Park electric energy storage optimization management system and method based on source network load storage

Abstract

The invention relates to the technical field of energy, and discloses a source network load storage-based park electric energy storage optimization management system and method, and the system comprises a data collection module, a data analysis module, an evaluation module and an optimization module, the data collection module collects data, the data analysis module carries out calculation, the evaluation module is used for evaluation, and the optimization module is used for strategy optimization. The above modules cooperate with each other to construct a complete energy management system, and the environment compensation coefficient of the energy storage device, the health degree of the energy storage device and the electric vehicle charging demand prediction accuracy index calculated by the data analysis module are not only used for assisting the evaluation module in judging the system operation state, but also used for providing a decision basis for the optimization module. Fine adjustment of charging and discharging strategies and capacity configuration of the energy storage equipment is achieved, and finally the effects of improving the energy utilization efficiency, prolonging the service life of the energy storage equipment, reducing the system operation cost and enhancing the stability and reliability of a power system are achieved.

Description

Technical Field

[0001] This invention relates to the field of energy technology, specifically to a park-based energy storage optimization management system and method based on source-grid-load-storage. Background Art

[0002] With the transformation of the energy structure and the rapid development of new energy technologies, energy management in industrial parks faces new challenges and opportunities. Traditional industrial park energy systems typically employ a single power supply model, lacking flexible allocation and optimized management of energy, and are ill-suited to the intermittent and fluctuating characteristics of new energy power generation. Simultaneously, with the widespread application of new types of electrical loads such as electric vehicles, the electrical load structure within industrial parks has become more complex, placing higher demands on the stability and reliability of the power system.

[0003] Against this backdrop, a park-based energy storage optimization management system based on power generation, grid, load, and storage has emerged. This system integrates distributed energy resources (such as solar and wind power), grid resources, electricity loads, and energy storage devices within the park to achieve efficient energy utilization and optimized management. However, existing technologies for managing energy storage devices have shortcomings. On the one hand, the performance of energy storage devices is significantly affected by environmental factors (such as temperature and humidity), but there is a lack of effective quantitative assessment and dynamic adjustment mechanisms. On the other hand, the health status of energy storage devices is difficult to monitor and assess in real time, leading to untimely equipment maintenance and affecting the stable operation of the system. Furthermore, the accuracy of predicting charging demand for new loads such as electric vehicles is low, resulting in unreasonable energy storage configuration and increasing the system's operating costs and management difficulty.

[0004] Therefore, there is an urgent need for a park energy storage optimization management system and method that can comprehensively consider environmental factors, equipment health status and load demand, so as to improve the overall performance, reliability and economy of the system and meet the needs of modern park energy management. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a park-based energy storage optimization management system and method based on source-grid-load-storage. It has the advantages of accurately quantifying environmental impact, real-time monitoring of equipment health, and efficient prediction of load demand. It solves the problems of energy storage equipment performance degradation due to environmental factors, untimely maintenance caused by lagging equipment health status monitoring, and unreasonable energy storage configuration due to low accuracy in predicting new load charging demand.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a park-based energy storage optimization management system based on source-grid-load-storage, including a data acquisition module, a data analysis module, an evaluation module, and an optimization module; The data acquisition module is responsible for collecting real-time operational data from various devices and systems within the park; The data analysis module is responsible for analyzing and calculating the collected data; The evaluation module assesses the health status of the energy storage equipment, system operating costs, and load response capabilities of the system based on the calculation results. The optimization module is responsible for optimizing the system's operating strategy based on the evaluation results.

[0007] Preferably, the data acquisition module includes a park power generation environment data acquisition unit, a park power load data acquisition unit, and an energy storage device data acquisition unit.

[0008] Preferably, the park power generation environment data acquisition unit collects park power generation environment data through temperature and humidity sensors, including the park power generation environment temperature change value per unit time. and the change in ambient humidity of the power generation environment in the park per unit time .

[0009] Preferably, the power load data acquisition unit within the park collects power load data within the park through smart meters and sensors, including the actual electric vehicle charging power. .

[0010] Preferably, the energy storage device data acquisition unit acquires energy storage device data through a communication interface and sensors, including the current number of discharges of the energy storage device. and the maximum allowable number of discharges of energy storage devices .

[0011] Preferably, the data analysis module includes an environmental adaptability assessment unit, an energy storage efficiency analysis unit, and a load forecasting unit.

[0012] Preferably, the environmental adaptability assessment unit calculates the environmental compensation coefficient of the energy storage device based on the park's power generation environment data. The calculation formula is as follows: ; In the formula, This represents the environmental compensation coefficient of energy storage equipment. This represents the change in ambient temperature in the power generation environment of the park per unit time. This represents the change in ambient humidity in the power generation environment of the park per unit time. Indicates the temperature threshold of the energy storage device. Indicates the humidity threshold of energy storage devices. , These represent the weighting coefficients for temperature and humidity in the environmental compensation coefficient of energy storage equipment, respectively.

[0013] Preferably, the energy storage efficiency analysis unit calculates the health status of the energy storage device based on the energy storage device data. The calculation formula is as follows: ; In the formula, Indicates the health status of energy storage devices. This indicates the current number of discharges of the energy storage device. Indicates the maximum number of discharges allowed for the energy storage device. This indicates the current energy conversion efficiency of the energy storage device. This indicates the response time of the energy storage device.

[0014] Preferably, the load forecasting unit calculates the electric vehicle charging demand forecasting accuracy index based on the electricity load data within the park. The calculation formula is as follows: ; In the formula, This indicates the accuracy index of electric vehicle charging demand forecasting. This represents the predicted charging power of electric vehicles. This indicates the actual charging power of the electric vehicle. This represents the total charging power of all electric vehicles during the predicted time period.

[0015] The optimized management method for park-based energy storage based on source-grid-load-storage includes the following steps: Step 1: Establish data acquisition, data analysis, evaluation, and optimization modules; Step 2: The data acquisition module collects real-time operational data from various devices and systems within the park; Step 3: The data analysis module analyzes and calculates the collected data; Step 4: The evaluation module assesses the health status of the energy storage equipment, system operating costs, and load response capabilities based on the analysis and calculation results. Step 5: The optimization module optimizes the system's operating strategy based on the evaluation results.

[0016] Compared with existing technologies, this invention provides a park-based energy storage optimization management system and method based on source-grid-load-storage, which has the following beneficial effects: 1. This invention calculates the environmental compensation coefficient of energy storage devices. This helps to assess the impact of quantitative environmental factors (temperature and humidity) on the performance of energy storage devices, thereby dynamically guiding the optimization module to adjust its operating strategy. Specifically, this is done based on the environmental compensation coefficient of the energy storage device. Within the range, adjust the charging and discharging strategy of the energy storage device in real time, when the environmental compensation coefficient of the energy storage device... When the energy storage device's environmental compensation coefficient is high, reduce its charging and discharging power to minimize wear and tear. When the energy storage device is in a low-temperature environment, its charging and discharging power is increased to make full use of its performance and ensure that it can maintain its optimal performance under different environmental conditions. This avoids performance degradation caused by environmental factors, thereby extending the service life of the energy storage device and reducing the overall operating cost of the system. This measure also ensures that the energy storage device can operate stably under various environmental conditions and enhances the overall reliability of the system.

[0017] 2. This invention calculates the health status of energy storage devices. This helps the assessment module to comprehensively evaluate the current operating status of energy storage equipment. Based on the health assessment results, it assists the optimization module in promptly identifying potential problems with the equipment, enabling early maintenance or replacement, and avoiding system downtime caused by equipment failure. When the health status of the energy storage equipment... When the health status of the energy storage equipment is high, maintain the current operating strategy to ensure efficient system operation; when the health status of the energy storage equipment is high... When the load is low, adjust the operating strategy to reduce equipment load and avoid overuse to ensure that the equipment operates in a healthy state, improve the overall performance of the system, reduce unnecessary maintenance costs and improve the economy of the system by optimizing the use and maintenance plan of the equipment, and ensure that the system can still operate stably when the equipment is in poor health, thus enhancing the overall reliability of the system.

[0018] 3. This invention calculates the accuracy index of electric vehicle charging demand prediction. The auxiliary evaluation module assesses the accuracy of existing electricity load forecasts, which helps the system optimize energy storage configuration. The optimization module predicts the accuracy index of electric vehicle charging demand. By rationally configuring the capacity and charging / discharging strategies of energy storage devices, the accuracy index of electric vehicle charging demand forecasting can be improved. When the energy storage capacity is high, reduce the reserved capacity of the energy storage device and optimize the charging and discharging strategy to improve the utilization efficiency of the energy storage device; when the accuracy index of electric vehicle charging demand forecasting is high... When the load is low, increase the reserved capacity of the energy storage equipment to enhance the system's regulation capability, thereby ensuring the stable operation of the system under different load demands. Attached Figure Description

[0019] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] Please see Figure 1 The park-based energy storage optimization management system based on source-grid-load-storage includes a data acquisition module, a data analysis module, an evaluation module, and an optimization module. The data acquisition module is responsible for collecting real-time operational data from various equipment and systems within the park, providing basic data support for subsequent data analysis, evaluation, and optimization. The data analysis module is responsible for analyzing and calculating the collected data, extracting the results, and providing a basis for system evaluation and optimization. The evaluation module assesses the health status of the energy storage devices, system operating costs, and load response capabilities based on the calculation results. The optimization module is responsible for optimizing the system's operating strategy based on the evaluation results, thereby improving the overall performance of the system.

[0022] The data acquisition module includes a power generation environment data acquisition unit for the park, a power load data acquisition unit for the park, and a data acquisition unit for energy storage equipment.

[0023] The power generation environment data acquisition unit in the industrial park collects environmental data about the power generation environment through temperature and humidity sensors, including the change in ambient temperature within the industrial park per unit time. and the change in ambient humidity of the power generation environment in the park per unit time .

[0024] The power load data acquisition unit within the park collects power load data through smart meters and sensors, including actual electric vehicle charging power. .

[0025] The energy storage device data acquisition unit collects data from the energy storage device through communication interfaces and sensors, including the current number of discharges of the energy storage device. and the maximum allowable number of discharges of energy storage devices .

[0026] The data analysis module includes an environmental adaptability assessment unit, an energy storage efficiency analysis unit, and a load forecasting unit.

[0027] The environmental adaptability assessment unit calculates the environmental compensation coefficient of the energy storage equipment based on the power generation environment data of the park. The calculation formula is as follows: ; In the formula, This represents the environmental compensation coefficient of energy storage equipment. This represents the change in ambient temperature in the power generation environment of the park per unit time. This represents the change in ambient humidity in the power generation environment of the park per unit time. Indicates the temperature threshold of the energy storage device. Indicates the humidity threshold of energy storage devices. , These represent the weighting coefficients for temperature and humidity in the environmental compensation coefficient of energy storage equipment, respectively. The advantage is that it allows for the calculation of the environmental compensation coefficient of energy storage devices. This helps to assess the impact of quantitative environmental factors (temperature and humidity) on the performance of energy storage devices, thereby dynamically guiding the optimization module to adjust its operating strategy. Specifically, this is done based on the environmental compensation coefficient of the energy storage device. Within the range, adjust the charging and discharging strategy of the energy storage device in real time, when the environmental compensation coefficient of the energy storage device... When the energy storage device's environmental compensation coefficient is high, reduce its charging and discharging power to minimize wear and tear. When the energy storage device is in a low-temperature environment, its charging and discharging power is increased to make full use of its performance and ensure that it can maintain its optimal performance under different environmental conditions. This avoids performance degradation caused by environmental factors, thereby extending the service life of the energy storage device and reducing the overall operating cost of the system. This measure also ensures that the energy storage device can operate stably under various environmental conditions and enhances the overall reliability of the system.

[0028] The energy storage performance analysis unit calculates the health status of the energy storage equipment based on the data from the energy storage equipment. The calculation formula is as follows: ; In the formula, This indicates the health status of energy storage equipment, comprehensively reflecting its health level under current operating conditions. It is expressed as a percentage (%) and used to assess the overall performance and lifespan of energy storage equipment, providing a basis for maintenance and replacement decisions. This indicates the current discharge count of the energy storage device, representing the number of discharge cycles the device has completed since it was put into use. It reflects the usage level of the energy storage device and is closely related to its lifespan. This indicates the maximum permissible number of discharge cycles for an energy storage device, specifically the maximum number of discharge cycles allowed within its design life. This indicates the current energy conversion efficiency of the energy storage device, which is the efficiency with which the energy storage device converts input energy into usable energy under its current operating conditions. The response time of an energy storage device is the time interval between receiving a charge / discharge command and actually starting to charge / discharge, expressed in seconds (s). It reflects the speed at which the energy storage device responds to system demands and affects the dynamic performance of the system. The advantage is that it calculates the health status of energy storage devices. This helps the assessment module to comprehensively evaluate the current operating status of energy storage equipment. Based on the health assessment results, it assists the optimization module in promptly identifying potential problems with the equipment, enabling early maintenance or replacement, and avoiding system downtime caused by equipment failure. When the health status of the energy storage equipment... When the health status of the energy storage equipment is high, maintain the current operating strategy to ensure efficient system operation; when the health status of the energy storage equipment is high... When the load is low, adjust the operating strategy to reduce equipment load and avoid overuse to ensure that the equipment operates in a healthy state, improve the overall performance of the system, reduce unnecessary maintenance costs and improve the economy of the system by optimizing the use and maintenance plan of the equipment, and ensure that the system can still operate stably when the equipment is in poor health, thus enhancing the overall reliability of the system.

[0029] The load forecasting unit calculates the accuracy index of electric vehicle charging demand forecasting based on the electricity load data within the park. The calculation formula is as follows: ; In the formula, The accuracy index for electric vehicle charging demand forecasting reflects how closely the predicted value matches the actual value. Higher accuracy indicates a more accurate system forecast. This represents the predicted charging power of electric vehicles, a predicted value for a future time period derived from historical data and prediction algorithms, expressed in kilowatts (kW). This represents the actual electric vehicle charging power, the value of electric vehicle charging power actually observed during the predicted time period, expressed in kilowatts (kW). This represents the total charging power of all electric vehicles during the prediction period. It serves as a normalization factor to ensure that the accuracy index does not change with the magnitude of the total charging power and remains within the range of [0, 1]. The unit is kilowatts (kW). The advantage is that it calculates the accuracy index of electric vehicle charging demand forecasting. The auxiliary evaluation module assesses the accuracy of existing electricity load forecasts, which helps the system optimize energy storage configuration. The optimization module predicts the accuracy index of electric vehicle charging demand. By rationally configuring the capacity and charging / discharging strategies of energy storage devices, the accuracy index of electric vehicle charging demand forecasting can be improved. When the energy storage capacity is high, reduce the reserved capacity of the energy storage device and optimize the charging and discharging strategy to improve the utilization efficiency of the energy storage device; when the accuracy index of electric vehicle charging demand forecasting is high... When the load is low, increase the reserved capacity of the energy storage equipment to enhance the system's regulation capability, thereby ensuring the stable operation of the system under different load demands.

[0030] The optimized management method for park-based energy storage based on source-grid-load-storage includes the following steps: Step 1: Establish data acquisition, data analysis, evaluation, and optimization modules; Step 2: The data acquisition module is responsible for collecting real-time operational data from various devices and systems within the park, providing basic data support for subsequent data analysis, evaluation, and optimization. Step 3: The data analysis module is responsible for analyzing and calculating the collected data, extracting the results, and providing a basis for system evaluation and optimization. Step 4: The evaluation module assesses the health status of the energy storage equipment, system operating costs, and load response capabilities based on the calculation results. Step 5: The optimization module is responsible for optimizing the system's operating strategy based on the evaluation results to improve the overall performance of the system.

[0031] The advantages are: by establishing data acquisition, data analysis, evaluation, and optimization modules, the data acquisition module is responsible for collecting real-time operational data from various devices and systems within the park; the data analysis module is responsible for analyzing and calculating the collected data; the evaluation module assesses the health status of the energy storage equipment, system operating costs, and load response capabilities based on the analysis and calculation results; and the optimization module optimizes the system's operating strategy based on the evaluation results. These modules work together to build a complete and efficient energy management system. The data analysis module calculates the environmental compensation coefficient of the energy storage equipment. Health status of energy storage equipment Accuracy index of electric vehicle charging demand forecast This invention not only assists the evaluation module in judging the system's operating status but also provides a basis for the optimization module to make decisions, enabling fine-grained adjustments to the charging and discharging strategies and capacity configurations of energy storage devices. Ultimately, this invention achieves the effects of improving energy utilization efficiency, extending the service life of energy storage devices, reducing system operating costs, and enhancing the stability and reliability of the power system. It effectively solves problems such as inflexible energy allocation, lagging equipment maintenance, and inaccurate load forecasting under the traditional park energy management model, meeting the urgent needs of modern parks for efficient energy management and optimized allocation.

[0032] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A park-based energy storage optimization management system based on source-grid-load-storage, characterized in that, It includes a data acquisition module, a data analysis module, an evaluation module, and an optimization module; The data acquisition module is responsible for collecting real-time operational data from various devices and systems within the park; The data analysis module is responsible for analyzing and calculating the collected data; The evaluation module assesses the health status of the energy storage equipment, system operating costs, and load response capabilities of the system based on the calculation results. The optimization module is responsible for optimizing the system's operating strategy based on the evaluation results.

2. The park-based energy storage optimization management system based on source-grid-load-storage as described in claim 1, characterized in that: The data acquisition module includes a power generation environment data acquisition unit for the park, a power load data acquisition unit for the park, and an energy storage device data acquisition unit.

3. The park-based energy storage optimization management system based on source-grid-load-storage as described in claim 2, characterized in that: The park's power generation environment data acquisition unit collects park power generation environment data through temperature and humidity sensors, including the change in park power generation environment temperature per unit time. and the change in ambient humidity of the power generation environment in the park per unit time .

4. The park-based energy storage optimization management system based on source-grid-load-storage as described in claim 2, characterized in that: The power load data acquisition unit within the park collects power load data within the park through smart meters and sensors, including actual electric vehicle charging power. .

5. The park-based energy storage optimization management system based on source-grid-load-storage as described in claim 2, characterized in that: The energy storage device data acquisition unit collects energy storage device data through a communication interface and sensors, including the current number of discharges of the energy storage device. and the maximum allowable number of discharges of energy storage devices .

6. The park-based energy storage optimization management system based on source-grid-load-storage as described in claim 1, characterized in that: The data analysis module includes an environmental adaptability assessment unit, an energy storage efficiency analysis unit, and a load forecasting unit.

7. The park-based energy storage optimization management system based on source-grid-load-storage as described in claim 6, characterized in that: The environmental adaptability assessment unit calculates the environmental compensation coefficient of the energy storage equipment based on the park's power generation environment data. The calculation formula is as follows: ； In the formula, This represents the environmental compensation coefficient of energy storage equipment. This represents the change in ambient temperature in the power generation environment of the park per unit time. This represents the change in ambient humidity in the power generation environment of the park per unit time. Indicates the temperature threshold of the energy storage device. Indicates the humidity threshold of energy storage devices. , These represent the weighting coefficients for temperature and humidity in the environmental compensation coefficient of energy storage equipment, respectively.

8. The park-based energy storage optimization management system based on source-grid-load-storage as described in claim 6, characterized in that: The energy storage efficiency analysis unit calculates the health status of the energy storage equipment based on the energy storage equipment data. The calculation formula is as follows: ； In the formula, Indicates the health status of energy storage devices. This indicates the current number of discharges of the energy storage device. Indicates the maximum number of discharges allowed for the energy storage device. This indicates the current energy conversion efficiency of the energy storage device. This indicates the response time of the energy storage device.

9. The park-based energy storage optimization management system based on source-grid-load-storage as described in claim 6, characterized in that: The load forecasting unit calculates the accuracy index of electric vehicle charging demand forecasting based on the electricity load data within the park. The calculation formula is as follows: ； In the formula, This indicates the accuracy index of electric vehicle charging demand forecasting. This represents the predicted charging power of electric vehicles. This indicates the actual charging power of the electric vehicle. This represents the total charging power of all electric vehicles during the predicted time period.

10. A method for optimized management of park-based energy storage based on source-grid-load-storage, characterized in that, Includes the following steps: Step 1: Establish data acquisition, data analysis, evaluation, and optimization modules; Step 2: The data acquisition module collects real-time operational data from various devices and systems within the park; Step 3: The data analysis module analyzes and calculates the collected data; Step 4: The evaluation module assesses the health status of the energy storage equipment, system operating costs, and load response capabilities based on the analysis and calculation results. Step 5: The optimization module optimizes the system's operating strategy based on the evaluation results.

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