A new energy power generation side energy storage intelligent scheduling method and system

Abstract

The application discloses a new energy power generation side energy storage intelligent scheduling method and system, the method comprises the following steps: obtaining historical data, and determining typical scene information reflecting wind power output and photovoltaic output based on the historical data; determining comprehensive benefit information of a micro-grid system according to the typical scene information; determining maximum comprehensive benefit according to the comprehensive benefit information, and determining a demand response strategy and an energy storage system operation strategy based on the maximum comprehensive benefit; constraining power generation side based on the demand response strategy and the energy storage system operation strategy, formulating a power generation strategy and a power purchase strategy based on minimizing power generation operation cost, and scheduling according to the power generation strategy and the power purchase strategy. The application can formulate a power generation strategy and a power purchase strategy under the premise of maximizing comprehensive benefit, so that the micro-grid system operation is more coordinated.

Description

Technical Field

[0001] This invention relates to the field of new energy dispatching technology, and in particular to a smart dispatching method and system for energy storage on the new energy power generation side. Background Technology

[0002] Energy shortage is a very important issue in the 21st century, and the best way to solve this problem is... One approach is to utilize new energy sources such as solar or wind power. However, wind power generation is characterized by randomness and fluctuations, making its output uncontrollable. This poses challenges to the stable operation and control of microgrid systems, potentially leading to curtailment and severe economic losses. Currently, a common method is to use energy storage systems to store or release electrical energy, thereby coordinating with grid dispatch commands, improving wind power performance, enhancing wind power efficiency, and improving power supply reliability and quality. However, current generation-side energy storage dispatching largely considers the multi-objective optimization problem from the overall perspective of the microgrid system, neglecting the comprehensive benefits of its internal components. Therefore, when implementing energy storage dispatching, it fails to fully consider the generation demand on the generation side and the energy dispatching requirements for the energy storage system, affecting the operational coordination of the microgrid system.

[0003] Therefore, existing technologies still need to be improved and enhanced. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a new energy power generation side energy storage intelligent scheduling method and system to address the above-mentioned defects of the prior art. The aim is to solve the problem that the existing technology cannot fully consider the power generation demand of the power generation side and the power scheduling demand of the energy storage system when realizing energy storage scheduling, which affects the operation coordination of the microgrid system.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for intelligent scheduling of energy storage on the new energy power generation side, wherein the method includes: Acquire historical data and determine typical scenario information reflecting wind power output and photovoltaic power output based on the historical data; Based on the typical scenario information, the comprehensive benefit information of the microgrid system is determined, including user satisfaction, grid stability, and renewable energy grid connection rate. Based on the comprehensive benefit information, the maximum comprehensive benefit is determined, and the demand response strategy and the energy storage system operation strategy are determined based on the maximum comprehensive benefit. The demand response strategy is used to reflect the power generation demand on the generation side, and the energy storage system operation strategy is used to reflect the power purchase demand from the generation side to the energy storage system. Based on the demand response strategy and the energy storage system operation strategy, constraints are imposed on the power generation side, and a power generation strategy and a power purchase strategy are formulated based on minimizing the power generation operating cost. The system is then dispatched according to the power generation strategy and the power purchase strategy.

[0006] In one implementation, the method further includes: The power generation strategy and the power purchase strategy are fed back to the microgrid system.

[0007] In one implementation, the step of acquiring historical data and determining typical scenario information reflecting wind power output and photovoltaic output based on the historical data includes: The historical data is obtained based on literature, and the historical data includes wind power output and photovoltaic output on a certain day in the preset area; Based on the historical data, taking the day-ahead power output prediction error of wind power and photovoltaic as the simulation object, several day-ahead power output prediction error scenarios of new energy represented by wind power and photovoltaic are randomly generated by Monte Carlo simulation method. Obtain the Euclidean distance between scenarios, and based on the Euclidean distance, eliminate useless scenarios from the day-ahead output prediction error scenarios of new energy sources represented by wind power and photovoltaics, to form the typical scenario information.

[0008] In one implementation, determining the comprehensive benefit information of the microgrid system based on the typical scenario information includes: Based on the typical scenario information, determine the wind power and / or photovoltaic power corresponding to the preset time period or preset moment in the typical scenario information; Based on the wind power and / or photovoltaic power, the user satisfaction, the grid stability, and the renewable energy grid connection rate are determined.

[0009] In one implementation, determining the maximum comprehensive benefit based on the comprehensive benefit information includes: Obtain the weighted data corresponding to the user satisfaction, the power grid stability, and the renewable energy grid connection rate, respectively. The maximum comprehensive benefit is obtained by weighted summation of the user satisfaction, the power grid stability, and the renewable energy grid connection rate.

[0010] In one implementation, determining the demand response strategy and the energy storage system operation strategy based on maximizing the overall benefits includes: Based on the maximized comprehensive benefits, the wind power load and photovoltaic load corresponding to the maximized comprehensive benefits are determined. The wind power load and the photovoltaic load respectively reflect the maximum amount of electricity that the wind farm and the photovoltaic power station can generate when the maximized comprehensive benefits are met. Based on the wind power load and the photovoltaic load, the wind power generation capacity and photovoltaic power generation capacity on the power generation side are determined respectively, and the demand response strategy is generated based on the wind power generation capacity and the photovoltaic power generation capacity. The predicted wind power generation and predicted photovoltaic power generation are obtained from the power generation side. Based on the wind power generation and the predicted wind power generation, the wind power compensation power is determined, and based on the photovoltaic power generation and the predicted photovoltaic power generation, the photovoltaic compensation power is determined. The energy storage system operation strategy is generated based on the wind power compensation power and the photovoltaic compensation power.

[0011] In one implementation, the constraint on the generation side based on the demand response strategy and the energy storage system operation strategy, so that the generation side formulates a generation strategy and a power purchase strategy based on minimizing the generation operating cost, includes: The operating costs of wind farms and photovoltaic power plants on the power generation side are obtained, and the power generation strategy is formulated based on the wind power generation capacity and the photovoltaic power generation capacity to minimize the operating costs. Obtain electricity price data for the energy storage system sold to external parties and determine the electricity purchase cost. Based on the wind power compensation power and the photovoltaic compensation power, formulate the electricity purchase strategy to minimize the electricity purchase cost.

[0012] Secondly, embodiments of the present invention also provide a smart dispatching system for energy storage on the new energy generation side, wherein the system includes: The typical scenario determination module is used to acquire historical data and determine typical scenario information reflecting wind power output and photovoltaic output based on the historical data. The comprehensive benefit determination module is used to determine the comprehensive benefit information of the microgrid system based on the typical scenario information. The comprehensive benefit information includes user satisfaction, grid stability, and renewable energy grid connection rate. The strategy information decision module is used to determine the maximum comprehensive benefit based on the comprehensive benefit information, and to determine the demand response strategy and the energy storage system operation strategy based on the maximum comprehensive benefit. The demand response strategy is used to reflect the power generation demand on the power generation side, and the energy storage system operation strategy is used to reflect the power purchase demand of the power generation side to the energy storage system. The generation-side strategy specification module is used to constrain the generation side based on the demand response strategy and the energy storage system operation strategy, formulate generation strategy and power purchase strategy based on minimizing generation operating costs, and perform scheduling according to the generation strategy and power purchase strategy.

[0013] Thirdly, embodiments of the present invention also provide a terminal device, which includes a memory, a processor, and a new energy generation-side energy storage intelligent scheduling program stored in the memory and capable of running on the processor. When the processor executes the new energy generation-side energy storage intelligent scheduling program, it implements the steps of the new energy generation-side energy storage intelligent scheduling method as described in any of the above schemes.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a smart scheduling program for energy storage on the new energy generation side. When the smart scheduling program for energy storage on the new energy generation side is executed by a processor, it implements the steps of the smart scheduling method for energy storage on the new energy generation side as described in any of the above schemes.

[0015] Beneficial Effects: Compared with existing technologies, this invention provides a smart scheduling method for energy storage on the new energy generation side. First, this invention acquires historical data and determines typical scenario information reflecting wind and solar power output based on this data. Then, based on the typical scenario information, it determines the comprehensive benefit information of the microgrid system, including user satisfaction, grid stability, and new energy grid connection rate. Next, based on the comprehensive benefit information, it determines how to maximize the comprehensive benefit, and based on this, it determines a demand response strategy and an energy storage system operation strategy. The demand response strategy reflects the power generation demand on the generation side, and the energy storage system operation strategy reflects the power purchase demand from the generation side to the energy storage system. Finally, based on the demand response strategy and the energy storage system operation strategy, constraints are imposed on the generation side, and a generation strategy and a power purchase strategy are formulated based on minimizing generation operating costs. Scheduling is then performed according to these generation and power purchase strategies. Therefore, this invention determines the demand response strategy and energy storage system operation strategy based on maximizing comprehensive benefit information, so that the generation side can formulate generation strategy and power purchase strategy based on the demand response strategy and energy storage system operation strategy respectively, thereby realizing the control of the generation side and the scheduling of the energy storage system, so as to achieve the coordination of microgrid system operation. Attached Figure Description

[0016] Figure 1 A flowchart illustrating a specific implementation of the intelligent scheduling method for energy storage on the new energy power generation side provided in this embodiment of the invention.

[0017] Figure 2 The diagram illustrates the operation of wind power output in the intelligent scheduling method for energy storage on the new energy generation side provided in this embodiment of the invention.

[0018] Figure 3 This is a schematic diagram of the operation of photovoltaic output in the intelligent scheduling method for energy storage on the new energy power generation side provided in the embodiments of the present invention.

[0019] Figure 4 This is a schematic diagram of the intelligent dispatching system for energy storage on the new energy power generation side provided in an embodiment of the present invention.

[0020] Figure 5 A schematic diagram of a terminal device provided in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0022] This embodiment provides a smart scheduling method for energy storage on the new energy generation side. Based on this method, control of the generation side and scheduling of the energy storage system can be achieved to ensure the coordinated operation of the microgrid system. In specific applications, this embodiment first acquires historical data and determines typical scenario information reflecting wind power output and photovoltaic output based on the historical data. Then, based on the typical scenario information, the comprehensive benefit information of the microgrid system is determined, including user satisfaction, grid stability, and new energy grid connection rate. Next, based on the comprehensive benefit information, the method for maximizing comprehensive benefits is determined, and a demand response strategy and an energy storage system operation strategy are determined based on maximizing comprehensive benefits. The demand response strategy reflects the power generation demand on the generation side, and the energy storage system operation strategy reflects the power purchase demand from the generation side to the energy storage system. Finally, based on the demand response strategy and the energy storage system operation strategy, constraints are imposed on the generation side, and a generation strategy and a power purchase strategy are formulated based on minimizing generation operating costs. Scheduling is then performed according to the generation strategy and the power purchase strategy. Therefore, this embodiment determines the demand response strategy and energy storage system operation strategy based on maximizing comprehensive benefit information, so that the generation side can formulate generation strategy and power purchase strategy based on the demand response strategy and energy storage system operation strategy respectively, thereby realizing the control of the generation side and the scheduling of the energy storage system, so as to achieve the coordination of microgrid system operation.

[0023] Exemplary methods The intelligent scheduling method for energy storage on the new energy generation side in this embodiment can be applied to terminal equipment, which can be a computer or a central control device. The terminal equipment can be connected to the user side, generation side, and energy storage system in the microgrid system, and is used to monitor the operation of the generation equipment on the generation side and the operation of the energy storage system in the microgrid system. It can also perform analysis and output relevant scheduling commands. Specifically, for example... Figure 1 As shown, the method includes the following steps: Step S100: Obtain historical data and determine typical scenario information reflecting wind power output and photovoltaic power output based on the historical data.

[0024] This embodiment can first obtain historical data, which includes pre-recorded wind power output and photovoltaic output in a certain area. Since wind power output and photovoltaic output are affected by factors such as time, season, and region, this embodiment needs to obtain typical scenario information from the historical data. The typical scenario information can reflect the characteristics of wind power output and photovoltaic processing in the area, so that comprehensive benefit information can be determined based on the typical scenario information in subsequent steps.

[0025] In one implementation, this embodiment includes the following steps when determining typical scenario information: Step S101: Obtain the historical data based on literature, the historical data including wind power output and photovoltaic power output on a certain day in a preset area; Step S102: Based on the historical data, taking the day-ahead power output prediction error of wind power and photovoltaic as the simulation object, several day-ahead power output prediction error scenarios of new energy represented by wind power and photovoltaic are randomly generated by Monte Carlo simulation method. Step S103: Obtain the Euclidean distance between scenarios, and based on the Euclidean distance, eliminate useless scenarios in the day-ahead output prediction error scenarios of new energy represented by wind power and photovoltaic power, and form the typical scenario information.

[0026] Specifically, this embodiment can collect specific data on wind power output and photovoltaic output in a certain area in advance and record them in literature to form historical data. For this purpose, this embodiment can also query existing literature to obtain the historical data. The historical data includes the wind power output and photovoltaic output of a preset area on a certain day. For example, for city A, wind power output exhibits a clear characteristic of high generation at night and low generation during the day. Figure 2 As shown, Figure 2 The curve of the operation of a wind farm in city A on a certain day is as follows: Figure 2 It is evident that the wind farm's output is high between 00:00 and 08:00, then gradually decreases, reaching a trough at 12:00 noon, and remains low for the rest of the time, exhibiting clear anti-peak characteristics and volatility. Similarly, for city A, photovoltaic power generation is significantly affected by irradiance, exhibiting a pattern of high daytime output and low nighttime output. Figure 3 shows the daily operating curve of a photovoltaic power station in city A. Figure 3It can be seen that the photovoltaic output reaches its peak at noon, and is zero between 9:00 PM and 6:00 AM, exhibiting a clear midday anti-peak characteristic and significant photovoltaic fluctuations. Based on the above example, when the terminal equipment obtains the historical data, it can determine the wind power output and photovoltaic output from this historical data.

[0027] Specifically, in the energy conversion process of wind power generation from wind energy to electricity, the uncertainties of wind speed and direction cause wind power generation to exhibit strong randomness, resulting in significant errors in wind power prediction. Furthermore, the error increases with the time scale of wind power prediction. Similarly, in the process of photovoltaic power generation from solar energy to electricity, the output prediction is also random due to the influence of solar radiation, cloud movement, and temperature changes. Moreover, the error caused by this randomness increases with the time scale of photovoltaic power prediction. Therefore, in this embodiment, after obtaining the wind power output and photovoltaic output, this embodiment uses the day-ahead output prediction errors of wind power and photovoltaic as the simulation object. Using the Monte Carlo simulation method, several day-ahead output prediction error scenarios representing new energy sources such as wind power and photovoltaic are randomly generated. After generating a large number of random scenarios, not all scenarios meet the actual needs; therefore, a scenario reduction method is needed to eliminate some useless scenarios. In this embodiment, the Euclidean distance between scenarios can be obtained, and based on the Euclidean distance, useless scenarios in the day-ahead power output prediction error scenarios of new energy sources represented by wind power and photovoltaics are eliminated to form the typical scenario information. The Euclidean distance is calculated using the following formula:

[0028] in, Euclidean distance; This is the k-th sample value of the i-th sample; is the k-th sampling value of the j-th sample; N is the total number of samplings. After calculating the Euclidean distance, this embodiment can eliminate scenes whose Euclidean distance exceeds a preset threshold based on the calculated Euclidean distance between scenes, and the remaining scenes form typical scene information.

[0029] Step S200: Based on the typical scenario information, determine the comprehensive benefit information of the microgrid system, which includes user satisfaction, grid stability, and renewable energy grid connection rate.

[0030] In this embodiment, the typical scenario information is obtained based on historical data, which includes specific data on wind power output and photovoltaic output in a certain region. Therefore, based on this typical scenario information, the comprehensive benefit information of the microgrid system can be determined. This comprehensive benefit information includes user satisfaction, grid stability, and renewable energy grid connection rate. User satisfaction reflects whether the wind power output and photovoltaic output in the typical scenario information meet the user's needs. Grid stability reflects the operational stability of the microgrid system, and the renewable energy grid connection rate reflects the ratio of renewable energy generation (wind power and photovoltaic power) to total power generation. A higher renewable energy grid connection rate indicates higher wind and photovoltaic power generation and lower traditional power generation.

[0031] In one implementation, this embodiment includes the following steps when determining the comprehensive benefit information: Step S201: Based on the typical scenario information, determine the wind power and / or photovoltaic power corresponding to the preset time period or preset moment in the typical scenario information; Step S202: Based on the wind power and / or photovoltaic power, determine the user satisfaction, the grid stability, and the renewable energy grid connection rate.

[0032] Specifically, this embodiment determines the wind power and / or photovoltaic power corresponding to a preset time period or preset moment in the typical scenario information based on the typical scenario information. For example, from Figure 2 and Figure 3The system can determine the wind power and photovoltaic power corresponding to a preset time period or preset moment. After obtaining the wind power and photovoltaic power at each moment on the power generation side, this embodiment can calculate the utilization rate of wind power and photovoltaic power based on the user's usage of wind power and photovoltaic power at each moment. The user satisfaction rate can be determined based on the utilization rate; a higher utilization rate results in higher user satisfaction. In one implementation, this embodiment pre-sets a correspondence table between utilization rate and user satisfaction, listing the user satisfaction rates corresponding to different utilization rates. Therefore, once the utilization rates of wind power and photovoltaic power are obtained, the corresponding user satisfaction rate can be found. Furthermore, since this embodiment determines the wind power and photovoltaic power at each moment, and the user's usage of wind power and photovoltaic power at each moment, it can further determine the storage status of wind power and photovoltaic power not used by the user side. Based on this, the grid stability can be evaluated. In this embodiment, when the wind and solar power generation on the generation side can fully meet the user's needs without the energy storage system participating, the grid stability is higher. Higher wind and solar power generation on the generation side indicates more electricity can be stored by the energy storage system, further enhancing grid stability. Similarly, this embodiment can pre-set wind and solar power corresponding to different levels of grid stability; therefore, the corresponding grid stability can be determined when the wind and solar power are obtained. Furthermore, this embodiment can calculate the ratio of new energy power generation (wind and solar power) to total power generation to obtain the new energy grid connection rate. In this embodiment, user satisfaction, grid stability, and new energy grid connection rate are all expressed numerically, providing a more intuitive reflection of comprehensive benefits.

[0033] Step S300: Based on the comprehensive benefit information, determine the maximum comprehensive benefit, and determine the demand response strategy and the energy storage system operation strategy based on the maximum comprehensive benefit. The demand response strategy is used to reflect the power generation demand on the power generation side, and the energy storage system operation strategy is used to reflect the power purchase demand of the power generation side to the energy storage system.

[0034] Once the comprehensive benefit information is obtained, this embodiment can determine the maximum comprehensive benefit. This maximum comprehensive benefit reflects the balance and coordination among user satisfaction, grid stability, and renewable energy grid connection rate, maximizing the operational coordination of the microgrid system. The microgrid system in this embodiment consists of a user side, a generation side, and an energy storage system. A two-layer optimization model is constructed within this microgrid system. After determining the maximum comprehensive benefit, the upper-layer optimization model determines the demand response strategy and the energy storage system operation strategy based on this maximum comprehensive benefit. The demand response strategy in this embodiment reflects the power generation demand of the generation side, i.e., the amount of electricity that the generation equipment needs to generate. The energy storage system operation strategy reflects the power purchase demand of the generation side from the energy storage system, i.e., the amount of electricity that needs to be purchased from the energy storage system when the power generated by the generation equipment cannot meet the user's demand.

[0035] Specifically, this embodiment first uses a weighted summation method to determine the maximum comprehensive benefit. First, weighted data are assigned to the user satisfaction, the power grid stability, and the renewable energy grid connection rate. Then, the user satisfaction, power grid stability, and renewable energy grid connection rate are weighted and summed to obtain the maximum comprehensive benefit.

[0036] In one implementation, this embodiment includes the following steps when obtaining the demand response strategy and the energy storage system operation strategy based on maximizing overall benefits: Step S301: Based on the maximized comprehensive benefits, determine the wind power load and solar power load corresponding to the maximized comprehensive benefits; Step S302: Based on the wind power load and the photovoltaic load, determine the wind power generation capacity and photovoltaic power generation capacity on the power generation side respectively, and generate the demand response strategy based on the wind power generation capacity and the photovoltaic power generation capacity; Step S303: Obtain the predicted wind power generation and predicted photovoltaic power generation on the power generation side, and determine the wind power compensation power based on the wind power generation and the predicted wind power generation, and determine the photovoltaic power compensation power based on the photovoltaic power generation and the predicted photovoltaic power generation. Step S304: Generate the energy storage system operation strategy based on the wind power compensation power and the photovoltaic compensation power.

[0037] Specifically, the upper-level optimization model in this embodiment first determines the user satisfaction, grid stability, and renewable energy grid connection rate corresponding to the maximized comprehensive benefit. Then, based on the user satisfaction, grid stability, and renewable energy grid connection rate, it determines the corresponding wind power load and solar power load. Since the wind power load and solar power load corresponding to the user satisfaction, grid stability, and renewable energy grid connection rate may not be the same, this embodiment can sequentially determine the wind power load and solar power load corresponding to user satisfaction, the wind power load and solar power load corresponding to grid stability, and the wind power load and solar power load corresponding to renewable energy grid connection rate, and then select the largest wind power load and solar power load as the wind power load and solar power load corresponding to the maximized comprehensive benefit. In this embodiment, the wind power load and solar power load respectively reflect the maximum electricity generated by the wind farm and the photovoltaic power station when satisfying the maximized comprehensive benefit. Once the wind power load and photovoltaic load are determined, the upper-level optimization model in this embodiment determines the wind power generation and photovoltaic power generation on the power generation side, respectively, and generates the demand response strategy based on the wind power generation and photovoltaic power generation. At this time, the determined wind power generation is the amount of electricity that the wind farm on the power generation side needs to generate, and the photovoltaic power generation is the amount of electricity that the photovoltaic power station on the power generation side needs to generate. Therefore, the demand response strategy is to reflect the request information of the wind power generation and the photovoltaic power generation.

[0038] Next, due to the influence of weather, location, and other factors on the actual power generation of wind farms and photovoltaic power stations, the actual power generation of wind farms and photovoltaic power stations will be less than the determined wind power generation and photovoltaic power generation on the power generation side. Therefore, this embodiment needs to obtain the predicted wind power generation and predicted photovoltaic power generation on the power generation side. The predicted wind power generation reflects the predicted actual power generation of the wind farm, and the predicted photovoltaic power generation reflects the predicted actual power generation of the photovoltaic power station. Specifically, the predicted wind power generation and predicted photovoltaic power generation can be obtained based on typical scenario information. Since historical data includes wind power output and photovoltaic output on a preset day within a certain area, and this embodiment has determined typical scenario information from historical data, the patterns of wind power output and photovoltaic output can be summarized based on the typical scenario information. Then, combined with weather, location, and other information, the predicted wind power generation and predicted photovoltaic power generation on the power generation side can be estimated. After determining the predicted wind power generation and predicted photovoltaic power generation on the power generation side, this embodiment can further determine the wind power compensation power and photovoltaic compensation power. The wind power compensation power equals the wind power generation power minus the predicted wind power generation power, and the photovoltaic power compensation power equals the photovoltaic power generation power minus the predicted photovoltaic power generation power. These wind power and photovoltaic power compensation powers reflect the amount of electricity that needs to be compensated from the energy storage system when the electricity generated on the power generation side cannot meet the user's demand. Therefore, the energy storage system operation strategy can be generated based on the wind power and photovoltaic power compensation powers, which involves transferring the load to the energy storage system. The energy storage system operation strategy reflects the request information for the wind power and photovoltaic power compensation powers.

[0039] Step S400: Based on the demand response strategy and the energy storage system operation strategy, constraints are imposed on the power generation side, and a power generation strategy and a power purchase strategy are formulated based on minimizing the power generation operating cost, and scheduling is carried out according to the power generation strategy and the power purchase strategy.

[0040] Once the upper-level optimization model determines the demand response strategy and the energy storage system operation strategy, it sends these strategies to the lower-level optimization model within the microgrid system. Upon receiving these strategies, the lower-level optimization model imposes constraints on the generation side and formulates a generation strategy and a power purchase strategy based on minimizing generation operating costs. Then, it performs scheduling according to these generation and power purchase strategies. In one implementation, the formulation of the power generation strategy and power purchase strategy in this embodiment includes the following steps: Step S401: Obtain the operating costs of wind farms and photovoltaic power plants on the power generation side, minimize the operating costs, and formulate the power generation strategy based on the wind power generation capacity and the photovoltaic power generation capacity; Step S402: Obtain the electricity price data sold by the energy storage system and determine the electricity purchase cost. Based on the wind power compensation power and the photovoltaic compensation power, formulate the electricity purchase strategy to minimize the electricity purchase cost.

[0041] Specifically, in this embodiment, the lower-level optimization model of the microgrid system obtains the operating costs of wind farms and photovoltaic power stations on the generation side. Then, by minimizing these operating costs, a generation strategy is specified based on the wind power generation and photovoltaic power generation. This generation strategy is used to control the operating status of the wind farms and photovoltaic power stations on the generation side, such as controlling the rotational speed of the wind farm's generating equipment and controlling the orientation of the photovoltaic arrays in the photovoltaic power station. This embodiment also obtains the electricity price data sold by the energy storage system and determines the electricity purchase cost. By minimizing this purchase cost, a purchase strategy is formulated based on the wind power compensation power and the photovoltaic compensation power. This purchase strategy is used to control the external discharge of the energy storage system and control the discharge amount. It can be seen that this embodiment formulates the generation and purchase strategies based on minimizing costs. Once the generation and purchase strategies are determined, the terminal equipment can control the operating status of the generating equipment on the generation side and the discharge status of the energy storage system, achieving better scheduling and facilitating the coordinated operation of the microgrid system. Furthermore, the terminal device in this embodiment also feeds back the power generation strategy and the power purchase strategy to the microgrid system in order to update the maximization of comprehensive benefits. Then, based on the updated maximization of comprehensive benefits, it redetermines the demand response strategy and the energy storage system operation strategy to formulate a new power generation strategy and power purchase strategy, thereby realizing the adjustment of the power generation strategy and power purchase strategy.

[0042] In summary, this embodiment first acquires historical data and determines typical scenario information reflecting wind power output and photovoltaic output based on this data. Then, based on the typical scenario information, it determines the comprehensive benefit information of the microgrid system, including user satisfaction, grid stability, and renewable energy grid connection rate. Next, based on the comprehensive benefit information, it determines how to maximize the comprehensive benefit, and based on this, it determines the demand response strategy and energy storage system operation strategy. The demand response strategy reflects the power generation demand on the generation side, and the energy storage system operation strategy reflects the power purchase demand from the generation side to the energy storage system. Finally, based on the demand response strategy and the energy storage system operation strategy, it constrains the generation side and formulates a generation strategy and a power purchase strategy based on minimizing generation operating costs, and performs scheduling according to these strategies. Therefore, this embodiment determines the demand response strategy and energy storage system operation strategy based on maximizing comprehensive benefit information, so that the generation side can formulate its own generation and power purchase strategies based on these strategies, thereby achieving control over the generation side and scheduling of the energy storage system to achieve coordinated operation of the microgrid system.

[0043] Exemplary System Based on the above embodiments, the present invention also provides a smart dispatching system for energy storage on the new energy power generation side, such as... Figure 4 As shown, the system includes: a typical scenario determination module 10, a comprehensive benefit determination module 20, a strategy information decision module 30, and a generation-side strategy specification module 40. Specifically, the typical scenario determination module 10 is used to acquire historical data and determine typical scenario information reflecting wind power output and photovoltaic output based on the historical data. The comprehensive benefit determination module 20 is used to determine the comprehensive benefit information of the microgrid system based on the typical scenario information, including user satisfaction, grid stability, and renewable energy grid connection rate. The strategy information decision module 30 is used to determine the maximization of comprehensive benefits based on the comprehensive benefit information, and to determine a demand response strategy and an energy storage system operation strategy based on the maximization of comprehensive benefits. The demand response strategy reflects the power generation demand on the generation side, and the energy storage system operation strategy reflects the power purchase demand from the generation side to the energy storage system. The generation-side strategy specification module 40 is used to constrain the generation side based on the demand response strategy and the energy storage system operation strategy, and to formulate a generation strategy and a power purchase strategy based on minimizing generation operating costs, and to perform scheduling according to the generation strategy and the power purchase strategy.

[0044] In one implementation, the system further includes: The feedback module is used to feed back the power generation strategy and the power purchase strategy to the microgrid system.

[0045] In one implementation, the typical scenario determination module 10 includes: The historical data acquisition unit is used to acquire the historical data based on literature, and the historical data includes wind power output and photovoltaic power output on a certain day in a preset area; The scenario random generation unit is used to randomly generate several scenarios of day-ahead power output prediction errors of new energy sources, represented by wind power and photovoltaics, based on the historical data and using the Monte Carlo simulation method, with wind power and photovoltaics as the simulation objects. The useless scene elimination unit is used to obtain the Euclidean distance between scenes, and based on the Euclidean distance, eliminate useless scenes in the day-ahead output prediction error scenarios of new energy represented by wind power and photovoltaic power, to form the typical scene information.

[0046] In one implementation, the comprehensive benefit determination module 20 includes: The power determination unit is used to determine the wind power and / or photovoltaic power corresponding to a preset time period or preset moment in the typical scenario information based on the typical scenario information. The power analysis unit is used to determine the user satisfaction, the grid stability, and the renewable energy grid connection rate based on the wind power and / or photovoltaic power.

[0047] In one implementation, the strategy information decision module 30 includes: The weighted data determination unit is used to obtain the weighted data corresponding to the user satisfaction, the power grid stability, and the renewable energy grid connection rate, respectively. The unit for determining the maximum comprehensive benefit is used to perform a weighted summation of the user satisfaction, the power grid stability, and the renewable energy grid connection rate to obtain the maximum comprehensive benefit.

[0048] In one implementation, the strategy information decision module 30 includes: The load data determination unit is used to determine the wind power load and photovoltaic load corresponding to the maximized comprehensive benefits based on the maximized comprehensive benefits. The demand response strategy generation unit is used to determine the wind power generation capacity and photovoltaic power generation capacity on the power generation side according to the wind power load and the photovoltaic load, respectively, and generate the demand response strategy according to the wind power generation capacity and the photovoltaic power generation capacity. The compensation power determination unit is used to obtain the predicted wind power generation power and the predicted photovoltaic power generation power on the power generation side, and determine the wind power compensation power based on the wind power generation power and the predicted wind power generation power, and determine the photovoltaic compensation power based on the photovoltaic power generation power and the predicted photovoltaic power generation power. An energy storage system operation strategy determination unit is used to generate the energy storage system operation strategy based on the wind power compensation power and the photovoltaic compensation power.

[0049] In one implementation, the power generation-side strategy specifying module 40 includes: A power generation strategy determination unit is used to obtain the operating costs of wind farms and photovoltaic power plants on the power generation side, and to formulate the power generation strategy based on the wind power generation power and the photovoltaic power generation power in order to minimize the operating costs. The electricity purchase strategy determination unit is used to obtain electricity price data sold by the energy storage system and determine the electricity purchase cost. Based on the wind power compensation power and the photovoltaic compensation power, the unit formulates the electricity purchase strategy to minimize the electricity purchase cost.

[0050] The working principle of each module in the new energy power generation side energy storage intelligent dispatch system of this embodiment is the same as that of each step in the above method embodiment, and will not be repeated here.

[0051] Based on the above embodiments, the present invention also provides a terminal device, which can be a computer or a central control device. The terminal device can be connected to the user side, the generation side, and the energy storage system in the microgrid system, and is used to monitor the operation of the generation equipment on the generation side and the operation of the energy storage system in the microgrid system. It can also perform analysis and output relevant scheduling commands. The principle block diagram of this terminal device can be as follows: Figure 5 As shown, the terminal device includes a processor and a memory connected via a system bus, both located in the host unit. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The network interface of the terminal device is used for communication with external terminals via network communication. When the computer program is executed by the processor, it implements a smart scheduling method for energy storage on the new energy generation side.

[0052] Those skilled in the art will understand that Figure 5 The block diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the terminal device to which the present invention is applied. The specific terminal device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0053] In one embodiment, a terminal device is provided, comprising a memory, a processor, and a method program for intelligent scheduling of energy storage on the new energy generation side stored in the memory and executable on the processor. When the processor executes the method program for intelligent scheduling of energy storage on the new energy generation side, it implements the following operation instructions: Acquire historical data and determine typical scenario information reflecting wind power output and photovoltaic power output based on the historical data; Based on the typical scenario information, the comprehensive benefit information of the microgrid system is determined, including user satisfaction, grid stability, and renewable energy grid connection rate. Based on the comprehensive benefit information, the maximum comprehensive benefit is determined, and the demand response strategy and the energy storage system operation strategy are determined based on the maximum comprehensive benefit. The demand response strategy is used to reflect the power generation demand on the generation side, and the energy storage system operation strategy is used to reflect the power purchase demand from the generation side to the energy storage system. Based on the demand response strategy and the energy storage system operation strategy, constraints are imposed on the power generation side, and a power generation strategy and a power purchase strategy are formulated based on minimizing the power generation operating cost. The system is then dispatched according to the power generation strategy and the power purchase strategy.

[0054] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, operational databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual operating data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0055] In summary, this invention discloses a method and system for intelligent scheduling of energy storage on the new energy generation side. The method includes: acquiring historical data and determining typical scenario information reflecting wind power output and photovoltaic output based on the historical data; determining comprehensive benefit information of the microgrid system based on the typical scenario information; determining the maximum comprehensive benefit based on the comprehensive benefit information, and determining a demand response strategy and an energy storage system operation strategy based on the maximum comprehensive benefit; constraining the generation side based on the demand response strategy and the energy storage system operation strategy, and formulating a generation strategy and a power purchase strategy based on minimizing generation operating costs, and performing scheduling based on the generation strategy and the power purchase strategy. This invention can formulate generation and power purchase strategies based on maximizing comprehensive benefits, making the operation of the microgrid system more coordinated.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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; and these 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 the present invention.

Claims

1. A method for intelligent scheduling of energy storage on the new energy power generation side, characterized in that, The method includes: Acquire historical data and determine typical scenario information reflecting wind power output and photovoltaic power output based on the historical data; Based on the typical scenario information, the comprehensive benefit information of the microgrid system is determined, including user satisfaction, grid stability, and renewable energy grid connection rate. Based on the comprehensive benefit information, the maximum comprehensive benefit is determined, and the demand response strategy and the energy storage system operation strategy are determined based on the maximum comprehensive benefit. The demand response strategy is used to reflect the power generation demand on the generation side, and the energy storage system operation strategy is used to reflect the power purchase demand from the generation side to the energy storage system. Based on the demand response strategy and the energy storage system operation strategy, constraints are imposed on the power generation side, and a power generation strategy and a power purchase strategy are formulated based on minimizing the power generation operating cost. Scheduling is then carried out according to the power generation strategy and the power purchase strategy. The step of determining the comprehensive benefit information of the microgrid system based on the typical scenario information includes: Based on the typical scenario information, determine the wind power and / or photovoltaic power corresponding to the preset time period or preset moment in the typical scenario information; Based on the wind power and / or photovoltaic power, the user satisfaction, the grid stability, and the renewable energy grid connection rate are determined. The step of determining the maximum comprehensive benefit based on the comprehensive benefit information includes: Obtain the weighted data corresponding to the user satisfaction, the power grid stability, and the renewable energy grid connection rate, respectively. The maximum comprehensive benefit is obtained by weighted summation of the user satisfaction, the power grid stability, and the renewable energy grid connection rate. The determination of demand response strategy and energy storage system operation strategy based on maximizing comprehensive benefits includes: Based on the maximized comprehensive benefits, the wind power load and photovoltaic load corresponding to the maximized comprehensive benefits are determined. The wind power load and the photovoltaic load respectively reflect the maximum amount of electricity that the wind farm and the photovoltaic power station can generate when the maximized comprehensive benefits are met. Based on the wind power load and the photovoltaic load, the wind power generation capacity and photovoltaic power generation capacity on the power generation side are determined respectively, and the demand response strategy is generated based on the wind power generation capacity and the photovoltaic power generation capacity. The predicted wind power generation and predicted photovoltaic power generation are obtained from the power generation side. Based on the wind power generation and the predicted wind power generation, the wind power compensation power is determined, and based on the photovoltaic power generation and the predicted photovoltaic power generation, the photovoltaic compensation power is determined. The energy storage system operation strategy is generated based on the wind power compensation power and the photovoltaic compensation power.

2. The intelligent dispatching method for energy storage on the new energy generation side according to claim 1, characterized in that, The method further includes: The power generation strategy and the power purchase strategy are fed back to the microgrid system.

3. The intelligent dispatching method for energy storage on the new energy generation side according to claim 1, characterized in that, The acquisition of historical data and the determination of typical scenario information reflecting wind power output and photovoltaic output based on the historical data include: The historical data is obtained based on literature, and the historical data includes wind power output and photovoltaic output on a certain day in the preset area; Based on the historical data, taking the day-ahead power output prediction error of wind power and photovoltaic as the simulation object, several day-ahead power output prediction error scenarios of new energy represented by wind power and photovoltaic are randomly generated by Monte Carlo simulation method. Obtain the Euclidean distance between scenarios, and based on the Euclidean distance, eliminate useless scenarios from the day-ahead output prediction error scenarios of new energy sources represented by wind power and photovoltaics, to form the typical scenario information.

4. The intelligent dispatching method for energy storage on the new energy generation side according to claim 1, characterized in that, The constraint on the generation side based on the demand response strategy and the energy storage system operation strategy, so that the generation side formulates a generation strategy and a power purchase strategy based on minimizing the generation operating cost, includes: The operating costs of wind farms and photovoltaic power plants on the power generation side are obtained, and the power generation strategy is formulated based on the wind power generation capacity and the photovoltaic power generation capacity to minimize the operating costs. Obtain electricity price data for the energy storage system sold to external parties and determine the electricity purchase cost. Based on the wind power compensation power and the photovoltaic compensation power, formulate the electricity purchase strategy to minimize the electricity purchase cost.

5. A smart dispatching system for energy storage on the new energy generation side, characterized in that, The system is used to implement the steps of the intelligent dispatching method for energy storage on the new energy generation side as described in any one of claims 1-4, and the system includes: The typical scenario determination module is used to acquire historical data and determine typical scenario information reflecting wind power output and photovoltaic output based on the historical data. The comprehensive benefit determination module is used to determine the comprehensive benefit information of the microgrid system based on the typical scenario information. The comprehensive benefit information includes user satisfaction, grid stability, and renewable energy grid connection rate. The strategy information decision module is used to determine the maximum comprehensive benefit based on the comprehensive benefit information, and to determine the demand response strategy and the energy storage system operation strategy based on the maximum comprehensive benefit. The demand response strategy is used to reflect the power generation demand on the power generation side, and the energy storage system operation strategy is used to reflect the power purchase demand of the power generation side to the energy storage system. The generation-side strategy specification module is used to constrain the generation side based on the demand response strategy and the energy storage system operation strategy, formulate generation strategy and power purchase strategy based on minimizing generation operating costs, and perform scheduling according to the generation strategy and power purchase strategy.

6. A terminal device, characterized in that, The terminal device includes a memory, a processor, and a new energy generation-side energy storage intelligent scheduling program stored in the memory and capable of running on the processor. When the processor executes the new energy generation-side energy storage intelligent scheduling program, it implements the steps of the new energy generation-side energy storage intelligent scheduling method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a smart scheduling program for energy storage on the new energy generation side. When the smart scheduling program for energy storage on the new energy generation side is executed by the processor, it implements the steps of the smart scheduling method for energy storage on the new energy generation side as described in any one of claims 1-4.

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