expected profit of the wind-storage power station in the day-ahead electricity energy market.

8. A bidding curve decision system for the day-ahead electricity market of energy storage power stations considering primary frequency regulation obligations, characterized in that: include: The data acquisition module is used to obtain the forecast curve of wind power station output for the next day, the upper and lower limits of the primary frequency regulation of the wind power station, and the forecast curve of the clearing price of the day-ahead electric energy market; A scenario generation module is used to generate a wind power output scenario set and a day-ahead electric energy market clearing price scenario set based on a wind power output forecast curve and a day-ahead electric energy market clearing price forecast curve, and to reduce the generated scenario set to obtain a reduced wind power output scenario set, a day-ahead electric energy market clearing price scenario set and their corresponding probabilities; The model building module is used to establish a bidding curve decision model for the day-ahead electricity market of wind and storage power stations taking into account the primary frequency regulation obligation. The model contains the objective function and constraints for maximizing the expected total revenue. The objective function considers the day-ahead electricity market revenue, deviation assessment penalty fees and energy storage operation costs. The constraints include primary frequency regulation capacity allocation constraints, wind and storage power station output constraints, wind power output constraints and energy storage operation constraints. The solution calculation module is used to solve the decision model to obtain the bidding curve submitted by the wind power station in the day-ahead electric energy market; based on the obtained bidding curve, the expected profit of the wind power station in the day-ahead electric energy market is calculated.

Images