A day-ahead and day-ahead optimization scheduling method for energy storage systems considering participation of optical storage in electricity energy-frequency modulation market

Abstract

This invention relates to a day-ahead-intraday optimal scheduling method for energy storage systems considering photovoltaic (PV) and energy storage participation in the electricity-frequency regulation market. Compared with existing technologies, it addresses the shortcomings of traditional PV-energy storage power plant operation modes, such as low energy storage utilization, long investment cost recovery period, lack of multi-timescale optimization considerations, and neglect of the dynamic impact of nonlinear lifetime losses on operating costs. The invention includes the following steps: generating typical PV output scenarios and operating losses; constructing a day-ahead two-stage stochastic optimal scheduling model; formulating real-time frequency regulation power allocation rules; and day-ahead-intraday multi-timescale optimal scheduling. This invention constructs a day-ahead-intraday optimization model for PV-energy storage combined systems participating in the electricity-frequency regulation market, achieving efficient allocation of energy storage resources between the electricity market and the frequency regulation market, improving the utilization rate of energy storage devices and the economic benefits of PV-energy storage combined power plants. Based on regional control deviations, different regulation intervals are divided, and a dynamic power allocation strategy for energy storage is formulated, enhancing the grid security support capability.

Description

Technical Field

[0001] This invention relates to the field of power system optimization dispatching technology, specifically a day-ahead and intraday optimization dispatching method for energy storage systems that considers the participation of photovoltaic and energy storage in the power-frequency regulation market. Background Technology

[0002] Driven by the ongoing "dual-carbon" strategic goals, the installed capacity of new energy sources, represented by photovoltaics, has experienced explosive growth, and photovoltaic power generation has become an important component of the new power system. However, the inherent randomness and intermittency of photovoltaic power generation also pose serious challenges to the frequency stability and supply-demand balance of the power grid. As an important flexible resource, energy storage has rapid bidirectional adjustment capabilities. Configuring a certain capacity of energy storage for photovoltaic power plants can effectively smooth out power fluctuations and improve the operating profitability of the power plants.

[0003] With the continuous improvement of my country's power market, the new energy green economy under the market model has become an inevitable path for my country's energy economy, and the participation of photovoltaic and energy storage combined power plants in the power market has become a trend. However, the energy storage systems built into traditional photovoltaic power plants are mostly used only for tracking planned output, resulting in low energy storage utilization and long investment cost recovery periods. In addition, existing methods lack multi-timescale collaborative optimization research on the simultaneous participation of photovoltaic and energy storage in the power market and frequency regulation market, and the existing mechanisms for energy storage participation in the frequency regulation market are too idealistic, failing to consider the actual frequency fluctuations of the power grid, rarely considering the impact of regional frequency regulation control strategies on model operation decisions, and often ignoring the dynamic impact of battery nonlinear lifetime loss on operating costs.

[0004] Therefore, how to leverage the flexibility of energy storage to synergistically optimize the returns of photovoltaic and energy storage participation in the spot and ancillary service markets amidst complex market pricing and settlement mechanisms has become a pressing technical challenge. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies, such as low energy storage utilization, long investment cost recovery period, lack of multi-timescale optimization, and neglect of the dynamic impact of nonlinear lifespan loss of energy storage on operating costs in traditional photovoltaic-storage power station operation modes. This invention provides a day-ahead-intraday optimization scheduling method for energy storage systems that considers photovoltaic and energy storage participation in the electricity-frequency regulation market to solve the above problems.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A day-ahead-intraday optimization scheduling method for energy storage systems that considers photovoltaic and energy storage participation in the electricity-frequency regulation market includes the following steps:

[0008] 11) Generation of typical photovoltaic power output scenarios and operating losses: Construct a typical photovoltaic power output scenario generation model to generate typical photovoltaic power output scenarios and probabilities; construct an operating loss cost model for energy storage systems to quantify battery cycle aging losses into operating loss costs.

[0009] 12) Construct a day-ahead two-stage stochastic optimization scheduling model: Based on typical photovoltaic power output scenarios and operating losses, construct a day-ahead two-stage stochastic optimization scheduling model with the goal of maximizing the total daily operating revenue of the photovoltaic-storage integrated system, and obtain the day-ahead electricity market application curve and frequency regulation capacity application plan;

[0010] 13) Formulate real-time frequency regulation power allocation rules: Divide different frequency regulation intervals according to the amplitude characteristics of regional control deviation, formulate real-time frequency regulation power allocation rules for photovoltaic-storage combined power stations, and set SOC feedback constraints for energy storage systems.

[0011] 14) Day-day to intraday multi-timescale optimization scheduling: Construct an intraday rolling optimization model, combine power allocation strategy and SOC feedback constraints, adjust energy storage output in real time to respond to AGC frequency regulation command and track day-day plan, output the power generation plan curve of the photovoltaic-storage joint system participating in the power market and frequency regulation market with optimal economics, and obtain the photovoltaic-storage collaborative optimization strategy that takes into account system economy and grid frequency stability.

[0012] The generation of typical photovoltaic power output scenarios and operating losses includes the following steps:

[0013] 21) Construct a typical photovoltaic power output scenario generation model.

[0014] Assume that photovoltaic power generation follows a normal distribution. ,in To predict photovoltaic power values, To represent the percentage fluctuation in photovoltaic power output, a set of photovoltaic power output scenarios following a probability distribution is generated using the Latin hypercube method. ,

[0015] The basic steps for scene reduction using a fast prior art elimination technique based on probabilistic distance are as follows:

[0016] 211) Set of computing scenarios Each pair of scenes ( , geometric distance The expression is shown in equation (1):

[0017] (1);

[0018] in, The number of time periods during which solar power is generated per day. This represents the photovoltaic power generation value of scenario s at time point i;

[0019] 212) Calculate each scene in the scene set Other scenarios minimum geometric distance And the minimum geometric distance is compared with the scene. Multiply the probabilities of occurrence to obtain the set. ;

[0020] 213) Find the set The minimum value, let the scenario corresponding to the minimum value be... ;

[0021] 214) Select a set of scenes In and Scene Scene with minimum geometric distance , in the context Alternative scenarios and the scene Add the probability to the scene On the probability of eliminating the scene To form a new set of scenes ;

[0022] 215) Determine if the number of remaining scenes meets the requirement; if not, repeat steps 211)-214); if the requirement is met, the scene reduction is complete.

[0023] 22) The Latin hypercube method is used to generate a scene set containing 500 photovoltaic power output scenarios. The scene is then reduced by using a fast prior elimination technique based on probabilistic distance to obtain 5 typical photovoltaic power generation scenarios and their corresponding probability sets P.

[0024] 23) Construct an operating loss cost model for the energy storage system.

[0025] Based on the relationship between the cycle life and the depth of discharge of lithium iron phosphate batteries, the exponential function method is used to fit the data to obtain the relationship between the cycle life and the depth of discharge of the energy storage battery, as shown in equation (2):

[0026] (2),

[0027] in, This indicates the cycle life of the energy storage battery at a given depth of discharge. This refers to the depth of discharge of the energy storage battery. It is a constant;

[0028] Relationship between depth of discharge of energy storage battery and output power of energy storage system Represented as:

[0029] (3),

[0030] in, For the charging and discharging efficiency of the energy storage system; Let t be the energy storage charging and discharging power at time t;

[0031] Operating loss rate of energy storage batteries The expression is:

[0032] (4),

[0033] Unit operating loss cost of energy storage system Represented as:

[0034] (5),

[0035] in, The unit cost of energy storage batteries, For energy storage capacity, This represents the single lifetime loss of energy storage due to charging and discharging operations during the t-th time period.

[0036] The construction of the two-stage stochastic optimization scheduling model includes the following steps:

[0037] 31) Construct a day-ahead optimization model for a photovoltaic-storage power station, with the objective function as follows:

[0038] (6),

[0039] in, For the number of scenes, Let be the probability of the s-th scenario occurring. This represents the system's total operating revenue up to date. To determine the revenue of a photovoltaic-storage integrated system participating in the electricity market under scenario s. To enable energy storage systems to participate in the frequency regulation market and generate revenue in scenario s, The cost of punishing the abandonment of sunlight For energy storage operation losses and costs,

[0040] The specific expression is shown in equation (7):

[0041] (7),

[0042] In the formula: The number of scheduling periods in the previous day. The day-ahead scheduling interval, Contributing to photovoltaic forecasting at time t under the current scenario s. and For energy storage charging and discharging power, and These represent the capacity price and mileage price for frequency modulation, respectively; m is the average mileage. The reported power for frequency modulation during time period t in the current scenario s; The frequency modulation performance factor is expressed as shown in equation (8); Cost of per unit of abandoned light penalty; The amount of light discarded at the current time; The unit operating loss cost of energy storage; The frequency regulation power coefficient represents the energy that the energy storage will charge and release during actual operation for every 1MW of frequency regulation power provided.

[0043] 32) Define the frequency modulation performance factor:

[0044] When the State of Charge (SOC) of the energy storage is around 50%, the frequency regulation performance is good. However, when the SOC approaches the upper or lower limits, the energy storage cannot respond to AGC commands in a timely manner, resulting in poor frequency regulation performance. Therefore, frequency regulation performance constraints are introduced. ,

[0045] (8),

[0046] in, Let t be the state of charge of the stored energy. and These are the maximum and minimum states of charge that the energy storage battery is physically allowed to have; and This serves as the upper and lower bounds of the state of charge for energy storage batteries participating in the frequency regulation market. This is the SOC deviation from the midpoint coefficient, and its value range is usually [0,1].

[0047] (9);

[0048] 33) The constraints for the day-ahead optimization model are set as follows:

[0049] Power balance constraints,

[0050] (10)

[0051] In the formula, The power output of the photovoltaic-storage combined system at time t under the current scenario s is...

[0052] (11),

[0053] In the formula, , These refer to the energy storage charging and discharging efficiencies, respectively. For the installed capacity of energy storage systems; This is the frequency regulation demand factor for the power grid. and These represent the state of charge of the energy storage system at time t and time t-1, respectively, under scenario s.

[0054] (12),

[0055] Energy storage at the start of the operating day's state of charge and state of charge at the end same,

[0056] (13)

[0057] The energy storage charging and discharging power constraints are as follows.

[0058] (14)

[0059] In the formula, These are binary variables introduced to distinguish the charging and discharging states of an energy storage system. =1 indicates that the energy storage system is in a charging state at time t under scenario s. =0 indicates that it is in a discharge state; To ensure the maximum charging and discharging power of the energy storage system, 0-1 integer variables are used to control that the energy storage charging and discharging states cannot occur simultaneously.

[0060] 34) Use a mathematical programming solver to solve the day-ahead optimization model of the photovoltaic-storage power station to obtain the application plan curves of the photovoltaic-storage system in the electricity market and frequency regulation market.

[0061] The process of formulating real-time frequency modulation power allocation rules includes the following steps:

[0062] 41) Historical data of AGC frequency modulation signals in the power market were selected for analysis. The original AGC commands were normalized and their amplitudes were mapped to the [-1,1] interval.

[0063] 42) A Gaussian mixture model is used to fit the probability distribution characteristics of historical data to obtain a probability distribution model;

[0064] 43) Based on the fitted probability distribution model, sampling and reconstruction are performed to obtain the AGC frequency modulation demand signal;

[0065] 44) Obtain the real-time ACE control range through the wide area monitoring system and energy management information system, and divide the ACE control area into four ranges based on the absolute value of ACE and the given static threshold value: emergency regulation zone, secondary emergency regulation zone, normal regulation zone and dead zone.

[0066] The actual frequency regulation output of energy storage is determined based on the ACE range in which the AGC frequency regulation demand signal is located:

[0067] when At this time, the ACE is in the emergency regulation zone. Ensuring grid frequency security is the primary control objective. To restore the system to a safe and stable state as quickly as possible, the dispatch center will force energy storage to regulate at maximum charging and discharging power, without restricting the state of charge of the energy storage.

[0068] (15)

[0069] in, It is the difference between the planned output value and the actual output value of the power grid. Let be the actual power regulation of the energy storage system at time t; ACE1, ACE2, and ACE3 are the thresholds for dividing the frequency regulation interval, with values ​​between [0,1].

[0070] when At this time, ACE is in the secondary emergency adjustment zone, and the energy storage maintains full-power operation. In this state, the energy storage prioritizes tracking the photovoltaic output, adjusting the deviation between the actual and planned photovoltaic output through charging and discharging, while also considering the constraints of the energy storage's own SOC on its maximum charging and discharging power.

[0071] (16);

[0072] when At this time, ACE is in the normal adjustment zone. At this time, the revenue of the photovoltaic and energy storage system is the control target. The dispatch center allocates power according to the frequency regulation output declared by the photovoltaic and energy storage system in the intraday market.

[0073] (17);

[0074] when At that time, the ACE is located in the regulation dead zone, and the frequency regulation power demand in the region is very small. The photovoltaic-storage combined system does not participate in grid regulation.

[0075] (18);

[0076] 45) Design maximum output constraint coefficient for energy storage batteries:

[0077] (1) When the energy storage is in the discharge state, if the energy storage battery has a high state of charge (SOC>70%), the energy storage battery discharges at the original power multiplied by an energy storage discharge regulation coefficient less than 1. The energy storage discharge regulation coefficient decreases as the state of charge decreases. When the energy storage state of charge is less than 0.7, the energy storage discharges at the original power.

[0078] (2) When the energy storage is in the charging state, if the energy storage battery has a low state of charge (SOC < 30%), the energy storage battery is charged by multiplying the original power by an energy storage charging regulation coefficient less than 1. The energy storage charging regulation coefficient decreases as the state of charge decreases. When the energy storage state of charge is greater than 0.3, the energy storage is discharged according to the original power.

[0079] In the discharge state, there is

[0080] (19)

[0081] in, The energy storage discharge regulation coefficient is denoted by SOC, which represents the energy storage state of charge.

[0082] In the charging state, there is

[0083] (20)

[0084] in, The energy storage charging regulation coefficient;

[0085] final operating power of energy storage battery The relationship with the state of charge constraint is as follows

[0086] (twenty one),

[0087] in, This represents the actual operating power of the energy storage system during the day.

[0088] The day-to-day multi-timescale optimization scheduling includes the following steps:

[0089] 51) Construct an intraday optimization model, the objective function of which is shown in the following formula:

[0090] ,(twenty two)

[0091] In the formula, The total daily operating revenue of the photovoltaic and energy storage system. To determine the daily revenue of a photovoltaic-storage integrated system participating in the electricity market under scenario s, To determine the daily revenue of energy storage systems participating in the frequency regulation market under scenario s, The daily energy storage operation loss cost of the energy storage system under scenario s. Cost of curtailment within the day The cost of assessing the deviation of the photovoltaic-storage system from the daily planned curve tracking under scenario s;

[0092] 52) Calculate the daily deviation assessment cost of the photovoltaic-storage system. The expression is shown in equation (25):

[0093] (twenty three),

[0094] (twenty four),

[0095] (25)

[0096] In the formula, Number of time periods within a day This refers to the electricity sales power of the photovoltaic and energy storage system at any given time during the day. The cost of deviation assessment at time t. and These are the lower and upper bounds of the scope of the tracking and assessment, respectively. This refers to the electricity price cost per unit of energy exceeding the assessment threshold. The maximum deviation stipulated in the performance evaluation regulations for the dispatching department. The power sold by the photovoltaic-storage integrated system at time t before the date;

[0097] 53) Based on the actual fluctuations in the power grid frequency and the output reported by the photovoltaic and energy storage system in the frequency regulation market, issue AGC frequency regulation commands;

[0098] Considering that real-time AGC frequency modulation commands can cause changes in the energy storage capacity and power, thus affecting the initial state of the photovoltaic-storage system's subsequent market participation, adjustments are made to account for uncertainties after each rolling window ends.

[0099] (26)

[0100] (27)

[0101] In the formula: This is the first revised energy storage operating power; The actual daily operating power of the energy storage system, and the actual power adjustment of the photovoltaic-storage system. It is obtained from the frequency modulation power allocation rules of the photovoltaic energy storage system;

[0102] 54) The mathematical programming solver is used to solve the intraday optimization model and output the power generation plan curve of the photovoltaic-storage joint system participating in the electricity market and frequency regulation market, which is the most economically optimal. Thus, the photovoltaic-storage synergistic optimization strategy that takes into account both system economy and grid frequency stability is obtained.

[0103] A computer-readable storage medium storing a computer program that, when executed by a processor, enables a day-ahead-intraday optimized scheduling method for energy storage systems that considers the participation of photovoltaic and energy storage in the electricity-frequency regulation market.

[0104] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, which, when executed by the processor, enables a day-ahead-intraday optimized scheduling method for energy storage systems that takes into account the participation of photovoltaic and energy storage in the electricity-frequency regulation market.

[0105] Beneficial effects

[0106] This invention provides a day-ahead-intraday optimization scheduling method for energy storage systems that considers photovoltaic (PV) and energy storage participation in the electricity-frequency regulation market. Compared with existing technologies, this method constructs a day-ahead-intraday optimization model for PV-energy storage joint systems participating in the electricity-frequency regulation market, achieving efficient allocation of energy storage resources between the electricity market and the frequency regulation market. This improves the utilization rate of energy storage equipment and the economic benefits of PV-energy storage joint power plants. Based on regional control deviations, different regulation intervals are divided, and a dynamic power allocation strategy for energy storage is formulated, thereby enhancing the grid security support capability.

[0107] This invention provides a multi-timescale optimization method that balances grid frequency security and the benefits of photovoltaic-storage power plants, and achieves optimal resource allocation through multi-timescale collaborative decision-making to improve the overall profitability of photovoltaic-storage systems in the electricity and frequency regulation markets.

[0108] The present invention has the following advantages:

[0109] First, the present invention divides the frequency regulation range based on the regional control deviation (ACE) and formulates a dynamic energy storage power allocation strategy, which ensures rapid power support during emergency periods and effectively balances the grid frequency regulation requirements with operational economy.

[0110] Second, this invention introduces a variable power charge and discharge constraint and an electrochemical energy storage lifetime decay model based on the state of charge (SOC), which effectively prevents battery overcharging and over-discharging and improves the lifespan of energy storage.

[0111] Third, this invention constructs a multi-timescale collaborative rolling optimization framework that takes into account photovoltaic uncertainties. By formulating multi-market application plans before the day and correcting forecast deviations in real time during the day, it effectively reduces the penalty for power deviation assessment and realizes the optimal resource allocation and maximum comprehensive benefits of photovoltaic-storage systems in the power and frequency regulation markets. Attached Figure Description

[0112] Figure 1 This is a sequence diagram of the method of the present invention;

[0113] Figure 2 This is a diagram showing the optimization results for scenario 1 involved in this invention;

[0114] Figure 3 This is a diagram showing the optimization results for scenario 2 involved in this invention. Detailed Implementation

[0115] To provide a better understanding of the structural features and effects achieved by the present invention, a detailed description is provided below, accompanied by preferred embodiments and accompanying drawings:

[0116] The technical challenge of this invention lies in simultaneously considering the uncertainty of photovoltaic output, the nonlinear lifetime loss of energy storage, and the power allocation of the photovoltaic-energy storage integrated system in both the electricity market and the frequency regulation market, while achieving coordinated optimization at both the day-ahead and intraday time scales. This results in high complexity in model construction and solution. This invention considers the simultaneous participation of the photovoltaic-energy storage integrated system in both the electricity market and the frequency regulation market, constructs a day-ahead-intraday multi-time-scale optimization model, and proposes a frequency regulation zoning control strategy and SOC feedback constraint regulation of energy storage output. This enables coordinated and optimized allocation of the photovoltaic-energy storage integrated system in both the electricity market and the frequency regulation market, which is beneficial for improving energy storage utilization, the operating benefits of the photovoltaic-energy storage integrated system, and grid frequency security.

[0117] like Figure 1 As shown, the present invention provides a day-ahead-intraday optimization scheduling method for energy storage systems that considers photovoltaic and energy storage participation in the electricity-frequency regulation market, comprising the following steps:

[0118] The first step is to generate typical photovoltaic (PV) output scenarios and operational losses. To accurately depict the uncertainty of PV output and the nonlinear lifetime loss of energy storage, firstly, a typical PV output scenario generation model is constructed to generate typical PV output scenarios and their corresponding probabilities; secondly, an operational loss cost model for the energy storage system is constructed to quantify the battery cycle aging loss into operational loss costs.

[0119] (1) Construct a typical photovoltaic power output scenario generation model,

[0120] Assume that photovoltaic power generation follows a normal distribution. ,in To predict photovoltaic power values, To represent the percentage fluctuation in photovoltaic power output, a set of photovoltaic power output scenarios following a probability distribution is generated using the Latin hypercube method. ,

[0121] The basic steps for scene reduction using a fast prior art elimination technique based on probabilistic distance are as follows:

[0122] A1 Computing Scenario Collection Each pair of scenes ( , geometric distance The expression is shown in equation (1):

[0123] (1);

[0124] in, The number of time periods during which solar power is generated per day. This represents the photovoltaic power generation value of scenario s at time point i;

[0125] A2) Calculate each scenario in the scenario set Other scenarios minimum geometric distance And the minimum geometric distance is compared with the scene. Multiply the probabilities of occurrence to obtain the set. ;

[0126] A3) Find the set The minimum value, let the scenario corresponding to the minimum value be... ;

[0127] A4) Select the scene set In and Scene Scene with minimum geometric distance , in the context Alternative scenarios and the scene Add the probability to the scene On the probability of eliminating the scene To form a new set of scenes ;

[0128] A5) Determine if the number of remaining scenes meets the requirement; if not, repeat steps 211)-214); if the requirement is met, the scene reduction is complete.

[0129] (2) A scene set containing 500 photovoltaic power output scenarios is generated by using the Latin hypercube method. The scene is reduced by using the fast prior elimination technique based on probability distance to obtain 5 typical photovoltaic power generation scenarios and their corresponding probability sets P.

[0130] (3) Construct an energy storage system operation loss cost model.

[0131] Based on the relationship between the cycle life and the depth of discharge of lithium iron phosphate batteries, the exponential function method is used to fit the data to obtain the relationship between the cycle life and the depth of discharge of the energy storage battery, as shown in equation (2):

[0132] (2),

[0133] in, This indicates the cycle life of the energy storage battery at a given depth of discharge. This refers to the depth of discharge of the energy storage battery. It is a constant;

[0134] Relationship between depth of discharge of energy storage battery and output power of energy storage system Represented as:

[0135] (3),

[0136] in, For the charging and discharging efficiency of the energy storage system; Let t be the energy storage charging and discharging power at time t;

[0137] Operating loss rate of energy storage batteries The expression is:

[0138] (4),

[0139] Unit operating loss cost of energy storage system Represented as:

[0140] (5),

[0141] in, The unit cost of energy storage batteries, For energy storage capacity, This represents the single-cycle lifespan loss of energy storage due to charging and discharging operations during the t-th time period.

[0142] The second step involves constructing a day-ahead two-stage stochastic optimization scheduling model: Since the photovoltaic-storage system needs to participate in both the electricity market and the frequency regulation market simultaneously, the allocation of the energy storage system between the two is interdependent and difficult to decide independently. To coordinate and determine the optimal electricity sales and frequency regulation application schemes during the day-ahead phase, a day-ahead two-stage stochastic optimization scheduling model is constructed based on typical photovoltaic output scenarios and an energy storage operation loss cost model. This model aims to maximize the total daily operating revenue of the photovoltaic-storage combined system, resulting in the day-ahead electricity market application curve and the frequency regulation capacity application plan.

[0143] (1) Construct a day-ahead optimization model for photovoltaic-storage power plants, with the objective function as follows:

[0144] (6),

[0145] in, For the number of scenes, Let be the probability of the s-th scenario occurring. This represents the system's total operating revenue up to date. To determine the revenue of a photovoltaic-storage integrated system participating in the electricity market under scenario s. To enable energy storage systems to participate in the frequency regulation market and generate revenue in scenario s, The cost of punishing the abandonment of sunlight For energy storage operation losses and costs,

[0146] The specific expression is shown in equation (7):

[0147] (7),

[0148] In the formula: The number of scheduling periods in the previous day. The day-ahead scheduling interval, Contributing to photovoltaic forecasting at time t under the current scenario s. and For energy storage charging and discharging power, and These represent the capacity price and mileage price for frequency modulation, respectively; m is the average mileage. The reported power for frequency modulation during time period t in the current scenario s; The frequency modulation performance factor is expressed as shown in equation (8); Cost of per unit of abandoned light penalty; The amount of light discarded at the current time; The unit operating loss cost of energy storage; The frequency regulation power coefficient represents the energy that the energy storage will charge and release during actual operation for every 1MW of frequency regulation power provided.

[0149] (2) Define the frequency modulation performance factor:

[0150] When the State of Charge (SOC) of the energy storage is around 50%, the frequency regulation performance is good. However, when the SOC approaches the upper or lower limits, the energy storage cannot respond to AGC commands in a timely manner, resulting in poor frequency regulation performance. Therefore, frequency regulation performance constraints are introduced. ,

[0151] (8),

[0152] in, Let t be the state of charge of the stored energy. and These are the maximum and minimum states of charge that the energy storage battery is physically allowed to have; and This serves as the upper and lower bounds of the state of charge for energy storage batteries participating in the frequency regulation market. This is the SOC deviation from the midpoint coefficient, and its value range is usually [0,1].

[0153] (9).

[0154] (3) The constraints of the day-ahead optimization model are set as follows:

[0155] Power balance constraints,

[0156] (10)

[0157] In the formula, The power output of the photovoltaic-storage combined system at time t under the current scenario s is...

[0158] (11),

[0159] In the formula, and These are the energy storage charging and discharging efficiencies, respectively. For the installed capacity of energy storage systems; This is the frequency regulation demand factor for the power grid. and These represent the state of charge of the energy storage system at time t and time t-1, respectively, under scenario s.

[0160] (12),

[0161] Energy storage at the start of the operating day's state of charge and state of charge at the end same,

[0162] (13)

[0163] The energy storage charging and discharging power constraints are as follows.

[0164] (14)

[0165] In the formula, These are binary variables introduced to distinguish the charging and discharging states of an energy storage system. =1 indicates that the energy storage system is in a charging state at time t under scenario s. =0 indicates that it is in a discharge state; To ensure the maximum charging and discharging power of the energy storage system, 0-1 integer variables are used to control that the energy storage charging and discharging states cannot occur simultaneously.

[0166] (4) Use a mathematical programming solver to solve the day-ahead optimization model of the photovoltaic-storage power station to obtain the application plan curves of the photovoltaic-storage system in the electricity market and frequency regulation market.

[0167] The third step is to formulate real-time frequency regulation power allocation rules: Real-time frequency regulation commands from the power grid are highly random. Energy storage needs to strike a balance between rapidly responding to grid commands and maintaining its own State of Charge (SOC) safety, requiring appropriate control strategies. To ensure that energy storage can respond promptly to urgent frequency regulation needs from the grid, while preventing overcharging and over-discharging of energy storage and thus protecting battery life, different frequency regulation zones are divided based on the amplitude characteristics of regional control deviations. Real-time frequency regulation power allocation rules for photovoltaic-energy storage combined power plants are then formulated, and SOC feedback constraints for the energy storage system are set.

[0168] (1) Historical data of AGC frequency modulation signal of a certain power market were selected for analysis. The original AGC command was normalized and its amplitude was mapped to the range of [-1,1].

[0169] (2) The probability distribution characteristics of historical data are fitted using a Gaussian mixture model to obtain the probability distribution model.

[0170] (3) Based on the fitted probability distribution model, sampling and reconstruction are performed to obtain the AGC frequency modulation demand signal.

[0171] (4) Obtain the real-time ACE control range through the wide area monitoring system and energy management information system, and divide the ACE control area into four ranges based on the absolute value of ACE and the given static threshold value: emergency regulation zone, secondary emergency regulation zone, normal regulation zone and dead zone.

[0172] The actual frequency regulation output of energy storage is determined based on the ACE range in which the AGC frequency regulation demand signal is located:

[0173] when At this time, the ACE is in the emergency regulation zone. Ensuring grid frequency security is the primary control objective. To restore the system to a safe and stable state as quickly as possible, the dispatch center will force energy storage to regulate at maximum charging and discharging power, without restricting the state of charge of the energy storage.

[0174] (15)

[0175] in, It is the difference between the planned output value and the actual output value of the power grid. Let be the actual power regulation of the energy storage system at time t; ACE1, ACE2, and ACE3 are the thresholds for dividing the frequency regulation interval, with values ​​between [0,1].

[0176] when At this time, ACE is in the secondary emergency adjustment zone, and the energy storage maintains full-power operation. In this state, the energy storage prioritizes tracking the photovoltaic output, adjusting the deviation between the actual and planned photovoltaic output through charging and discharging, while also considering the constraints of the energy storage's own SOC on its maximum charging and discharging power.

[0177] (16);

[0178] when At this time, ACE is in the normal adjustment zone. At this time, the revenue of the photovoltaic and energy storage system is the control target. The dispatch center allocates power according to the frequency regulation output declared by the photovoltaic and energy storage system in the intraday market.

[0179] (17);

[0180] when At that time, the ACE is located in the regulation dead zone, and the frequency regulation power demand in the region is very small. The photovoltaic-storage combined system does not participate in grid regulation.

[0181] (18).

[0182] (5) Maximum output constraint coefficient of the energy storage battery:

[0183] When the energy storage is in a discharge state, if the energy storage battery has a high state of charge (SOC>70%), the energy storage battery discharges at its original power multiplied by an energy storage discharge regulation coefficient less than 1. The energy storage discharge regulation coefficient decreases as the state of charge decreases. When the energy storage state of charge is less than 0.7, the energy storage discharges at its original power.

[0184] When the energy storage is in the charging state, if the energy storage battery has a low state of charge (SOC < 30%), the energy storage battery is charged by multiplying the original power by an energy storage charging regulation coefficient less than 1. The energy storage charging regulation coefficient decreases as the state of charge decreases. When the energy storage state of charge is greater than 0.3, the energy storage is discharged at the original power.

[0185] In the discharge state, there is

[0186] (19)

[0187] in, The energy storage discharge regulation coefficient is denoted by SOC, which represents the energy storage state of charge.

[0188] In the charging state, there is

[0189] (20)

[0190] in, The energy storage charging regulation coefficient;

[0191] final operating power of energy storage battery The relationship with the state of charge constraint is as follows

[0192] (twenty one),

[0193] in, This represents the actual operating power of the energy storage system during the day.

[0194] The fourth step is multi-timescale optimization scheduling from day-ahead to intraday. During intraday operation, the difference between actual photovoltaic output and predicted values, and the continuous changes in AGC commands that alter the energy storage SOC state, make it difficult to execute the day-ahead plan according to the original scheme. To promptly correct actual output deviations and improve the overall operational benefits of the photovoltaic-energy storage system, an intraday rolling optimization model is constructed. Combining power allocation strategies and SOC feedback constraints, the energy storage output is adjusted in real time to respond to AGC frequency regulation commands and track the day-ahead plan. This outputs the optimal power generation plan curve for the photovoltaic-energy storage joint system to participate in the electricity market and frequency regulation market, resulting in a photovoltaic-energy storage collaborative optimization strategy that balances system economics and grid frequency stability.

[0195] (1) Construct an intraday optimization model, the objective function of which is shown in the following equation:

[0196] ,(twenty two)

[0197] In the formula, The total daily operating revenue of the photovoltaic and energy storage system. To determine the daily revenue of a photovoltaic-storage integrated system participating in the electricity market under scenario s, To determine the daily revenue of energy storage systems participating in the frequency regulation market under scenario s, The daily energy storage operation loss cost of the energy storage system under scenario s. Cost of curtailment within the day The cost of assessing the deviation of the daily tracking plan curve for the photovoltaic-storage system under scenario s.

[0198] The specific formula expression is the same as that of equation (7), except for the different time scale.

[0199] (2) Calculate the daily deviation assessment cost of the photovoltaic-storage system The expression is shown in equation (25):

[0200] (twenty three),

[0201] (twenty four),

[0202] (25)

[0203] In the formula, Number of time periods within a day This refers to the electricity sales power of the photovoltaic and energy storage system at any given time during the day. The cost of deviation assessment at time t. and These are the lower and upper bounds of the scope of the tracking and assessment, respectively. This refers to the electricity price cost per unit of energy exceeding the assessment threshold. The maximum deviation stipulated in the performance evaluation regulations for the dispatching department. The power sold by the photovoltaic-storage integrated system at time t is the power output of the system.

[0204] (3) Issue AGC frequency regulation commands based on the actual fluctuation of the power grid frequency and the output reported by the photovoltaic and energy storage system in the frequency regulation market;

[0205] Considering that real-time AGC frequency modulation commands can cause changes in the energy storage capacity and power, thus affecting the initial state of the photovoltaic-storage system's subsequent market participation, adjustments are made to account for uncertainties after each rolling window ends.

[0206] (26)

[0207] (27)

[0208] In the formula: This is the first revised energy storage operating power; The actual daily operating power of the energy storage system, and the actual power adjustment of the photovoltaic-storage system. It is derived from the frequency modulation power allocation rules of the photovoltaic energy storage system.

[0209] (4) The mathematical programming solver is used to solve the intraday optimization model and output the power generation plan curve of the photovoltaic-storage joint system participating in the electricity market and frequency regulation market, thus obtaining the photovoltaic-storage synergistic optimization strategy that takes into account both system economy and grid frequency stability.

[0210] To verify the effectiveness of the proposed operating strategy, two scenarios were set up to compare the optimization results, as follows.

[0211] Scenario 1: Photovoltaics and energy storage jointly participate in the electricity market and frequency regulation market, and determine the actual frequency regulation output of energy storage based on the AGC zoning control strategy;

[0212] Scenario 2: Photovoltaic and energy storage systems jointly participate in the electricity market, but do not participate in the frequency regulation market.

[0213] The optimization results for the two scenarios are as follows: Figure 2 and Figure 3As shown, observing the optimization results of Scenario 1 reveals that during the periods of 0:00-6:00 and 19:00-0:00 the next day, photovoltaic power is not generating electricity, and energy storage cannot engage in peak-valley arbitrage. Therefore, energy storage can obtain additional revenue by investing in the frequency regulation market and declaring frequency regulation capacity. In Scenario 2, the photovoltaic-energy storage power station only participates in the electricity market, and energy storage remains inactive, with the SOC unchanged. During the period of 6:00-7:00, photovoltaic power begins to generate electricity, and the electricity price is relatively low. Energy storage uses some of its capacity for charging to prepare for selling electricity at higher prices later, and the declared frequency regulation capacity is reduced. During the period of 10:00-2:00, the electricity price fluctuates significantly, and energy storage reduces its declared frequency regulation capacity, using the capacity for peak-valley arbitrage in the electricity market. During the period of 2:00-7:00, since the electricity price does not fluctuate much, the optimizer for Scenario 1 judges that the revenue from energy storage participating in frequency regulation is greater than that from energy storage participating in peak-valley arbitrage, so it chooses to declare a large amount of frequency regulation capacity. In Scenario 2, since it only participates in the electricity market, energy storage is used entirely for peak-valley arbitrage during the 7:00-17:00 period. For example... Figure 2 and Figure 3 As shown, scenario 1 has a higher energy storage utilization rate and greater flexibility and economy compared to scenario 2.

[0214] Table 1 shows the power plant revenue under different scenarios. For scenario 2, the photovoltaic-storage combined power plant only participates in the electricity market, and the energy storage is used entirely for peak-valley arbitrage. Therefore, the electricity market revenue is the highest among the three schemes. Since the energy storage does not need to respond to high-frequency AGC frequency regulation signals, the energy storage aging cost is low. However, the revenue from the frequency regulation market is forfeited, resulting in low energy storage utilization and a total revenue of only 95,075 yuan. For scenario 1, since photovoltaic and energy storage participate in both the electricity market and the frequency regulation market, the energy storage chooses to participate in the frequency regulation market during idle periods and adopts a frequency regulation zone control strategy. The energy storage does not perform frequency regulation in dead zones and performs full-power frequency regulation in emergency regulation zones, reducing the frequent operation of the energy storage and improving the grid frequency support. More frequency regulation market revenue is obtained with a smaller energy storage aging cost, and the total revenue reaches 107,477 yuan, which is significantly higher than that of scenario 2. It can be seen that the method proposed in this invention significantly improves the economic efficiency of system operation and the safety of the grid, and improves the utilization rate of energy storage equipment.

[0215] Table 1. Revenue of photovoltaic-storage combined power plants under different scenarios

[0216]

[0217] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A day-ahead-intraday optimized scheduling method for energy storage systems considering photovoltaic and energy storage participation in the electricity-frequency regulation market, characterized in that, Includes the following steps: 11) Generation of typical photovoltaic power output scenarios and operating losses: Construct a typical photovoltaic power output scenario generation model to generate typical photovoltaic power output scenarios and probabilities; construct an operating loss cost model for energy storage systems to quantify battery cycle aging losses into operating loss costs. 12) Construct a day-ahead two-stage stochastic optimization scheduling model: Based on typical photovoltaic power output scenarios and operating losses, construct a day-ahead two-stage stochastic optimization scheduling model with the goal of maximizing the total daily operating revenue of the photovoltaic-storage integrated system, and obtain the day-ahead electricity market application curve and frequency regulation capacity application plan; 13) Formulate real-time frequency regulation power allocation rules: Divide different frequency regulation intervals according to the amplitude characteristics of regional control deviation, formulate real-time frequency regulation power allocation rules for photovoltaic-storage combined power stations, and set SOC feedback constraints for energy storage systems. 14) Day-day to intraday multi-timescale optimization scheduling: Construct an intraday rolling optimization model, combine power allocation strategy and SOC feedback constraints, adjust energy storage output in real time to respond to AGC frequency regulation command and track day-day plan, output the power generation plan curve of the photovoltaic-storage joint system participating in the power market and frequency regulation market with optimal economics, and obtain the photovoltaic-storage collaborative optimization strategy that takes into account system economy and grid frequency stability.

2. The day-ahead-intraday optimization scheduling method for energy storage systems considering photovoltaic and energy storage participation in the electricity-frequency regulation market, as described in claim 1, is characterized in that... The generation of typical photovoltaic power output scenarios and operating losses includes the following steps: 21) Construct a typical photovoltaic power output scenario generation model. Assume that photovoltaic power generation follows a normal distribution. ,in To predict photovoltaic power values, To represent the percentage fluctuation in photovoltaic power output, a set of photovoltaic power output scenarios following a probability distribution is generated using the Latin hypercube method. , The basic steps for scene reduction using a fast prior art elimination technique based on probabilistic distance are as follows: 211) Set of computational scenarios Each pair of scenes ( , geometric distance The expression is shown in equation (1): （1）； in, The number of time periods during which solar power is generated per day. This represents the photovoltaic power generation value of scenario s at time point i; 212) Calculate each scene in the scene set Other scenarios minimum geometric distance And the minimum geometric distance is compared with the scene. Multiply the probabilities of occurrence to obtain the set. ; 213) Find the set The minimum value, let the scenario corresponding to the minimum value be... ; 214) Select a set of scenes In and Scene Scene with minimum geometric distance , in the context Alternative scenarios and the scene Add the probability to the scene On the probability of eliminating the scene To form a new set of scenes ; 215) Determine if the number of remaining scenes meets the requirement; if not, repeat steps 211)-214); if the requirement is met, the scene reduction is complete. 22) The Latin hypercube method is used to generate a scene set containing 500 photovoltaic power output scenarios. The scene is then reduced by using a fast prior elimination technique based on probabilistic distance to obtain 5 typical photovoltaic power generation scenarios and their corresponding probability sets P. 23) Construct an operating loss cost model for the energy storage system. Based on the relationship between the cycle life and the depth of discharge of lithium iron phosphate batteries, the exponential function method is used to fit the data to obtain the relationship between the cycle life and the depth of discharge of the energy storage battery, as shown in equation (2): （2）， in, This indicates the cycle life of the energy storage battery at a given depth of discharge. The depth of discharge of the energy storage battery. It is a constant; Relationship between depth of discharge of energy storage battery and output power of energy storage system Represented as: （3）， in, For the charging and discharging efficiency of the energy storage system; Let t be the energy storage charging and discharging power at time t; Operating loss rate of energy storage batteries The expression is: （4）， Unit operating loss cost of energy storage system Represented as: （5）， in, The unit cost of energy storage batteries, For energy storage capacity, This represents the single lifetime loss of energy storage due to charging and discharging operations during the t-th time period.

3. The day-ahead-intraday optimization scheduling method for energy storage systems considering photovoltaic and energy storage participation in the electricity-frequency regulation market, as described in claim 2, is characterized in that... The construction of the two-stage stochastic optimization scheduling model includes the following steps: 31) Construct a day-ahead optimization model for a photovoltaic-storage power station, with the objective function as follows: （6）， in, For the number of scenes, Let be the probability of the s-th scenario occurring. This represents the system's total operating revenue up to date. To determine the revenue of a photovoltaic-storage integrated system participating in the electricity market under scenario s. To enable energy storage systems to participate in the frequency regulation market and generate revenue in scenario s, The cost of punishing the abandonment of sunlight For energy storage operation losses and costs, The specific expression is shown in equation (7): （7）， In the formula: The number of scheduling periods in the previous day. The day-ahead scheduling interval, Contributing to photovoltaic forecasting at time t under the current scenario s. and For energy storage charging and discharging power, and These represent the capacity price and mileage price for frequency modulation, respectively; m is the average mileage. The reported power for frequency modulation during time period t under the scenario s; The frequency modulation performance factor is expressed as shown in equation (8); Cost of per unit of abandoned light penalty; The amount of light discarded at the current time; The unit operating loss cost of energy storage; The frequency regulation power coefficient represents the energy that the energy storage will charge and release during actual operation for every 1MW of frequency regulation power provided. 32) Define the frequency modulation performance factor: When the State of Charge (SOC) of the energy storage is around 50%, the frequency regulation performance is good. However, when the SOC approaches the upper or lower limits, the energy storage cannot respond to AGC commands in a timely manner, resulting in poor frequency regulation performance. Therefore, frequency regulation performance constraints are introduced. , （8）， in, Let t be the state of charge of the stored energy. and These are the maximum and minimum states of charge that the energy storage battery is physically allowed to have; and This serves as the upper and lower bounds of the state of charge for energy storage batteries participating in the frequency regulation market. This is the SOC deviation from the midpoint coefficient, and its value range is usually [0,1]. （9）； 33) The constraints for the day-ahead optimization model are set as follows: Power balance constraints, （10）， In the formula, The power output of the photovoltaic-storage integrated system at time t under the current scenario s is... （11）， In the formula, , These refer to the energy storage charging and discharging efficiencies, respectively. For the installed capacity of energy storage systems; This is the frequency regulation demand factor for the power grid. and These represent the state of charge of the energy storage system at time t and time t-1, respectively, under scenario s. （12）， Energy storage at the start of the operating day's state of charge and state of charge at the end same, （13）， The energy storage charging and discharging power constraints are as follows. （14）， In the formula, These are binary variables introduced to distinguish the charging and discharging states of an energy storage system. =1 indicates that the energy storage system is in a charging state at time t under scenario s. =0 indicates that it is in a discharge state; To ensure the maximum charging and discharging power of the energy storage system, 0-1 integer variables are used to control that the energy storage charging and discharging states cannot occur simultaneously. 34) Use a mathematical programming solver to solve the day-ahead optimization model of the photovoltaic-storage power station to obtain the application plan curves of the photovoltaic-storage system in the electricity market and frequency regulation market.

4. The day-ahead-intraday optimization scheduling method for energy storage systems considering photovoltaic and energy storage participation in the electricity-frequency regulation market, as described in claim 3, is characterized in that... The process of formulating real-time frequency modulation power allocation rules includes the following steps: 41) Historical data of AGC frequency modulation signals in the power market were selected for analysis. The original AGC commands were normalized and their amplitudes were mapped to the [-1,1] interval. 42) A Gaussian mixture model is used to fit the probability distribution characteristics of historical data to obtain a probability distribution model; 43) Based on the fitted probability distribution model, sampling and reconstruction are performed to obtain the AGC frequency modulation demand signal; 44) Obtain the real-time ACE control range through the wide area monitoring system and energy management information system, and divide the ACE control area into four ranges based on the absolute value of ACE and the given static threshold value: emergency regulation zone, secondary emergency regulation zone, normal regulation zone and dead zone. The actual frequency regulation output of energy storage is determined based on the ACE range in which the AGC frequency regulation demand signal is located: when At this time, the ACE is in the emergency regulation zone. Ensuring grid frequency security is the primary control objective. To restore the system to a safe and stable state as quickly as possible, the dispatch center will force energy storage to regulate at maximum charging and discharging power, without restricting the state of charge of the energy storage. （15）， in, It is the difference between the planned output value and the actual output value of the power grid. Let be the actual power regulation of the energy storage system at time t; ACE1, ACE2, and ACE3 are the thresholds for dividing the frequency regulation interval, with values ​​between [0,1]. when At this time, ACE is in the secondary emergency adjustment zone, and the energy storage maintains full-power operation. In this state, the energy storage prioritizes tracking the photovoltaic output, adjusting the deviation between the actual and planned photovoltaic output through charging and discharging, while also considering the constraints of the energy storage's own SOC on its maximum charging and discharging power. （16）； when At this time, ACE is in the normal adjustment zone. At this time, the revenue of the photovoltaic and energy storage system is the control target. The dispatch center allocates power according to the frequency regulation output declared by the photovoltaic and energy storage system in the intraday market. （17）； when At that time, the ACE is located in the regulation dead zone, and the frequency regulation power demand in the region is very small. The photovoltaic-storage combined system does not participate in grid regulation. （18）； 45) Design maximum output constraint coefficient for energy storage batteries: (1) When the energy storage is in the discharge state, if the energy storage battery has a high state of charge (SOC>70%), the energy storage battery discharges at the original power multiplied by an energy storage discharge regulation coefficient less than 1. The energy storage discharge regulation coefficient decreases as the state of charge decreases. When the energy storage state of charge is less than 0.7, the energy storage discharges at the original power. (2) When the energy storage is in the charging state, if the energy storage battery has a low state of charge (SOC < 30%), the energy storage battery is charged by multiplying the original power by an energy storage charging regulation coefficient less than 1. The energy storage charging regulation coefficient decreases as the state of charge decreases. When the energy storage state of charge is greater than 0.3, the energy storage is discharged according to the original power. In the discharge state, there is （19）， in, The energy storage discharge regulation coefficient is denoted by SOC, which represents the energy storage state of charge. In the charging state, there is （20）， in, The energy storage charging regulation coefficient; final operating power of energy storage battery The relationship with the state of charge constraint is as follows （21）， in, This represents the actual operating power of the energy storage system during the day.

5. A day-ahead-intraday optimization scheduling method for energy storage systems considering photovoltaic and energy storage participation in the electricity-frequency regulation market, as described in claim 4, is characterized in that... The day-to-day multi-timescale optimization scheduling includes the following steps: 51) Construct an intraday optimization model, the objective function of which is shown in the following formula: ，（22） In the formula, The total daily operating revenue of the photovoltaic and energy storage system. To determine the daily revenue of a photovoltaic-storage integrated system participating in the electricity market under scenario s, To determine the daily revenue of energy storage systems participating in the frequency regulation market under scenario s, The daily energy storage operation loss cost of the energy storage system under scenario s. Cost of curtailment within the day The cost of assessing the deviation of the photovoltaic-storage system from the daily planned curve tracking under scenario s; 52) Calculate the daily deviation assessment cost of the photovoltaic-storage system. The expression is shown in equation (25): （23）， （24）， （25）， In the formula, Number of time periods within a day This refers to the electricity sales power of the photovoltaic and energy storage system at any given time during the day. The cost of deviation assessment at time t. and These are the lower and upper limits of the tracking and assessment scope, respectively. This refers to the electricity price cost per unit of energy exceeding the assessment threshold. The maximum deviation stipulated in the performance evaluation regulations for the dispatching department. The power sold by the photovoltaic-storage integrated system at time t before the date; 53) Based on the actual fluctuations in the power grid frequency and the output reported by the photovoltaic and energy storage system in the frequency regulation market, issue AGC frequency regulation commands; Considering that real-time AGC frequency modulation commands can cause changes in the energy storage capacity and power, thus affecting the initial state of the photovoltaic-storage system's subsequent market participation, adjustments are made to account for uncertainties after each rolling window ends. （26）， （27）， In the formula: This is the first revised energy storage operating power; The actual daily operating power of the energy storage system, and the actual power adjustment of the photovoltaic-storage system. It is obtained from the frequency modulation power allocation rules of the photovoltaic energy storage system; 54) The mathematical programming solver is used to solve the intraday optimization model and output the power generation plan curve of the photovoltaic-storage joint system participating in the electricity market and frequency regulation market, which is the most economically optimal. Thus, the photovoltaic-storage synergistic optimization strategy that takes into account both system economy and grid frequency stability is obtained.

6. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, enables the day-ahead-intraday optimization scheduling method for an energy storage system that considers photovoltaic and energy storage participation in the electricity-frequency regulation market, as described in any one of claims 1-5.

7. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it can implement the day-ahead-intraday optimization scheduling method for energy storage systems that considers the participation of photovoltaic and energy storage in the power-frequency regulation market, as described in any one of claims 1-5.

Images