Energy storage system energy-saving control method and system

Abstract

The present invention relates to the field of energy storage system regulation, and specifically to an energy storage system energy-saving regulation method and system, comprising: an energy management module, a cost calculation module and a charging and discharging strategy module; the energy management module calculates the grid load of a specific period by analyzing the peak power consumption period of the grid and weather factors; the cost calculation module estimates the charging cost of the corresponding period by analyzing the electricity market price, battery wear and operating cost; the charging and discharging strategy module combines the grid load data provided by the energy management module and the charging cost estimation of the cost calculation module, as well as the current power of the energy storage system, to decide whether to charge, and provides an intelligent and cost-effective operation strategy for the energy storage system by comprehensively analyzing the grid load, power cost and energy storage status, which not only optimizes energy utilization, but also reduces operating costs, while improving the stability and efficiency of the grid and battery system.

Description

Technical Field

[0001] The present invention provides an energy storage system energy-saving control method and system, and relates to the field of energy storage system control. Background Art

[0002] The energy storage system energy-saving control system is a key technology used to optimize energy management and power system operation. As the demand for regulation capabilities of the power system continues to increase, especially with the expansion of the scale of new energy development, the advantages of new energy storage systems are gradually becoming more prominent. Specifically, the energy storage system energy-saving control system is a set of technologies and strategies that aims to optimize grid loads, improve energy efficiency, and achieve energy conservation and carbon emission reduction through energy storage equipment (such as batteries, compressed air energy storage, etc.).

[0003] However, the energy storage system regulation of the existing technology has the following shortcomings:

[0004] In existing technologies, there is a lack of ability to accurately predict grid load, especially during extreme weather conditions or seasonal changes. This results in the inability of energy storage systems to efficiently respond to the actual needs of the grid, which affects the efficiency of overall energy management. Existing systems fail to fully consider the impact of electricity market price fluctuations and battery operating costs (such as maintenance and depreciation), resulting in inaccurate charging cost estimates. This results in charging during periods with higher costs, thereby increasing operating costs. Existing energy storage systems lack flexible charging and discharging strategies and cannot be dynamically adjusted according to grid load, charging costs, and battery status. This leads to inefficient energy utilization and failure to maximize economic benefits. Battery maintenance and depreciation are not adequately managed in existing technologies, resulting in premature battery aging or performance degradation. The lack of effective battery health management measures shortens battery life and increases replacement and maintenance costs. There is no mechanism for different modules in existing energy storage systems to work together efficiently, which affects overall operating efficiency and responsiveness. In general, the main defects of existing energy storage system technologies include insufficient prediction capabilities, incomplete cost estimates, rigid strategies, poor battery management, and limited system synergy. Summary of the invention

[0005] The present invention provides an energy storage system energy-saving control method and system, which solves the technical problems of low performance and economic benefits in the energy storage system control system of the prior art. The technical solutions adopted are as follows:

[0006] An energy storage system energy-saving control system includes: an energy management module, a cost calculation module and a charging and discharging strategy module; the energy management module calculates the grid load of a specific period by analyzing the peak power consumption period of the grid and weather factors; the cost calculation module estimates the charging cost of the corresponding period by analyzing the power market price, battery wear and operating costs; the charging and discharging strategy module combines the grid load data provided by the energy management module and the charging cost estimate of the cost calculation module, as well as the current power of the energy storage system, to decide whether to charge.

[0007] Preferably, the energy management module includes: collecting grid usage data, that is, collecting historical electricity usage data from the data management system of the grid operator, collecting weather data about temperature through the meteorological bureau or meteorological service agency, the grid usage data includes historical electricity usage data and weather conditions, and the weather conditions are specifically temperature; identifying daily stable electricity usage period, daily peak electricity usage period and daily low-peak electricity usage period according to historical electricity usage data; identifying temperature-suitable weather, cold weather and hot weather according to weather condition data; combining the identification of historical electricity usage data and weather data, using time series analysis combined with a prediction model to estimate the grid load of the upcoming period, wherein different weight values ​​are assigned according to daily stable electricity usage period, daily peak electricity usage period and daily low-peak electricity usage period to reflect the impact of different periods of the day on the grid load, and different weight values ​​are assigned according to temperature-suitable weather, cold weather and hot weather to reflect the impact of weather on the grid load;

[0008] Analyze historical electricity usage data to identify periods of steady, peak, and low daily usage. This can usually be done by comparing electricity usage over different time periods.

[0009] Set the percentage of power load to the total power load to define different time periods:

[0010] Peak hours: electricity load accounts for more than 70% of the total electricity load;

[0011] Stable period: power load is between 30% and 70% of the total power load;

[0012] Off-peak period: the electricity load accounts for less than 30% of the total electricity load.

[0013] Set specific temperature thresholds to define:

[0014] Suitable weather: Temperature between 5℃ and 30℃;

[0015] Cold weather: temperature below 5℃;

[0016] Hot weather: temperature exceeds 30℃;

[0017] Preferably, the time series analysis combined with the forecast model estimation formula includes:

[0018] L(t)=W t× L t +W w× L w

[0019] Where, L(t) is the predicted percentage of grid load in time period t, W t is the weight value assigned based on the daily electricity consumption stable period, daily electricity consumption peak period or daily electricity consumption low peak period, reflecting the impact of different time periods on the grid load. t is the average grid load percentage for time period t based on historical electricity consumption data, W w is the weight value assigned based on suitable temperature weather, cold weather or hot weather, reflecting the impact of different weather conditions on the grid load. w Based on the historical average grid load percentage at the current temperature value according to weather conditions, a method for predicting grid load in a specific period of time is provided. This method combines the historical electricity consumption trend and the impact of weather conditions to help predict future grid load conditions, thereby providing important information for the management and regulation of energy storage systems;

[0020] In order to determine the weights for different weather conditions and different time periods, it is necessary to consider their impact on the grid load. Usually, these weights are set based on historical data analysis and actual experience in grid operation. The following is a setting method, and the actual weights can be adjusted according to specific circumstances and available data:

[0021] Weight value W based on different weather conditions w :

[0022] Suitable temperature weather: moderate impact, set the weight to 0.5;

[0023] Cold weather: Since cold weather may lead to higher heating demand and thus increase grid load, a higher weight of 0.8 is set;

[0024] Hot weather: Similarly, hot weather usually increases the load on the grid due to increased use of cooling equipment such as air conditioners, and the weight is also set to 0.8;

[0025] Weight value W at different time periods t :

[0026] Daily stable electricity consumption period: During these periods, the grid load is relatively stable and the weight can be lower, such as 0.4;

[0027] Daily peak electricity consumption period: Since the grid load increases significantly during peak hours, the weight should be higher, such as 0.9;

[0028] Daily off-peak electricity consumption period: During off-peak hours, the grid load is low and the weight can be set to 0.3.

[0029] Preferably, the cost calculation module includes:

[0030] Collect real-time pricing information on the electricity market, including peak and valley prices, and analyze electricity prices in the forecast period based on the collected pricing information; collect battery operating costs of the energy storage system, including maintenance costs and battery degradation and depreciation fees; estimate the total charging cost of the energy storage system in a specific period based on the electricity price in the forecast period combined with the battery operating costs;

[0031] Use real-time data interfaces or API services provided by electricity market operators to obtain real-time information on peak and valley electricity prices through these interfaces; deploy automated systems to regularly collect pricing information from electricity market databases, including real-time and historical price data; use statistical methods to analyze historical electricity price data and predict price trends in future time periods; collect data on battery maintenance and degradation from the BMS of the energy storage system; collect historical records and cost data on battery maintenance, replacement and repair.

[0032] Preferably, the estimation formula of the total charging cost includes:

[0033] C(t)=P(t)×E(t)+O(t);

[0034] Among them, C(t) is the total charging cost in time period t, P(t) is the electricity price in time period t, E(t) is the expected charging amount in time period t, and O(t) is the battery operating cost.

[0035] The battery operating cost O(t) for a specific period of time can be estimated using the following calculation formula:

[0036] O(t)=M(t)+D(t)+A(t)

[0037] in:

[0038] M(t) represents the maintenance cost;

[0039] D(t) represents the depreciation cost of the battery;

[0040] A(t) represents other relevant operating costs.

[0041] When calculating each cost, technicians need to determine the exact value based on actual operating data and historical records. These data can be based on past maintenance records, performance data provided by battery manufacturers, and actual observations during operation. Such calculations can provide a more comprehensive perspective on the economics and efficiency of energy storage systems.

[0042] Preferably, the charging and discharging strategy module includes:

[0043] Collect grid load data L(t), charging cost C(t) and current power of the energy storage system B(t); analyze grid load, charging cost and current power of the energy storage system, and decide whether to charge based on the preset threshold through the decision formula; if charging is decided, calculate the charging amount D(t), taking into account the power limit and operating efficiency of the energy storage system.

[0044] Preferably, the decision formula includes:

[0045]

[0046] Where F(L(t), C(t), B(t)) is the charging demand function, which combines the grid load, charging cost and current power to evaluate the charging demand. G(L(t), B(t)) is the recommended charging capacity function calculated based on the grid load and current power. y is the preset charging threshold, and B max It is the upper limit of the energy storage system power; it means that when the charging demand function meets the charging threshold, the charging operation is performed and the charging value obtained by the charging capacity function is charged;

[0047] In order to achieve system control and automation, ensure that the charging operation is automatically performed when function F meets a specific threshold, and adjust the charging power and time according to the power calculated by function G, the following specific technical means can be used:

[0048] The algorithms are integrated directly into the control logic of the energy storage system using industrial automation software such as SCADA (Supervisory Control and Data Acquisition System) or DCS (Distributed Control System), which can monitor the system status in real time and execute control commands based on the algorithm output.

[0049] Program the PLC to read real-time data (including grid load, electricity price, weather conditions, and energy storage system status) and calculate the values ​​of functions F and G.

[0050] The PLC logic is set to automatically start the charging process when the value of F exceeds a predetermined threshold, and adjust the charging power and time according to the value of G. For example, if G recommends a charge amount that is a certain percentage of the energy storage system capacity, the PLC will adjust the charger output accordingly.

[0051] Implement relevant data processing and calculation modules in the energy management system. This includes writing scripts or programs to process real-time data, calculate F and G, and generate control instructions.

[0052] Ensure data exchange and command transmission between the PLC and the battery management system (BMS) of the energy storage system through industrial communication protocols such as Modbus and Profibus; configure the BMS to receive and execute charging instructions from the PLC; and conduct sufficient system testing before actual deployment, including simulating charging responses under different grid conditions and energy storage states.

[0053] Preferably, the calculation formula of the charging demand function F(L(t), C(t), B(t)) is:

[0054]

[0055] Where F is the charging demand value, L(t) is the predicted grid load percentage in time period t, C(t) is the total charging cost in time period t, B(t) is the current power of the energy storage system, and L max is the historical maximum grid load percentage, C max is the highest charging cost in the same time period as time t, B max is the upper limit of the energy storage system power, W1, W2 and W3 are weight coefficients respectively.

[0056] Preferably, the calculation formula of the charge capacity function G(L(t), B(t)) is:

[0057] G=K×(L max -L(t))×(B max -B(t)

[0058] Among them, G is the charge value, K is the adjustment factor, L(t) is the predicted grid load percentage in time period t, B(t) is the current power of the energy storage system, and L max is the historical maximum grid load percentage, B max It is the upper limit of energy storage system capacity.

[0059] A method for energy storage system energy saving and control, using the energy storage system energy saving and control system.

[0060] The present invention has the following beneficial effects:

[0061] 1. By analyzing historical electricity consumption data and current weather conditions (such as temperature), the energy management module can accurately predict the grid load during a specific period of time. This predictive capability is crucial for optimizing the operation of the energy storage system, especially under extreme weather conditions or seasonal changes. As grid demand and weather conditions continue to change, the energy management module can flexibly adjust to ensure that the energy storage system always responds to grid demand in the most appropriate way. By accurately predicting and responding to grid load, the energy management module helps the energy storage system store energy when it is most needed and release energy when demand is low, thereby improving overall energy efficiency.

[0062] 2. Accurately calculate the charging cost (including electricity market price and battery operating cost) during a specific period of time. The cost calculation module helps the energy storage system to charge when the cost is lowest, thereby reducing operating costs. It combines electricity prices and battery operating costs (including maintenance, depreciation, etc.) to provide operators with a comprehensive cost analysis, support more informed economic decisions, consider battery depreciation and maintenance costs, help optimize battery use and life management, and avoid unnecessary cost expenditures.

[0063] 3. Taking into account the grid load, charging cost and current power of the energy storage system, the charging and discharging strategy module determines the best charging time and level. This intelligent decision helps to make full use of low-cost electricity while ensuring that the battery is not overcharged. By optimizing the charging time and level, energy waste is reduced, energy utilization efficiency is improved, and system collaboration is enhanced: the work of the charging and discharging strategy module is closely dependent on the load forecast of the energy management module and the cost analysis of the cost calculation module. This collaborative work helps to achieve the overall optimization and efficient operation of the energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 , one A method for energy storage system energy saving and control and a schematic diagram of the main flow of the system;

[0065] Figure 2 , one A schematic diagram of the working process of an energy storage system energy-saving control method and a system energy management module;

[0066] Figure 3 , one A schematic diagram of the working process of an energy storage system energy-saving control method and a system cost calculation module;

[0067] Figure 4 , one A schematic diagram of the logic judgment of an energy storage system energy-saving control method and a system discharge strategy module. DETAILED DESCRIPTION

[0068] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0069] Embodiment 1:

[0070] An energy storage system energy-saving control system includes: an energy management module, a cost calculation module and a charging and discharging strategy module; the energy management module calculates the grid load of a specific period by analyzing the peak power consumption period of the grid and weather factors; the cost calculation module estimates the charging cost of the corresponding period by analyzing the power market price, battery wear and operating costs; the charging and discharging strategy module combines the grid load data provided by the energy management module and the charging cost estimate of the cost calculation module, as well as the current power of the energy storage system, to decide whether to charge.

[0071] The technical principles and expected effects of the energy storage system energy-saving control system are based on the collaborative work of three main modules: energy management module, cost calculation module and charging and discharging strategy module. The following are the specific technical principles of each module and the effects of the overall system:

[0072] The energy management module uses historical and real-time data to analyze the peak hours of power consumption and weather factors in the power grid to predict the load of the power grid during a specific period of time. It uses time series analysis, forecasting models and other technologies to combine power consumption data and meteorological information (such as temperature and humidity) to estimate the load demand of the power grid.

[0073] The cost calculation module predicts the electricity cost for the corresponding period by analyzing the real-time pricing information of the electricity market, including peak and valley prices. It also calculates the total charging cost for a specific period by taking into account battery wear and operating costs (including maintenance, battery degradation and depreciation fees, etc.).

[0074] The charging and discharging strategy module combines the grid load data provided by the energy management module, the charging cost estimate of the cost calculation module, and the current power of the energy storage system to make a decision on whether to charge. The decision algorithm is used to determine when to charge and the amount of charging, thereby optimizing energy utilization and cost-effectiveness.

[0075] By accurately predicting grid load and charging costs, the system can charge at the most appropriate time and improve energy efficiency. Combining electricity market prices and battery operating costs to make charging decisions helps to charge at the lowest cost, thereby reducing overall operating costs. By adjusting the charging and discharging strategy, the system helps the grid operate stably during peak hours, especially when there is high demand for electricity or extreme weather. Considering the operating and maintenance costs of the battery can more effectively manage the use of the battery and extend its service life.

[0076] Overall, this energy storage system energy-saving control system provides an intelligent and cost-effective operation strategy for the energy storage system by comprehensively analyzing the grid load, electricity cost and energy storage status, which not only optimizes energy utilization, but also reduces operating costs, while improving the stability and efficiency of the grid and battery system.

[0077] Specifically, the energy management module includes: collecting grid usage data, that is, collecting historical electricity usage data from the data management system of the grid operator, and collecting weather data about temperature through the meteorological bureau or meteorological service agency, the grid usage data includes historical electricity usage data and weather conditions, and the weather conditions are specifically temperature; identifying daily stable electricity usage time periods, daily peak electricity usage time periods and daily low-peak electricity usage time periods according to historical electricity usage data; identifying temperature-suitable weather, cold weather and hot weather according to weather condition data; combining the identification of historical electricity usage data and weather data, using time series analysis combined with a prediction model to estimate the grid load of the upcoming period, wherein different weight values ​​are assigned according to daily stable electricity usage time periods, daily peak electricity usage time periods and daily low-peak electricity usage time periods to reflect the impact of different time periods of the day on the grid load, and different weight values ​​are assigned according to temperature-suitable weather, cold weather and hot weather to reflect the impact of weather on the grid load;

[0078] Analyze historical electricity usage data to identify periods of steady, peak, and low daily usage. This can usually be done by comparing electricity usage over different time periods.

[0079] Set the percentage of power load to the total power load to define different time periods:

[0080] Peak hours: electricity load accounts for more than 70% of the total electricity load;

[0081] Stable period: power load is between 30% and 70% of the total power load;

[0082] Off-peak period: the electricity load accounts for less than 30% of the total electricity load.

[0083] Set specific temperature thresholds to define:

[0084] Suitable weather: Temperature between 5℃ and 30℃;

[0085] Cold weather: temperature below 5℃;

[0086] Hot weather: temperature exceeds 30℃;

[0087] The working principle and effect of the above technical solution are:

[0088] Collect historical electricity consumption data from the grid operator's data management system, including electricity consumption in each time period; collect temperature-related weather data through the meteorological bureau or meteorological service agency, and consider the impact of temperature on electricity consumption; analyze historical electricity consumption data to distinguish between stable periods, peak periods, and low peak periods of daily electricity consumption. This is usually based on the distribution and fluctuation of electricity consumption throughout the day. Based on the collected weather data, identify different weather conditions, such as suitable temperatures, cold and hot, and understand the impact of these conditions on electricity consumption. Use time series analysis and forecasting models (such as autoregressive moving average models) to combine historical electricity consumption and weather data to predict the grid load for the upcoming period. Assign weight values ​​to different electricity consumption periods and weather conditions to reflect the degree of their impact on the grid load.

[0089] By combining historical electricity consumption data and weather conditions, grid load can be predicted more accurately, especially under extreme weather conditions. Prediction information can help the energy storage system optimize charging and discharging strategies, for example, charging in advance when peak load is predicted and discharging when low peak is predicted. Accurate load forecasting helps reduce energy costs. For example, charging during off-peak hours can reduce electricity costs. Energy storage systems can more effectively balance grid loads, especially during peak hours or under adverse weather conditions, thereby improving the stability and reliability of the overall grid. In summary, the energy management module can significantly improve the performance of the energy storage system by efficiently integrating and analyzing key data, which helps to better adapt to and support grid operations.

[0090] Specifically, the time series analysis combined with the forecast model estimation formula includes:

[0091] L(t)=W t× L t +W w× L w

[0092] Where, L(t) is the predicted percentage of grid load in time period t, W t is the weight value assigned based on the daily electricity consumption stable period, daily electricity consumption peak period or daily electricity consumption low peak period, reflecting the impact of different time periods on the grid load. t is the average grid load percentage for time period t based on historical electricity consumption data, W w is the weight value assigned based on suitable temperature weather, cold weather or hot weather, reflecting the impact of different weather conditions on the grid load. w Based on the historical average grid load percentage at the current temperature value according to weather conditions, a method for predicting grid load in a specific period of time is provided. This method combines the historical electricity consumption trend and the impact of weather conditions to help predict future grid load conditions, thereby providing important information for the management and regulation of energy storage systems;

[0093] In order to determine the weights for different weather conditions and different time periods, it is necessary to consider their impact on the grid load. Usually, these weights are set based on historical data analysis and actual experience in grid operation. The following is a setting method, and the actual weights can be adjusted according to specific circumstances and available data:

[0094] Weight value W based on different weather conditions w :

[0095] Suitable temperature weather: moderate impact, set the weight to 0.5;

[0096] Cold weather: Since cold weather may lead to higher heating demand and thus increase grid load, a higher weight of 0.8 is set;

[0097] Hot weather: Similarly, hot weather usually increases the load on the grid due to increased use of cooling equipment such as air conditioners, and the weight is also set to 0.8;

[0098] Weight value W at different time periods t :

[0099] Daily stable electricity consumption period: During these periods, the grid load is relatively stable and the weight can be lower, such as 0.4;

[0100] Daily peak electricity consumption period: Since the grid load increases significantly during peak hours, the weight should be higher, such as 0.9;

[0101] Daily off-peak electricity consumption period: During off-peak hours, the grid load is low and the weight can be set to 0.3.

[0102] The principles and technical effects of the above technical solutions are:

[0103] L(t) is the percentage load on the grid predicted for a specific time period t. This is the output of the formula and represents the proportion of the grid load relative to the maximum load or average load during time period t.

[0104] W t The weights are based on the time of day (smooth, peak, or off-peak). These weights reflect the relative impact of different time periods on the grid load. For example, the weights for peak hours may be higher because the grid load usually increases at this time.

[0105] L t It is the average grid load percentage for the same period t in history. This indicator is based on historical data and reflects the typical electricity consumption level during that period.

[0106] W wThe weights are based on weather conditions (suitable, cold, or hot). These weights reflect the impact of different weather conditions on the grid load. For example, in extremely hot weather, the grid load may increase due to the increased use of cooling equipment such as air conditioners, so the weight of extremely hot weather is higher.

[0107] L w The percentage of historical average grid load at current temperature conditions. This data reflects typical electricity usage under specific weather conditions.

[0108] The comprehensive analysis of two key influencing factors (time and weather) makes the forecast more accurate, especially in extreme weather conditions or special periods (such as holidays). It can adapt to different electricity consumption scenarios and conditions based on historical trends and changes in weather patterns.

[0109] Provides energy storage systems with important information about when it is best to charge or discharge, helping to optimize energy management and operating strategies.

[0110] In summary, this approach not only takes into account the impact of historical electricity consumption trends and weather conditions on grid load, but also provides a flexible and accurate forecasting tool, which is of great significance to the efficient operation of energy storage systems and grid stability.

[0111] Specifically, the cost calculation module includes:

[0112] Collect real-time pricing information on the electricity market, including peak and valley prices, and analyze electricity prices in the forecast period based on the collected pricing information; collect battery operating costs of the energy storage system, including maintenance costs and battery degradation and depreciation fees; estimate the total charging cost of the energy storage system in a specific period based on the electricity price in the forecast period combined with the battery operating costs;

[0113] Use real-time data interfaces or API services provided by electricity market operators to obtain real-time information on peak and valley electricity prices; deploy automated systems to regularly collect pricing information from electricity market databases, including real-time and historical price data; use statistical methods to analyze historical electricity price data and predict price trends in future periods; collect data on battery maintenance and degradation from the BMS of energy storage systems; collect historical records and cost data on battery maintenance, replacement and repair;

[0114] Principles and technical effects of the above technical solutions:

[0115] Monitor electricity markets in real time and collect data on peak and valley prices. This can be done by connecting to the electricity market operator’s data interface or using a market data service.

[0116] Use time series analysis and forecasting models to analyze and predict electricity prices for future periods.

[0117] Data collection on battery operating costs includes battery maintenance costs and degradation costs due to battery use. This information can be obtained from the battery management system (BMS) or operation records of the energy storage system.

[0118] Combined with electricity price forecasts and battery operating costs for upcoming periods, the total cost of charging the energy storage system during a specific period is calculated. This estimate can take into account the expected charging volume, battery life, maintenance frequency and other relevant factors. By accurately estimating the charging cost, the energy storage system can choose the most economical period to charge, thereby reducing the overall energy cost. Combining the analysis of electricity prices and battery operating costs can help achieve economic and efficient operation of energy storage systems, especially when electricity market prices are volatile. Providing detailed information on when and under what conditions charging is most economical helps make more informed operational decisions. By considering the maintenance and degradation costs of batteries, it helps to better manage battery use and extend battery life.

[0119] In summary, this cost calculation module provides an effective method for the cost-effectiveness management of energy storage systems. By comprehensively considering the dynamic changes of the electricity market and the operating costs of batteries, the energy storage system can find a balance between economic benefits and battery maintenance.

[0120] Specifically, the estimation formula of the total charging cost includes:

[0121] C(t)=P(t)×E(t)+O(t);

[0122] Among them, C(t) is the total charging cost in time period t, P(t) is the electricity price in time period t, E(t) is the expected charging amount in time period t, and O(t) is the battery operating cost.

[0123] The battery operating cost O(t) for a specific period of time can be estimated using the following calculation formula:

[0124] O(t)=M(t)+D(t)+A(t)

[0125] in:

[0126] M(t) represents the maintenance cost;

[0127] D(t) represents the depreciation cost of the battery;

[0128] A(t) represents other relevant operating costs.

[0129] When calculating each cost, technicians need to determine the exact value based on actual operating data and historical records. These data can be based on past maintenance records, performance data provided by battery manufacturers, and actual observations during operation. Such calculations can provide a more comprehensive perspective on the economics and efficiency of energy storage systems.

[0130] Charging costs include electricity prices and battery operating costs.

[0131] P(t) represents the electricity price at a specific time period t, which can be obtained through electricity market data.

[0132] E(t) is the estimated charge amount during that period, which is usually based on the demand of the energy storage system and the grid load forecast.

[0133] O(t) represents the operating cost of the battery, which is further broken down into maintenance costs M(t), depreciation costs D(t), and other operating costs A(t).

[0134] Battery operating cost O(t):

[0135] Maintenance costs M(t) include costs for regular inspections, repairs, and replacement of parts.

[0136] Battery depreciation cost D(t) refers to the cost of the battery as its value decreases over time.

[0137] Other operating costs A(t) include management expenses, software update costs, etc.

[0138] The calculation of these costs needs to be based on actual operating data and historical records.

[0139] Improved accuracy of cost estimates: By combining real-time electricity prices and battery operating costs, the formula can provide more accurate charging cost estimates. Accurate cost calculations help energy storage systems charge when costs are lowest and optimize economic benefits. Considering battery maintenance and depreciation costs helps better manage battery life and efficiency and avoid overuse or loss. : Provides comprehensive cost information to support more effective energy storage system operation and management decisions. This charging cost estimation formula not only helps energy storage systems plan and perform charging operations more accurately, but also provides important guidance on economics and battery maintenance, thereby improving the efficiency and benefits of the overall system.

[0140] Specifically, the charging and discharging strategy module includes:

[0141] Collect grid load data L(t), charging cost C(t) and current energy storage system power B(t); analyze grid load, charging cost and current energy storage system power, and decide whether to charge based on the preset thresholds and decision-making formula; if charging is decided, calculate the charging amount D(t), taking into account the energy storage system power limit and operating efficiency;

[0142] Principles and technical effects of the above technical solutions:

[0143] Collect grid load data L(t), which includes real-time grid load or predicted load information.

[0144] Collect the charging cost C(t), including the electricity market price and battery operating cost.

[0145] The current power B(t) of the energy storage system is obtained, usually through the battery management system (BMS) of the energy storage system.

[0146] Comprehensively analyze the grid load, charging cost and current power to decide whether to charge.

[0147] The decision-making formula combines these data with the preset threshold to determine whether it is the right time to charge.

[0148] If it is decided to charge, the appropriate charging amount D(t) is further calculated.

[0149] The calculation of charging capacity takes into account the power limit and operating efficiency of the energy storage system to ensure that it does not overcharge, while also considering economic feasibility.

[0150] Through intelligent analysis and decision-making, the charging time and level are optimized to improve energy efficiency. Selecting the lowest cost period for charging helps reduce overall energy costs. Considering the battery's power limit and health status helps extend battery life. The charging strategy can be flexibly adjusted according to changes in grid load and market prices to improve the adaptability and responsiveness of the system. This charging and discharging strategy module provides an efficient, economical and reliable charging management solution for the energy storage system by combining key operating data and intelligent decision-making, which helps to achieve optimal energy utilization and economic benefits of system operation.

[0151] Specifically, the decision formula includes:

[0152]

[0153] Where F(L(t), C(t), B(t)) is the charging demand function, which combines the grid load, charging cost and current power to evaluate the charging demand. G(L(t), B(t)) is the recommended charging capacity function calculated based on the grid load and current power. y is the preset charging threshold, and B maxIt is the upper limit of the energy storage system power; it means that when the charging demand function meets the charging threshold, the charging operation is performed and the charging value obtained by the charging capacity function is charged;

[0154] In order to achieve system control and automation, ensure that the charging operation is automatically performed when function F meets a specific threshold, and adjust the charging power and time according to the power calculated by function G, the following specific technical means can be used:

[0155] The algorithms are integrated directly into the control logic of the energy storage system using industrial automation software such as SCADA (Supervisory Control and Data Acquisition System) or DCS (Distributed Control System), which can monitor the system status in real time and execute control commands based on the algorithm output.

[0156] Program the PLC to read real-time data (including grid load, electricity price, weather conditions, and energy storage system status) and calculate the values ​​of functions F and G.

[0157] The PLC logic is set to automatically start the charging process when the value of F exceeds a predetermined threshold, and adjust the charging power and time according to the value of G. For example, if G recommends a charge amount that is a certain percentage of the energy storage system capacity, the PLC will adjust the charger output accordingly.

[0158] Implement relevant data processing and calculation modules in the energy management system. This includes writing scripts or programs to process real-time data, calculate F and G, and generate control instructions.

[0159] Ensure data exchange and command transmission between the PLC and the battery management system (BMS) of the energy storage system through industrial communication protocols such as Modbus and Profibus; configure the BMS to receive and execute charging instructions from the PLC; and conduct sufficient system testing before actual deployment, including simulating charging responses under different grid conditions and energy storage states.

[0160] Specifically, the calculation formula of the charging demand function F(L(t), C(t), B(t)) is:

[0161]

[0162] Where F is the charging demand value, L(t) is the predicted grid load percentage in time period t, C(t) is the total charging cost in time period t, B(t) is the current power of the energy storage system, and L max is the historical maximum grid load percentage, C max is the highest charging cost in the same time period as time t, B max is the upper limit of the energy storage system power, W1, W2 and W3 are weight coefficients respectively.

[0163] The settings of W1, W2 and W3 reflect the influence of grid load, charging cost and battery capacity on charging demand decision. The weight allocation depends on the actual importance of these factors in a specific environment.

[0164] This formula provides a method based on comprehensive analysis of multiple factors to determine the charging needs of the energy storage system, prompting smarter charging decisions. By accurately controlling the timing and magnitude of charging, it helps to improve energy utilization and reduce operating costs. The formula can flexibly adjust the charging strategy according to changes in real-time data and market conditions, enhancing the system's adaptability to different operating environments. Considering the current battery power and charging cost can help avoid overcharging and unnecessary operating expenses, and protect battery health.

[0165] In summary, the design of the charging demand function F(L(t), C(t), B(t)) aims to take into account the grid load, charging cost and battery status, and to achieve an accurate estimation of the charging demand of the energy storage system, thereby improving the economic efficiency and operational effectiveness of the entire system.

[0166] Specifically, the calculation formula of the charge capacity function G(L(t), B(t)) is:

[0167] G=K×(L max -L(t))×(B max -B(t)

[0168] Among them, G is the charge value, K is the adjustment factor, L(t) is the predicted grid load percentage in time period t, B(t) is the current power of the energy storage system, and L max is the historical maximum grid load percentage, B max It is the upper limit of energy storage system capacity.

[0169] L max -L(t) reflects the remaining capacity of the grid load. When the grid load is lower than the historical maximum, the difference is larger, which means there are more charging opportunities.

[0170] B max -B(t) indicates how much more charge the energy storage system can accept. When the current charge is lower than the maximum charge, the difference is larger, which means the energy storage system can accept more charge.

[0171] K is a regulation factor that adjusts the calculated charge to ensure it meets grid demand while not exceeding the energy storage system’s charging capacity.

[0172] This formula can recommend more charging when the grid load is low and the energy storage system power is low, which helps to charge when the cost-effectiveness is the highest. The formula can dynamically adjust the charging amount according to the changes in the real-time grid load and energy storage system power, improving the adaptability and flexibility of the energy storage system. Considering the upper limit of the energy storage system power can help avoid overcharging and protect the battery life. Intelligent calculation of the charging amount helps balance the grid load, especially during low-load periods.

[0173] In summary, the charging capacity function G(L(t), B(t)) provides an optimized charging strategy for the energy storage system by integrating the mathematical relationship between the grid load and the current power of the energy storage system, aiming to maximize energy efficiency and maintain battery health.

[0174] A method for energy storage system energy saving and control, using the energy storage system energy saving and control system.

Claims

1. An energy storage system energy-saving control system, characterized in that: include: Energy management module, cost calculation module and charging and discharging strategy module; The energy management module calculates the grid load during a specific period by analyzing the peak power consumption period and weather factors of the grid; The cost calculation module estimates the charging cost of the corresponding period by analyzing the electricity market price, battery wear and operating costs; The charging and discharging strategy module combines the grid load data provided by the energy management module, the charging cost estimation of the cost calculation module, and the current power of the energy storage system to decide whether to charge; The energy management module comprises: Collecting grid usage data, the grid usage data including historical electricity usage data and weather conditions, the weather conditions specifically being temperature; Identify daily stable electricity consumption periods, daily peak electricity consumption periods, and daily low electricity consumption periods based on historical electricity consumption data; Identify suitable temperature weather, cold weather and hot weather based on weather condition data; Combined with the identification of historical electricity consumption data and weather data, time series analysis is used in combination with the forecasting model to estimate the grid load in the upcoming period. Different weight values ​​are assigned according to the daily stable electricity consumption period, daily peak electricity consumption period and daily low electricity consumption period to reflect the impact of different periods of the day on the grid load. Different weight values ​​are assigned according to temperature-suitable weather, cold weather and hot weather to reflect the impact of weather on the grid load. The formula for time series analysis combined with forecast model estimation includes: L(t)=W t× L t +W w× L w Where, L(t) is the predicted percentage of grid load in time period t, W t is the weight value assigned based on the daily electricity consumption stable period, daily electricity consumption peak period or daily electricity consumption low peak period, L t is the average grid load percentage for time period t based on historical electricity consumption data, W w is the weight value assigned to suitable weather, cold weather or hot weather based on temperature, L w is the historical average grid load percentage at the current temperature value according to weather conditions; The cost calculation module includes: Collecting real-time pricing information on the electricity market, including peak and valley prices, and analyzing electricity prices in a forecast period based on the collected pricing information; Collecting the battery operating costs of the energy storage system, wherein the battery operating costs include maintenance costs and battery degradation costs; Estimate the total cost of charging the energy storage system during a specific period of time based on the electricity price during the forecast period combined with the battery operating cost; The estimated formula for the total charging cost includes: C(t)=P(t)×E(t)+O(t); Where C(t) is the total charging cost in time period t, P(t) is the electricity price in time period t, E(t) is the expected charging amount in time period t, and O(t) is the battery operating cost; The charging and discharging strategy module includes: Collect grid load data L(t), charging cost C(t) and current power of energy storage system B(t); Analyze the grid load, charging cost and current power of the energy storage system, and decide whether to charge based on the preset threshold and decision formula; If you decide to charge, calculate the charge amount D(t), taking into account the energy storage system's upper limit and operating efficiency; The decision formula includes: Where F(L(t), C(t), B(t)) is the charging demand function, which combines the grid load, charging cost and current power to evaluate the charging demand. G(L(t), B(t)) is the recommended charging capacity function calculated based on the grid load and current power. y is the preset charging threshold, and B max It is the upper limit of the energy storage system’s capacity; Indicates that when the charging demand function meets the charging threshold, the charging operation is performed and the charging capacity value obtained by the charging capacity function is charged; The calculation formula of the charging demand function F(L(t), C(t), B(t)) is: Where F is the charging demand value, L(t) is the predicted grid load percentage in time period t, C(t) is the total charging cost in time period t, B(t) is the current power of the energy storage system, and L max is the historical maximum grid load percentage, C max is the highest charging cost in the same time period as time t, B max is the upper limit of the energy storage system power, W1, W2 and W3 are weight coefficients respectively; The calculation formula of the charge capacity function G(L(t), B(t)) is: G=K×(L max -L(t))×(B max -B(t)) Among them, G is the charge value, K is the adjustment factor, L(t) is the predicted grid load percentage in time period t, B(t) is the current power of the energy storage system, and L max is the historical maximum grid load percentage, B max The upper limit of the energy storage system capacity.

2. A method for energy storage system energy saving and control, characterized in that: Use the energy storage system energy-saving control system described in claim 1.

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