Power station peak regulation method based on hybrid energy storage and related device

Abstract

The invention discloses a hybrid energy storage-based power station peak regulation method and a related device. The method comprises the following steps of: constructing a hybrid energy storage allocation model; predicting the future load demand of the power station, and matching energy storage equipment corresponding to the load demand through the hybrid energy storage deployment model; formulating a power station peak regulation strategy based on the load demand prediction result, determining the specific type of energy storage equipment, and carrying out peak regulation on the power station; and the load change of the power station after peak regulation is monitored, so that the energy storage equipment can store and release electric energy at the optimal opportunity. According to the method, a hybrid energy storage deployment model is constructed, the future load demand of a power station is accurately predicted, corresponding energy storage equipment is matched through the model, a scientific peak regulation strategy is formulated, and meanwhile, the load change of the power station after peak regulation is monitored, so that the energy storage equipment stores and releases electric energy at the optimal opportunity; therefore, power station peak regulation efficiency and accuracy are improved, and stable operation of a power grid is guaranteed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power system peak regulation, and particularly relates to a power station peak regulation method based on a hybrid energy storage and a related device. BACKGROUND

[0002] With the rapid development of the power system and the large-scale access of new energy, the load demand of the power station presents an increasingly complex and variable trend. In order to meet the requirements of stable operation of the power grid and power quality, the power station peak regulation technology has become a research hotspot in the field of electric power. The traditional power station peak regulation mainly relies on conventional energy such as thermal power units, however, this method has the disadvantages of slow response speed, high energy consumption, poor flexibility, etc., and is difficult to meet the demand of modern power system for fast and accurate peak regulation.

[0003] In recent years, with the continuous progress and gradual reduction of the cost of energy storage technology, the hybrid energy storage system is widely used in power station peak regulation due to its advantages of fast response speed, high energy density, long cycle life, etc. The hybrid energy storage system is usually composed of multiple types of energy storage devices, such as battery energy storage, super capacitor energy storage, pumped storage, etc. These energy storage devices each have their own characteristics and are suitable for different load scenarios.

[0004] However, how to effectively allocate various energy storage devices in the hybrid energy storage system to meet the load demand of the power station and realize the optimized utilization of the energy storage devices is a technical problem currently faced. The traditional peak regulation method often relies on experience judgment or simple rule control, and lacks scientificity and accuracy. SUMMARY

[0005] The purpose of the present application is to provide a power station peak regulation method based on a hybrid energy storage and a related device, to solve the technical defects in the prior art that the traditional peak regulation method often relies on experience judgment or simple rule control, and lacks scientificity and accuracy.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions: In a first aspect, a power station peak regulation method based on a hybrid energy storage is provided, comprising: constructing a hybrid energy storage allocation model; predicting the future load demand of the power station, and matching the energy storage devices corresponding to the load demand through the hybrid energy storage allocation model; based on the load demand prediction result, formulating a power station peak regulation strategy, determining the specific type of the energy storage devices, and regulating the power station; monitoring the load change of the power station after peak regulation, so that the energy storage devices store and release electric energy at the best opportunity.

[0007] Further, the construction of the hybrid energy storage allocation model specifically comprises: Collect the operation state data of the hybrid energy storage power station throughout the year, and divide a large amount of the operation state data into months as the division condition, so that different months correspond to different operation state data; Based on the annual production plan, in combination with the change trend of the operation state data, determine the load fluctuation time period of the hybrid energy storage power station; Use the months included in the load fluctuation time period of the hybrid energy storage power station and the operation state data corresponding to the months as construction data to construct a hybrid energy storage deployment model.

[0008] Further, the operation state data includes battery capacity change data, charging and discharging efficiency data, and battery life data.

[0009] Further, the future load demand of the power station is predicted, and the energy storage device corresponding to the load demand is matched through the hybrid energy storage deployment model, specifically including: The time series analysis is adopted to combine the historical load data of the power station, the weather forecast information and the production plan to predict the future load demand of the power station; The hybrid energy storage deployment model is used to simulate the load demand characteristics of the power station in the future, and the energy storage device corresponding to the load demand is matched.

[0010] Further, the energy storage device includes a battery energy storage device, a compressed air energy storage device, and a pumped hydro energy storage device; According to the technical parameters and economic costs of each type of energy storage device, the capacity and configuration ratio of each type of energy storage device are determined.

[0011] Further, based on the load demand prediction result, a power station peak shaving strategy is formulated, the specific type of the energy storage device is determined, and the power station is peak shaved, specifically including: According to the load prediction result, a power station peak shaving strategy is formulated, and the specific type of the energy storage device participating in peak shaving is determined.

[0012] In a second aspect, a power station peak shaving system based on hybrid energy storage is provided, including: The construction module is used to construct a hybrid energy storage deployment model; The prediction module is used to predict the future load demand of the power station; The matching module is used to match the energy storage device corresponding to the load demand; The strategy formulation module is used to formulate a power station peak shaving strategy; The monitoring module is used to monitor the load change of the power station after peak shaving.

[0013] In a third aspect, a mobile device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the hybrid energy storage based power station peak shaving method as described above when executing the computer program.

[0014] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, wherein the computer program is executable by a processor to implement the steps of the hybrid energy storage based power station peak shaving method as described above.

[0015] In a fifth aspect, a computer program product is provided, comprising computer instructions for instructing a computing device to perform the operations corresponding to the hybrid energy storage based power station peak shaving method as described above.

[0016] Compared with the prior art, the present application has the following beneficial effects: 1. The method can construct a hybrid energy storage deployment model, accurately predict future load demand of the power station, and develop a scientific peak shaving strategy by matching the corresponding energy storage device through the model, while monitoring the load change of the power station after peak shaving, so as to store and release electric energy at the best opportunity, thereby improving the efficiency and accuracy of power station peak shaving and ensuring the stable operation of the power grid.

[0017] 2. By dividing the operation state data by month and combining the annual production plan and load fluctuation period to construct the model, the model is more close to the actual operation, and the pertinence and accuracy of the model are improved. Secondly, the model considers the energy storage device deployment strategy under different months and different load fluctuation conditions, so that the model has stronger applicability and can be applied to hybrid energy storage power stations of different years and different production plans.

[0018] 3. By collecting and analyzing battery capacity change data, the change of the battery's energy storage capacity under different working conditions can be accurately understood, so that the performance of the battery under different load demands can be more accurately predicted. By collecting and analyzing the charging and discharging efficiency data, the energy loss of the battery under different charging and discharging states can be evaluated, and then the charging and discharging strategy can be optimized to improve the overall efficiency of the energy storage system. By collecting and analyzing the battery life data, the remaining life of the battery under different use conditions can be predicted, so that the charging and discharging plan of the battery can be reasonably arranged to prolong the service life of the battery.

[0019] 4. By time series analysis, combined with historical load data, weather forecast information and production plan, factors affecting load demand can be more comprehensively considered to improve the accuracy of load prediction.

[0020] 5. By comprehensively considering the economic cost of each type of energy storage device, the optimal configuration ratio can be formulated, so that the total cost of the entire energy storage system is minimized and the economic benefit is maximized.

[0021] 6. By accurate load prediction and reasonable peak shaving strategy, the power generation plan of the power station and the charge and discharge plan of the energy storage device can be optimized, unnecessary power generation and energy storage operation can be reduced, energy consumption and cost can be reduced, and the operation efficiency of the power station can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0023] Figure 1 A flow chart of the power station peak shaving method based on hybrid energy storage provided by the present application is provided. Figure 2 A principle diagram of the power station peak shaving system based on hybrid energy storage provided by the present application is provided. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, not all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0025] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those skilled in the art on the basis of the embodiments in the present application without creative labor are within the scope of protection of the present application.

[0026] It should be noted that: similar numbers and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0027] In the description of the embodiments of the present application, it should be noted that if the terms "upper", "lower", "horizontal", "inner" and the like indicating the orientation or position relationship are based on the orientation or position relationship shown in the drawings, or the orientation or position relationship when the product of the present application is usually placed, which is only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as limiting the device or element indicated to have a specific orientation, to be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second" and the like are only used for differentiation and cannot be understood as indicating or implying relative importance.

[0028] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly inclined. For example, "horizontal" only means that its direction is relatively more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly inclined.

[0029] In the description of the embodiments of the present application, it should be noted that unless otherwise explicitly specified and limited, if the terms "arrangement", "installation", "connection", "connection" appear, they should be understood in a broad sense. For example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0030] With the rapid development of power systems and the large-scale access of new energy, the load demand of power stations presents an increasingly complex and variable trend. In order to meet the requirements of stable operation of power grid and power quality, power station peak shaving technology has become a research hotspot in the field of electric power. Traditional power station peak shaving mainly relies on conventional energy such as thermal power units, however, this method has the disadvantages of slow response speed, high energy consumption, poor flexibility, etc., and is difficult to meet the demand of modern power system for fast and accurate peak shaving.

[0031] In recent years, with the continuous progress and gradual reduction of energy storage technology, hybrid energy storage systems are widely used in power station peak shaving due to their fast response speed, high energy density, long cycle life and other advantages. Hybrid energy storage systems are usually composed of multiple types of energy storage devices, such as battery energy storage, supercapacitor energy storage, pumped storage, etc. These energy storage devices have their own characteristics and are suitable for different load scenarios.

[0032] However, how to effectively allocate various energy storage devices in the hybrid energy storage system to meet the load demand of the power station and realize the optimized utilization of the energy storage devices is a technical problem currently faced. Traditional peak shaving methods often rely on experience judgment or simple rule control, lacking scientificity and accuracy.

[0033] To solve the above technical defects, the inventors provide a hybrid energy storage-based power station peak shaving method and related device.

[0034] The application will be described in further detail below with reference to the drawings: In a first aspect, the embodiments of the present application provide a hybrid energy storage-based power station peak shaving method, as shown in the accompanying drawings, comprising: Figure 1 S101, a hybrid energy storage deployment model is constructed; illustratively, first, the annual operation state data of the hybrid energy storage power station is collected, and a large amount of the operation state data is divided into months as the division condition, so that different months correspond to different operation state data; then, based on the annual production plan, the change trend of the operation state data is combined to determine the load fluctuation time period of the hybrid energy storage power station; finally, the months included in the load fluctuation time period of the hybrid energy storage power station and the operation state data corresponding to the months are used as the construction data to construct the hybrid energy storage deployment model. In the above steps, by monitoring and recording the annual operation data of the hybrid energy storage power station, including the charging and discharging state, power output, state of charge (SOC) of the energy storage device, etc., basic data is provided for subsequent analysis; the annual operation state data is divided according to months, so that each month corresponds to a specific set of operation state data, facilitating independent analysis and comparison of data in different months. Combined with the annual production plan and the change trend of the operation state data, it is analyzed that the load of the hybrid energy storage power station fluctuates greatly in which time period, and these time periods will be the focus of attention for subsequent peak shaving strategy development. The determined load fluctuation time period and the corresponding operation state data are used to construct the hybrid energy storage deployment model, which will be able to reflect the energy storage device deployment strategy of the hybrid energy storage power station under different months and different load fluctuation conditions.

[0035] Moreover, by dividing the operation state data according to months and combining the annual production plan and the load fluctuation time period to construct the model, the model is more close to the actual operation condition, improving the pertinence and accuracy of the model. At the same time, the model considers the energy storage device deployment strategy under different months and different load fluctuation conditions, so that the model has stronger applicability and can be applied to hybrid energy storage power stations of different years and different production plans. Furthermore, through the model, a more scientific energy storage device deployment strategy can be developed, so that in the time period with greater load fluctuation, the energy storage device can be reasonably deployed to meet the peak shaving demand of the power station and improve the utilization rate and efficiency of the energy storage device.

[0036] ​Further, the collected operation state data of the mixed energy storage power station throughout the year includes battery capacity change data, charging and discharging efficiency data, and battery life data; wherein the battery capacity is an important indicator to measure the energy storage capacity of the battery. By collecting and analyzing the battery capacity change data, the change of the energy storage capacity of the battery under different working conditions can be accurately understood, so that the performance of the battery under different load demands can be more accurately predicted, and when the mixed energy storage deployment model is constructed, the configuration and scheduling strategy of the energy storage device can be more accurately determined. The charging and discharging efficiency directly affects the energy conversion efficiency of the energy storage system. By collecting and analyzing the charging and discharging efficiency data, the energy loss of the battery under different charging and discharging states can be evaluated, and then the charging and discharging strategy can be optimized to improve the overall efficiency of the energy storage system. The battery life is a key factor for long-term stable operation of the energy storage system. By collecting and analyzing the battery life data, the remaining life of the battery under different use conditions can be predicted, so that the charging and discharging plan of the battery can be reasonably arranged to prolong the service life of the battery.

[0037] S102, predicting the future load demand of the power station, and matching the energy storage device corresponding to the load demand through the mixed energy storage deployment model; for example, time series analysis is adopted, combined with historical load data of the power station, weather forecast information and production plan, to predict the future load demand of the power station, and through the mixed energy storage deployment model, the future load demand characteristics of the power station are simulated, and the energy storage device corresponding to the load demand is matched.

[0038] Wherein, the historical load data of the power station is analyzed by time series analysis using mathematical statistical method, the law and trend of load change are identified, the weather forecast information can provide future weather conditions such as temperature, humidity, wind speed, etc., which have a direct impact on the load demand of the power station. Combined with the weather forecast information, the future load demand can be more accurately predicted. The production plan of the power station, such as equipment maintenance, shutdown plan, etc., will also affect the future load demand. Considering these factors when predicting can improve the accuracy of prediction. The mixed energy storage deployment model can simulate the characteristics of the future load demand of the power station, including the fluctuation range and change speed of the load, etc. According to the simulated load demand characteristics, the model can match the most suitable energy storage device, including the type, capacity, charging and discharging rate, etc. of the device, to meet the peak shaving demand of the power station.

[0039] In specific applications, by time series analysis, combined with historical load data, weather forecast information and production plan, the factors affecting load demand can be more comprehensively considered, the accuracy of load prediction can be improved, accurate load prediction is the basis for formulating peak shaving strategy, which helps to optimize the configuration and scheduling of energy storage devices and improve the operation efficiency of power station; secondly, the mixed energy storage deployment model can simulate the characteristics of future load demand of power station, according to the change of load demand, the most suitable energy storage device is matched, and the optimized matching of energy storage device can improve the response speed and flexibility of energy storage system, better meet the peak shaving demand of power station.

[0040] Further, the energy storage device includes a battery energy storage device, a compressed air energy storage device, and a pumped hydro energy storage device; wherein the capacity and configuration ratio of each type of energy storage device are determined according to the technical parameters and economic cost of each type of energy storage device. Different types of energy storage devices have different economic costs. For example, although the initial investment cost of pumped hydro energy storage device is high, its operation and maintenance cost is low, and it has long service life and high efficiency, which has good economy in the long run. Although the initial investment cost of battery energy storage device is moderate, its charging and discharging efficiency is high, and the response speed is fast, which is suitable for peak shaving scenarios that require fast response. The cost of compressed air energy storage device is between the two, which gradually decreases with the progress of technology, and is suitable for large-scale energy storage scenarios. By comprehensively considering the economic cost of each type of energy storage device, the optimal configuration ratio can be determined to minimize the total cost of the entire energy storage system and maximize the economic benefit. At the same time, different types of energy storage devices have different technical characteristics. For example, pumped hydro energy storage device is suitable for large-scale and long-time energy storage demand, but its response speed is relatively slow; battery energy storage device has fast response speed, but its energy storage capacity is relatively limited; compressed air energy storage device has certain energy storage capacity and response speed. By reasonably configuring different types of energy storage devices, the redundancy of the entire energy storage system can be improved to ensure that other energy storage devices can supplement in time when a certain energy storage device fails or cannot meet the load demand, thereby ensuring the stable operation of the power grid.

[0041] The load demand of power station is diverse, including peak-valley difference, seasonal fluctuation, etc., and different types of energy storage devices have different peak shaving capabilities. For example, pumped hydro energy storage device is suitable for responding to long-time load fluctuation, and battery energy storage device is suitable for responding to short-time load impact. By determining the capacity and configuration ratio of each type of energy storage device according to its technical parameters and economic cost, the energy storage capacity can be flexibly configured to meet different peak shaving demands and improve the adaptability and flexibility of the energy storage system.

[0042] S103, based on the load demand prediction result, formulate the power station peak shaving strategy, determine the specific type of the energy storage device, and determine the peak shaving of the power station; for example, according to the load prediction result, the power station peak shaving strategy is formulated, and the specific type of the energy storage device participating in the peak shaving is determined. First, the future load demand of the power station is accurately predicted to obtain the trend and specific value of the load change, analyze the load prediction result, and identify the peak period, valley period and fluctuation of the load; then, according to the load prediction result, the peak shaving strategy of the power station is formulated, including determining the target, time and amplitude of peak shaving; considering the power generation capacity of the power station, the capacity and response speed of the energy storage device and other factors, to ensure the feasibility and effectiveness of the peak shaving strategy. Finally, according to the demand of the peak shaving strategy, the technical characteristics and economic cost of different types of energy storage devices are analyzed, and the most suitable type of energy storage device for peak shaving demand is selected, such as battery energy storage device, compressed air energy storage device or pumped storage energy storage device, etc., to determine the capacity and configuration mode of the selected energy storage device to meet the requirements of the peak shaving strategy.

[0043] In the above process, through accurate load prediction and reasonable peak shaving strategy, the power generation plan of the power station and the charge and discharge plan of the energy storage device can be optimized, unnecessary power generation and energy storage operation can be reduced, energy consumption and cost can be reduced, and the operation efficiency of the power station can be improved. At the same time, as an important part of the power grid, the peak shaving capacity of the power station has an important influence on the stability of the power grid, through reasonable peak shaving strategy and selection of energy storage device, the load fluctuation can be smoothed, the failure risk of the power grid can be reduced, the support capacity of the power station to the power grid can be improved, and the stability and reliability of the power grid can be enhanced. Moreover, different types of energy storage devices have different technical characteristics and economic costs, which are suitable for different peak shaving scenarios, by selecting the most suitable type of energy storage device according to the peak shaving demand, the rational application and development of energy storage technology can be promoted, the innovation progress of energy storage technology can be promoted, the cost of energy storage can be reduced, and the commercialization application level of energy storage technology can be improved.

[0044] S104, monitoring the load change of the power station after peak shaving, so that the energy storage device stores and releases electric energy at the best opportunity; for example, by monitoring the load change of the power station after peak shaving in real time, the best storage and release opportunity of the energy storage device can be accurately judged. In the load valley period, when the power grid supply is excessive, the energy storage device can timely store electric energy; in the load peak period, when the power grid supply is insufficient, the energy storage device can quickly release electric energy, thereby optimizing the use efficiency of the energy storage device and reducing energy waste. Moreover, through the storage and release of electric energy by the energy storage device at the best opportunity, the load fluctuation of the power grid can be effectively balanced, not only the power shortage or overload phenomenon caused by load fluctuation can be reduced, but also the reliability and quality of electric energy can be improved, and the stable operation of the power grid can be ensured. Especially in the power grid with a high proportion of renewable energy generation, the role of the energy storage device is more significant, which can make up for the intermittency and instability of renewable energy generation.

[0045] Additionally, by optimizing the efficiency of energy storage devices, reducing unnecessary charging and discharging operations, the wear and tear and maintenance costs of energy storage devices can be reduced. At the same time, by releasing the energy of energy storage devices during peak load periods, the dependence on high-cost power generation equipment (such as oil or gas generators) can be reduced, thereby reducing the operating costs of the power station. Real-time monitoring of load changes and the use of energy storage devices can better promote the consumption of renewable energy. During the peak period of renewable energy generation, when the power grid is oversupplied, energy storage devices can store electricity in time; during the peak load period, when the power grid is undersupplied, energy storage devices can release electricity, thereby reducing dependence on fossil fuels and improving the utilization rate of renewable energy. The storage and release of electricity by energy storage devices at the best opportunity can improve the flexibility and response speed of the power grid. In the face of sudden load changes or power grid failures, energy storage devices can quickly release electricity to provide emergency support for the power grid, ensuring the stable operation of the power grid, which is of great significance to improving the disaster resistance and response to emergencies of the power grid.

[0046] In a second aspect, a hybrid energy storage-based power station peak shaving system is provided, as shown in Figure 2 comprising: a construction module for constructing a hybrid energy storage deployment model; a prediction module for predicting future load demand of the power station; a matching module for matching energy storage devices corresponding to the load demand; a strategy formulation module for formulating a power station peak shaving strategy; a monitoring module for monitoring load changes of the power station after peak shaving.

[0047] By constructing a hybrid energy storage deployment model, the system can fully utilize the advantages of different types of energy storage devices (battery storage, pumped storage and compressed air storage), and flexibly deploy them according to actual needs, so that the power station can better adapt to fluctuations in load, especially during peak and off-peak periods, and quickly adjust output power to meet the needs of the power grid.

[0048] The prediction module can accurately predict the future load demand of the power station, so that the system can plan the charging and discharging strategy of the energy storage device in advance to avoid waste of resources. By matching the energy storage devices corresponding to the load demand, the system can ensure that the most suitable energy storage resources can be quickly called upon when needed, improving the utilization efficiency of resources.

[0049] Formulating a reasonable power station peak shaving strategy can reduce the dependence of the power station on traditional peak shaving power sources, such as reducing the use of oil or gas generators, thereby reducing operating costs. By optimizing the charging and discharging strategy of energy storage devices, the service life of the equipment can also be extended, reducing the cost of maintenance and replacement.

[0050] The hybrid energy storage system can quickly respond to changes in the load of the power grid, provide stable power support, and help maintain the voltage and frequency stability of the power grid. In the event of a power grid failure or emergency, the hybrid energy storage system can serve as a backup power source to provide power support for critical loads and enhance the anti-interference ability of the power grid.

[0051] The hybrid energy storage system can store excess power generated by renewable energy and release it during peak demand, helping to improve the utilization of renewable energy. By optimizing the charging and discharging strategy of the energy storage device, the fluctuation of renewable energy generation can be reduced, making it more stable to access the power grid.

[0052] In a third aspect, a mobile device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the hybrid energy storage-based power station peak shaving method as described above when executing the computer program.

[0053] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program, wherein the computer program is executed by a processor to implement the steps of the hybrid energy storage-based power station peak shaving method as described above.

[0054] In a fifth aspect, a computer program product is provided, comprising computer instructions for instructing a computing device to perform operations corresponding to the hybrid energy storage-based power station peak shaving method as described above.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit the protection scope thereof. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be changed, modified or replaced by equivalents by those skilled in the art after reading the present application. However, these changes, modifications or equivalent replacements are all within the protection scope of the claims of the present application.

Claims

1. A method for peak shaving of a power station based on hybrid energy storage, characterized in that, The method comprises the following steps: constructing a hybrid energy storage deployment model; predicting future load demand of the power station and matching energy storage devices corresponding to the load demand through the hybrid energy storage deployment model; formulating a power station peak shaving strategy based on the prediction result of the load demand, determining specific types of the energy storage devices, and performing peak shaving on the power station; monitoring load changes of the power station after peak shaving to enable the energy storage devices to store and release electric energy at the best time.

2. The hybrid energy storage based power plant peak shaving method of claim 1, wherein, The step of constructing the hybrid energy storage deployment model specifically comprises: collecting annual operation state data of the hybrid energy storage power station, dividing a large amount of the operation state data into months as a division condition so that different months correspond to different operation state data; determining load fluctuation time periods of the hybrid energy storage power station based on annual production plans and in combination with change trends of the operation state data; constructing the hybrid energy storage deployment model by taking months included in the load fluctuation time periods of the hybrid energy storage power station and the operation state data corresponding to the months as construction data.

3. The hybrid energy storage based power plant peak shaving method of claim 2, wherein, The operation state data comprises battery capacity change data, charging and discharging efficiency data, and battery life data.

4. The hybrid energy storage based power plant peak shaving method of claim 1, wherein, The step of predicting future load demand of the power station and matching energy storage devices corresponding to the load demand through the hybrid energy storage deployment model specifically comprises: predicting future load demand of the power station by using time series analysis in combination with historical load data of the power station, weather forecast information, and production plans; simulating load demand characteristics of the power station in the future through the hybrid energy storage deployment model to match energy storage devices corresponding to the load demand.

5. The hybrid energy storage based power plant peak shaving method of claim 4, wherein, The energy storage devices comprise battery energy storage devices, compressed air energy storage devices, and pumped hydro energy storage devices; wherein capacities and configuration ratios of the energy storage devices of different types are determined according to technical parameters and economic costs of the energy storage devices of different types.

6. The hybrid energy storage based power plant peak shaving method of claim 1, wherein, The step of formulating a power station peak shaving strategy based on the prediction result of the load demand, determining specific types of the energy storage devices, and performing peak shaving on the power station specifically comprises: formulating a power station peak shaving strategy and determining specific types of energy storage devices participating in peak shaving according to the prediction result of the load demand.

7. A hybrid energy storage based power plant peak shaving system, characterized in that, The method comprises the following steps: a construction module for constructing a hybrid energy storage deployment model; a prediction module for predicting future load demand of the power station; a matching module for matching energy storage devices corresponding to the load demand; a strategy formulation module for formulating a power station peak shaving strategy; a monitoring module for monitoring load changes of the power station after peak shaving.

8. A mobile device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the hybrid energy storage-based power station peak shaving method according to any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to implement the steps of the hybrid energy storage-based power station peak shaving method according to any one of claims 1-6.

10. A computer program product comprising computer instructions, characterized in that, The computer instructions instruct the computing device to perform operations corresponding to the hybrid energy storage-based power station peak shaving method according to any one of claims 1-6.

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