A method and system for online rolling correction of energy storage operation strategy of new energy station

Abstract

The present invention relates to an online rolling correction method and system for a new energy station energy storage operation strategy, and its main technical features are: constructing a new energy storage aging cost model according to the new energy storage characteristics; constructing a power station profit objective function; constructing a frequency modulation market round mathematical model; constructing a real-time market round mathematical model; constructing a power station total profit model including a frequency modulation market round model, a real-time market round sequence model, and a new energy storage aging cost model; at the operating time τ, inputting various short-term and ultra-short-term forecast information, optimizing the energy storage operation strategy according to the power station total profit model, outputting the energy storage operation strategy; and rolling updating various forecast information. The present invention is reasonably designed, realizes the rolling optimization control function of the new energy storage operation strategy, enhances the availability of new energy power generation and various market information forecast data, and can formulate the best control decision plan for the operation of new energy stations on the basis of reducing the impact of system uncertainty.

Description

Technical Field

[0001] The present invention belongs to the technical field of power generation, and relates to a new energy storage operation strategy, and in particular to an online rolling correction method and system for a new energy station energy storage operation strategy. Background Art

[0002] The main idea of ​​the energy storage operation strategy in traditional new energy stations is to obtain the optimal solution for a future optimization cycle in the initial optimization stage and arrange the charging and discharging power for all future periods at one time. This open-loop control method cannot use the latest (more accurate) prediction information, nor can it flexibly adjust the energy storage control strategy according to the latest situation. Therefore, it is a static decision-making method. The control strategy obtained by the solution may deviate greatly from the actual situation, and lead to economic loss of the power station.

[0003] Under the conditions of the power market, the uncertainty of the external environment has increased significantly. In addition to the uncertainty of the power generation of the new energy station itself, various market information is also uncertain. The open-loop optimization control technology of the new energy station cannot meet the decision-making requirements in this highly uncertain environment. Therefore, it is necessary to develop a real-time (online) rolling optimization scheduling strategy and formulate a dynamic control plan. As the actual operation time of the new energy station approaches, the uncertainty faced by the new energy station will decrease, and various forecast information will become more accurate.

[0004] In summary, in the power market environment, various factors change more frequently, and the traditional energy storage equipment operation strategy has large errors and is difficult to adapt to the needs of the development of the situation. Therefore, how to accurately control the operation of energy storage in new energy stations in the power market environment is an urgent problem that needs to be solved. Summary of the invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a method and system for online rolling correction of energy storage operation strategies of new energy stations with reasonable design and accurate control.

[0006] The present invention solves the existing technical problems by adopting the following technical solutions:

[0007] An online rolling correction method for a new energy station energy storage operation strategy comprises the following steps;

[0008] Step 1: Construct a new energy storage aging cost model based on the characteristics of new energy storage;

[0009] Step 2: Construct the power station profit objective function, make the best decision on the day ahead based on various day-ahead forecast information, optimize the time interval to cover the entire trading day, and use this as the basis for the energy storage operation strategy to optimize the day-ahead decision;

[0010] Step 3: Construct a mathematical model of the frequency modulation market rounds;

[0011] Step 4: Construct a real-time market round mathematical model;

[0012] Step 5: Construct a total profit model of a power station including a frequency regulation market round model, a real-time market round sequence model, and a new energy storage aging cost model;

[0013] Step 6: At runtime τ, input pairs from τ+1 to τ+T RT Based on the various short-term and ultra-short-term forecast information of the time interval, the energy storage status at time τ+1 is calculated according to the total profit model of the power station in step 5, the energy storage operation strategy is optimized, and the energy storage operation strategy at time τ+1 is output;

[0014] Step 7: As the time window moves forward by one period of time, update various types of prediction information and repeat step 6.

[0015] Furthermore, the new energy storage aging cost model includes an aging cost model of energy storage batteries that is sensitive to the number of cycles and an aging cost model of energy storage batteries that is sensitive to the depth of discharge;

[0016] The cycle-number-sensitive energy storage battery aging cost model is:

[0017]

[0018] in, represents the aging cost of energy storage i in period t; k i The slope of the curve representing the effect of the number of cycles of the energy storage battery i on the available energy storage capacity; represents the investment cost of energy storage i; represents the depth of the charging or discharging process;

[0019] The discharge depth-sensitive energy storage battery aging cost model is:

[0020]

[0021] Among them, k p is a constant parameter between 0.8 and 2.1; Indicates the maximum number of cycles at 100% charge and discharge depth (DOD).

[0022] Furthermore, the objective function constructed in step 2 is the total profit of the power station, including real-time market revenue, frequency regulation revenue and aging cost of energy storage.

[0023] Furthermore, the frequency modulation income of the frequency modulation market round mathematical model mainly includes the sum of the income from upward adjustment, downward adjustment and default. The frequency modulation market round mathematical model:

[0024]

[0025] Among them, r RG,τ represents the frequency regulation profit of the new energy station in the time period τ, It indicates the price increase of frequency regulation in period t predicted in period τ; It indicates that the frequency regulation in time period t is predicted to reduce the electricity price in time period τ; It indicates that the default electricity price of frequency regulation increase in period t is predicted in period τ; It means that the default electricity price of frequency regulation reduction in period t is predicted in period τ; It represents the frequency regulation increase capacity of the new energy station in the period t of the decision period τ; It represents the frequency regulation reduction capacity of the new energy station in the period t of the decision period τ; It represents the frequency regulation increase capacity of the new energy station in the period t when the decision is made in the period τ; represents the frequency regulation reduction capacity of the new energy station in the period t when the decision is made during the period τ; T RG Indicates the number of time intervals for the FM market round optimization;

[0026] The constraints of the above FM market round mathematical model include:

[0027] Constraint 1: The real-time power and power of energy storage device i at the new energy station are expressed as:

[0028]

[0029]

[0030]

[0031] represents the real-time power output of energy storage device i at the new energy station in time period t during the decision period τ; represents the real-time charging power of energy storage device i at the new energy station during time period t of the decision period τ; represents the real-time discharge power of energy storage device i of the new energy station in time period t of the decision period τ; represents the storage capacity of energy storage equipment i in the new energy station in time period t of the decision period τ; ∈ represents the duration of each time period; represents the charging efficiency of energy storage device i; represents the discharge efficiency of energy storage device i;

[0032] Constraint 2: The power and power constraints of the energy storage device are:

[0033]

[0034]

[0035]

[0036]

[0037] represents the frequency regulation increase capacity of the energy storage equipment i in the new energy station in the time period t of the decision period τ; represents the frequency regulation reduction capacity of the energy storage equipment i in the new energy station in the time period t of the decision period τ; represents the minimum charging power of energy storage device i; represents the maximum charging power of energy storage device i; represents the minimum discharge power of energy storage device i; represents the maximum discharge power of energy storage device i; E ES,min,i Represents the minimum storage capacity of energy storage device i; E ES,max,i Indicates the maximum storage capacity of energy storage device i; represents the initial storage capacity of energy storage device i in time period τ; Represents the real-time total power output of the energy storage equipment of the new energy station in time period t of the decision period τ;

[0038] Constraint 3: The constraints on energy storage frequency regulation are:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] In the above formula, It represents the real-time power output of the new energy equipment in the new energy station in the time period t of the decision period τ; Represents the real-time total power output of the energy storage equipment of the new energy station in time period t of the decision period τ; It represents the frequency regulation increase capacity actually provided by the new energy station in time period t of the decision period τ; represents the actual frequency regulation down-regulation capacity of the new energy station in period t of the decision period τ; Indicates the maximum delivery capacity of the new energy station; It represents the real-time power output of the new energy equipment in the new energy station in the time period t of the decision period τ; It indicates the time period t in which the renewable energy station abandons wind and solar power in the time period τ; represents the predicted actual power output of the new energy in the prediction period t during the period τ; It represents the frequency regulation reduction capacity of the new energy station in the period t of the decision period τ; It represents the frequency regulation increase capacity actually provided by the new energy station in time period t of the decision period τ; It represents the proportion of frequency regulation increase capacity actually called by the market operation agency in the forecast period t during the period τ; It indicates the proportion of frequency regulation reduction capacity actually called by the market operation agency in the forecast period t during the period τ.

[0049] Furthermore, the income of the real-time market in the real-time market round mathematical model is the sum of the electricity income of the real-time market new energy and the electricity income of the real-time market energy storage. The real-time market round mathematical model is:

[0050]

[0051] Among them, r RT,τ It represents the real-time market profit of the new energy station in the whole day during the decision period τ; represents the real-time electricity price in the prediction period t during the period τ; represents the predicted actual power output of the new energy in the prediction period t during the period τ; Represents the real-time power output of energy storage device i at the new energy station during time period t.

[0052] The constraints of the real-time market round mathematical model are the same as those of the frequency modulation market round model.

[0053] Furthermore, the total profit model of the power station is:

[0054]

[0055] Among them, r τ represents the total profit considering FM and real-time markets, r RG,τ represents the frequency regulation income of the renewable energy station in the time period τ, r RT,τ represents the full-day real-time market revenue of the new energy station in the time period τ, Represents the aging cost of energy storage i in period t.

[0056] Furthermore, the step 6 performs the following operations on the new energy station from τ+1 to τ+T RTThe relevant parameters of the time period are predicted, including the new energy power generation output forecast, frequency regulation electricity price forecast, real-time market electricity price forecast, frequency regulation winning capacity forecast and battery aging cost calculation forecast.

[0057] An online rolling correction system for energy storage operation strategy of a new energy station, comprising:

[0058] New energy storage aging cost model construction module: used to construct a new energy storage aging cost model based on the characteristics of new energy storage. The new energy storage aging cost model includes a cycle-sensitive energy storage battery aging cost model and a discharge depth-sensitive energy storage battery aging cost model.

[0059] Power plant profit objective function construction module: It is used to make the best decision on the day-ahead based on various day-ahead forecast information, optimize the time interval to cover the entire trading day, and use this as the basis for the energy storage operation strategy to optimize the day-ahead decision. The objective function is the total profit of the power plant, including real-time market revenue, frequency regulation revenue, and aging cost of energy storage;

[0060] Frequency modulation market round mathematical model construction module: used to construct a frequency modulation market round mathematical model, which includes the sum of the revenues of upward adjustment, downward adjustment and default;

[0061] Real-time market round mathematical model construction module: used for the real-time market round mathematical model, which includes the sum of the real-time new energy electricity revenue and the real-time energy storage electricity revenue;

[0062] Power station total profit model construction module: used to construct the power station total profit model, which includes three parts: frequency regulation market round model, real-time market round sequence model and new energy storage aging cost model;

[0063] Execution module: for input pairs from τ+1 to τ+T RT Based on the various short-term and ultra-short-term forecast information of the time interval, the energy storage status at time τ+1 is calculated according to the total profit model of the power station, the energy storage operation strategy is optimized, and then the energy storage operation strategy at time τ+1 is output;

[0064] Rolling optimization module: used to continuously update various types of forecast information in each rolling optimization during the execution process.

[0065] The advantages and positive effects of the present invention are:

[0066] 1. The present invention is reasonably designed, realizes the rolling optimization control function of the new energy storage operation strategy, enhances the availability of new energy power generation and various market information forecast data, and can reasonably analyze the operation of new energy stations and formulate the best regulation and decision-making plan on the basis of reducing the impact of system uncertainty.

[0067] 2. The present invention introduces the model predictive control method into the field of active power control of new energy stations, adopts a rolling optimization strategy in the control strategy, replaces the global optimum with local optimization, uses measured information feedback correction to enhance the robustness of the control, and can use the improvement of prediction accuracy to bring additional benefits to the decision-making of new energy stations.

[0068] 3. The present invention can perform rolling multi-step forecasts on the active power of renewable energy power generation and various market information, thereby improving the reliability of forecast data.

[0069] 4. The present invention optimizes the objective function within a limited time domain, implements the solved control value in a rolling manner, and only issues the control command of the next time period to each control device in the station, rather than all time periods, making the control method more flexible.

[0070] 5. The present invention can maintain the optimal (local optimal) operation control target within a certain control time and adapt to the uncertain environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The present invention is a flow chart of the online rolling correction method of the energy storage operation strategy of the new energy station. DETAILED DESCRIPTION

[0072] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings.

[0073] The design concept of the present invention is: in the power market environment, in addition to the uncertainty of power generation of the new energy station itself, various market information is also uncertain. The open-loop new energy station optimization control technology cannot meet the decision-making requirements in this highly uncertain environment. Therefore, it is necessary to develop a real-time (online) rolling optimization scheduling strategy and formulate a dynamic control plan.

[0074] The principle of rolling optimization is: in the strategy formulation period τ, based on the future optimization time interval (from τ+1 to τ+T RT The short-term and ultra-short-term forecast information of the time interval (τ+1) is used to formulate the control strategy for the optimized time interval, but only the control strategy for the τ+1 period is implemented. Next, the time window moves forward by one time period and the next rolling optimization is performed. By continuously updating various forecast information in each rolling optimization, and taking the real-time system status as input.

[0075] Based on the above design concept, the present invention proposes an online rolling correction method for the energy storage operation strategy of a new energy station in the power market environment, such as Figure 1 As shown, the following steps are included:

[0076] Step 1: Construct a new energy storage aging cost model based on the characteristics of new energy storage.

[0077] The aging cost of renewable energy storage is a real-time changing process. Since the aging cost model of renewable energy storage is related to the type of renewable energy storage, the present invention constructs different renewable energy storage cost aging models according to the type of renewable energy storage, and uses them as a component of the revenue of renewable energy stations.

[0078] In this step, the aging cost of the energy storage during operation is determined There are two new energy storage battery aging cost models:

[0079] (1) The cycle-sensitive energy storage battery aging cost model is:

[0080]

[0081] in, represents the aging cost of energy storage i in period t; k i The slope of the curve representing the effect of the number of cycles of the energy storage battery i on the available energy storage capacity; represents the investment cost of energy storage i; Indicates the depth of the charging or discharging process.

[0082] (2) The aging cost model of energy storage batteries that are sensitive to discharge depth is:

[0083]

[0084] Among them, k p is a constant parameter between 0.8 and 2.1; Indicates the maximum number of cycles at 100% charge and discharge depth (DOD).

[0085] Step 2: Construct the power plant profit objective function.

[0086] The objective function is the total profit of the power station, including real-time market revenue, frequency regulation revenue and aging cost of energy storage. In order to simplify the model, the present invention considers that the real-time market revenue includes the revenue of the day-ahead market.

[0087] In this step, the optimal decision for the day-ahead is made based on various types of day-ahead forecast information, and the time interval is optimized to cover the entire trading day. This serves as the basis for the energy storage operation strategy, that is, optimization is performed on the day-ahead decision.

[0088] Step 3: Construct a mathematical model of FM market rounds.

[0089] The frequency regulation revenue mainly includes the sum of the revenue from upward regulation, downward regulation and default. Among them, the constraints include energy storage power and electricity constraints; power grid output constraints on new energy sites; power abandonment constraints, etc.

[0090] Since the impact of the real-time market needs to be considered when executing the FM market round, this step constructs the following FM market round mathematical model:

[0091]

[0092] Among them, r RG,τ represents the frequency regulation profit of the new energy station in the time period τ, It indicates the price increase of frequency regulation in period t predicted in period τ; It indicates that the frequency regulation in time period t is predicted to reduce the electricity price in time period τ; It indicates that the default electricity price of frequency regulation increase in period t is predicted in period τ; It means that the default electricity price of frequency regulation reduction in period t is predicted in period τ; It represents the frequency regulation increase capacity of the new energy station in the period t of the decision period τ; It represents the frequency regulation reduction capacity of the new energy station in the period t of the decision period τ; It represents the frequency regulation increase capacity of the new energy station in the period t when the decision is made in the period τ; represents the frequency regulation reduction capacity of the new energy station in the period t when the decision is made during the period τ; T RG Indicates the number of time intervals for the FM market round optimization.

[0093] The constraints are as follows:

[0094] Constraint 1: The real-time power and power of energy storage device i at the new energy station are expressed as:

[0095]

[0096]

[0097]

[0098] represents the real-time power output of energy storage device i at the new energy station in time period t during the decision period τ; represents the real-time charging power of energy storage device i at the new energy station during time period t of the decision period τ; represents the real-time discharge power of energy storage device i of the new energy station in time period t of the decision period τ; represents the storage capacity of energy storage equipment i in the new energy station in the time period t of the decision period τ; ∈: represents the duration of each time period; represents the charging efficiency of energy storage device i; Represents the discharge efficiency of energy storage device i.

[0099] Constraint 2: The power and power constraints of the energy storage device are:

[0100]

[0101]

[0102]

[0103]

[0104] represents the frequency regulation increase capacity of the energy storage equipment i in the new energy station in the time period t of the decision period τ; represents the frequency regulation reduction capacity of the energy storage equipment i in the new energy station in the time period t of the decision period τ; represents the minimum charging power of energy storage device i; represents the maximum charging power of energy storage device i; represents the minimum discharge power of energy storage device i; represents the maximum discharge power of energy storage device i; E ES,min,i Represents the minimum storage capacity of energy storage device i; E ES,max,i Indicates the maximum storage capacity of energy storage device i; represents the initial storage capacity of energy storage device i in time period τ; Represents the real-time total power output of the energy storage equipment of the new energy station in time period t of the decision period τ.

[0105] Constraint 3: The constraints on energy storage frequency regulation are:

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115] It represents the real-time power output of the new energy equipment in the new energy station in the time period t of the decision period τ; Represents the real-time total power output of the energy storage equipment of the new energy station in time period t of the decision period τ; It represents the frequency regulation increase capacity actually provided by the new energy station in time period t of the decision period τ; represents the actual frequency regulation down-regulation capacity of the new energy station in period t of the decision period τ; Indicates the maximum delivery capacity of the new energy station; It represents the real-time power output of the new energy equipment in the new energy station in the time period t of the decision period τ; It indicates the time period t in which the renewable energy station abandons wind and solar power in the time period τ; represents the predicted actual power output of the new energy in the prediction period t during the period τ; It represents the frequency regulation reduction capacity of the new energy station in the period t of the decision period τ; It represents the frequency regulation increase capacity actually provided by the new energy station in time period t of the decision period τ; It represents the proportion of frequency regulation increase capacity actually called by the market operation agency in the forecast period t during the period τ; It indicates the proportion of frequency regulation reduction capacity actually called by the market operation agency in the forecast period t during the period τ.

[0116] Step 4: Construct a real-time market round mathematical model.

[0117] The income of the real-time market is the sum of the electricity income of the new energy in the real-time market and the electricity income of the energy storage in the real-time market. The constraints are the same as those of the mathematical model of the frequency regulation market round.

[0118] Since the clearing result of the frequency modulation market is a known boundary condition when executing the real-time market round, the mathematical model of the real-time market round constructed in this step is as follows:

[0119]

[0120] Among them, r RT,τ It represents the real-time market profit of the new energy station in the whole day during the decision period τ; represents the real-time electricity price in the prediction period t during the period τ; represents the predicted actual power output of the new energy in the prediction period t during the period τ; It represents the real-time power output of energy storage equipment i at the new energy station in period t. The constraints are the same as those of the frequency regulation market round model.

[0121] Step 5: Establish the total profit model of the power station.

[0122] The total profit (total income) is composed of three parts: real-time market income, frequency regulation market income and energy storage aging cost. That is, the total profit (total income) includes three parts: frequency regulation market round model, real-time market round sequence model and new energy storage aging cost model. For simplicity, this patent classifies the day-ahead market income as real-time market income.

[0123] The mathematical model of the total profit of the power plant established in this step is as follows:

[0124]

[0125] Among them, r τ Represents the total profit considering both FM and real-time markets.

[0126] Step 6: At runtime τ, input pairs from τ+1 to τ+T RT Based on various short-term and ultra-short-term forecast information of the time interval, the state of energy storage at time τ+1 is calculated according to the total profit model of the power station in step 5, the energy storage operation strategy is optimized, and then the energy storage operation strategy at time τ+1 is output.

[0127] In this step, the new energy station is from τ+1 to τ+T RT The relevant parameters of the time period are predicted, including new energy power generation output prediction, frequency regulation electricity price prediction, real-time market electricity price prediction, frequency regulation winning capacity prediction, battery aging cost calculation, etc.

[0128] Step 7: As the time window moves forward by one period of time, update various types of prediction information and repeat step 6.

[0129] In this step, the next rolling optimization is performed as the time window moves forward, by continuously updating various types of prediction information in each rolling optimization and taking the real-time system status as input.

[0130] Based on the above-mentioned online rolling correction method for energy storage operation strategy of new energy station, the present invention also proposes an online rolling correction system for energy storage operation strategy of new energy station in power market environment, including:

[0131] (1) New energy storage aging cost model construction module: used to construct a new energy storage aging cost model based on the characteristics of new energy storage. The new energy storage aging cost model includes a cycle-sensitive energy storage battery aging cost model and a discharge depth-sensitive energy storage battery aging cost model.

[0132] (2) Power plant profit objective function construction module: It is used to make the best decision on the day-ahead based on various types of day-ahead forecast information, optimize the time interval to cover the entire trading day, and use this as the basis for the energy storage operation strategy, that is, optimize the day-ahead decision. The objective function is the total profit of the power plant, including real-time market revenue, frequency regulation revenue, and the aging cost of energy storage.

[0133] (3) FM market round mathematical model construction module: used to construct the FM market round mathematical model, which includes the sum of the income from upward adjustment, downward adjustment and default.

[0134] (4) Real-time market round mathematical model construction module: used for the real-time market round mathematical model, which includes the sum of the real-time new energy electricity revenue and the real-time energy storage electricity revenue.

[0135] (5) Power station total profit model construction module: used to construct the power station total profit model, which includes three parts: frequency regulation market cycle model, real-time market cycle sequence model and new energy storage aging cost model.

[0136] (6) Execution module: used to input the input from τ+1 to τ+T RT Based on various short-term and ultra-short-term forecast information in the time interval, the state of energy storage at time τ+1 is calculated according to the total profit model of the power station, the energy storage operation strategy is optimized, and then the energy storage operation strategy at time τ+1 is output.

[0137] (7) Rolling optimization module: used to continuously update various types of prediction information in each rolling optimization during the execution process.

[0138] It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive. Therefore, the present invention includes but is not limited to the embodiments described in the specific implementation manner. Any other implementation manners derived by those skilled in the art based on the technical solution of the present invention also fall within the scope of protection of the present invention.

Claims

1. A method for online rolling correction of energy storage operation strategy of a new energy station, characterized by: The steps include: Step 1: Construct a new energy storage aging cost model based on the characteristics of new energy storage; Step 2: Construct the power station profit objective function, make the best decision on the day ahead based on various day-ahead forecast information, optimize the time interval to cover the entire trading day, and use this as the basis for the energy storage operation strategy to optimize the day-ahead decision; Step 3: Construct a mathematical model of the frequency modulation market rounds; Step 4: Construct a real-time market round mathematical model; Step 5: Construct a total profit model of a power station including a frequency regulation market round model, a real-time market round sequence model, and a new energy storage aging cost model; Step 6: At runtime τ, input pairs from τ+1 to τ+T RT Based on the various short-term and ultra-short-term forecast information of the time interval, the energy storage status at time τ+1 is calculated according to the total profit model of the power station in step 5, the energy storage operation strategy is optimized, and the energy storage operation strategy at time τ+1 is output; Step 7: As the time window moves forward by one time period, various types of prediction information are updated and step 6 is repeated; The new energy storage aging cost model includes a cycle-number-sensitive energy storage battery aging cost model and a discharge depth-sensitive energy storage battery aging cost model; The cycle-number-sensitive energy storage battery aging cost model is: in, represents the aging cost of energy storage i in period t; k i The slope of the curve representing the effect of the number of cycles of the energy storage battery i on the available energy storage capacity; represents the investment cost of energy storage i; Indicates the depth of the charging or discharging process; The discharge depth-sensitive energy storage battery aging cost model is: Among them, k p is a constant parameter between 0.8 and 2.1; Indicates the maximum number of cycles at 100% charge and discharge depth (DOD); The objective function constructed in step 2 is the total profit of the power station, including real-time market revenue, frequency regulation revenue and aging cost of energy storage; The total profit model of the power station is: Among them, r τ represents the total profit considering FM and real-time markets, r RG,τ represents the frequency regulation income of the renewable energy station in the time period τ, r RT,τ represents the full-day real-time market revenue of the new energy station in the time period τ, Represents the aging cost of energy storage i in period t.

2. According to claim 1, a new energy station energy storage operation strategy online rolling correction method is characterized by: The frequency modulation income of the frequency modulation market round mathematical model mainly includes the sum of the income from upward adjustment, downward adjustment and default. The frequency modulation market round mathematical model: Among them, r RG,τ represents the frequency regulation profit of the new energy station in the time period τ, It indicates the price increase of frequency regulation in period t predicted in period τ; It indicates that the frequency regulation in time period t is predicted to reduce the electricity price in time period τ; It indicates that the default electricity price of frequency regulation increase in period t is predicted in period τ; It means that the default electricity price of frequency regulation reduction in period t is predicted in period τ; It represents the frequency regulation increase capacity of the new energy station in the period t of the decision period τ; It represents the frequency regulation reduction capacity of the new energy station in the period t of the decision period τ; It represents the frequency regulation increase capacity of the new energy station in the period t when the decision is made in the period τ; represents the frequency regulation reduction capacity of the new energy station in the period t when the decision is made during the period τ; T RG Indicates the number of time intervals for the FM market round optimization; The constraints of the above FM market round mathematical model include: Constraint 1: The real-time power and power of energy storage device i at the new energy station are expressed as: represents the real-time power output of energy storage device i of the new energy station in time period t during the decision period τ; represents the real-time charging power of energy storage device i at the new energy station during time period t of the decision period τ; represents the real-time discharge power of energy storage device i of the new energy station in time period t of the decision period τ; represents the storage capacity of energy storage equipment i in the new energy station in time period t of the decision period τ; ∈ represents the duration of each time period; represents the charging efficiency of energy storage device i; represents the discharge efficiency of energy storage device i; Constraint 2: The power and power constraints of the energy storage device are: represents the frequency regulation increase capacity of the energy storage equipment i in the new energy station in the time period t of the decision period τ; represents the frequency regulation reduction capacity of the energy storage equipment i in the new energy station in the time period t of the decision period τ; represents the minimum charging power of energy storage device i; represents the maximum charging power of energy storage device i; represents the minimum discharge power of energy storage device i; represents the maximum discharge power of energy storage device i; E ES,min,i Represents the minimum storage capacity of energy storage device i; E ES,max,i Indicates the maximum storage capacity of energy storage device i; represents the initial storage capacity of energy storage device i in time period τ; Represents the real-time total power output of the energy storage equipment of the new energy station in time period t of the decision period τ; Constraint 3: The constraints on energy storage frequency regulation are: In the above formula, It represents the real-time power output of the new energy equipment in the new energy station in the time period t of the decision period τ; Represents the real-time total power output of the energy storage equipment of the new energy station in time period t of the decision period τ; It represents the frequency regulation increase capacity actually provided by the new energy station in time period t of the decision period τ; represents the actual frequency regulation down-regulation capacity of the new energy station in period t of the period τ decision; Indicates the maximum delivery capacity of the new energy station; It represents the real-time power output of the new energy equipment in the new energy station in the time period t of the decision period τ; It indicates the time period t in which the renewable energy station abandons wind and solar power in the time period τ; represents the predicted actual power output of the new energy in the prediction period t during the period τ; It represents the frequency regulation reduction capacity of the new energy station in the period t of the decision period τ; It represents the frequency regulation increase capacity actually provided by the new energy station in time period t of the decision period τ; It represents the proportion of frequency regulation increase capacity actually called by the market operation agency in the forecast period t during the period τ; It indicates the proportion of frequency regulation reduction capacity actually called by the market operation agency in the forecast period t during the period τ.

3. According to claim 1, a new energy station energy storage operation strategy online rolling correction method is characterized by: The income of the real-time market in the real-time market round mathematical model is the sum of the electricity income of the real-time market new energy and the electricity income of the real-time market energy storage. The real-time market round mathematical model is: Among them, r RT,τ It represents the full-day real-time market profit of the new energy station in the decision period τ; represents the real-time electricity price in the prediction period t during the period τ; represents the predicted actual power output of the new energy in the prediction period t during the period τ; Represents the real-time power output of energy storage device i at the new energy station during time period t; The constraints of the real-time market round mathematical model are the same as those of the frequency modulation market round model.

4. The online rolling correction method for the energy storage operation strategy of a new energy station according to claim 1 is characterized by: Step 6 performs the following operations on the new energy station from τ+1 to τ+T RT The relevant parameters of the time period are predicted, including the new energy power generation output forecast, frequency regulation electricity price forecast, real-time market electricity price forecast, frequency regulation winning capacity forecast and battery aging cost calculation forecast.

5. A system for implementing the online rolling correction method of the energy storage operation strategy of the new energy station according to any one of claims 1 to 4, comprising: New energy storage aging cost model construction module: used to construct a new energy storage aging cost model based on the characteristics of new energy storage. The new energy storage aging cost model includes a cycle-sensitive energy storage battery aging cost model and a discharge depth-sensitive energy storage battery aging cost model. Power plant profit objective function construction module: It is used to make the best decision on the day-ahead based on various day-ahead forecast information, optimize the time interval to cover the entire trading day, and use this as the basis for the energy storage operation strategy to optimize the day-ahead decision. The objective function is the total profit of the power plant, including real-time market revenue, frequency regulation revenue, and aging cost of energy storage; Frequency modulation market round mathematical model construction module: used to construct a frequency modulation market round mathematical model, which includes the sum of the revenues of upward adjustment, downward adjustment and default; Real-time market round mathematical model construction module: used for the real-time market round mathematical model, which includes the sum of the real-time new energy electricity revenue and the real-time energy storage electricity revenue; Power station total profit model construction module: used to construct the power station total profit model, which includes three parts: frequency regulation market round model, real-time market round sequence model and new energy storage aging cost model; Execution module: for input pairs from τ+1 to τ+T RT Based on the various short-term and ultra-short-term forecast information of the time interval, the energy storage status at time τ+1 is calculated according to the total profit model of the power station, the energy storage operation strategy is optimized, and then the energy storage operation strategy at time τ+1 is output; Rolling optimization module: used to continuously update various types of forecast information in each rolling optimization during the execution process.

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