Electric energy economy evaluation method and device based on storage battery

Abstract

The invention discloses an electric energy economy evaluation method and device based on a storage battery. The electric energy economy evaluation method based on the storage battery comprises the following steps: acquiring a real-time power load condition to predict a future power demand; according to the future power demand, adjusting a charging and discharging strategy of an energy storage battery to realize balance of power load; and analyzing the economy of the total cost, wherein the total cost comprises the energy purchase cost, the peak power cost and the power transmission network cost. According to the technical scheme, power load balance and peak load shifting can be achieved, especially the problem that the power demand is too high in the peak period is solved, the operation cost of a power grid is remarkably reduced, and the operation efficiency and reliability of a power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power storage, and in particular to a method, device, equipment and storage medium for evaluating the economic efficiency of electric energy based on a storage battery. Background Art

[0002] Modern society is increasingly dependent on electricity. Electricity is an indispensable infrastructure not only in traditional fields such as industrial manufacturing and power transmission, but also in emerging fields such as information technology, medical equipment, and communication networks. The demand for electricity is extremely high during peak hours, while it is relatively low during off-peak hours. This uneven demand for electricity poses a severe test to the stability and economy of the power grid. Rapid urbanization around the world has led to a rapid increase in electricity demand in urban areas, but the speed of grid expansion and upgrading cannot fully keep up with the pace of demand growth. Remote areas and developing countries often face power shortages and instability due to insufficient infrastructure construction.

[0003] With the advancement of technology and the enhancement of environmental awareness, renewable energy technologies such as solar energy and wind energy have gradually matured, and their application scope has continued to expand, becoming an important force in promoting global energy transformation. However, renewable energy is intermittent and volatile, resulting in unstable power generation, which poses a major challenge to the balance and stability of the power grid. Existing energy storage technologies mainly include battery energy storage, pumped storage, compressed air energy storage, etc. Among them, battery energy storage has become the most widely used energy storage technology due to its flexibility and high energy density. Despite this, battery energy storage technology still faces many challenges in terms of energy density, charging and discharging efficiency, cycle life, cost and safety, which limits its large-scale commercial application. Global climate change and environmental pollution problems are becoming increasingly severe, and reducing greenhouse gas emissions and protecting the environment have become the consensus of governments and all sectors of society. Promoting the application of renewable energy and improving energy utilization efficiency are the keys to achieving the goal of carbon neutrality, and energy storage technology plays an important role in this process. Governments of various countries have introduced a series of policies and regulations to support and promote the development and application of energy storage technology. Such as financial subsidies, tax incentives, green certificates, etc. For example, the Chinese government has clearly stated that it will focus on promoting the innovation and application of energy storage technology and support the construction and operation of independent energy storage power stations.

[0004] Independent energy storage power stations can store excess electricity when electricity demand is low, release electricity during peak hours, balance supply and demand, and optimize grid operation. By providing auxiliary services such as frequency regulation and voltage support, energy storage power stations can effectively improve the stability and reliability of the grid and reduce power outages caused by power fluctuations. Energy storage power stations can smooth out fluctuations in renewable energy generation, improve the grid's ability to accept renewable energy, and promote the use of more clean energy such as solar energy and wind energy. In the event of power outage emergencies, natural disasters, etc., independent energy storage power stations can serve as a reliable backup power source to ensure the power supply of critical loads.

[0005] In summary, the background of independent energy storage power stations involves multiple aspects such as the contradiction between electricity supply and demand, technical challenges, environmental protection and policy promotion, which provides broad prospects and urgent needs for the research and application of independent energy storage power stations. By solving the current technical shortcomings and application challenges, independent energy storage power stations will provide a solid guarantee for the realization of an efficient, safe and low-carbon future power system. Grid operators not only have to pay for the total energy demand, but also for the highest power demand from the upper grid. Therefore, reducing the demand for electricity during peak hours is the main concern of grid operators. Summary of the invention

[0006] The present invention provides a method, device, equipment and storage medium for evaluating the economic efficiency of electric energy based on storage batteries, which can be used to achieve balancing of electric loads and peak load shifting, especially to solve the problem of excessive power demand during peak hours, significantly reduce power grid operating costs, and improve the operating efficiency and reliability of the power system.

[0007] According to a first aspect of the present invention, there is provided a method for evaluating the economic efficiency of electric energy based on a storage battery, the method comprising:

[0008] Obtain real-time power load conditions to predict future power demand;

[0009] According to the future power demand, adjusting the charging and discharging strategy of the energy storage battery to achieve a balance of power load;

[0010] Analyze the economics of the total cost, which includes purchased energy costs, peak power costs, and transmission network costs.

[0011] In one embodiment, it further includes:

[0012] The energy content of the energy storage battery at the current moment depends on the energy content and power at the previous moment.

[0013] In one embodiment, it further includes:

[0014] The degradation degree of the energy storage battery is calculated according to any one or more of the remaining power, discharge depth, charging rate, usage time, energy storage battery temperature and cycle type.

[0015] In one embodiment, it further includes:

[0016] Establish a high-precision load forecasting model and use deep learning algorithms to predict future electricity demand;

[0017] The forecast accuracy of the electricity demand is evaluated according to at least two different forecast error levels.

[0018] In one embodiment, it further includes:

[0019] The total cost of electricity procurement is revised based on power peak shaving and power valley filling.

[0020] In one embodiment, it further includes:

[0021] Limit the lower limit of the remaining power of the energy storage battery to be greater than or equal to the energy content of the energy storage battery at the previous moment;

[0022] The product of the remaining power of the energy storage battery and the rated energy capacity is limited to be greater than or equal to the energy content of the energy storage battery at the previous moment.

[0023] According to a second aspect of the present invention, there is provided an electric energy economy evaluation device based on a storage battery, comprising:

[0024] An acquisition module, used to obtain real-time power load conditions to predict future power demand;

[0025] An adjustment module, used for adjusting the charging and discharging strategy of the energy storage battery according to the future power demand to achieve a balance of power load;

[0026] An analysis module is used to analyze the economic feasibility of the total cost, wherein the total cost includes the purchased energy cost, the peak power cost and the transmission network cost.

[0027] According to a third aspect of the present invention, there is provided an electronic device, the electronic device comprising: a communication interface, a processor, and a memory;

[0028] The memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, any of the above-mentioned methods for evaluating the economic efficiency of electric energy based on storage batteries is implemented.

[0029] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a computer (e.g., a processor in the computer), implement any of the above-mentioned methods for evaluating the economic efficiency of electric energy based on storage batteries.

[0030] In summary, the present invention provides a method and device for evaluating the economic efficiency of electric energy based on storage batteries, the method comprising: obtaining real-time power load conditions to predict future power demand; adjusting the charging and discharging strategy of the energy storage battery according to the future power demand to achieve a balance in the power load; and analyzing the economic efficiency of the total cost, the total cost including the cost of purchasing energy, the peak power cost, and the transmission network cost. The technical solution of the present application can achieve the balance of power load and peak load shifting, especially solve the problem of excessive power demand during peak hours, significantly reduce the operating cost of the power grid, and improve the operating efficiency and reliability of the power system.

[0031] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0032] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 A flow chart of a method for evaluating the economic efficiency of electric energy based on a storage battery provided by an embodiment of the present invention;

[0035] Figure 2 A flowchart of another method for evaluating the economic efficiency of electric energy based on a storage battery provided by an embodiment of the present invention;

[0036] Figure 3 A flowchart of another method for evaluating the economic efficiency of electric energy based on a storage battery provided in an embodiment of the present invention;

[0037] Figure 4 A structural diagram of an electric energy economy evaluation device based on a storage battery provided by an embodiment of the present invention;

[0038] Figure 5 A structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0040] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0041] like Figure 1 As shown, the present invention provides a method for evaluating the economic efficiency of electric energy based on a storage battery, and the method for evaluating the economic efficiency of electric energy based on a storage battery includes:

[0042] In step S11, real-time power load conditions are obtained to predict future power demand;

[0043] In step S12, according to the future power demand, the charging and discharging strategy of the energy storage battery is adjusted to achieve a balance of power load;

[0044] In step S13, the economic feasibility of the total cost is analyzed, the total cost including the purchased energy cost, the peak power cost and the transmission network cost.

[0045] In one embodiment, energy storage systems play an increasingly important role in modern power systems. With the continuous growth of global demand for electricity, especially driven by the popularization of renewable energy, the application of energy storage systems has gradually become an important means to ensure power supply, improve grid stability and achieve low-carbon goals, and explain the role of energy storage systems in peak load shaving, load forecasting, economic optimization and battery management.

[0046] Real-time acquisition of power load conditions. Power load has obvious time fluctuations, usually with peaks during the day and night, and troughs in the middle of the night. In order to cope with this fluctuation, it is necessary to store energy during the trough period and release electricity during the peak period to balance the supply and demand of electricity, which depends on accurate prediction of future power demand. By combining historical data with the real-time monitoring results of the transformer, the energy storage system can generate a high-precision load forecasting model, using the recurrent neural network (RNN) and gated recurrent unit (GRU) in deep learning technology, so that the system can make accurate predictions based on past power consumption data and real-time information. The load forecasting model is not simply based on historical trends, but through continuous self-learning, it identifies complex load patterns and potential change trends. Different levels of prediction error will directly affect the effectiveness of the peak shaving and valley filling strategy, and the accuracy of the prediction determines whether the charging and discharging strategy is adjusted in a timely manner. Therefore, by evaluating the impact of different prediction errors on the strategy, the load forecasting accuracy is further optimized so that the energy storage system can still operate stably and efficiently under increased uncertainty.

[0047] An important function of the energy storage system is to balance the power load by dynamically adjusting the charging and discharging strategy, especially in terms of peak load reduction and valley filling. During peak load periods, the power demand surges, while the demand is low during valley periods. This unevenness increases the burden on the power grid. The energy storage system monitors the operating status of the energy storage battery in real time through the energy management system, and dynamically adjusts the charging and discharging strategy according to the changes in power demand. Specifically, the energy storage system can charge the battery during the valley load period to store excess power; and release the stored power during the peak load period to reduce the demand pressure on the power grid. A method for dynamic adjustment of the charging and discharging power threshold is designed. The power threshold is adjusted at any time using real-time load forecast information, so that the energy storage system can flexibly respond to load fluctuations. The peak load of the power grid can be alleviated without increasing energy procurement, thereby achieving the dual goals of economy and stability. In addition, the addition of deep learning models further improves the intelligence level of the energy storage system. Through the adaptive adjustment function of the deep learning model, the charging and discharging strategy can be updated in real time to cope with load changes, ensuring that even in the case of prediction errors, the stability of the power grid can be guaranteed to a certain extent. It plays a significant role in reducing peak loads, reducing energy losses and improving system efficiency, and is the core advantage of energy storage systems in modern power networks.

[0048] Economic analysis is a key consideration in the practical application of energy storage systems. In order to ensure the cost-effectiveness of energy storage systems, the system costs are divided into energy purchase costs, peak power costs and transmission network costs. The operation strategy of the energy storage system is precisely controlled to minimize the economic burden of the entire system. Energy purchase costs refer to the cost of purchasing electricity. In the peak-shaving and valley-filling strategy, the energy storage system is charged during the valley period, and energy is accumulated during the period of low electricity prices, so as to be discharged during the peak electricity price period, thereby reducing energy procurement expenditures. The second is the peak power cost, which is mainly related to the electricity price and grid load during the peak electricity demand period. The energy storage system releases the stored electricity during the peak load period, which can reduce the peak power cost and reduce the dependence on peak electricity consumption. Finally, there is the transmission network cost. The energy storage system can reduce the load on the grid, thereby reducing the pressure of power transmission and the corresponding maintenance cost. Through the objective function, these three cost factors are combined to optimize the operation of the energy storage system. This objective function effectively avoids the additional costs incurred in the peak-shaving and valley-filling process by considering the worst-case energy procurement cost changes, so that the energy storage system can achieve a balance between economy and stability. Through this comprehensive economic analysis, the energy storage system can achieve the best input-output ratio and meet the needs of commercialization and large-scale applications.

[0049] The degradation of energy storage batteries is a key factor affecting the long-term economic benefits of energy storage systems. Batteries will experience a certain degree of degradation during the charging and discharging process, especially in high-frequency and high-intensity charging and discharging cycles, the battery capacity and charging and discharging efficiency will gradually decrease. Therefore, battery degradation assessment is an indispensable part of energy storage system management. Through a multi-factor degradation assessment method, including considering factors such as the remaining battery power, discharge depth, charging rate, usage time, temperature and cycle type. The above factors have a significant impact on the health of the battery in actual operation. Through a comprehensive evaluation of these factors, the energy storage system can formulate a more reasonable charging and discharging strategy to avoid excessive use of the battery, thereby extending the battery life. Discharge depth and charging rate are the main factors affecting battery degradation. Deeper discharge will lead to greater energy flow and increase the degradation rate of the battery. High charging rate will accelerate the heat accumulation of the battery and affect its life. By dynamically managing the depth and frequency of charge and discharge through an intelligent control system, the energy storage system can minimize battery losses while ensuring grid demand. This not only extends the battery life, but also effectively reduces the cost of battery replacement, providing a strong guarantee for the economy of the energy storage system.

[0050] Peak-shaving and valley-filling is the core strategy for energy storage systems to reduce total costs. The peak-shaving and valley-filling strategy reduces dependence on peak power in the power grid by charging during low load periods and discharging during peak load periods, while using low electricity prices during valley periods to store electricity, thereby reducing total power procurement costs. Through deep learning technology and objective function optimization, energy storage systems can dispatch electricity more flexibly when electricity prices fluctuate greatly. Energy storage systems can not only use market electricity price fluctuations to reduce procurement costs, but also further optimize the supply and demand relationship of the power grid through their ability to respond to different electricity price periods, ensuring the stability of the system during peak electricity consumption periods. In the power market, through a reasonable peak-shaving and valley-filling strategy, energy storage systems can help power suppliers effectively control operating expenses and increase profit margins. In addition, in the context of the global promotion of a low-carbon economy, this strategy of energy storage systems is also in line with the policy orientation of governments in various countries for the application of renewable energy. For example, policies such as tax incentives and financial subsidies provided by the government provide further support for the development and application of energy storage systems. Energy storage systems are not only more attractive in terms of economics, but also meet policy requirements, laying the foundation for the low-carbon transformation of future power systems.

[0051] Energy storage systems have an application prospect that cannot be ignored in future power systems. With the growth of global electricity demand and the intensification of environmental problems, energy storage systems, as a key technology that can balance electricity supply and demand, improve grid stability, and support the development of renewable energy, have gradually attracted attention. Independent energy storage power stations have unique advantages in the power grid system. They can not only provide electricity during peak loads, but also improve the stability and reliability of the power grid through services such as frequency regulation and voltage support. In response to emergencies such as extreme weather and natural disasters, independent energy storage power stations can also serve as emergency backup power sources to ensure the power supply of critical loads. By real-time monitoring and optimizing the operating status of energy storage batteries, energy storage systems can significantly improve the risk resistance of power systems and provide users with stable and reliable power guarantees. In addition, the construction of independent energy storage systems can also reduce dependence on traditional fossil energy, thereby reducing greenhouse gas emissions and promoting the realization of environmental protection goals. In the future, with the advancement of energy storage technology and the further reduction of costs, the application of battery energy storage systems will be more extensive. Modular design allows energy storage systems to be flexibly expanded according to demand, providing possibilities for application scenarios of different scales. In this context, energy storage systems are not only gradually maturing in technology, but also their acceptance and application prospects in the market will continue to expand.

[0052] Energy storage systems can effectively improve the stability and economy of power systems through optimization measures such as real-time power load monitoring and demand forecasting, dynamic adjustment of charging and discharging strategies, economic analysis, battery degradation assessment, and power peak-filling. The widespread application of energy storage systems is not only commercially feasible, but also of great significance in promoting energy transformation and addressing global climate change. By systematically solving the challenges of energy storage technology in use and optimizing its cost-effectiveness, energy storage systems will become an indispensable part of future power systems, providing a solid guarantee for achieving efficient, safe, and low-carbon power supply.

[0053] An economic evaluation method considering the participation of electrochemical energy storage in peak load shifting and valley filling includes the following steps:

[0054] In the first step, a battery storage control scheme is proposed, combined with a deep learning load forecasting model, for total grid load peak shaving under actual conditions. Based on historical load data and combined with real-time transformer measurement results, the power threshold based on the day-ahead peak power forecast is frequently updated to reduce the dependence on load forecasting and improve the economy of the system.

[0055] Step 2: Use deep learning technology to predict the load, establish a high-precision load forecasting model, and evaluate the impact of different forecast error levels on strategy performance. By evaluating load forecasts at different accuracy levels, the accuracy and reliability of load forecasting can be improved.

[0056] Step three: adopt modular design, allowing the energy storage capacity and power output to be freely expanded according to actual needs. The energy storage unit adopts a pluggable module design, which is convenient for system expansion and maintenance to improve economic benefits. Each module can work independently or in collaboration, which significantly improves the flexibility and maintainability of the system. This design enables the system to respond quickly to changes in load demand and adapt to application scenarios of different scales.

[0057] Step 4: Use the energy management system to monitor and optimize the operating status of the battery energy storage system in real time to reduce energy loss and operating costs. This method achieves effective reduction of peak load and maximization of economic benefits by optimizing the charging and discharging strategy of the energy storage system.

[0058] In step 1, a rule-based battery storage control scheme is proposed, which aims to significantly improve the energy storage capacity of the energy storage device by defining an objective function for evaluating the control scheme. The main goal of DSO is to reduce the power peak without worsening other related cost components. To this end, two additional cost factors are considered in the objective function to avoid adverse effects on other cost components. This scheme innovatively solves the problem of difficult optimization of energy storage systems in the field of grid peak load management. The total cost C tot It consists of three different components:

[0059] C tot =C epex +C peak +C use (1)

[0060] Among them C epex , C peak and C use They represent the total daily cost of energy purchase, the total monthly cost of peak power demand, and the total daily cost of transmission and distribution network use, respectively. The theoretical total energy cost based on EPEX prices is included in the objective function as a worst-case measure of the change in energy procurement costs due to peak load shaving. In this way, the control scheme is prevented from reducing peak power costs at the expense of energy procurement and grid use costs.

[0061] The components of the total cost include:

[0062]

[0063] C peak =C peak max(P 1d (t)) (3)

[0064]

[0065] Among them, N and P 1d (t) are the number of time steps per day and the power demand at time step t. imp (t) and E′ imp (t) represents the total energy cost charged for energy procurement and network usage cost respectively. Penalty factor C epex , C peak and C use They are the EPEX day-ahead spot price, the monthly peak electricity price (daily usage) and the grid usage fee.

[0066] In step two, it includes real-time monitoring of power load conditions and prediction of future peak power demand periods through advanced data acquisition technology and machine learning algorithms; dynamically adjusting the charging and discharging strategies of the battery energy storage system according to the prediction results to achieve power load balance and peak shaving and valley filling. The present invention innovatively solves the problem of excessive power demand during peak hours in the field of power load management, significantly reduces grid operating costs, and improves the operating efficiency and reliability of the power system. It includes real-time monitoring of power load conditions and prediction of future peak power demand periods through advanced data acquisition technology and machine learning algorithms; dynamically adjusting the charging and discharging strategies of the battery energy storage system according to the prediction results to achieve power load balance and peak shaving and valley filling.

[0067] The present invention selects recurrent neural networks (RNNs) with gated recurrent units (GRU) as the core of the prediction system. GRUs are a simple version of gated RNNs suitable for processing time data.

[0068] g(t) = ReLU(MLP p (P 1d (t))+MLP u (u(t))) (5)

[0069] Among them, MLP p and MLP u are multilayer perceptrons (MLPs) with a single hidden layer, ReLU activation function, and a linear output layer. Both the hidden and output layers have the same size k.

[0070] From the above modules, we get a vector set and process it by the time encoder. The y layer is processed by the final multi-layer perceptron (MLP), and the predicted target vector is y(t). Through a variant of the model, future peak loads can be predicted directly. The actual prediction target and predicted value of the optimized model are simply as follows:

[0071] y(t)=max k∈{1,.....h} P 1d (t+k) (6)

[0072]

[0073] Where t is each time step, P 1d Enter values ​​for the model.

[0074] Furthermore, in step 3, the algorithm developed by the present invention is applied to improve and optimize the battery energy storage system (battery energy storage system) project. The battery energy storage system has the advantages of rapid growth time, versatility, high energy density and efficiency, and low operation and maintenance costs.

[0075] The battery energy storage system model is developed taking into account its main technical constraints. By using the battery energy storage system round-trip efficiency n rtp (β) At the beginning of year β, assuming the charge and discharge efficiency, n ch and n dis , are equal, as follows:

[0076]

[0077] Battery Energy Storage System Power Battery P bess (t) The energy storage system consists of two components, the charging power P ch (t) and discharge power P dis (t):

[0078] P bess (t) = P ch (t)-P dis (t) (9)

[0079] P bess (t) represents a discharge operation, the charge and discharge power are non-negative, subject to the maximum charge and discharge power limit, equal to the AC rated power P of the inverter nom :

[0080] 0≤P ch (t)≤P nom (10)

[0081] 0≤P dis (t)≤P nom (11)

[0082] The energy content of the battery energy storage system at time t depends on the energy content and power at time step t-1, so the following recursive relationship should be satisfied:

[0083]

[0084] Where Δt is the time step, n inv is the converter efficiency of the battery energy storage system. For the limit of energy content, it can be calibrated by the state of charge SOC(t), which is defined as the energy content EBattery Energy Storage System (t) per time step and the rated energy capacity E nom (t). In particular, the upper and lower bounds of SOC, SOC max and SOC min The requirements are predefined based on the technical characteristics of the battery. Therefore, the following constraints are used for the energy content:

[0085] SOC min ≤E bess (t-1)≤SoC max E nom (13)

[0086] Furthermore, in step 4, the energy management system is used to optimize the operating state of the battery energy storage system, reduce energy loss and operating costs, thereby achieving effective reduction of peak load and maximizing economic benefits. In the operation cycle of the transmission and distribution network, energy storage mainly considers investment and construction costs, operation and maintenance costs, the benefits of "low storage and high release" and government subsidy benefits. Therefore, the economic efficiency of the battery energy storage system can be expressed as:

[0087] C eco =C inv +C op +C pro -C sub (14)

[0088] Where: C inv represents the investment and construction cost of the battery energy storage system; C op represents the operation and maintenance cost; C pro Represents the benefits of the battery energy storage system; C sub Represents the government subsidy income.

[0089]

[0090] Where: r represents the discount rate; y b Indicates the operating life of the battery energy storage system; C p Indicates the cost per unit charge and discharge power of the battery energy storage system; Represents the absolute value of the charging and discharging power of the battery energy storage system; C e Represents the unit capacity cost of the battery energy storage system; E N Indicates the rated capacity of the battery energy storage system’s charging and discharging power.

[0091] C op =C main -C upgrade (16)

[0092] Where: C main Represents the operation and maintenance cost of the battery energy storage system throughout its life cycle; C upgrade Represents the benefits of delaying distribution network upgrades.

[0093] The technical solution in this embodiment can achieve balancing of power load and peak shaving and valley filling, especially solve the problem of excessive power demand during peak hours, significantly reduce power grid operating costs, and improve the operating efficiency and reliability of the power system.

[0094] In one embodiment, Figure 2 As shown, the following steps S21-S22 are also included:

[0095] In step S21, a high-precision load prediction model is established, and a deep learning algorithm is used to predict future power demand;

[0096] In step S22 , the prediction accuracy of the power demand is evaluated according to at least two different prediction error levels.

[0097] In one embodiment, in modern power systems, load forecasting is a key link in achieving peak load shaving and valley filling and optimizing the allocation of power resources. The accuracy of the prediction model directly determines the effectiveness of the charging and discharging strategy of the energy storage system. Deep learning technology is used to construct a load prediction model, and the importance of high-precision prediction is emphasized. Deep learning algorithms, especially recurrent neural networks (RNNs) and gated recurrent units (GRUs), are applied to the load prediction model, which can predict future power demand based on historical power load data and real-time monitoring data (such as transformer measurements). The goal of the model is to identify the fluctuation characteristics of power load over time, so as to accurately predict the demand during peak and valley periods, so as to reasonably plan the charging and discharging operations of the energy storage system. In practical applications, the fluctuation of power load is affected by many factors, such as weather, time, people's electricity usage habits, etc. The advantage of deep learning algorithms is that they can handle complex nonlinear relationships and improve the prediction accuracy by continuously self-learning and adjusting model parameters. The use of high-precision load prediction models can help energy storage systems respond to load changes in a timely manner and achieve more efficient peak load shaving and valley filling operations. By combining historical data and deep learning algorithms, frequently updated high-precision load forecasts can be achieved, thus supporting the operation of electrochemical energy storage systems under real-time conditions.

[0098] Deep learning algorithms play a central role in load forecasting. By using advanced data acquisition technology and deep learning algorithms, the system can monitor power load in real time and make predictions. It is not limited to simple time series analysis, but uses gated units and multi-layer perceptrons (MLP) to process complex patterns in historical data, so that details such as seasonality, diurnal changes, and unexpected fluctuations can be captured, improving the accuracy and robustness of the forecast. The advantage of deep learning algorithms is that they can adapt to large amounts of data and perform well in processing nonlinear data patterns. In particular, structures such as GRU can capture long-term dependencies without increasing the complexity of the model, so that the model can not only predict short-term loads, but also predict load change trends over a longer time span. In this way, the changing trend of power demand can be more accurately reflected, providing a reliable basis for the real-time response of the energy storage system.

[0099] Forecast error evaluation is an important step in verifying the reliability of the load forecasting model. Different forecast error levels (such as mean square error (MSE) and mean absolute error (MAE)) can help system managers understand the performance of the model under different accuracy requirements, so as to better adjust the charging and discharging strategy. By evaluating the impact of different error levels on the load forecasting strategy, the charging and discharging strategy of the energy storage system can be optimized to improve the overall efficiency and economy of the system. In practical applications, due to the volatility of power load, the forecasting model is often interfered by uncontrollable factors. Therefore, based on the evaluation of multiple error levels, the system can allow the existence of errors within a certain range while ensuring the effectiveness of the energy storage system. For example, when the load forecast error is low, the energy storage system can charge and discharge according to the forecast demand; while in the case of high error, a more conservative charging and discharging strategy can be adopted to prevent energy storage loss or demand mismatch caused by error. Through high-precision prediction based on deep learning, the system can evaluate the load demand at different accuracy levels to ensure effective peak shaving and valley filling under the influence of errors. This method improves the flexibility and reliability of load forecasting and helps maintain the stability and economy of the energy storage system in the face of unpredictable load changes.

[0100] A high-precision load forecasting model is established through historical data and deep learning algorithms. The model aims to capture the temporal fluctuation characteristics of power load in order to accurately predict future power demand. Using deep learning algorithms (such as GRU, etc.) to process complex load data can effectively improve the accuracy of the forecast. By identifying the nonlinear characteristics in load changes, the system can obtain more accurate load forecasts and provide data support for the charging and discharging operations of the energy storage system. After the forecast is completed, the forecast results are evaluated at least two different error levels to determine the accuracy and reliability of the forecast model. This step ensures the adaptability of the system in dealing with load changes, and even in the presence of errors, the energy storage strategy can be dynamically adjusted to achieve a stable power supply. Not only does it effectively support the intelligent operation of the energy storage system in terms of technology, but it also provides guarantees in terms of economy and stability. By continuously optimizing the load forecasting accuracy and error level evaluation, the energy storage system can better adapt to the fluctuations in actual load demand, achieve the goals of peak-to-valley filling, reduce operating costs and improve grid stability.

[0101] In one embodiment, Figure 3 As shown, the following steps S31-S32 are also included:

[0102] In step S31, the lower limit of the remaining power of the energy storage battery is limited to be greater than or equal to the energy content of the energy storage battery at the previous moment;

[0103] In step S32, the product of the remaining power of the energy storage battery and the rated energy capacity is limited to be greater than or equal to the energy content of the energy storage battery at the previous moment.

[0104] In one embodiment, during the operation of the energy storage system, in order to ensure the safety and effectiveness of the energy storage battery, strict restrictions are imposed on its remaining power and energy capacity. The purpose of these restrictions is to ensure the service life, stability and economy of the energy storage battery.

[0105] The lower bound of the remaining power of the energy storage battery is limited to be greater than or equal to the energy content of the energy storage battery at the previous moment, and a lower bound is set for the remaining power of the energy storage battery to ensure that the battery has sufficient power reserve at any time. The operation of the energy storage battery is optimized by constraining its energy content. Specifically, the energy content of the energy storage battery is associated with the energy content of the previous moment at each time step, so the lower bound of the remaining power at the current moment cannot be lower than the energy content of the previous moment. By setting the lower bound of the remaining power of the battery to not less than the energy content of the previous moment, the battery power can be prevented from being too low. The deep discharge of the energy storage battery will accelerate its degradation. Therefore, setting a minimum power lower bound can effectively reduce the number of deep discharges and extend the service life of the battery. The degree of battery degradation is significantly affected by the depth of discharge, and the degradation of the battery can be slowed down by limiting the depth of discharge. In the management of energy storage batteries, future load requirements need to be considered. The lower bound of the battery energy content at the current moment is not lower than the previous moment, ensuring that the system can meet the basic power supply needs in the next time step, especially when the sudden load increases, it can provide stable power output. By limiting the lower limit of the remaining battery power to no less than the energy content at the previous moment, the stable operation of the energy storage battery is ensured, potential problems caused by low power are avoided, and a safe operating condition is provided for the energy storage system.

[0106] The product of the remaining power and the rated energy capacitance of the energy storage battery is limited to be greater than or equal to the energy content of the energy storage battery at the previous moment, ensuring that the operation of the energy storage battery will not exceed the limit of its rated energy capacitance. Certain conditions must be met between the remaining power of the energy storage battery and its rated energy capacitance, so that the battery can be ensured to operate safely and efficiently within its design capacity. The rated energy capacitance of the energy storage battery is the maximum value of its design capacity. By limiting the product of the remaining power and the rated energy capacitance to be greater than the energy content at the previous moment, it can be ensured that the battery will not exceed its design limit due to excessive use. The charging and discharging operations of the energy storage battery must take into account the capacity constraints of the battery to avoid the risks of overload use. Limiting the product of the remaining power and the rated energy capacitance of the battery helps to maintain the charging and discharging efficiency of the battery. The efficiency of the battery will decrease when charging and discharging under high load. By limiting the power and capacity, its charging and discharging process can be optimized to ensure that the battery is in a more efficient working state. The life of the energy storage battery is closely related to factors such as the depth of charge and discharge and the number of cycles. Limiting the product of the remaining power and the rated capacity prevents the battery from operating beyond the rated capacity range, reduces unnecessary deep discharge, helps extend the battery life, and reduces replacement and maintenance costs.

[0107] By limiting the remaining power of the battery to no less than the energy content of the previous moment, it can be ensured that the battery has a basic power reserve within each time step, preventing the adverse effects of low power on system stability. Limiting the product relationship between the remaining power of the battery and the rated energy capacitance further ensures that the use of the battery will not exceed the design capacity, avoiding the decline in efficiency and shortened life due to excessive use. It not only ensures the stability and life of the battery, but also improves the adaptability of the system in dealing with load fluctuations. These restrictions together build a balanced power management system, so that the energy storage system can achieve stable and efficient peak-shaving and valley-filling operations in actual operation, meet the changes in power demand while maintaining the economy and long life of the battery. By strictly limiting the remaining power of the energy storage battery and its relationship with the rated energy capacitance, the energy storage system can provide stable power supply, extend battery life, and improve the overall system operation efficiency under load demand fluctuations.

[0108] In one embodiment, Figure 4 FIG. 1 is a block diagram of an energy economy evaluation device based on a storage battery according to an exemplary embodiment. Figure 4 As shown, the electric energy economy evaluation device based on the storage battery includes an acquisition module 41, an adjustment module 42 and an analysis module 43.

[0109] The acquisition module 41 is used to acquire real-time power load conditions to predict future power demand;

[0110] The adjustment module 42 is used to adjust the charging and discharging strategy of the energy storage battery according to the future power demand to achieve a balance of power load;

[0111] The analysis module 43 is used to analyze the economic feasibility of the total cost, which includes the cost of purchasing energy, the peak power cost and the transmission network cost.

[0112] The acquisition module 41, the adjustment module 42 and the analysis module 43 included in the block diagram of the electric energy economy evaluation device based on the storage battery are controlled to execute the electric energy economy evaluation method based on the storage battery described in any of the above embodiments.

[0113] like Figure 5 As shown, the present invention provides an electronic device 500, the electronic device comprising: a communication interface, a processor 501, and a memory 502;

[0114] The memory 502 is used to store program instructions, which, when executed by the processor 501 that is communicatively connected to the memory 502 through the communication interface, obtains real-time power load conditions to predict future power demand; adjusts the charging and discharging strategies of the energy storage battery according to the future power demand to achieve power load balance; and analyzes the economic feasibility of the total cost, which includes the cost of purchasing energy, peak power cost and transmission network cost.

[0115] The present invention provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, real-time power load conditions are obtained to predict future power demand; according to the future power demand, the charging and discharging strategy of the energy storage battery is adjusted to achieve a balance of the power load; and the economic feasibility of the total cost is analyzed, wherein the total cost includes the cost of purchasing energy, the peak power cost and the transmission network cost.

[0116] It should be understood that the specific features, operations and details described hereinabove about the method of the present invention may also be similarly applied to the device and system of the present invention, or, vice versa. In addition, each step of the method of the present invention described above may be performed by the corresponding parts or units of the device or system of the present invention.

[0117] It should be understood that each module / unit of the device of the present invention can be implemented in whole or in part by software, hardware, firmware or a combination thereof. Each module / unit can be embedded in the processor of the computer device in the form of hardware or firmware or independent of the processor, or can be stored in the memory of the computer device in the form of software for the processor to call to perform the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0118] In one embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores computer instructions executable by the processor, and the computer instructions instruct the processor to execute each step of the method of the embodiment of the present invention when executed by the processor. The computer device can be a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities in a broad sense. In one embodiment, the computer device may include a processor, a memory, a network interface, a communication interface, etc. connected through a system bus. The processor of the computer device can be used to provide necessary computing, processing and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. An operating system, a computer program, etc. may be stored in or on the non-volatile storage medium. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the computer device can be used to connect and communicate with external devices through a network. The steps of the method of the present invention are executed by the processor.

[0119] The present invention may be implemented as a computer-readable storage medium having a computer program stored thereon, which causes the steps of the method of an embodiment of the present invention to be executed when executed by a processor. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be performed by one or more computer devices or processors, and one or more other method steps / operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step / operation, or perform two or more method steps / operations.

[0120] It will be understood by those skilled in the art that the method steps of the present invention can be completed by instructing related hardware such as a computer device or a processor through a computer program, and the computer program can be stored in a non-temporary computer-readable storage medium, and the steps of the present invention are executed when the computer program is executed. Depending on the circumstances, any reference to memory, storage, database or other media herein may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (which can achieve balancing of power loads and peak-shaving and valley-filling, especially solving the problem of excessive power demand during peak hours, significantly reducing power grid operating costs, and improving the operating efficiency and reliability of the power system. PROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0121] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the economic efficiency of electric energy based on storage batteries, characterized in that: include: Obtain real-time power load conditions to predict future power demand; According to the future power demand, adjusting the charging and discharging strategy of the energy storage battery to achieve a balance of power load; Analyze the economics of the total cost, which includes purchased energy costs, peak power costs, and transmission network costs.

2. The method for evaluating the economic efficiency of electric energy based on storage batteries according to claim 1, characterized in that: Also includes: The energy content of the energy storage battery at the current moment depends on the energy content and power at the previous moment, as shown in the following formula:

3. The method for evaluating the economic efficiency of electric energy based on storage batteries according to claim 1, characterized in that: Also includes: The degradation degree of the energy storage battery is calculated according to any one or more of the remaining power, discharge depth, charging rate, usage time, energy storage battery temperature and cycle type.

4. The method for evaluating the economic efficiency of electric energy based on storage batteries according to claim 1, characterized in that: Also includes: Establish a high-precision load forecasting model and use deep learning algorithms to predict future electricity demand; The forecast accuracy of the electricity demand is evaluated according to at least two different forecast error levels.

5. The method for evaluating the economic efficiency of electric energy based on storage batteries according to claim 1, characterized in that: Also includes: The total cost of electricity procurement is revised based on power peak shaving and power valley filling.

6. The method for evaluating the economic efficiency of electric energy based on storage batteries according to claim 3, characterized in that: Also includes: Limit the lower limit of the remaining power of the energy storage battery to be greater than or equal to the energy content of the energy storage battery at the previous moment; The product of the remaining power of the energy storage battery and the rated energy capacity is limited to be greater than or equal to the energy content of the energy storage battery at the previous moment.

7. An electric energy economic evaluation device based on a storage battery, characterized in that: include: An acquisition module, used to obtain real-time power load conditions to predict future power demand; An adjustment module, used for adjusting the charging and discharging strategy of the energy storage battery according to the future power demand to achieve a balance of power load; An analysis module is used to analyze the economic feasibility of the total cost, wherein the total cost includes the purchased energy cost, the peak power cost and the transmission network cost.

8. The electric energy economy evaluation device based on storage battery according to claim 7, characterized in that: The acquisition module, the adjustment module and the analysis module are controlled to execute the electric energy economy evaluation method based on the storage battery as described in any one of claims 1-6.

9. An electronic device, characterized in that: include: Communication interface, processor, memory; Wherein, the memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, the electronic device implements the electric energy economy evaluation method based on the storage battery as described in any one of claims 1 to 6.

10. A computer-readable storage medium having program instructions stored thereon, characterized in that: When the program instructions are executed by a computer, the computer implements the method for evaluating the economic efficiency of electric energy based on storage batteries as described in any one of claims 1 to 6.

Images