A method and device for generating an electric energy storage market transaction declaration strategy

Abstract

The application discloses a kind of electric energy storage market transaction declaration strategy generation method and device, method includes: according to electric energy storage climbing first data, electric energy storage climbing second data, electric energy storage frequency modulation first data and electric energy storage frequency modulation second data are constructed with electric energy storage expected comprehensive income maximization as target electric energy storage declaration strategy optimization model, and determine electric energy storage declaration strategy optimization model electric energy storage declaration constraint condition;Optimization solution is carried out to electric energy storage declaration strategy optimization model, obtains electric energy storage frequency modulation market declaration strategy and climbing market declaration strategy, according to electric energy storage frequency modulation market declaration strategy generation first dispatching instruction, so that frequency modulation market adjusts power generation output according to first dispatching instruction, according to climbing market declaration strategy generation second dispatching instruction, so that climbing market adjusts power generation output according to second dispatching instruction;Realize the optimization of the declaration strategy of two kinds of market transaction varieties of frequency modulation service and climbing service.

Description

Technical Field

[0001] This application relates to the cross-technical fields of electricity market and power dispatch, and in particular to a method and apparatus for generating transaction declaration strategies in the energy storage market. Background Technology

[0002] In recent years, the rapid development of new energy sources such as wind power and photovoltaics has led to greater uncertainty in power grid operation, making the demand for flexible regulation resources such as energy storage more urgent. To meet the regulation resource needs of power grid operation under the large-scale integration of new energy sources, power grids in various provinces and regions are accelerating the construction of electricity markets, with frequency regulation markets and ramp-up markets becoming the focus of current research and application.

[0003] The frequency regulation market is a power market trading instrument with frequency regulation services as the underlying asset. Frequency regulation services are essentially an automatic power generation output regulation function. Power sources that win bids in the frequency regulation market reserve a certain frequency regulation capacity and adjust their power generation output in real time according to the instructions of the dispatching agency's automatic power generation control system during operation to address second-level power generation and consumption deviations. The ramp-up market is a power market trading instrument with ramp-up services as the underlying asset. Ramp-up services are essentially a rapid power generation output regulation function. Power sources with remaining ramp-up capacity rapidly adjust their power generation output within their power generation range according to dispatching instructions to address minute-level power generation and consumption deviations. Both frequency regulation and ramp-up services are power generation output regulation functions, but their response times and mechanisms differ in regulating power generation and consumption deviations, which also means that a power source cannot simultaneously provide both frequency regulation and ramp-up services.

[0004] However, the development of energy storage is still in its early stages, and research on its participation in the electricity market is insufficient. In particular, there is currently no effective method for formulating application strategies for market trading varieties such as ramp-up markets, which have emerged in recent years due to the rapid development of new energy sources. Summary of the Invention

[0005] This application provides a method and apparatus for generating transaction declaration strategies in the energy storage market, which is used to optimize the declaration strategies for two types of market transaction products: frequency regulation service and ramp-up service.

[0006] In view of this, the first aspect of this application provides a method for generating a trading strategy for the energy storage market, including:

[0007] Based on the first data of energy storage ramp-up, the second data of energy storage ramp-up, the first data of energy storage frequency regulation, and the second data of energy storage frequency regulation, an energy storage application strategy optimization model is constructed with the goal of maximizing the expected comprehensive benefits of energy storage, and the energy storage application constraints of the energy storage application strategy optimization model are determined.

[0008] The optimization model for the energy storage application strategy is optimized and solved to obtain the energy storage frequency regulation market application strategy and the ramp-up market application strategy;

[0009] A first dispatch instruction is generated according to the energy storage frequency regulation market application strategy, causing the frequency regulation market to adjust its power generation output according to the first dispatch instruction. A second dispatch instruction is generated according to the ramp-up market application strategy, causing the ramp-up market to adjust its power generation output according to the second dispatch instruction.

[0010] Optionally, the process of acquiring the first data for energy storage ramp-up is as follows:

[0011] The probability of the first distribution is predicted based on historical trading data of the ramp market and the ramp demand on trading days.

[0012] The first data for energy storage ramp-up is calculated based on the first distribution probability, the energy storage application ramp-up capacity, and the price of the energy storage application ramp-up capacity.

[0013] Optionally, the process of acquiring the first data for energy storage frequency regulation is as follows:

[0014] The probability of the second distribution is predicted based on historical trading data of the frequency modulation market and the frequency modulation demand on trading days.

[0015] The first data for energy storage frequency regulation is calculated based on the second distribution probability, the declared frequency regulation capacity of energy storage, and the price of the declared frequency regulation capacity of energy storage.

[0016] Optionally, the step of predicting the first distribution probability based on historical transaction data of the ramping market and the ramping demand on the trading day includes:

[0017] Calculate the expected ramp-up capacity for the trading day based on the generator set's start-up and shutdown schedule;

[0018] Based on the ramping demand and expected ramping capacity declared on the trading day, the ramping market clearing price under the same ramping demand and declared ramping capacity is extracted from historical data of the ramping market. The maximum and minimum values ​​of the extracted ramping market clearing price are used to determine the predicted range of fluctuation of the ramping market clearing price.

[0019] The predicted market clearing price fluctuation range is divided into multiple intervals. The number of times the historical clearing price occurs in each interval is counted, and the ratio of the number of times the historical clearing price occurs in each interval to the sum of the number of times the historical clearing price occurs in all intervals is calculated to obtain the first distribution probability.

[0020] Optionally, the step of predicting the second distribution probability based on historical trading data of the frequency modulation market and frequency modulation demand on trading days includes:

[0021] Calculate the expected frequency regulation capacity to be declared on the trading day based on the start-up and shutdown methods of generator sets on the trading day;

[0022] Based on the frequency regulation demand and expected frequency regulation capacity declared on the trading day, the frequency regulation market clearing price under the same frequency regulation demand and declared frequency regulation capacity is extracted from historical data of the frequency regulation market. The range of fluctuation of the predicted frequency regulation service clearing price is determined based on the maximum and minimum values ​​of the extracted frequency regulation market clearing price.

[0023] The predicted frequency modulation market clearing price fluctuation range is divided into multiple intervals. The number of times the historical clearing price occurs in each interval is counted, and the ratio of the number of times the historical clearing price occurs in each interval to the sum of the number of times the historical clearing price occurs in all intervals is calculated to obtain the second distribution probability.

[0024] Optionally, the process of acquiring the second data for energy storage ramp-up is as follows:

[0025] Construct a quadratic function for the application of ramp-up capacity for energy storage, and calculate the second data for energy storage ramp-up using the quadratic function for the application of ramp-up capacity for energy storage.

[0026] Optionally, the process of acquiring the second data for energy storage frequency regulation is as follows:

[0027] Construct a quadratic function for the frequency regulation capacity application of energy storage, and calculate the second data for energy storage frequency regulation using the quadratic function for the frequency regulation capacity application of energy storage.

[0028] Optionally, the process for obtaining the expected comprehensive benefits of the energy storage is as follows:

[0029] The difference between the first data point and the second data point for energy storage ramp-up is calculated to obtain the first expected return.

[0030] The difference between the first data and the second data of energy storage frequency regulation is calculated to obtain the second expected return.

[0031] The first expected return and the second expected return are weighted and summed according to the winning bid state function to obtain the comprehensive expected return of energy storage.

[0032] Optionally, the energy storage application constraints include energy storage application ramp-up capacity constraints and energy storage application frequency regulation capacity constraints.

[0033] The energy storage application ramp-up capacity constraints include:

[0034] The declared ramp-up capacity of electric energy storage shall not exceed the difference between the maximum charging and discharging power of the electric energy storage and the planned maximum charging and discharging power of the electric energy storage.

[0035] The declared ramp-up capacity of electric energy storage shall not exceed the difference between the maximum ramp-up power of electric energy storage and the ramp-up power already arranged in the power energy plan;

[0036] The change in the amount of energy storage capacity under the maximum ramping capability response during any given period shall not exceed the remaining energy storage capacity adjustment space during that period.

[0037] The frequency regulation capacity constraints for energy storage applications include:

[0038] The declared frequency regulation capacity of energy storage shall not exceed the difference between the maximum charging and discharging power of energy storage and the planned maximum charging and discharging power of energy storage.

[0039] The change in the amount of energy stored under the maximum frequency regulation capability response during any given period shall not exceed the remaining energy storage capacity adjustment space during that period.

[0040] The second aspect of this application provides an energy storage market transaction reporting device, comprising:

[0041] The model building unit is used to construct an energy storage application strategy optimization model with the goal of maximizing the expected comprehensive benefits of energy storage based on the first data of energy storage ramping, the second data of energy storage ramping, the first data of energy storage frequency regulation, and the second data of energy storage frequency regulation, and to determine the energy storage application constraints of the energy storage application strategy optimization model.

[0042] The model solving unit is used to optimize and solve the energy storage application strategy optimization model to obtain the energy storage frequency regulation market application strategy and the ramp-up market application strategy.

[0043] The generation unit is configured to generate a first dispatch instruction based on the energy storage frequency regulation market application strategy, so that the frequency regulation market adjusts the power generation output according to the first dispatch instruction, and generate a second dispatch instruction based on the ramp-up market application strategy, so that the ramp-up market adjusts the power generation output according to the second dispatch instruction.

[0044] As can be seen from the above technical solutions, this application has the following advantages:

[0045] This application provides a method for generating an energy storage market transaction declaration strategy, comprising: constructing an energy storage declaration strategy optimization model with the objective of maximizing the expected comprehensive benefits of energy storage based on energy storage ramp-up first data, energy storage ramp-up second data, energy storage frequency regulation first data, and energy storage frequency regulation second data, and determining the energy storage declaration constraints of the energy storage declaration strategy optimization model; optimizing and solving the energy storage declaration strategy optimization model to obtain an energy storage frequency regulation market declaration strategy and a ramp-up market declaration strategy; generating a first dispatch instruction based on the energy storage frequency regulation market declaration strategy, causing the frequency regulation market to adjust power generation output according to the first dispatch instruction; and generating a second dispatch instruction based on the ramp-up market declaration strategy, causing the ramp-up market to adjust power generation output according to the second dispatch instruction.

[0046] In this application, an objective function based on maximizing expected returns is constructed, and the operational constraints that energy storage needs to meet in providing frequency regulation and ramp-up services are considered. An optimization model for energy storage application strategy is constructed to achieve the optimization of application strategy. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 A flowchart illustrating a method for generating an energy storage market transaction declaration strategy, provided in an embodiment of this application;

[0049] Figure 2 This is a schematic diagram of a device for submitting transactions in the energy storage market, provided as an embodiment of this application. Detailed Implementation

[0050] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0051] For easier understanding, please refer to Figure 1 This application provides a method for generating a transaction declaration strategy in the energy storage market, including:

[0052] Step 101: Based on the first data of energy storage ramp-up, the second data of energy storage ramp-up, the first data of energy storage frequency regulation, and the second data of energy storage frequency regulation, construct an energy storage application strategy optimization model with the goal of maximizing the expected comprehensive benefits of energy storage, and determine the energy storage application constraints of the energy storage application strategy optimization model.

[0053] The process of acquiring the first data for energy storage ramp-up includes:

[0054] First, based on historical trading data of the ramp-up market and the ramp-up demand on trading days, the probability of the first distribution is predicted.

[0055] To obtain historical data on the ramp-up market and the ramp-up demand on each trading day, the historical data is obtained from the data disclosed by the market operator. This historical data includes the ramp-up demand for each historical trading day, the declared ramp-up capacity for each historical trading day, and the clearing price for ramp-up services for each historical trading day. The ramp-up demand on each trading day is calculated and disclosed by the market operator based on the grid operation needs before market trading submissions.

[0056] In this embodiment of the application, the specific process of predicting the first distribution probability based on historical transaction data of the ramping market and the ramping demand on trading days can be as follows:

[0057] A1. Calculate the expected ramp-up capacity to be declared on the trading day based on the generator set's start-up and shutdown schedule on the trading day;

[0058] Before submitting a ramp-up market transaction application, the market operator needs to disclose the unit start-up and shutdown methods for that operating day. The total expected ramp-up capacity declared across the entire network is the sum of the expected ramp-up capacities of all units, while the expected ramp-up capacity declared for any given unit is its last declared ramp-up capacity under the same start-up method. Therefore, the total expected ramp-up capacity declared across the entire network can be expressed as:

[0059]

[0060] In the formula, R DC This represents the expected ramp-up capacity for all transactions across the network on the trading day. For the expected ramp-up capacity of generator set g, For generator set g, this represents the previously declared ramp-up capacity under the same start-up mode. NG represents the number of generator sets.

[0061] A2. Based on the ramping demand and expected ramping capacity declared on the trading day, extract ramping market parameters from historical ramping market data under the same ramping demand and declared ramping capacity, and determine the predicted ramping market parameter fluctuation range based on the maximum and minimum values ​​of the extracted ramping market parameters.

[0062] A3. Divide the predicted market parameter fluctuation range into multiple intervals, count the number of times the historical parameter appears in each interval, and calculate the ratio of the number of times the historical parameter appears in each interval to the sum of the number of times the historical parameter appears in all intervals to obtain the first probability distribution.

[0063] In some exemplary embodiments, the ramp-up market parameter can be the ramp-up market clearing price, and the predicted ramp-up market parameter can be the predicted ramp-up market clearing price. The fluctuation range of the predicted ramp-up market clearing price is divided into multiple intervals, and the frequency of historical clearing prices in each interval is counted. The percentage of these frequencies represents the probability within that interval. Therefore, the clearing price of the ramp-up service on a trading day can be expressed as a distribution function as follows:

[0064]

[0065] In the formula, pro C The probability is the first distribution. To predict the probability of different price ranges for ramp-up services clearing out, p Cmax p Cmin These represent the maximum and minimum values ​​of the predicted range for the clearing price fluctuation of the ramp-up service, respectively; p C1 p C2 ...p CNC-1 The following are the boundary values ​​for each interval of the predicted ramp-up service clearing price, p FC NC represents the number of intervals within the price range of ramp-up services, where NC is the clearing price.

[0066] Secondly, the first data for energy storage ramp-up is calculated based on the first distribution probability and the energy storage application ramp-up capacity;

[0067] The energy storage bidding strategy needs to consider the expected bidding results in both the ramp-up market and the frequency regulation market, and assess the relationship between the bid price and the clearing price. The revenue after winning a bid in the energy storage ramp-up market can be represented as the product of the expected successful ramp-up capacity and the predicted ramp-up market clearing price. In this embodiment, considering that the ramp-up capacity clearing price is a probability function, the first data point for energy storage ramp-up is the expected value of the interval between the expected successful ramp-up capacity and the ramp-up service clearing price being higher than the bid price, which can be expressed as:

[0068]

[0069] In the formula, s represents electrical energy storage. This is the first data point for energy storage ramp-up. For energy storage, apply for ramp-up capacity pricing, p Cmax To predict the maximum range of price fluctuations for ramp-up services clearing, pro C The probability is the first distribution. For the application of ramp-up capacity for energy storage, p FC The clearing price for ramp-up services is determined. If the clearing price is lower than the bid price, the energy storage unit will not win the ramp-up market bid, and its revenue will be zero. If the clearing price is higher than the bid price, the clearing price will be used for settlement. In particular, since the individual energy storage units' declared ramp-up capacity and frequency regulation capacity are relatively small, they become marginal units, and the probability of partially declared capacity is low. Therefore, in this embodiment, it is assumed that if the energy storage unit wins the bid for the ramp-up market or frequency regulation market, its declared ramp-up capacity or frequency regulation capacity will be fully awarded.

[0070] The process of acquiring the first data for energy storage frequency regulation includes:

[0071] First, predict the second distribution probability based on historical trading data of the frequency modulation market and frequency modulation demand on trading days;

[0072] To obtain historical trading data and frequency regulation demand for each trading day in the frequency regulation market, the historical data is obtained from the data disclosed by the market operator. This historical data includes the frequency regulation demand for each historical trading day, the declared frequency regulation capacity for each historical trading day, and the clearing price of frequency regulation services for each historical trading day. The frequency regulation demand for each trading day is calculated and disclosed by the market operator based on the grid operation needs before market trading is declared.

[0073] In this embodiment of the application, the specific process of predicting the second distribution probability based on historical trading data of the frequency modulation market and the frequency modulation demand on trading days can be as follows:

[0074] B1. Calculate the expected frequency regulation capacity to be declared on the trading day based on the generator set's start-up and shutdown methods on the trading day;

[0075] Before submitting a frequency regulation market transaction application, the market operator needs to disclose the unit start-up and shutdown methods for that operating day. The total expected frequency regulation capacity declared across the entire network is the sum of the expected frequency regulation capacities of all units, while the expected frequency regulation capacity declared for any given unit is its last declared frequency regulation capacity under the same start-up method. Therefore, the total expected frequency regulation capacity declared across the entire network can be expressed as:

[0076]

[0077] In the formula, R DF The expected frequency regulation capacity to be submitted across the entire network on the trading day. For the expected frequency regulation capacity of generator unit g, For generator set g, the frequency regulation capacity was last declared under the same start-up mode. NG represents the number of generator sets.

[0078] B2. Based on the frequency regulation demand and expected frequency regulation capacity declared on the trading day, extract the frequency regulation market parameters under the same frequency regulation demand and declared frequency regulation capacity from the historical data of the frequency regulation market, and determine the predicted fluctuation range of the frequency regulation market parameters based on the maximum and minimum values ​​of the extracted frequency regulation market parameters.

[0079] B3. Divide the predicted frequency modulation market parameter fluctuation range into multiple intervals, count the number of times the historical parameter appears in each interval, and calculate the ratio of the number of times the historical parameter appears in each interval to the sum of the number of times the historical parameter appears in all intervals to obtain the second distribution probability.

[0080] In some exemplary embodiments, the frequency modulation market parameter can be the frequency modulation market clearing price, and the predicted frequency modulation market parameter can be the predicted frequency modulation market clearing price. The fluctuation range of the predicted frequency modulation market clearing price is divided into multiple intervals, and the frequency of occurrence of the historical clearing price in each interval is counted. The percentage of occurrences represents the probability within that interval. Therefore, the frequency modulation service clearing price on a trading day can be represented by a distribution function as follows:

[0081]

[0082] In the formula, pro F The second distribution probability, To predict the probability of different price ranges for FM service clearing, p Fmax p Fmin These represent the maximum and minimum values ​​of the predicted range for the clearing price fluctuation of frequency regulation services; p F1 p F2 ... p FNF-1 The following are the boundary values ​​for each interval of the predicted frequency modulation service clearing price, p FF NF represents the clearing price for frequency modulation services, and NF is the number of intervals that divide the fluctuation range of the clearing price for frequency modulation services.

[0083] Secondly, the first data for energy storage frequency regulation is calculated based on the second distribution probability and the declared frequency regulation capacity of energy storage.

[0084] The revenue from winning bids in the energy storage frequency regulation market can be expressed as the product of the expected winning frequency regulation capacity and the predicted market clearing price. In this embodiment, considering that the market clearing price is a probability function, the first data for energy storage frequency regulation is the expected value of the interval between the expected winning frequency regulation capacity and the market clearing price being higher than the bid price, which can be expressed as:

[0085]

[0086] In the formula, s represents electrical energy storage. This is the first data point for frequency regulation of energy storage. To apply for frequency regulation capacity pricing for energy storage, p Fmax To predict the maximum range of fluctuations in the clearing price of frequency modulation services, pro F The second distribution probability, For the application of frequency regulation capacity for energy storage, p FF The clearing price for frequency regulation services. If the clearing price in the frequency regulation market is lower than the bid price, the energy storage frequency regulation market will not be successful and its revenue will be zero; if the clearing price in the frequency regulation market is higher than the bid price, the settlement will be based on the clearing price.

[0087] The process of acquiring the second data for energy storage ramp-up includes:

[0088] A quadratic function for the application of ramp-up capacity for energy storage is constructed, and the second data for energy storage ramp-up is calculated using this quadratic function. The constructed quadratic function for the application of ramp-up capacity can be:

[0089]

[0090] In the formula, This is the second data point for energy storage ramp-up. Apply for ramp-up capacity for electric energy storage; and The coefficients are, in order, the quadratic coefficient, the linear coefficient, and the constant coefficient of the second data for energy storage ramp-up.

[0091] The process of acquiring the second data for energy storage frequency regulation includes:

[0092] A quadratic function for the declared frequency regulation capacity of energy storage is constructed, and the second data for energy storage frequency regulation is calculated using this quadratic function. The constructed quadratic function for the declared frequency regulation capacity can be:

[0093]

[0094] In the formula, This is the second data point for frequency regulation of energy storage. Apply for frequency regulation capacity for energy storage; and The coefficients are, in order, the quadratic coefficient, the linear coefficient, and the constant coefficient of the second data for energy storage frequency regulation.

[0095] The process of obtaining the expected comprehensive benefits of energy storage includes:

[0096] The difference between the first data point and the second data point for energy storage ramp-up is calculated to obtain the first expected return.

[0097] The difference between the first data and the second data of energy storage frequency regulation is calculated to obtain the second expected return.

[0098] The expected comprehensive return of energy storage is obtained by weighted summation of the first and second expected returns based on the winning bid state function.

[0099] The purpose of this application is to construct an expected return optimization target, which serves as the application target for energy storage transactions in the ramp-up market and frequency regulation market. Specifically, based on the first data of energy storage ramp-up, the second data of energy storage ramp-up, the first data of energy storage frequency regulation, and the second data of energy storage frequency regulation, an energy storage application strategy optimization model is constructed with the goal of maximizing the expected comprehensive return of energy storage. The formula for calculating the expected comprehensive return of energy storage is as follows:

[0100]

[0101] In the formula, CRe s For the expected comprehensive benefits of energy storage, This is the first data point for energy storage ramp-up. This is the second data point for energy storage ramp-up. This is the first data point for frequency regulation of energy storage. This is the second data point for frequency regulation of energy storage. The state function for the energy storage ramp-up service was awarded. The state function for winning bids in the frequency regulation service for energy storage. The objective function of the energy storage application strategy optimization model is Max CRe. s .

[0102] After determining the application targets, it is necessary to further determine the constraints that the application for energy storage must meet, including four aspects: the application price and capacity for ramp-up services, the application price and capacity for frequency regulation services.

[0103] 1. Determine the ramp-up capability of energy storage (i.e., the ramp-up capacity declared for energy storage). Based on the established energy plan declaration strategy, the ramp-up capability provided by energy storage is limited by three factors: charging / discharging power, ramp-up power, and storage capacity.

[0104] (1) Regarding charging and discharging power, the ramping capability should not exceed the difference between the maximum charging and discharging power of the energy storage and the planned maximum charging and discharging power of the energy storage, which can be expressed as:

[0105]

[0106] In the formula, P s DCmax P is the maximum charge / discharge power of the electrical energy storage s. s DDCmax This represents the maximum charging and discharging power in the electrical energy plan.

[0107] (2) Regarding ramping power, the ramping capacity should not exceed the difference between the maximum ramping power of the energy storage and the ramping power already scheduled in the energy plan, which can be expressed as:

[0108]

[0109] In the formula, P s SCmax P is the maximum ramp power of the electric energy storage s. s DSCmax This represents the maximum ramp power in the electricity energy plan.

[0110] (3) Regarding energy storage capacity, the ramping capability should ensure that the change in energy storage capacity under the maximum ramping capability response at any given time period does not exceed the remaining energy storage adjustment space for that time period. This requirement is equivalent to the remaining energy storage capacity at the time corresponding to the maximum energy storage capacity in the energy plan not exceeding the change in energy storage capacity under the maximum ramping capability response; and the energy storage capacity at the time corresponding to the minimum energy storage capacity not being lower than the change in energy storage capacity under the maximum ramping response. The change in energy storage capacity under the maximum ramping response is the energy storage capacity under the declared ramping capacity in one ramping response cycle. The above constraints can be expressed as:

[0111]

[0112]

[0113] In the formula, These represent the maximum and minimum energy storage capacities of electrical energy storage (s) in the electricity energy plan, respectively. These represent the maximum and minimum energy storage capacities of electrical energy storage, T. C This refers to the hill-climbing response cycle duration.

[0114] 2. Determine the frequency regulation capability of energy storage (i.e., the declared frequency regulation capacity of energy storage). Based on the established energy plan declaration strategy, the frequency regulation capability that energy storage can provide is limited by both charging / discharging power and storage capacity:

[0115] (1) Regarding charging and discharging power, the frequency regulation capability should not exceed the difference between the maximum charging and discharging power of the energy storage and the planned maximum charging and discharging power of the energy storage, which can be expressed as:

[0116]

[0117] (2) Regarding energy storage, the frequency regulation capability should ensure that the change in energy storage capacity under the maximum frequency regulation capability response at any given time period does not exceed the remaining energy storage adjustment space for that time period. This requirement is equivalent to the remaining energy storage capacity at the time corresponding to the maximum energy storage capacity in the energy plan not exceeding the change in energy storage capacity under the maximum frequency regulation capability response; and the energy storage capacity at the time corresponding to the minimum energy storage capacity not being lower than the change in energy storage capacity under the maximum frequency regulation response. The change in energy storage capacity under the maximum frequency regulation response is the energy storage capacity of the declared frequency regulation capacity in one frequency regulation response cycle. The above constraints can be expressed as:

[0118]

[0119]

[0120] In the formula, T F This is the duration of the frequency modulation response period.

[0121] 3. Determine the price constraints for the energy storage ramp-up market and frequency regulation market. Price declarations for both the ramp-up market and frequency regulation market must comply with market trading rules and be submitted within the prescribed range, which can be expressed as follows:

[0122]

[0123]

[0124] In the formula, p DCmax p DCmin These represent the upper and lower limits of the declared price in the ramp-up market, p DFmax p DFmin These are the upper and lower limits for the declared price in the FM market.

[0125] 4. Determine the bidding status constraints, requiring that energy storage can only win bids in one of the frequency regulation market and the ramp-up market, which can be expressed as:

[0126]

[0127] The above process can be used to determine the energy storage application constraints of the energy storage application strategy optimization model.

[0128] Step 102: Optimize and solve the energy storage application strategy optimization model to obtain the energy storage frequency regulation market application strategy and the ramp-up market application strategy.

[0129] The optimal application strategy is obtained by optimizing the energy storage application strategy model. The model contains differential terms, which can be solved using the interior-point method. Since these differential terms can be simplified using a step function, the optimization problem can be transformed into a mixed-integer convex quadratic programming problem, which can be solved using the branch-and-bound method or by calling commercial software packages such as Cplex. This yields the application strategies for the energy storage frequency regulation market and the energy storage ramp-up market. The frequency regulation market application strategy includes the application price for frequency regulation capacity and the application price under different frequency regulation capacities; the ramp-up market application strategy includes the application price for ramp-up capacity and the application price under different ramp-up capacities.

[0130] Step 103: Generate a first dispatch instruction based on the energy storage frequency regulation market application strategy, so that the frequency regulation market adjusts the power generation output according to the first dispatch instruction; generate a second dispatch instruction based on the ramp-up market application strategy, so that the ramp-up market adjusts the power generation output according to the second dispatch instruction.

[0131] After obtaining the energy storage frequency regulation market application strategy and the energy storage ramp-up market application strategy, the dispatching agency can display, prompt, or recommend these strategies. The dispatching agency can generate a first dispatching instruction based on the energy storage frequency regulation market application strategy, so that the frequency regulation market can adjust its power generation output according to the first dispatching instruction. The dispatching agency can generate a second dispatching instruction based on the energy storage ramp-up market application strategy, so that the ramp-up market can adjust its power generation output according to the second dispatching instruction.

[0132] This application proposes a probabilistic prediction method for clearing prices in the frequency regulation and ramp-up markets. Based on the probabilistic prediction of clearing prices in the ramp-up and frequency regulation markets, an objective function based on maximizing expected returns is constructed. Considering the operational constraints that energy storage needs to meet in providing frequency regulation and ramp-up services, an energy storage application strategy optimization model is constructed to achieve the optimized formulation of application strategies.

[0133] The above is an embodiment of a method for reporting transactions in the energy storage market provided by this application. The following is an embodiment of a device for reporting transactions in the energy storage market provided by this application.

[0134] Please refer to Figure 2 This application provides an energy storage market transaction declaration device, comprising:

[0135] The model building unit is used to construct an energy storage application strategy optimization model with the goal of maximizing the expected comprehensive benefits of energy storage based on the first data of energy storage ramping, the second data of energy storage ramping, the first data of energy storage frequency regulation, and the second data of energy storage frequency regulation, and to determine the energy storage application constraints of the energy storage application strategy optimization model.

[0136] The model solving unit is used to optimize and solve the energy storage application strategy optimization model to obtain the energy storage frequency regulation market application strategy and the ramp-up market application strategy.

[0137] The generation unit is used to generate a first dispatch instruction based on the energy storage frequency regulation market application strategy, so that the frequency regulation market adjusts the power generation output according to the first dispatch instruction, and to generate a second dispatch instruction based on the ramp-up market application strategy, so that the ramp-up market adjusts the power generation output according to the second dispatch instruction.

[0138] This application embodiment constructs an objective function based on maximizing expected revenue, and considers the operational constraints that energy storage needs to meet in providing frequency regulation and ramp-up services, and constructs an energy storage application strategy optimization model to achieve the optimized formulation of application strategies.

[0139] This application also provides a computer-readable storage medium for storing program code, which, when executed by a processor, implements the energy storage market transaction declaration method in the aforementioned method embodiments.

[0140] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0141] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0142] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0143] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0144] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0145] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0146] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of this application through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0147] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for generating a trading strategy for the energy storage market, characterized in that, include: Based on the first data of energy storage ramp-up, the second data of energy storage ramp-up, the first data of energy storage frequency regulation, and the second data of energy storage frequency regulation, an energy storage application strategy optimization model is constructed with the goal of maximizing the expected comprehensive benefits of energy storage, and the energy storage application constraints of the energy storage application strategy optimization model are determined. The process of acquiring the first data for energy storage ramp-up includes: Based on historical transaction data of the ramping market and the first distribution probability of ramping demand on trading days, the historical transaction data of the ramping market includes historical ramping demand and historical declared ramping capacity. Calculate the first data for energy storage ramp-up based on the first distribution probability and the energy storage application ramp-up capacity; The process of acquiring the second data for energy storage ramping includes: Construct a quadratic function for the application ramp-up capacity of electric energy storage, and calculate the second data for the ramp-up capacity of electric energy storage using the quadratic function for the application ramp-up capacity of electric energy storage; The process of acquiring the first data for energy storage frequency regulation includes: The second distribution probability is predicted based on historical transaction data of the frequency modulation market and frequency modulation demand on trading days. The historical transaction data of the frequency modulation market includes historical frequency modulation demand and historical frequency modulation capacity declaration. The first data for energy storage frequency regulation is calculated based on the second distribution probability and the declared frequency regulation capacity of energy storage. The process of acquiring the second data for energy storage frequency regulation includes: Construct a quadratic function for the applied frequency regulation capacity of energy storage, and calculate the second data for energy storage frequency regulation using the quadratic function for the applied frequency regulation capacity of energy storage; The optimization model for the energy storage application strategy is optimized and solved to obtain the energy storage frequency regulation market application strategy and the ramp-up market application strategy; A first dispatch instruction is generated according to the energy storage frequency regulation market application strategy, causing the frequency regulation market to adjust its power generation output according to the first dispatch instruction. A second dispatch instruction is generated according to the ramp-up market application strategy, causing the ramp-up market to adjust its power generation output according to the second dispatch instruction.

2. The method for generating an energy storage market transaction declaration strategy according to claim 1, characterized in that, The prediction of the first distribution probability based on historical transaction data of the ramping market and the ramping demand on trading days includes: Calculate the expected ramp-up capacity for the trading day based on the generator set's start-up and shutdown schedule; Based on the ramp-up demand and expected ramp-up capacity declared on the trading day, ramp-up market parameters under the same ramp-up demand and declared ramp-up capacity are extracted from historical data of the ramp-up market. The fluctuation range of the predicted ramp-up market parameters is determined based on the maximum and minimum values ​​of the extracted ramp-up market parameters. The predicted market parameter fluctuation range is divided into multiple intervals. The number of times the historical parameter appears in each interval is counted, and the ratio of the number of times the historical parameter appears in each interval to the sum of the number of times the historical parameter appears in all intervals is calculated to obtain the first distribution probability.

3. The method for generating an energy storage market transaction declaration strategy according to claim 1, characterized in that, The prediction of the second distribution probability based on historical trading data of the frequency modulation market and frequency modulation demand on trading days includes: Calculate the expected frequency regulation capacity to be declared on the trading day based on the start-up and shutdown methods of generator sets on the trading day; Based on the frequency regulation demand and expected frequency regulation capacity declared on the trading day, frequency regulation market parameters under the same frequency regulation demand and declared frequency regulation capacity are extracted from historical frequency regulation market data, and the fluctuation range of the predicted frequency regulation market parameters is determined based on the maximum and minimum values ​​of the extracted frequency regulation market parameters. The predicted frequency modulation market parameter fluctuation range is divided into multiple intervals. The number of times the historical parameter appears in each interval is counted, and the ratio of the number of times the historical parameter appears in each interval to the sum of the number of times the historical parameter appears in all intervals is calculated to obtain the second distribution probability.

4. The method for generating an energy storage market transaction declaration strategy according to claim 1, characterized in that, The process of obtaining the expected comprehensive benefits of the energy storage includes: The difference between the first data point and the second data point for energy storage ramp-up is calculated to obtain the first expected return. The difference between the first data and the second data of energy storage frequency regulation is calculated to obtain the second expected return. The first expected return and the second expected return are weighted and summed according to the winning bid state function to obtain the comprehensive expected return of energy storage.

5. The method for generating an energy storage market transaction declaration strategy according to claim 1, characterized in that, The constraints for energy storage application include energy storage application ramp-up capacity constraints and energy storage application frequency regulation capacity constraints. The energy storage application ramp-up capacity constraints include: The declared ramp-up capacity of electric energy storage shall not exceed the difference between the maximum charging and discharging power of the electric energy storage and the planned maximum charging and discharging power of the electric energy storage. The declared ramp-up capacity of electric energy storage shall not exceed the difference between the maximum ramp-up power of electric energy storage and the ramp-up power already arranged in the power energy plan; The change in the amount of energy storage capacity under the maximum ramping capability response during any given period shall not exceed the remaining energy storage capacity adjustment space during that period. The frequency regulation capacity constraints for energy storage applications include: The declared frequency regulation capacity of energy storage shall not exceed the difference between the maximum charging and discharging power of energy storage and the planned maximum charging and discharging power of energy storage. The change in the amount of energy stored under the maximum frequency regulation capability response during any given period shall not exceed the remaining energy storage capacity adjustment space during that period.

6. A device for submitting transaction declarations in the energy storage market, characterized in that, include: The model building unit is used to construct an energy storage application strategy optimization model with the goal of maximizing the expected comprehensive benefits of energy storage based on the first data of energy storage ramping, the second data of energy storage ramping, the first data of energy storage frequency regulation, and the second data of energy storage frequency regulation, and to determine the energy storage application constraints of the energy storage application strategy optimization model. The process of acquiring the first data for energy storage ramp-up includes: Based on historical transaction data of the ramping market and the first distribution probability of ramping demand on trading days, the historical transaction data of the ramping market includes historical ramping demand and historical declared ramping capacity. Calculate the first data for energy storage ramp-up based on the first distribution probability and the energy storage application ramp-up capacity; The process of acquiring the second data for energy storage ramping includes: Construct a quadratic function for the application ramp-up capacity of electric energy storage, and calculate the second data for the ramp-up capacity of electric energy storage using the quadratic function for the application ramp-up capacity of electric energy storage; The process of acquiring the first data for energy storage frequency regulation includes: The second distribution probability is predicted based on historical transaction data of the frequency modulation market and frequency modulation demand on trading days. The historical transaction data of the frequency modulation market includes historical frequency modulation demand and historical frequency modulation capacity declaration. The first data for energy storage frequency regulation is calculated based on the second distribution probability and the declared frequency regulation capacity of energy storage. The process of acquiring the second data for energy storage frequency regulation includes: Construct a quadratic function for the applied frequency regulation capacity of energy storage, and calculate the second data for energy storage frequency regulation using the quadratic function for the applied frequency regulation capacity of energy storage; The model solving unit is used to optimize and solve the energy storage application strategy optimization model to obtain the energy storage frequency regulation market application strategy and the ramp-up market application strategy. The generation unit is configured to generate a first dispatch instruction based on the energy storage frequency regulation market application strategy, so that the frequency regulation market adjusts the power generation output according to the first dispatch instruction, and generate a second dispatch instruction based on the ramp-up market application strategy, so that the ramp-up market adjusts the power generation output according to the second dispatch instruction.

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