Energy storage optimization method, device and equipment under various power loads

Abstract

The invention discloses an energy storage optimization method, device and equipment under various power loads, and relates to the technical field of energy storage optimizing.The method comprises the steps that firstly, energy storage configuration schemes corresponding to different types of user loads on a user side are determined through an energy storage configuration optimization objective function and optimization constraint conditions; then evaluating energy storage configuration schemes corresponding to different types of users by adopting an analytic hierarchy process to obtain an optimal energy storage configuration scheme corresponding to a user side; and on the basis of the optimal energy storage configuration scheme, carrying out energy storage configuration on the user side, and carrying out energy storage scheduling on the configured user side by adopting a day-ahead optimization scheduling model, so that full-cycle economic quantitative evaluation and dominant multi-dimensional value of energy storage are realized, and a theoretical basis and decision support are provided for large-scale popularization of energy storage of the user side.

Description

Technical Field

[0001] This application relates to the field of energy storage optimization technology, specifically to an energy storage optimization method, apparatus, and equipment under various power loads. Background Technology

[0002] With the accelerated global energy structure transitioning towards low-carbon development and rapid socio-economic progress, traditional power systems face multiple challenges, including the mismatch between the dynamic characteristics of loads and the spatiotemporal distribution of system regulation capabilities, supply-demand imbalances caused by large peak-valley differences, and declining equipment utilization and reduced economic efficiency due to reliance on continuous expansion of transmission and transformation equipment. To improve the reliability and quality of power supply, the application of distributed generation in load centers is receiving increasing attention. The concept of Integrated Energy Systems (IES) enables the complementary and coordinated development of multiple energy sources and enhances the flexibility and reliability of distributed energy, providing crucial technical support for building a clean, low-carbon, safe, and efficient modern energy system. However, IES may lead to some energy waste during self-generation and self-consumption. The rational configuration of energy storage systems not only helps improve operational economics but also, through their bidirectional energy regulation characteristics, optimizes cross-period power transmission, alleviates peak-hour pressure, and improves the overall economic efficiency of the power supply system.

[0003] User-side energy storage configurations mainly include various types such as chemical battery energy storage, compressed air energy storage, pumped hydro storage, and supercapacitors. Among them, chemical battery energy storage has become the preferred choice for user-side energy storage due to its modular deployment, high power density, and rapid response capabilities. However, this technology still faces challenges in practical applications, including high initial capital investment, low overall return on investment, and long payback periods, significantly hindering its large-scale promotion and application. Therefore, how to achieve the scientific configuration of user-side energy storage and improve the economic efficiency of electricity consumption has become a crucial area that the energy storage industry urgently needs to address.

[0004] Energy storage offers multidimensional value through load forecasting, output tracking, and power optimization. For users, it generates revenue through peak shaving and valley filling arbitrage. However, high costs remain a barrier to large-scale deployment, and traditional economic analyses struggle to quantify the technological value of energy storage. To address these challenges, a comprehensive evaluation system is needed to assess development prospects, technological performance, and economic benefits. Current progress indicates the need for a holistic approach to energy storage configuration to balance technological performance and economic feasibility, ensuring sustainable and cost-effective integration into modern energy systems.

[0005] Current domestic and international research largely focuses on energy storage technology itself or single application scenarios, lacking differentiated configuration and evaluation methods for different user types (such as commercial, industrial, and residential). Furthermore, traditional economic analysis struggles to quantify the multidimensional value of energy storage, such as the implicit benefits of peak shaving and valley filling, reduced capacity charges, and increased renewable energy consumption. Therefore, there is an urgent need to establish a scientific and comprehensive energy storage configuration and evaluation system. Summary of the Invention

[0006] The purpose of this application is to provide an energy storage optimization method, device, and equipment under various power loads, which solves the problems of not fully considering the differences in user load characteristics, lacking a systematic full life cycle economic assessment, and / or failing to effectively integrate the implicit benefits of energy storage.

[0007] This application is achieved through the following technical solution:

[0008] The first aspect of this application provides an energy storage optimization method under various power loads, including:

[0009] An energy storage configuration optimization objective function and optimization constraints are constructed, and the energy storage configuration schemes corresponding to different types of user loads on the user side are determined by using the energy storage configuration optimization objective function as the objective and the optimization constraints as the constraints.

[0010] The analytic hierarchy process (AHP) is used to evaluate energy storage configuration schemes for different types of users and obtain the optimal energy storage configuration scheme for the user side.

[0011] Based on the optimal energy storage configuration scheme, energy storage is configured on the user side, and the day-ahead optimization scheduling model is used to schedule the configured energy storage on the user side, so as to realize energy storage optimization under various power loads.

[0012] In one possible implementation, the objective function for optimizing energy storage configuration is constructed as follows:

[0013] ;

[0014] in, Represents net profit, and max represents finding the maximum value. This represents the arbitrage profit from utilizing the time-shifting characteristics of electrical energy storage. This represents the benefit of reduced electricity costs for users. This indicates the residual value of batteries and their potential for resource utilization. This indicates the initial investment cost of energy storage. This indicates the daily operation and maintenance costs of energy storage.

[0015] In one possible implementation, the optimization constraints include: integrated energy system power balance constraints, energy conversion state constraints, state of charge constraints, energy conversion power constraints of electric energy storage, power and capacity scale constraints of energy storage configuration, continuity constraints of electric energy storage state of charge, and peak shaving load constraints.

[0016] In one possible implementation, the energy storage configuration optimization objective function is used as the objective, and optimization constraints are used as limitations, to determine the energy storage configuration schemes corresponding to different types of user loads on the user side, including:

[0017] Using the energy storage configuration optimization objective function as the objective and optimization constraints as the limitations, the energy storage configuration power and capacity corresponding to different types of user loads on the user side are optimized to obtain the energy storage configuration schemes corresponding to different types of user loads on the user side.

[0018] In one possible implementation, the analytic hierarchy process (AHP) is used to evaluate energy storage configuration schemes for different types of users to obtain the optimal energy storage configuration scheme for the user side, including:

[0019] The target layer is constructed based on the optimal configuration results of user-side energy storage; the criterion layer is constructed based on the net income, cost recovery period and rate of return of user-side energy storage throughout its entire life cycle; and the scheme layer is constructed based on the energy storage configuration schemes corresponding to different types of users.

[0020] Obtain the first judgment matrix of the criterion layer relative to the target layer, and obtain the weight coefficients corresponding to each decision indicator in the criterion layer based on the first judgment matrix;

[0021] Obtain the second judgment matrix of the scheme layer relative to the criterion layer, and determine the weight coefficient of each energy storage configuration scheme for each decision indicator in the criterion layer based on the second judgment matrix;

[0022] Based on the weight coefficients of each decision indicator in the criteria layer and the weight coefficients of the energy storage configuration scheme for each decision indicator in the criteria layer, the comprehensive weight coefficients corresponding to the energy storage configuration scheme are determined.

[0023] Based on the comprehensive weighting coefficients corresponding to the energy storage configuration schemes, the optimal energy storage configuration scheme for the user side is determined among the energy storage configuration schemes corresponding to different types of users.

[0024] In one possible implementation, the method for obtaining the net profit is as follows:

[0025] ;

[0026] in, Indicates net income, This represents the arbitrage profit from utilizing the time-shifting characteristics of electrical energy storage. This represents the benefit of reduced electricity costs for users. This indicates the residual value of batteries and their potential for resource utilization. This indicates the initial investment cost of energy storage. This indicates the daily operation and maintenance costs of energy storage.

[0027] The method for obtaining the cost recovery period is as follows:

[0028] ;

[0029] in, Indicates the cost payback period for user's energy storage. This represents the total revenue that energy storage can obtain over its entire life cycle, where T represents the number of days the energy storage device operates per year.

[0030] The method for obtaining the rate of return is as follows:

[0031] ;

[0032] Where M represents the rate of return.

[0033] In one possible implementation, after obtaining the first judgment matrix of the criterion layer relative to the target layer, the method further includes:

[0034] Perform a consistency check on the first judgment matrix, and if the consistency check of the first judgment matrix fails, re-obtain the first judgment matrix;

[0035] Perform a consistency check on the second judgment matrix, and if the consistency check of the second judgment matrix fails, re-obtain the second judgment matrix.

[0036] In one possible implementation, the day-ahead optimization scheduling model is:

[0037] ;

[0038] in, This represents the energy interaction benefits during the daily scheduling cycle. Indicates minimization. This represents the power demand on the user side during the x-th time period. This represents the charging power of energy storage during the x-th time period of the day. This represents the discharge power of the energy storage system during the x-th time period of the day. This represents the length of the x-th time period.

[0039] Based on the same inventive concept, a second aspect of this application provides an energy storage optimization device under various power loads, comprising:

[0040] The energy storage configuration optimization module is used to construct the energy storage configuration optimization objective function and optimization constraints, and to determine the energy storage configuration schemes corresponding to different types of user loads on the user side, with the energy storage configuration optimization objective function as the objective and the optimization constraints as the constraints.

[0041] The energy storage configuration evaluation module is used to evaluate the energy storage configuration schemes corresponding to different types of users using the analytic hierarchy process (AHP) to obtain the optimal energy storage configuration scheme for the user side.

[0042] The energy storage scheduling optimization module is used to configure energy storage on the user side based on the optimal energy storage configuration scheme, and to schedule the configured energy storage on the user side using the day-ahead optimization scheduling model, so as to realize energy storage optimization under various power loads.

[0043] Based on the same inventive concept, a third aspect of this application provides an electronic device, including a processor and a memory;

[0044] The memory stores computer-executed instructions;

[0045] The processor executes computer execution instructions stored in the memory, causing the processor to perform the energy storage optimization method under various power loads as described in any of the first aspects.

[0046] Compared with the prior art, this application has the following advantages and beneficial effects:

[0047] This application provides a method, apparatus, and equipment for optimizing energy storage under various power loads. First, by determining the energy storage configuration schemes corresponding to different types of user loads on the user side through an energy storage configuration optimization objective function and optimization constraints, considering the differences in user load characteristics, the analytic hierarchy process (AHP) is then used to evaluate the energy storage configuration schemes corresponding to different types of users, obtaining the optimal energy storage configuration scheme for the user side. Based on the optimal energy storage configuration scheme, energy storage is configured on the user side, and a day-ahead optimization scheduling model is used to schedule the configured energy storage on the user side. This achieves a full-cycle economic quantitative evaluation, reveals the multi-dimensional value of energy storage, and provides a theoretical basis and decision support for the large-scale promotion of user-side energy storage. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the exemplary embodiments of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0049] Figure 1This is a flowchart illustrating an energy storage optimization method under various power loads proposed in an embodiment of this application.

[0050] Figure 2 This is a typical daily load curve and time-of-use electricity price curve for commercial users proposed in the embodiments of this application.

[0051] Figure 3 This is a typical daily load curve and time-of-use electricity price curve for industrial users proposed in the embodiments of this application.

[0052] Figure 4 This is a typical daily load curve and time-of-use electricity price curve for residential users proposed in the embodiments of this application.

[0053] Figure 5 This is an energy conversion curve of energy storage within the daytime scheduling cycle proposed in this application embodiment.

[0054] Figure 6 This is a daily load curve of user B after configuring energy storage according to the embodiments of this application.

[0055] Figure 7 This is a schematic diagram of the structure of an energy storage optimization device under various power loads proposed in an embodiment of this application.

[0056] Figure 8 This is a schematic diagram of the structure of an energy storage optimization device under various power loads proposed in an embodiment of this application.

[0057] Among them, 701-Energy storage configuration optimization module, 702-Energy storage configuration evaluation module, 703-Energy storage scheduling optimization module, 801-Memory, 802-Processor, and 803-Bus. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this application are only for explaining this application and are not intended to limit this application.

[0059] like Figure 1 As shown in the embodiments of this application, an energy storage optimization method under various power loads is provided, including:

[0060] S101. Construct an energy storage configuration optimization objective function and optimization constraints, and determine the energy storage configuration schemes corresponding to different types of user loads on the user side, with the energy storage configuration optimization objective function as the objective and the optimization constraints as the constraints.

[0061] For different power loads on the user side, this application determines an energy storage configuration scheme through the energy storage configuration optimization objective function and optimization constraints, and then selects the scheme with the greatest benefit from these energy storage configuration schemes for energy storage configuration optimization.

[0062] S102. Use the analytic hierarchy process (AHP) to evaluate the energy storage configuration schemes for different types of users and obtain the optimal energy storage configuration scheme for the user side.

[0063] The optimal energy storage configuration scheme essentially refers to the energy storage configuration scheme that can generate the greatest benefit when deploying energy storage at a certain power load on the user side, thereby achieving energy storage optimization under various power loads.

[0064] S103. Based on the optimal energy storage configuration scheme, energy storage is configured on the user side, and the configured user side is scheduled using the day-ahead optimization scheduling model to achieve energy storage optimization under various power loads.

[0065] By optimizing the scheduling model to schedule energy storage on the configured user side, the benefits of energy storage have been further improved, and comprehensive energy storage optimization under various power loads has been achieved.

[0066] This application embodiment configures suitable energy storage on the user side, which not only alleviates the power transmission pressure of the system during peak hours and improves the peak-valley characteristics of the user load curve, but also, under the background of time-of-use pricing in the electricity market, utilizes the spatiotemporal shifting characteristics of the energy storage system to charge when electricity prices are low and discharge when electricity prices are high, thereby generating certain revenue and forming a virtuous cycle between the supply and demand sides of the power system. The resulting technical effects are as follows:

[0067] (1) An integrated model for energy storage configuration and evaluation for multiple types of users has been established, realizing quantitative evaluation of economic efficiency throughout the entire life cycle and making the multidimensional value of energy storage explicit.

[0068] (2) By integrating subjective experience and objective data through the AHP model, the scientific nature of the assessment is improved. Combined with day-ahead dispatch optimization, dynamic response to changes in electricity prices is achieved, further improving revenue and providing theoretical basis and decision support for the large-scale promotion of user-side energy storage.

[0069] (3) The results of the case analysis show that configuring energy storage on the user side can smooth the peak load curve on the user side, but there are certain differences in the economics of configuring energy storage for different users: configuring energy storage for users with high load utilization and whose peak and valley load periods match the peak and valley time-of-use electricity price periods can significantly improve their economics, such as industrial users and commercial users.

[0070] (4) The time-of-use energy conversion strategy of current user energy storage has a significant dynamic correlation with the electricity market price curve. Optimizing its energy conversion strategy can effectively improve the electricity consumption economy of the user side. At the same time, the time-of-use electricity price mechanism of the electricity market has a significant impact on the economics of the user side. The large-scale application of energy storage on the user side also requires the support of relevant policies.

[0071] The user-side energy storage configuration model takes maximizing net revenue over its entire lifecycle as its objective function, and its configuration model is shown in the following formula. The net revenue of energy storage includes arbitrage revenue from the energy time-shifting characteristics of the energy storage system, system revenue from smoothing peak loads and reducing the output power limit of the distribution network, revenue from reduced electricity costs for users, revenue from special subsidies for new energy infrastructure, and the residual value of batteries for resource utilization. The system cost includes the initial investment cost and daily operation and maintenance cost of electrochemical energy storage.

[0072] In one possible implementation, the objective function for optimizing energy storage configuration is constructed as follows:

[0073] ;

[0074] in, Represents net profit, and max represents finding the maximum value. This represents the arbitrage profit from utilizing the time-shifting characteristics of electrical energy storage. This represents the benefit of reduced electricity costs for users. This indicates the residual value of batteries and their potential for resource utilization. This indicates the initial investment cost of energy storage. This indicates the daily operation and maintenance costs of energy storage.

[0075] Under the time-of-use dynamic pricing mechanism of the electricity spot market, user-side energy storage can charge during off-peak hours and discharge during peak hours, utilizing the peak-valley price difference for charging and discharging in the corresponding periods. This allows for the smoothing of peak loads and the maintenance of supply-demand balance in the power system during peak load periods, while also generating arbitrage profits. The arbitrage profit of energy storage over its entire lifecycle is expressed as:

[0076] ;

[0077] ;

[0078] In the formula, Arbitrage profits from the time shift of energy in electrical energy storage; To calculate the present value factor of compound interest, For electricity price increase rate, D represents the premium rate; D represents the lifespan of the energy storage battery. The arbitrage profit of an energy storage device through energy time shifting is defined as the 24 time periods of a day; T represents the number of operating days of the energy storage device per year, t represents the number of days variable, and t=1,2,…,T; Let x be the electricity price for time period x; , These represent the energy conversion power during the energy storage period x the actual energy conversion power, i.e., charging power and discharging power. , These represent the energy conversion states of electrical energy storage during time period x. This represents the length of the x-th time period.

[0079] Users with significant load peak-valley differences can pay electricity bills based on a fixed monthly load capacity. When applying for dedicated fixed-capacity billing, configuring energy storage can intelligently optimize the capacity fee metering benchmark, thereby reducing the user's monthly fixed-capacity electricity bill and generating certain benefits.

[0080] ;

[0081] ;

[0082] In the formula, To reduce electricity costs for users, R2 is the number of days in a month, taken as 30; R2 is the reduction of the user's fixed capacity electricity cost within a cycle. It is the user's fixed capacity electricity fee within a cycle; The energy storage configuration power limit is required to reduce peak load to baseline load; This refers to the rated energy conversion efficiency of the energy storage system.

[0083] BESS resource utilization benefits Initial cost of the residual value recovery body It is related, and the relationship is as follows:

[0084] ;

[0085] In the formula, For the resource utilization benefits of electrochemical energy storage, The residual value recovery coefficient of batteries at different stages; the total life cycle cost of energy storage mainly includes: initial construction cost and operation and maintenance cost.

[0086] The initial construction cost of energy storage consists of lithium batteries and their installation costs. The initial construction cost of user-side BESS (Battery-Assisted Storage System) is mainly determined by the configuration capacity and power of the energy storage, which are determined by the size of the user-side load, and can be expressed as:

[0087] ;

[0088] In the formula, The initial construction cost of electric energy storage; and These are the unit power cost and capacity cost of BESS configuration; Configure the rated capacity for energy storage.

[0089] The daily operation and maintenance costs of energy storage mainly consist of battery performance degradation costs and system operation and maintenance costs, which can be expressed as:

[0090] ;

[0091] In the formula: This indicates the daily operation and maintenance cost of energy storage. The annual operating and maintenance cost per unit power.

[0092] In one possible implementation, the optimization constraints include: integrated energy system power balance constraints, energy conversion state constraints, state of charge constraints, energy conversion power constraints of electric energy storage, power and capacity scale constraints of energy storage configuration, continuity constraints of electric energy storage state of charge, and peak shaving load constraints.

[0093] Optionally, constraints can be established during the process and the power and capacity of the energy storage configuration can be optimized, as follows.

[0094] The power balance constraint of the integrated energy system is:

[0095] ;

[0096] In the formula, The conversion power between energy storage during time period x and the power of the integrated energy system; This refers to the output power of the gas turbine. The power demand on the user side within time period x; Let x be the output power of the energy storage during the time period x.

[0097] The energy conversion state constraints are:

[0098] ;

[0099] In the formula, , The energy conversion state of the stored energy is represented by binary signals, where... This is a discharge state identifier, and its state is represented as follows: 0 corresponds to energy storage and absorption of electrical energy; 1 corresponds to energy storage and output of electrical energy. This is a charging state identifier, with states represented as follows: 0 corresponds to energy storage output; 1 corresponds to energy storage output. This constraint restricts the energy storage from simultaneously being charged and discharged.

[0100] The state of charge (SOC) constraint is as follows:

[0101] The state of charge of the energy storage capacity must be stably maintained within the upper and lower limits of the energy storage capacity:

[0102] ;

[0103] In the formula, The state of charge of electrical energy storage during time period x can be understood as the average charge value; , These correspond to the upper and lower limits of energy storage capacity, respectively.

[0104] The energy conversion power constraint for electric energy storage is: the transient energy conversion power of the energy storage device must be limited to the maximum allowable power, and its cumulative energy value must not exceed the total installed capacity constraint.

[0105] ;

[0106] ;

[0107] The continuity constraint of the state of charge of electrical energy storage is:

[0108] ;

[0109] In the formula, The state of charge of the stored electrical energy in time period x+1. and These correspond to the energy conversion efficiency of energy storage.

[0110] After energy storage smooths out peak loads, the system operating load should decrease. Therefore, the peak load constraint is:

[0111] ;

[0112] In the formula, The peak load within one cycle; To reduce peak rate.

[0113] The power and capacity constraints for energy storage configurations are as follows:

[0114] ;

[0115] In the formula, This is the ratio factor between power and capacity.

[0116] In one possible implementation, the energy storage configuration optimization objective function is used as the objective, and optimization constraints are used as limitations, to determine the energy storage configuration schemes corresponding to different types of user loads on the user side, including:

[0117] Using the energy storage configuration optimization objective function as the objective and optimization constraints as the limitations, the energy storage configuration power and capacity corresponding to different types of user loads on the user side are optimized to obtain the energy storage configuration schemes corresponding to different types of user loads on the user side.

[0118] In one possible implementation, the analytic hierarchy process (AHP) is used to evaluate energy storage configuration schemes for different types of users to obtain the optimal energy storage configuration scheme for the user side, including:

[0119] The target layer is constructed based on the optimal configuration results of user-side energy storage; the criterion layer is constructed based on the net income, cost recovery period and rate of return of user-side energy storage throughout its entire life cycle; and the scheme layer is constructed based on the energy storage configuration schemes corresponding to different types of users.

[0120] Obtain the first judgment matrix of the criterion layer relative to the target layer, and obtain the weight coefficients corresponding to each decision indicator in the criterion layer based on the first judgment matrix;

[0121] Obtain the second judgment matrix of the scheme layer relative to the criterion layer, and determine the weight coefficient of each energy storage configuration scheme for each decision indicator in the criterion layer based on the second judgment matrix;

[0122] Based on the weight coefficients of each decision indicator in the criteria layer and the weight coefficients of the energy storage configuration scheme for each decision indicator in the criteria layer, the comprehensive weight coefficients corresponding to the energy storage configuration scheme are determined.

[0123] Based on the comprehensive weighting coefficients corresponding to the energy storage configuration schemes, the optimal energy storage configuration scheme for the user side is determined among the energy storage configuration schemes corresponding to different types of users.

[0124] In one possible implementation, the method for obtaining the net profit is as follows:

[0125] ;

[0126] in, Indicates net income, This represents the arbitrage profit from utilizing the time-shifting characteristics of electrical energy storage. This represents the benefit of reduced electricity costs for users. This indicates the residual value of batteries and their potential for resource utilization. This indicates the initial investment cost of energy storage. This indicates the daily operation and maintenance costs of energy storage.

[0127] The method for obtaining the cost recovery period is as follows:

[0128] ;

[0129] in, Indicates the cost payback period for user's energy storage. This represents the total revenue that energy storage can obtain over its entire life cycle, where T represents the number of days the energy storage device operates per year.

[0130] The method for obtaining the rate of return is as follows:

[0131] ;

[0132] Where M represents the rate of return.

[0133] In one possible implementation, after obtaining the first judgment matrix of the criterion layer relative to the target layer, the method further includes:

[0134] Perform a consistency check on the first judgment matrix, and if the consistency check of the first judgment matrix fails, re-obtain the first judgment matrix;

[0135] Perform a consistency check on the second judgment matrix, and if the consistency check of the second judgment matrix fails, re-obtain the second judgment matrix.

[0136] For example, this section constructs an evaluation model based on AHP. The model structure is divided into an objective layer, a criterion layer, and a scheme layer. The objective layer aims to optimize the user-side energy storage configuration; the criterion layer uses the net revenue, cost payback period, and rate of return over the entire life cycle of user-side energy storage as decision indicators; and the scheme layer presents energy storage schemes configured according to different user-side loads.

[0137] Based on authoritative guidance, the priorities of indicators at each level are ranked, and the nine-level scaling method in the Analytic Hierarchy Process (AHP) is used to determine the priority ratio of adjacent indicators, thus constructing a corresponding judgment matrix. In this paper, the net life-cycle benefit, cost recovery period, and rate of return of user-side energy storage in the criteria layer of the energy storage assessment model are selected as decision indicators. For the target layer, the optimal configuration power and capacity of user-side energy storage are determined, requiring the construction of a 3×3 judgment matrix.

[0138] ;

[0139] in, Represents the judgment matrix. This represents the element in the k-th row and the j-th element, which indicates the importance of the k-th indicator relative to the j-th indicator. k=1,2,…,n, j=1,2,…,n, where n represents the total number of indicators, which is set to 3 here.

[0140] To verify the rationality of the constructed judgment matrix Y and eliminate potential biases caused by subjective judgments, a consistency test is required. This test can be performed by calculating the consistency ratio (CR) and the consistency index (CI). The calculation process is as follows: [Eigenvalues ​​are missing from the original text]. Its corresponding feature vector is ( , , After standardizing the feature vectors, the decision indicators of the corresponding criterion layer for the target layer are obtained. Weighting coefficients ( , , ), can be compared The magnitude of the coefficient reflects the relative importance of the criterion layer coefficient in the system evaluation.

[0141] ;

[0142] ;

[0143] ;

[0144] In the formula: n is the index number; This represents the k-th weight coefficient. It is a random consistency index.

[0145] When the consistency ratio (CR) does not exceed 0.1, matrix Y passes the consistency check, and its standardized eigenvectors... This can be used as a weighting coefficient. When the consistency ratio (CR) exceeds 0.1, the judgment matrix needs to be reset until it passes the consistency check. The judgment matrix can be input by experts, and then the subjective influence can be eliminated through the above analysis.

[0146] The comprehensive weighting coefficients of the scheme layer and their consistency verification can be performed as follows.

[0147] Assume the weight coefficients of the three schemes in the scheme layer to the nth indicator in the criterion layer are as follows: The weighting coefficients here can be obtained using the method described above. Therefore, the overall weighting coefficients for the scheme layer are:

[0148] ;

[0149] In the formula: Let m be the comprehensive weighting coefficient for scheme m. For example, the user side may include commercial user A, industrial user B, and residential user C. Then, the energy storage configuration schemes corresponding to commercial user A, industrial user B, and residential user C can be determined respectively.

[0150] Assume the consistency index of the three alternatives in the alternative layer with the decision index n in the criterion layer is: The random consistency index is Then the consistency ratio CR of the overall weight coefficient of the scheme layer is:

[0151] ;

[0152] When the consistency ratio (CR) is less than 0.1, the overall weighting coefficient of the scheme layer meets the consistency requirements. If the consistency ratio (CR) is higher than 0.1, the overall weighting coefficient of the scheme layer needs to be readjusted. Finally, the optimal configuration scheme is selected by comparing the overall weighting coefficients. Since there is a positive correlation between the weighting coefficient and the economic indicators of energy storage, a higher weighting coefficient corresponds to a better energy storage system configuration scheme.

[0153] In one possible implementation, the day-ahead optimization scheduling model is:

[0154] ;

[0155] in, This represents the energy interaction benefits during the daily scheduling cycle. Indicates minimization. This represents the power demand on the user side during the x-th time period. This represents the charging power of energy storage during the x-th time period of the day. This represents the discharge power of the energy storage system during the x-th time period of the day. This represents the length of the x-th time period.

[0156] Electricity user load curves are influenced by multiple factors, exhibiting periodicity, continuity, and variability. This chapter selects three types of user loads for specific case studies to evaluate user-side energy storage configuration and optimize operation. The annual average time-of-use load curves and local time-of-use electricity price curves for commercial user A, industrial user B, and residential user C are shown below. Figure 2 , Figure 3 , Figure 4 As shown.

[0157] A detailed solution analysis was performed on the energy storage configuration model and the configuration evaluation model. Table 1 shows the rated power and rated capacity of the energy storage. Table 2 shows the economic index coefficient results of the evaluation model. The comprehensive weight coefficients of each level in AHP are shown in Table 8. Table 3 shows the judgment matrix of the criterion layer, and Tables 4-6 show the judgment matrices of the three indicators of relative net income, payback period, and return on investment at the scheme layer, respectively. Table 7 shows the index weights of the criterion layer. Table 8 shows the comprehensive weight coefficients of the scheme layer on the criterion layer. Table 9 shows the impact of different peak-valley electricity price differences on the economics of the user side.

[0158] Table 1 Rated power and rated capacity of energy storage configuration

[0159]

[0160] Table 2 Evaluation results of energy storage configurations for different users

[0161]

[0162] Table 3 Judgment Matrix of Criterion Layer

[0163]

[0164] Table 4. Judgment Matrix (Net Benefit) of the Solution Layer

[0165]

[0166] Table 5. Judgment Matrix (Payback Period) for the Scheme Layer

[0167]

[0168] Table 6. Judgment Matrix (Return on Investment) for the Solution Layer

[0169]

[0170] Table 7. Indicator Weights of the Criterion Layer

[0171]

[0172] Table 8. Overall weighting coefficients of the scheme layer to the criterion layer.

[0173]

[0174] Table 9. Impact of different peak-valley electricity price differences on user-side economics.

[0175]

[0176] As shown in Table 1, under the condition that unforeseen events are not considered, users who configure energy storage can recover their costs and obtain a certain profit in about 6 to 8 years, with a rate of return of over 35%. This proves that configuring energy storage can improve the electricity economy for users.

[0177] As can be seen from Table 2, User B has the best overall evaluation result, followed by User A; User C has the worst evaluation result.

[0178] Table 3-8 shows the process results of evaluating different energy storage configuration schemes using the Analytic Hierarchy Process (AHP). Analysis of the results shows that, among the comprehensive weightings of the indicators, net income has the largest weight, followed by payback period, while the rate of return has a relatively smaller weight.

[0179] The results in Table 9 show that as the peak-valley electricity price difference increases, net income and rate of return continue to improve, while the cost recovery period continues to decrease, reflecting that the economics of the user side are affected by the peak-valley electricity price difference.

[0180] Combination Figures 2 to 4 Analysis shows that User A has a high load utilization rate and significant peak-to-valley load and electricity price differences. Configuring energy storage allows for arbitrage by charging during off-peak hours and discharging during peak hours, thus reducing user electricity costs. User C, on the other hand, has a low load utilization rate and smaller peak-to-valley load and electricity price differences, limiting the efficiency of energy storage utilization and resulting in lower net profits. User B's load curve is similar to User A's, and they receive revenue from the same sources. However, their peak load is higher, and their electricity price premium is more significant. User B can generate more arbitrage profits during peak hours, making energy storage the most economical option for User B.

[0181] After the user's electrochemical energy storage configuration is determined, User B, which showed the best improvement in user-side electricity economy during the configuration evaluation phase, is selected. Based on its optimal configuration of rated power and capacity and real-time load data from the electricity market, a day-ahead optimization dispatch model is established to optimize its energy storage equipment and time-of-use energy conversion strategy within the integrated energy system, further improving user-side electricity economy. The energy storage charge / discharge power curve for User B is shown below. Figure 5 As shown in Figure 6, the load curve for user B after configuring energy storage is also shown. Combined with... Figure 3 As can be seen from the time-of-use pricing, energy storage charges when electricity prices are low and discharges when prices are high. Furthermore, after configuring energy storage, user B's load curve peak value decreased while its valley value significantly increased, verifying that configuring energy storage can improve the electricity economy on the user side.

[0182] like Figure 7 As shown, based on the same inventive concept, another embodiment of this application also provides an energy storage optimization device under various power loads, including:

[0183] The energy storage configuration optimization module 701 is used to construct the energy storage configuration optimization objective function and optimization constraints, and to determine the energy storage configuration schemes corresponding to different types of user loads on the user side, with the energy storage configuration optimization objective function as the objective and the optimization constraints as the constraints.

[0184] The energy storage configuration evaluation module 702 is used to evaluate the energy storage configuration schemes corresponding to different types of users using the analytic hierarchy process (AHP) to obtain the optimal energy storage configuration scheme for the user side.

[0185] The energy storage scheduling optimization module 703 is used to configure energy storage on the user side based on the optimal energy storage configuration scheme, and to schedule the configured energy storage on the user side using the day-ahead optimization scheduling model, so as to realize energy storage optimization under various power loads.

[0186] The energy storage optimization device under various power loads provided in this application embodiment can execute the above-mentioned method and technical solution. Its principle and beneficial effects are similar, and will not be described again here.

[0187] like Figure 8 As shown, based on the same inventive concept, this application embodiment also provides an energy storage optimization device under various power loads, including a processor 802 and a memory 801; the memory 801 and the processor 802 are interconnected via a bus 803.

[0188] The memory 801 stores computer-executed instructions;

[0189] The processor 802 executes the computer execution instructions stored in the memory 801, causing the processor 802 to execute an energy storage optimization method under multiple power loads as described in any embodiment of this application.

[0190] For specific examples, memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). Furthermore, the processor may include a main processor and coprocessors. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0191] This application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the energy storage optimization method under various power loads described in any of the above embodiments.

[0192] This application embodiment may also provide a computer program product, including a computer program that, when executed by a processor, implements the energy storage optimization method under various power loads described in any of the above embodiments.

[0193] All or part of the steps in the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof.

[0194] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0195] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0196] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0197] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0198] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. An energy storage optimization method under multiple power loads, characterized in that, include: An energy storage configuration optimization objective function and optimization constraints are constructed, and the energy storage configuration schemes corresponding to different types of user loads on the user side are determined by using the energy storage configuration optimization objective function as the objective and the optimization constraints as the constraints. The analytic hierarchy process (AHP) is used to evaluate energy storage configuration schemes for different types of users and obtain the optimal energy storage configuration scheme for the user side. Based on the optimal energy storage configuration scheme, energy storage is configured on the user side, and the day-ahead optimization scheduling model is used to schedule the configured energy storage on the user side, so as to realize energy storage optimization under various power loads.

2. The energy storage optimization method under various power loads according to claim 1, characterized in that, The objective function for optimizing energy storage configuration is as follows: ； in, Represents net profit, and max represents finding the maximum value. This represents the arbitrage profit from utilizing the time-shifting characteristics of electrical energy storage. This represents the benefit of reduced electricity costs for users. This indicates the residual value of batteries and their potential for resource utilization. This indicates the initial investment cost of energy storage. This indicates the daily operation and maintenance cost of energy storage.

3. The energy storage optimization method under various power loads according to claim 1, characterized in that, The optimization constraints include: integrated energy system power balance constraints, energy conversion state constraints, state of charge constraints, energy conversion power constraints of electric energy storage, power and capacity constraints of energy storage configuration, continuity constraints of electric energy storage state of charge, and peak shaving load constraints.

4. The energy storage optimization method under various power loads according to claim 1, characterized in that, Using the energy storage configuration optimization objective function as the objective and optimization constraints as the limitations, determine the energy storage configuration schemes corresponding to different types of user loads on the user side, including: Using the energy storage configuration optimization objective function as the objective and optimization constraints as the limitations, the energy storage configuration power and capacity corresponding to different types of user loads on the user side are optimized to obtain the energy storage configuration schemes corresponding to different types of user loads on the user side.

5. The energy storage optimization method under various power loads according to claim 1, characterized in that, The analytic hierarchy process (AHP) is used to evaluate energy storage configuration schemes for different types of users to obtain the optimal energy storage configuration scheme for the user side, including: The target layer is constructed based on the optimal configuration results of user-side energy storage; the criterion layer is constructed based on the net income, cost recovery period and rate of return of user-side energy storage throughout its entire life cycle; and the scheme layer is constructed based on the energy storage configuration schemes corresponding to different types of users. Obtain the first judgment matrix of the criterion layer relative to the target layer, and obtain the weight coefficients corresponding to each decision indicator in the criterion layer based on the first judgment matrix; Obtain the second judgment matrix of the scheme layer relative to the criterion layer, and determine the weight coefficient of each energy storage configuration scheme for each decision indicator in the criterion layer based on the second judgment matrix; Based on the weight coefficients of each decision indicator in the criteria layer and the weight coefficients of the energy storage configuration scheme for each decision indicator in the criteria layer, the comprehensive weight coefficients corresponding to the energy storage configuration scheme are determined. Based on the comprehensive weighting coefficients corresponding to the energy storage configuration schemes, the optimal energy storage configuration scheme for the user side is determined among the energy storage configuration schemes corresponding to different types of users.

6. The energy storage optimization under various power loads according to claim 5, characterized in that, The method for obtaining the net income is as follows: ； in, Indicates net income, This represents the arbitrage profit from utilizing the time-shifting characteristics of electrical energy storage. This represents the benefit of reduced electricity costs for users. This indicates the residual value of batteries and their potential for resource utilization. This indicates the initial investment cost of energy storage. This indicates the daily operation and maintenance costs of energy storage. The method for obtaining the cost recovery period is as follows: ； in, Indicates the cost payback period for user's energy storage. This represents the total revenue that energy storage can obtain over its entire life cycle, where T represents the number of days the energy storage device operates per year. The method for obtaining the rate of return is as follows: ； Where M represents the rate of return.

7. The energy storage optimization under various power loads according to claim 5, characterized in that, After obtaining the first judgment matrix of the criterion layer relative to the target layer, the following steps are also included: Perform a consistency check on the first judgment matrix, and if the consistency check of the first judgment matrix fails, re-obtain the first judgment matrix; Perform a consistency check on the second judgment matrix, and if the consistency check of the second judgment matrix fails, re-obtain the second judgment matrix.

8. The energy storage optimization under various power loads according to claim 1, characterized in that, The day-ahead optimization scheduling model is as follows: ； in, This represents the energy interaction benefits during the daily scheduling cycle. Indicates minimization. This represents the power demand on the user side during the x-th time period. This represents the charging power of energy storage during the x-th time period of the day. This represents the discharge power of the energy storage system during the x-th time period of the day. This represents the length of the x-th time period.

9. An energy storage optimization device under multiple power loads, characterized in that, include: The energy storage configuration optimization module is used to construct the energy storage configuration optimization objective function and optimization constraints, and to determine the energy storage configuration schemes corresponding to different types of user loads on the user side, with the energy storage configuration optimization objective function as the objective and the optimization constraints as the constraints. The energy storage configuration evaluation module is used to evaluate the energy storage configuration schemes corresponding to different types of users using the analytic hierarchy process (AHP) to obtain the optimal energy storage configuration scheme for the user side. The energy storage scheduling optimization module is used to configure energy storage on the user side based on the optimal energy storage configuration scheme, and to schedule the configured energy storage on the user side using the day-ahead optimization scheduling model, so as to realize energy storage optimization under various power loads.

10. An electronic device, characterized in that, Including processor and memory; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the energy storage optimization method under various power loads as described in any one of claims 1 to 8.

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