Energy storage control system with multiple flexible adjustment capabilities

Abstract

This invention proposes an energy storage control system that provides multiple flexible adjustment capabilities. Based on four adjustment functions—peak shaving, frequency regulation, wind power plan tracking, and reserve—it calculates the benefits of energy storage participating in each function during the day-ahead optimization phase by establishing benefit models for each function and considering wind power plan deviations and changes in the energy storage's state of charge (SOC). This optimizes the power capacity of energy storage participating in peak shaving, frequency regulation, and reserve in future time periods. During the real-time control phase, based on the energy storage's response to real-time frequency deviations and utilizing ultra-short-term wind power forecasts four hours in advance, it optimizes the real-time power value of energy storage participating in wind power plan tracking. The total power output of energy storage is the sum of the calculation results for all functions. By modeling the benefits of each function and considering changes in the energy storage's SOC over a longer period, the system optimizes the output of energy storage for each function, achieving joint optimization control of energy storage participating in multi-timescale adjustment functions and maximizing the effectiveness and benefits of energy storage as a flexible grid adjustment resource.

Description

Technical Field

[0001] This invention belongs to the field of energy storage system optimization control technology, and particularly relates to an energy storage control system that provides multiple flexible adjustment capabilities. Background Technology

[0002] As the penetration rate of renewable energy generation increases, greater demands are being placed on the peak-shaving, frequency regulation, and reserve capabilities of the power system. Battery energy storage technology offers rapid response and high control precision. With the decreasing cost of energy storage, it can be used to regulate the grid-connected power of renewable energy sources and provide ancillary services. Currently, the application of energy storage technology in power systems has become a research hotspot.

[0003] Extensive research has been conducted both domestically and internationally on the application of energy storage in power systems. Through bidirectional control of energy storage, it discharges during peak load periods and charges during off-peak periods, reducing the peak-to-valley load difference. Utilizing the rapid response characteristics of energy storage, it responds to grid frequency deviations, charging rapidly when the frequency is high and discharging rapidly when the frequency is low, maintaining system frequency stability. Energy storage can assist in the grid connection of new energy sources, improving their accuracy in tracking planned power generation. It can also provide system backup, ensuring the safety and stability of the power system. Current energy storage control strategies primarily target single functions, resulting in some energy storage capacity not being fully utilized. When energy storage provides multiple services (functions), a comprehensive optimization method needs to be designed based on the demand and benefits of adjusting functions at different time scales, as well as the inherent constraints of energy storage. Currently, a complete control strategy is lacking. Summary of the Invention

[0004] In view of this, in order to overcome the defects and shortcomings of the existing technology, the purpose of this invention is to provide an energy storage control system that provides multiple flexible adjustment capabilities. An optimized control model is established to provide four functions of energy storage at the same time: peak shaving, frequency regulation, wind power plan tracking, and backup. This reduces the capacity idleness of energy storage providing a single service and can coordinate the adjustment needs of different time scales.

[0005] Based on four regulation functions—peak shaving, frequency regulation, wind power plan tracking, and reserve—the system optimizes energy storage participation in each function during the day-ahead optimization phase by establishing benefit models for each function. It considers wind power plan deviations and changes in the energy storage's state of charge (SOC), optimizing the power capacity of energy storage for peak shaving, frequency regulation, and reserve in future time periods. During the real-time control phase, based on the energy storage's response to real-time frequency deviations, it utilizes ultra-short-term wind power forecasts four hours in advance to optimize the real-time power value of energy storage participating in wind power plan tracking. The total energy storage output is the sum of the calculation results for all functions. By modeling the benefits of each function and considering long-term changes in energy storage SOC, the system optimizes the output provided by energy storage for each function, achieving joint optimization control of energy storage participation in multi-timescale regulation functions and maximizing the effectiveness and benefits of energy storage as a flexible grid regulation resource.

[0006] The present invention specifically adopts the following technical solution:

[0007] An energy storage control system providing multiple flexible adjustment capabilities, characterized in that it is based on a computer system and includes:

[0008] The price forecasting module is used to read the day-ahead power forecast data of the target power system and the day-ahead power forecast data of new energy sources including wind power and photovoltaics; and to forecast the prices of each service provided in each time period based on the price range for providing peak shaving, frequency regulation, wind power plan tracking and backup services.

[0009] The energy storage power day-ahead optimization control module establishes a revenue model for four functions—peak shaving, frequency regulation, wind power plan tracking, and reserve—based on price forecast results. It calculates the revenue of energy storage providing various functions, considers wind power plan deviations and changes in the state of charge (SOC) of energy storage, and optimizes the power capacity of energy storage participating in peak shaving, frequency regulation, and reserve for each time period of the next day.

[0010] The real-time optimization and control module for energy storage power, based on the day-ahead optimization results of energy storage power and the real-time response results of energy storage to frequency deviation, utilizes the ultra-short-term predicted power of wind power four hours in advance to optimize the real-time power value of energy storage participating in wind power plan tracking. The total power output of energy storage is the superposition of various functional calculation results.

[0011] Furthermore, based on load forecasting, wind power output forecasting, and photovoltaic forecasting, the system determines the demand and shortage of peak shaving, frequency regulation, and reserve capacity for the next day, and predicts the compensation prices for deep peak shaving, frequency regulation, and reserve capacity for each period.

[0012] To formulate a power generation plan based on the day-ahead forecast of wind power, and to determine the energy storage demand of the wind power tracking plan for the next day, the forecast power for the next day is corrected based on the error between the historical measured power and the corresponding day-ahead forecast power.

[0013] By combining the four aforementioned revenue models, a day-ahead optimization control model for energy storage is established to optimize the power capacity of energy storage participating in peak shaving, frequency regulation, and reserve in each time period of the following day.

[0014] During the real-time control period, based on the optimized peak-shaving, frequency regulation, and reserve power, and according to the deviation between the real-time grid frequency and the rated frequency, the energy storage power participating in frequency regulation and the wind power planned tracking power value are calculated in real time; the real-time output value of energy storage is the sum of the outputs provided for each function.

[0015] Furthermore, the mathematical models for the benefits of the four functions—peak shaving, frequency regulation, wind power plan tracking, and reserve—are as follows:

[0016]

[0017] In the formula: Gm G f G s These refer to the revenue generated from energy storage participating in peak shaving, frequency regulation, and backup services, respectively. Let represent the power of energy storage participating in peak shaving, frequency regulation, wind power plan tracking, and reserve at time i, respectively. The unit prices for energy storage participation in peak shaving, frequency regulation mileage, wind power grid connection, and standby are respectively, C. p C is penalized for participating in wind power program tracking. soc Let λ be the absolute value of the energy storage SOC deviation of 50%, and λ be the coefficient of the SOC deviation value. Let P be the minimum or maximum SOC that the stored energy can reach at time i. i d Let P be the day-ahead power forecast correction value for the wind farm at time i. i p Let E be the planned power generation capacity of the wind farm at time i. p Let α be the planned power generation of the wind farm during time period T, α be the allowable deviation of the wind farm's power output from the tracking plan curve, t be the time interval for energy storage power output control, and T be the longest period for optimized energy storage power output control.

[0018] Furthermore, the objective expression of the day-ahead optimization control model for energy storage is:

[0019] f = max(G) m +G f +G s -C p -C soc (2)

[0020] The formula for calculating SOC constraints is:

[0021]

[0022] In the formula: Let SOC be the minimum and maximum possible SOC of the stored energy at time t-1, respectively. The minimum or maximum SOC of the stored energy at time t are given by SOC. min SOC max These are the lower and upper limits of the SOC for energy storage, respectively, and E is the rated capacity of energy storage;

[0023] The formula for calculating the energy storage power constraint is as follows:

[0024]

[0025] In the formula: P min P max These are the upper and lower limits of the energy storage capacity, respectively.

[0026] The calculation formulas for peak shaving, frequency regulation, and reserve constraints are as follows:

[0027]

[0028] And estimate the frequency regulation revenue based on the historical mileage-capacity ratio.

[0029] Furthermore, the energy storage adjusts its power based on the deviation between the real-time grid frequency and the rated frequency. The formula for calculating the energy storage power participating in frequency regulation is as follows:

[0030]

[0031] In the formula: δ% is the wind power frequency regulation droop rate, set at 2%, P N f is the rated power of the wind power. i f0, f d These are the real-time frequency, rated frequency, and frequency modulation dead zone of the power grid at time i, respectively.

[0032] The energy storage power real-time optimization control module utilizes the wind power ultra-short-term prediction power and considers the changes in energy storage SOC in the next four hours to optimize the energy storage participation in wind power plan tracking in the next four hours. The ultra-short-term prediction power time scale is 4 hours and the time resolution is 15 minutes.

[0033] The peak shaving real-time power is the peak shaving power capacity optimized a day before, the frequency regulation real-time power cannot exceed the frequency regulation power capacity optimized a day before, and the reserve capacity is the reserve power capacity optimized a day before.

[0034] Compared with the prior art, the present invention and its preferred embodiment have the following advantages: considering that the energy storage system provides four functions at the same time, namely peak shaving, frequency regulation, wind power plan tracking, and backup, and utilizing the ultra-short-term predicted power of wind power to establish an optimal output control model for energy storage, the energy storage benefits can be maximized. Attached Figure Description

[0035] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0036] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0037] Figure 1 , Figure 2 This is a flowchart and system module diagram of the optimized control process for real-time energy storage that provides multiple functions, provided by an embodiment of the present invention.

[0038] Figures 3-7 This is a rendering of an application example of the present invention. Detailed Implementation

[0039] To make the features and advantages of this patent more apparent and understandable, specific embodiments are provided below for detailed explanation:

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. The components described and shown in the accompanying drawings can generally be combined and designed in different configurations. Therefore, the following detailed description of selected embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but only to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0041] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0042] like Figure 1 , Figure 2 The energy storage control system with various flexible adjustment capabilities provided in this embodiment includes:

[0043] The price forecasting module is used to read the day-ahead power forecast data of the target power system and the day-ahead power forecast data of new energy sources including wind power and photovoltaics; and to forecast the prices of each service provided in each time period based on the price range for providing peak shaving, frequency regulation, wind power plan tracking and backup services.

[0044] The energy storage power day-ahead optimization control module establishes a revenue model for each service based on price forecast results, calculates the revenue of energy storage participating in each service, and optimizes the power capacity of energy storage participating in peak shaving, frequency regulation and reserve for each time period of the next day, taking into account wind power planning deviations and changes in energy storage state of charge (SOC).

[0045] The real-time optimization and control module for energy storage power, based on the day-ahead optimization results of energy storage power and the real-time response results of energy storage to frequency deviation, utilizes the ultra-short-term predicted power of wind power four hours in advance to optimize the real-time power value of energy storage participating in wind power plan tracking. The total power output of energy storage is the superposition of the calculation results of various functions.

[0046] like Figure 1 As shown, the working process of this system includes the following steps:

[0047] 1) Energy storage provides four functions: peak shaving, frequency regulation, wind power plan tracking, and backup. Revenue models are established for each function.

[0048] 2) Based on load forecasting, wind power output forecasting, and photovoltaic forecasting, determine the demand and shortage of system peak shaving, frequency regulation, and reserve capacity for the next day, and predict the compensation prices for deep peak shaving, frequency regulation, and reserve capacity for each period.

[0049] 3) Based on the day-ahead forecast power of wind power, a power generation plan is formulated. At the same time, in order to determine the energy storage demand of the wind power tracking plan for the next day, the forecast power for the next day is corrected according to the error between the historical measured power and the corresponding day-ahead forecast power.

[0050] 4) Combine the four functions of step (1) to establish a day-ahead optimization control model for energy storage, and optimize the power capacity of energy storage participating in peak shaving, frequency regulation and reserve for each period of the next day;

[0051] 5) During the real-time control period, based on the peak shaving, frequency regulation and reserve power optimized in step (4), the power of energy storage participating in frequency regulation and the wind power plan tracking power value are calculated in real time according to the deviation between the real-time frequency of the power grid and the rated frequency; the superposition of the calculation results of each function is the real-time output value of energy storage.

[0052] In this embodiment, the mathematical models for the benefits of energy storage participating in peak shaving, frequency regulation, wind power plan tracking, and backup in step (1) are as follows:

[0053]

[0054] In the formula: G m G f G s The benefits of energy storage participating in four functions are as follows: peak shaving, frequency regulation, and backup. Let represent the power of energy storage participating in peak shaving, frequency regulation, wind power plan tracking, and reserve at time i, respectively. The unit prices for energy storage participation in peak shaving, frequency regulation mileage, wind power grid connection, and standby are respectively, C. p C is penalized for participating in wind power program tracking. soc Let λ be the absolute value of the energy storage SOC deviation of 50%, and λ be the coefficient of the SOC deviation value. Let P be the minimum or maximum SOC that the stored energy can reach at time i. i d Let P be the day-ahead power forecast correction value for the wind farm at time i. i p Let E be the planned power generation capacity of the wind farm at time i. p Let α be the planned power generation of the wind farm during time period T, α be the allowable deviation of the wind farm's power output from the tracking plan curve, t be the time interval for energy storage power output control, and T be the longest period for optimized energy storage power output control.

[0055] In this embodiment, the objective expression of the day-ahead optimization control model for energy storage in step (4) is:

[0056] f = max(G) m +G f +G s -C p -C soc (2)

[0057] The formula for calculating SOC constraints is:

[0058]

[0059] In the formula: Let SOC be the minimum and maximum possible SOC of the stored energy at time t-1, respectively. The minimum or maximum SOC of the stored energy at time t are given by SOC. min SOC max These represent the lower and upper limits of the State of Charge (SOC) for energy storage, respectively, and E represents the rated capacity of the energy storage.

[0060] The formula for calculating energy storage power constraints is:

[0061]

[0062] In the formula: P min P max These represent the upper and lower limits of energy storage capacity, respectively.

[0063] The calculation formulas for peak shaving, frequency regulation, and reserve constraints are as follows:

[0064]

[0065] In this embodiment, the frequency modulation revenue in step (4) is estimated based on the historical mileage-capacity ratio.

[0066] In this embodiment, in step (5), the energy storage power is adjusted according to the deviation between the real-time frequency of the power grid and the rated frequency. The formula for calculating the energy storage power participating in frequency regulation is as follows:

[0067]

[0068] In the formula: δ% is the wind power frequency regulation droop rate, set at 2%, P N f is the rated power of the wind power. i f0, f d , respectively, are the real-time frequency, rated frequency, and frequency regulation dead zone of the power grid at time i.

[0069] In this embodiment, the energy storage real-time optimization control model in step (5) utilizes the wind power ultra-short-term prediction power and considers the change of energy storage SOC in the next four hours to optimize the energy storage participation in wind power plan tracking power in the next four hours. The ultra-short-term prediction power time scale should be 4 hours and the time resolution should be 15 minutes.

[0070] In this embodiment, in step (5), the peak-shaving real-time power is the day-ahead optimized peak-shaving power capacity, the frequency modulation real-time power cannot exceed the day-ahead optimized frequency modulation power capacity, and the reserve capacity is the day-ahead optimized reserve power capacity.

[0071] Application examples

[0072] Based on the system design provided above, this embodiment uses Matlab software to write relevant programs and demonstrates the implementation effect using a case study.

[0073] Operating environment:

[0074] Intel Core i3-10105 CPU 3.7GHz, 16GB RAM, Microsoft Windows 10x64

[0075] Gurobi 9.5.1

[0076] Matlab 2020a

[0077] Implementation results:

[0078] This application example is based on... Figure 1 The energy storage optimization control process optimizes and controls the energy storage output. The energy storage and wind farm configurations are shown in Table 1.

[0079] Table 1 Energy Storage and Wind Power Parameter Configuration

[0080]

[0081] According to the strategy of this embodiment, energy storage is optimized to provide output power for four functions simultaneously: peak shaving, frequency regulation, and reserve. The predicted price is as follows: Figures 3-5 As shown; the total energy storage output and energy storage SOC are respectively as follows: Figures 6-7 As shown.

[0082] Table 2: Energy Storage Provides Multiple Benefits

[0083] Peak shaving / 10,000 yuan FM / 10,000 yuan Planned tracking / 10,000 yuan Reserves / 10,000 yuan Total revenue / 10,000 yuan 1.78 5.90 12.33 0.18 20.19

[0084] Table 2 shows the revenue and total revenue of energy storage participating in various functions. Energy storage generates significant revenue by providing wind power plan tracking functionality, as wind power is largely tracked according to the generation plan. Therefore, the revenue from energy storage providing plan tracking alone is approximately RMB 123,300, while the revenue from providing multiple functions reaches RMB 201,900, representing a 64% increase compared to providing plan tracking functionality alone. Therefore, this strategy can significantly improve the revenue of energy storage compared to providing wind power plan tracking alone.

[0085] The logic program design scheme in the above solution provided in this embodiment can be stored in a computer-readable storage medium in the form of code, and implemented in the form of a computer program. The basic parameter information required for calculation is input through computer hardware, and the calculation result is output.

[0086] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] This invention is described with reference to methods, apparatus (devices), and computer program products according to embodiments of the invention. It should be understood that each process can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing functions specified in one or more processes.

[0088] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the function specified in one or more processes.

[0089] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing a function specified in one or more processes.

[0090] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0091] This patent is not limited to the above-described preferred embodiments. Anyone can derive other forms of energy storage control systems that provide multiple flexible adjustment capabilities based on the guidance of this patent. All equivalent changes and modifications made within the scope of this patent application shall fall within the scope of this patent.

Claims

1. An energy storage control system providing a plurality of flexible regulation capabilities, comprising: The computer system comprises: a price prediction module configured to read load day-ahead power prediction data of a target power system and day-ahead power prediction data of new energy power sources including wind power and photovoltaic power, and predict prices of each service in each period according to a price interval of peak regulation, frequency regulation, wind power plan tracking and standby service; an energy storage power day-ahead optimization control module configured to establish a benefit model of the four functions of peak regulation, frequency regulation, wind power plan tracking and standby based on the price prediction result, calculate benefits of the energy storage providing various functions, consider wind power plan deviation and energy storage state of charge (SOC) change, and optimize power capacity of the energy storage participating in peak regulation, frequency regulation and standby in each period of the next day; an energy storage power real-time optimization control module configured to use four-hour ultra-short-term prediction power of wind power based on the energy storage power day-ahead optimization result and real-time response result of the energy storage to frequency deviation, optimize real-time power value of the energy storage participating in wind power plan tracking, and superimpose the total power output of the energy storage on the calculation results of various functions; based on load prediction, wind power output prediction and photovoltaic prediction, judge system peak regulation, frequency regulation and standby capacity demand and tightness of the next day, and predict deep peak regulation, frequency regulation and standby compensation prices in each period; based on day-ahead prediction power of wind power, establish a power generation plan, correct the next day prediction power according to errors of historical measured power and corresponding day-ahead prediction power, and judge demand of the energy storage for wind power tracking plan output of the next day; combine the four benefit models to establish an energy storage day-ahead optimization control model, and optimize power capacity of the energy storage participating in peak regulation, frequency regulation and standby in each period of the next day; in a real-time control period, based on the optimized peak regulation, frequency regulation and standby power, according to deviation of real-time frequency and rated frequency of the power grid, optimize and calculate frequency regulation power and wind power plan tracking power value of the energy storage in real time; and the real-time output value of the energy storage is the sum of the outputs of providing various functions. the mathematical models of the four functions of peak regulation, frequency regulation, wind power plan tracking and standby are respectively: wherein: G m , G f , G s are the benefits of energy storage participating in peak regulation, frequency regulation, and backup service, respectively, are the power of energy storage participating in peak regulation, frequency regulation, wind power plan tracking, and backup at the ith moment, respectively, are the unit prices of energy storage participating in peak regulation, frequency regulation, wind power transmission, and backup, respectively, C p is the penalty of energy storage participating in wind power plan tracking, C soc is the absolute value of energy storage SOC deviating from 50%, and λ is the coefficient of SOC deviation value, are the minimum or maximum SOC that energy storage can have at the ith moment, P i d is the day-ahead forecast power correction value of the wind farm at the ith moment, P i p is the planned power generation of the wind farm at the ith moment, E p is the planned power generation of the wind farm in T period, α is the allowed deviation of wind farm output in tracking the plan curve, t is the time interval of energy storage output control, and T is the longest period of energy storage output optimization control.

2. The energy storage control system providing multiple flexible regulation capabilities according to claim 1, wherein: a target expression of the energy storage day-ahead optimization control model is: f = max(G m + G f + G s - C p - C soc )(2) a calculation formula of the SOC constraint is: In the formula: respectively the minimum and maximum SOC that the energy storage can assume at time t-1, respectively the minimum or maximum SOC that the energy storage can assume at time t, SOC min , SOC max respectively the lower and upper limits of the SOC of the energy storage, E being the rated capacity of the energy storage; a calculation formula of the energy storage power constraint is: wherein: P min , P max are the upper and lower limits of the stored power, respectively; a calculation formula of the peak regulation, frequency regulation and standby constraint is: and estimate frequency regulation benefits according to historical mileage-capacity ratios.

3. The energy storage control system providing multiple flexible regulation capabilities according to claim 2, wherein: the energy storage adjusts the energy storage power according to deviation of real-time frequency and rated frequency of the power grid, and a calculation formula of the energy storage power participating in frequency regulation is: Wherein: δ% is the wind power frequency modulation adjustment rate, set to 2%, P N is the wind power rated power, f i , f0, f d are the i-th moment of the real-time frequency of the power grid, the rated frequency, and the frequency modulation dead zone, respectively. the energy storage power real-time optimization control module uses wind power ultra-short-term prediction power, considers SOC change of the energy storage in the next four hours, optimizes the energy storage participating in wind power plan tracking power in the next four hours, the time scale of the ultra-short-term prediction power is four hours, and the time resolution is 15 minutes; the peak regulation real-time power is the peak regulation power capacity of the day-ahead optimization, the frequency regulation real-time power cannot exceed the frequency regulation power capacity of the day-ahead optimization, and the standby capacity is the standby power capacity of the day-ahead optimization.

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