A method for evaluating the ability of energy storage to participate in frequency regulation ancillary services in the power system

Abstract

The present invention provides a method for evaluating the ability of energy storage to participate in the frequency regulation ancillary service of a power system, belonging to the technical field of power system control; it solves the difficult problem of evaluating the frequency regulation ability of distributed resources in the existing power market; it includes the following steps: constructing an optimization model, the optimization model includes an objective function, uncertainty, constraint conditions and profitability analysis; the objective function consists of two parts, the first part is to maximize the frequency regulation income of the energy storage market entity during the period t ; the second part is intraday scheduling; uncertainty is used to declare the price and capacity of frequency regulation services based on the predicted frequency regulation demand and winning bid price; profitability analysis is used to judge whether the expected income of the energy storage frequency regulation equipment participating in the frequency regulation service is reasonable; model solution; the present invention is applied to a power system containing energy storage.

Description

Technical Field

[0001] The present invention provides a method for evaluating the ability of energy storage to participate in the frequency regulation ancillary service of the power system, belonging to the technical field of power system control. Background Art

[0002] In traditional power systems, frequency regulation services mainly rely on centralized generating units, especially thermal power plants. These generating units can adjust their output power according to the needs of the power grid to maintain the frequency stability of the power grid. However, with the large-scale access of renewable energy and the continuous development of the power market, the demand for and challenges of frequency regulation services are also increasing continuously. The intermittency and unpredictability of renewable energy such as wind energy and solar energy have increased the operational complexity of the power grid and put forward higher requirements for frequency regulation services. In addition, the power market reform has made the providers of frequency regulation services no longer limited to traditional power generation enterprises, but extended to market players including virtual power plants and third-party independent operators.

[0003] The development of energy storage technology has provided new frequency regulation resources for the power system. Due to its characteristics of fast response and flexible regulation, the energy storage system has become an indispensable frequency regulation resource in the power system, helping the power grid balance supply and demand and maintaining the stable and safe operation of the power grid. With the development of energy storage technology, its role in the power market has become increasingly important. However, in practical applications, it still faces a series of challenges: (1) Frequency regulation ability evaluation: Accurately evaluating the frequency regulation ability of the energy storage system is the premise for realizing its effective scheduling and control. At present, there is a lack of accurate methods to evaluate the frequency regulation performance of energy storage devices, including their response speed, regulation depth, and duration, etc. (2) Real-time scheduling and control: The energy storage system needs to be scheduled and controlled according to the real-time power grid demand, which requires efficient algorithms and models to process real-time data and make rapid decisions. (3) Cost-benefit analysis: The economy of energy storage frequency regulation services is the key to their commercialization. It is necessary to conduct a detailed analysis of the operating costs and revenues of energy storage devices to ensure their economic feasibility. (4) Market mechanism and policy support: The effective operation of energy storage frequency regulation services requires corresponding market mechanisms and policy support. At present, the power markets in many regions of the country have not been fully opened, lacking a clear pricing mechanism and incentive policies for energy storage frequency regulation services. Summary of the Invention

[0004] In order to solve the problem of evaluating the frequency regulation ability of distributed resources in the existing power market, the present invention proposes a method for evaluating the ability of energy storage to participate in the frequency regulation ancillary service of the power system, and realizes the effective management of frequency regulation services and the maximization of cost-benefit by constructing an optimization model.

[0005] The technical solution adopted by the present invention is: A method for evaluating the ability of energy storage to participate in the frequency regulation ancillary service of the power system, comprising the following steps:

[0006] Step 1: Construction of the optimization model. The optimization model includes an objective function, uncertainty, constraint conditions, and profitability analysis.

[0007] The objective function consists of two parts. The first part is to maximize the frequency regulation revenue of the energy storage market entity during the time period t where the frequency regulation revenue is determined by the product of the winning bid price and the frequency regulation mileage during this time period t . The second part is the intraday scheduling. According to the randomness of the AGC command, calculate the weighted value of the frequency regulation power cost for all possible values and minimize the frequency regulation ancillary service cost.

[0008] The uncertainty is used to declare the price and capacity of the frequency regulation service based on the predicted frequency regulation demand and the winning bid price.

[0009] The constraint conditions include frequency regulation command-related constraints, declared capacity constraints, power constraints of energy storage frequency regulation equipment, power balance constraints of electrical energy storage frequency regulation equipment, and correlation relationship constraints between frequency regulation mileage and the power of energy storage frequency regulation equipment.

[0010] The profitability analysis is used to determine whether the expected revenue of the energy storage frequency regulation equipment participating in the frequency regulation service is reasonable.

[0011] Step 2: Model solution: After obtaining the required parameters, use an optimization method to solve the optimization model to obtain the final declaration decision of the energy storage market entity.

[0012] The expression of the objective function is as follows:

[0013] ;

[0014] In the formula: x is a set composed of decision variables, ;

[0015] b t is the winning bid price of the frequency regulation ancillary service of the energy storage market entity during the time period t ; is the total expected value of the frequency regulation mileage of the energy storage market entity during the time period t , that is, the cumulative frequency regulation mileage of the energy storage market entity based on the AGC command during this time period; is the probability of the unit AGC command power value, and the probability distributions in different time periods t are also different; k represents the t th sub-moment t’ under the time period and the k th value, represents the t th sub-moment t’AGC command value per unit declared capacity; Q t For the energy storage market entity during the time period t Declared capacity of frequency regulation ancillary service; For the energy storage frequency regulation equipment during the time period t Sub-moment at t’ Operating power; Δ t’ Is the duration; t’ Is the AGC command scheduling moment of the frequency regulation operating day, ; For the time period t Sub-moment at t’ Real-time electricity price; T Is a set composed of multiple frequency regulation time periods.

[0016] The frequency regulation power represented by the probability distribution of the uncertainty Is described as Indicates that during intraday scheduling, the power grid issues an AGC scheduling command according to the declared capacity of the frequency regulation ancillary service of the winning energy storage market entity during the time period t Declared capacity of frequency regulation ancillary service Q t The energy storage frequency regulation equipment needs to dynamically adjust the frequency regulation power To follow the AGC command.

[0017] The expressions of the frequency regulation command related constraints are as follows:

[0018] ;

[0019] ;

[0020] Δ is the maximum allowable deviation value.

[0021] The expressions of the declared capacity constraints are as follows:

[0022] ;

[0023] Q max Is the maximum capacity of the energy storage frequency regulation equipment participating in the frequency regulation ancillary service.

[0024] The expressions of the power constraints of the energy storage frequency regulation equipment are as follows:

[0025] ;

[0026] ;

[0027] For the energy storage frequency regulation equipment during the time period t Sub-moment at t’ Basic operating power;p max The maximum power that the energy storage frequency regulation device is allowed to reach.

[0028] The expression of the power balance constraint of the electric energy storage frequency regulation device is as follows:

[0029] ;

[0030] ;

[0031] In the formula: is the remaining energy of the energy storage frequency regulation device in time period t , E t-1 is the remaining energy of the previous time period, Δ t’ is the duration; E min , E max are respectively the minimum and maximum capacity ranges that the energy storage frequency regulation device is allowed to reach.

[0032] The expression of the correlation relationship constraint between the frequency regulation mileage and the power of the energy storage frequency regulation device is as follows:

[0033] .

[0034] The expression of the cost - effectiveness analysis is as follows:

[0035] ;

[0036] The sum on the left side of the formula represents the expected revenue of the energy storage frequency regulation device in all time periods, and the sum on the right side represents the charge - discharge operation cost calculated according to the real - time electricity price in all time periods; if the above inequality holds, the participation of the energy storage frequency regulation device in frequency regulation services is economically cost - effective.

[0037] In step two, the mixed - integer linear programming method is used to solve the objective function and constraint conditions to obtain the optimal winning bid price and capacity as the final declaration decision of the energy storage market entity.

[0038] The beneficial effects of the present invention compared with the prior art are as follows: Through the method of the present invention, the participation strategies of market entities can be effectively evaluated and optimized, the frequency regulation efficiency and economy of the power system can be improved, and at the same time, greater economic benefits can be brought to market entities, providing a flexible and market - operation - rule - compliant declaration decision - making tool for market entities. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be further described below with reference to the accompanying drawings:

[0040] Figure 1 is the flow chart of the method of the present invention;

[0041] Figure 2 This is the solution flowchart of the optimization model of the present invention. Specific implementation manner

[0042] As Figure 1-2 shown, the present invention provides a method for evaluating the ability of energy storage to participate in the frequency regulation ancillary service of the power system. By constructing an optimization model for the energy storage market entity applicable to the policy rules and frequency regulation ancillary service market conditions in different regions, it is used to evaluate the frequency regulation ability of the energy storage market entity during each frequency regulation period, and the corresponding frequency regulation bidding strategy is obtained by solving, so as to maximize the benefits of energy storage market entities such as virtual power plants participating in the frequency regulation ancillary service. The constructed optimization model can optimize the declaration of the day-ahead frequency regulation service and the specific energy storage charge and discharge scheduling within the day.

[0043] The method specifically includes the following steps:

[0044] Step 1: Construction of the optimization model. The optimization model includes an objective function, uncertainty, constraint conditions, and cost-effectiveness analysis, where:

[0045] 1) Construction of the objective function. The objective function consists of two parts. The first part is to maximize the frequency regulation income of the energy storage market entity during the period t . The frequency regulation income is determined by the product of the winning bid price and the frequency regulation mileage (also known as the frequency regulation depth) during this period; the second part is the within-day scheduling. According to the randomness of the AGC command, the weighted value of the frequency regulation power cost under all possible values is calculated, and the frequency regulation ancillary service cost is minimized; the specific expression is as follows:

[0046] (1);

[0047] In the formula: x is a set composed of decision variables, ;

[0048] b t is the winning bid price of the energy storage market entity for the frequency regulation ancillary service during the period t . According to the frequency regulation ancillary service policy rules in different regions, if the unit wins the bid, the frequency regulation winning bid clearing price is calculated according to its declared price; in the present invention b t is used as a known variable, and is given by the energy storage market entity based on historical clearing data and combined with its own historical frequency regulation performance;

[0049] is the total expected frequency regulation mileage of the energy storage market entity during the period t , that is, the cumulative frequency regulation mileage (also called the regulation depth) of the energy storage market entity based on the AGC command during this period;

[0050] is the probability of the unit AGC command power value, and the probability distributions at different times t are also different; k represents the sub-moment t of time period t’ under the k th value, represents the AGC command value of the unit declared capacity at the sub-moment t of time period t’ ;

[0051] is the operating power of the energy storage frequency regulation equipment at the sub-moment t of time period t’ ; Δ t’ is the duration, which may be in minutes or seconds in actual situations; t’ is the AGC command scheduling moment of the frequency regulation operation day, in minutes or seconds, ;

[0052] is the real-time electricity price at the sub-moment t of time period t’ , that is, the real-time peak, valley, and flat electricity prices in the electricity market on the same day;

[0053] T is a set composed of multiple frequency regulation time periods. According to the frequency regulation ancillary service policy rules in different regions, it can be specifically divided into five, six, or other frequency regulation time periods.

[0054] Among them, the energy storage market entity refers to an operator, which may be an independent energy storage power station, a virtual power plant operator, a power sales company, etc.; the energy storage frequency regulation equipment is a specific energy storage physical equipment. When participating in the electricity market, the declaration and clearing processes are based on the entity; during actual scheduling, it is based on the equipment.

[0055] 2) Uncertainty construction

[0056] The energy storage system needs to declare the price and capacity of the frequency regulation service based on the predicted frequency regulation demand and the winning bid price. Given the uncertainty of the AGC command, a probability distribution is used for description.

[0057] Let AGC t,t’ be the AGC command at the sub-moment t of time period t’ , be the AGC command value of the unit declared capacity at the sub-moment t of time period t’ , that is, the value of the AGC command that the energy storage market entity needs to follow per unit declared capacity. That is to say, the power grid will adjust according to the declared capacity of the energy storage market entity Qt to determine the amount of AGC commands that each unit capacity needs to respond to. is a random variable with a probability distribution . During intraday scheduling, the power grid issues AGC (Automatic Generation Control) scheduling commands according to the declared capacity of the winning energy storage market entities Q t The winning energy storage frequency modulation equipment needs to dynamically adjust the frequency modulation power to follow the AGC commands, expressed as .

[0058] 3) Constraints: including constraints related to frequency modulation commands, declared capacity constraints, power constraints of energy storage frequency modulation equipment, power balance constraints of electrical energy storage frequency modulation equipment, and power correlation relationship constraints between frequency modulation mileage and energy storage frequency modulation equipment. Specifically:

[0059] The expressions of the constraints related to frequency modulation commands are as follows:

[0060] (2-1);

[0061] (2-2);

[0062] Equation (2-1) indicates that the frequency modulation command AGC t,t’ depends on the optimization variables of the corresponding time period Q t ; Equation (2-2) indicates that at any time, it is ensured that the frequency modulation power of the energy storage equipment is as close as possible to the AGC command, is the corresponding frequency modulation power; Δ is the maximum allowable deviation value.

[0063] The expressions of the declared capacity constraints are as follows:

[0064] (3);

[0065] Equation (3) indicates that the energy storage frequency modulation equipment must declare the frequency modulation capacity it is willing to provide in each time period of the day-ahead market; Q t is the declared capacity of the frequency modulation ancillary service of the energy storage market entity in the time period t (unit: MW); Q max is the maximum capacity that the energy storage frequency modulation equipment can participate in the frequency modulation ancillary service.

[0066] The expressions of the power constraints of the energy storage frequency modulation equipment are as follows:

[0067] (4-1);

[0068] (4 - 2);

[0069] Equation (4 - 1) indicates that the energy storage operation power consists of two parts: the frequency regulation power and the basic power (the power reserved by the energy storage); (4 - 2) indicates that the energy storage operation power needs to be within the range of the equipment operation regulations. is the operation power of the energy storage frequency regulation equipment at the sub - moment t of the time period t’ ; is the frequency regulation power of the energy storage frequency regulation equipment at the sub - moment t of the time period t’ ; is the basic operation power of the energy storage frequency regulation equipment at the sub - moment t of the time period t’ ; p max is the maximum power that the energy storage frequency regulation equipment is allowed to reach.

[0070] The expression of the charge - discharge balance constraint of the electrical energy storage frequency regulation equipment is as follows:

[0071] (5 - 1);

[0072] (5 - 2);

[0073] In the formula: is the remaining energy of the energy storage frequency regulation equipment at the time period t ; E t-1 is the remaining energy of the previous time period, Δ t’ is the duration; E min , E max are respectively the minimum and maximum capacity ranges that the energy storage frequency regulation equipment is allowed to reach, to avoid over - charge and over - discharge affecting the service life of the equipment.

[0074] The expression of the constraint on the relationship between the frequency regulation mileage and the power of the energy storage frequency regulation equipment is as follows:

[0075] (6 - 1);

[0076] Equation (6 - 1) is the constraint on the relationship between the frequency regulation mileage and the power of the energy storage frequency regulation equipment, ensuring that the total expected frequency regulation mileage t at the time period R t is equal to the cumulative amount of the frequency regulation power at all sub - moments within this time period. Linearize Equation (6 - 1), introduce auxiliary variables p + , p - , so that:

[0077] (6 - 2);

[0078] Then there is a constraint:

[0079] (6 - 3);

[0080] (6 - 4);

[0081] (6 - 5);

[0082] The original formula (6 - 1) is transformed into:

[0083] (6 - 6);

[0084] Where: p + Represents the sub - moment t of the time period t’ under which the positive deviation of the frequency - modulation power; p - Represents the sub - moment t of the time period t’ under which the negative deviation of the frequency - modulation power; z t,t’ is a binary variable, indicating whether to select the positive deviation ( t at the sub - moment t’ of the time period z t,t’ = 1 represents the positive deviation, z t,t’ = 0 represents the negative deviation); M is a sufficiently large positive number to ensure that when z t,t’ = 1 p + can take any non - negative value, and when z t,t’ = 0 p + = 0; Formulas (6 - 3) and (6 - 4) ensure that at any moment p + and p - cannot be non - zero at the same time; The constraint formula (6 - 5) means that p + and p - The sum of is equal to the absolute value of the frequency - modulation power ;

[0085] 4) The expression for the cost - effectiveness analysis is as follows:

[0086] (7);

[0087] If the expected revenue is greater than or equal to the cost, it is cost-effective to participate in frequency regulation services; the summation on the left side of Equation (7) represents the expected revenue of the energy storage frequency regulation equipment over all time periods, and the summation on the right side represents the charge and discharge operation costs calculated at the real-time electricity price over all time periods. If Equation (7) holds, the participation of the energy storage frequency regulation equipment in frequency regulation services is economically viable.

[0088] Step 2: Model solution, including parameter acquisition and optimization method.

[0089] 1. Parameter acquisition description:

[0090] b t It is obtained by the energy storage market entity based on historical clearing data and combined with its own historical frequency regulation performance;

[0091] Obtained through prediction and other means in the day-ahead stage;

[0092] T Obtained according to the frequency regulation ancillary service policy rules in different regions;

[0093] Q max They are the parameters of the energy storage frequency regulation equipment;

[0094] Δ t’ The actual situation may be in minutes or seconds; t’ It is in minutes or seconds;

[0095] p max Obtained from the operation parameter plate of the energy storage frequency regulation equipment;

[0096] Issued by the power grid;

[0097] Obtained by fitting and statistics from historical data.

[0098] 2. Optimization method

[0099] Based on the actual rules of the frequency regulation ancillary service market in different regions, each energy storage market entity completes declaration and clearing in the day-ahead stage and needs to strictly execute according to the AGC command during the day. The optimization model of the present invention adopts a method based on the expected value model for optimization. By ensuring equipment constraints, the frequency regulation capacity is retained at any time to ensure that there is sufficient frequency regulation power and SOC reserve space outside the equipment power baseline. Among them, SOC (State of Charge) refers to the state of charge of the battery, also known as the remaining power.

[0100] As Figure 2 shown, the specific solution process is as follows:

[0101] Start; Parameter acquisition: Collect all necessary parameters, including frequency modulation time period, quotation range, electricity price, equipment power limit, etc.; Initialization: Determine the initial winning bid price and declared capacity of the energy storage market entity; The optimization goal is to maximize the expected revenue; Solve the mixed-integer linear programming: Use the mixed-integer linear programming method to solve the objective function and constraints to obtain the optimal winning bid price and capacity; Output the optimal winning bid price and capacity: Obtain the optimal winning bid price and capacity as the final declaration decision of the energy storage market entity; End.

[0102] The present invention aims to effectively evaluate and optimize the frequency modulation ability of energy storage market entities and improve the frequency modulation efficiency and economy of the power system by constructing an optimization model. Through the method of the present invention, a flexible declaration decision-making tool that conforms to the market operation rules is provided for energy storage market entities, and at the same time, technical support is provided for the development of the power market.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the ability of energy storage to participate in power system frequency regulation auxiliary services, characterized by: The following steps are involved: Step 1: Construction of an optimization model, which includes an objective function, uncertainty, constraints, and cost-effectiveness analysis; The objective function consists of two parts. The first part is to maximize the energy storage market players in the period t The FM revenue during the period is t The second part is the intraday dispatch, which calculates the weighted value of the frequency regulation power cost under all possible values ​​according to the randomness of the AGC instructions, and minimizes the frequency regulation auxiliary service cost; The expression of the objective function is as follows: ； Where: x is the set of decision variables, ; b t For energy storage market players in the period t The winning bid price for frequency regulation ancillary services; For energy storage market players in the period t The expected value of the total frequency regulation mileage, that is, the cumulative frequency regulation mileage of the energy storage market players based on the AGC instructions during this period; is the probability of the unit AGC command power value, in different time periods t The probability distribution of is also different; k Indicates time period t Sub-moment t’ Down No. k A value, Indicates during the period t Sub-moment t’ The AGC command value of the unit declared capacity; Q t For energy storage market players in the period t The declared capacity of frequency regulation auxiliary services; For energy storage frequency regulation equipment in the period t Sub-moment t’ The operating power of t’ for duration; t’ is the AGC instruction dispatching time on the frequency modulation operation day, ; For the period t Sub-moment t’ Real-time electricity prices; T A collection of multiple frequency modulation time periods; The uncertainty is used to declare the price and capacity of frequency regulation services based on the predicted frequency regulation demand and the winning bid price; The uncertainty is the uncertainty of the AGC instruction, which is described by probability distribution; set up AGC t,t’ For the period t Sub-moment t’ The AGC instruction, For the period t Sub-moment t’ The AGC instruction value of the unit declared capacity is the value of the AGC instruction that the energy storage market entity needs to follow for each unit declared capacity. In other words, the power grid will follow the capacity declared by the energy storage market entity. Q t To determine the amount of AGC instructions that each unit capacity needs to respond to, is a random variable with a probability distribution During the day's dispatch, the power grid will allocate the capacity based on the capacity declared by the winning energy storage market player. Q t Issue AGC dispatch instructions, and the winning energy storage frequency modulation equipment needs to dynamically adjust the frequency modulation power To follow the AGC instruction, it is expressed as ; The constraints include frequency regulation instruction related constraints, declared capacity constraints, energy storage frequency regulation equipment power constraints, power balance constraints of electric energy storage frequency regulation equipment, and power correlation constraints between frequency regulation mileage and energy storage frequency regulation equipment; The expressions of frequency modulation instruction related constraints are as follows: ； ； Δ is the maximum allowable deviation value; The cost-effectiveness analysis is used to determine whether the expected benefits of the energy storage frequency regulation equipment participating in the frequency regulation service are cost-effective; Step 2: Model solution: After obtaining the required parameters, the optimization method is used to solve the optimization model to obtain the final declaration decision of the energy storage market entity.

2. A method for evaluating the ability of energy storage to participate in power system frequency regulation auxiliary services according to claim 1, characterized in that: The expression for declaring capacity constraints is as follows: ； Q max The maximum capacity of energy storage frequency regulation equipment participating in frequency regulation auxiliary services.

3. The method for evaluating the ability of energy storage to participate in power system frequency regulation auxiliary services according to claim 1, characterized in that: The expression of power constraint of energy storage frequency regulation equipment is as follows: ； ； For energy storage frequency regulation equipment in the period t Sub-moment t’ Basic operating power; p max The maximum power allowed for energy storage and frequency regulation equipment.

4. The method for evaluating the ability of energy storage to participate in power system frequency regulation auxiliary services according to claim 1, characterized in that: The expression of the power balance constraint of the electric energy storage frequency regulation equipment is as follows: ； ； Where: For energy storage frequency regulation equipment in the period t The remaining energy, E t-1 is the residual energy of the previous period, Δ t’ for duration; E min , E max They are respectively the minimum and maximum capacity ranges allowed for energy storage and frequency regulation equipment.

5. The method for evaluating the ability of energy storage to participate in power system frequency regulation auxiliary services according to claim 1, characterized in that: The expression for the constraint of the relationship between frequency regulation mileage and the power of energy storage frequency regulation equipment is as follows: 。 6. A method for evaluating the ability of energy storage to participate in power system frequency regulation auxiliary services according to any one of claims 2 to 5, characterized in that: The expression of cost-effectiveness analysis is as follows: ； The sum on the left side of the formula represents the expected revenue of the energy storage frequency regulation equipment in all time periods, and the sum on the right side represents the charging and discharging operation costs calculated based on the real-time electricity price in all time periods; if the above inequality holds, the participation of the energy storage frequency regulation equipment in the frequency regulation service is economically viable.

7. A method for evaluating the ability of energy storage to participate in power system frequency regulation auxiliary services according to claim 6, characterized in that: In step 2, the mixed integer linear programming method is used to solve the objective function and constraints to obtain the optimal winning bid price and capacity as the final declaration decision of the energy storage market player.

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