Novel energy storage auxiliary decision scheduling method and system

Abstract

The invention discloses a novel energy storage auxiliary decision scheduling method and system, and belongs to the technical field of power grid scheduling. Determining battery information of energy storage batteries in the energy storage power stations based on the energy storage information, and constructing operation portraits of the energy storage power stations; predicting the capacity attenuation condition of the energy storage power station based on the battery information and the operation portrait; power grid regulation and control are carried out, and the response capability of the energy storage power station is evaluated based on the battery information and the operation portrait; determining a scheduling strategy of the power grid to the energy storage power station based on the evaluation result; the performance of each power station is predicted and optimized more accurately while a maintenance plan is optimized and the service life of a battery is prolonged. The scheduling strategy of the energy storage power station is optimized according to the response capability of the energy storage power station to power grid scheduling, the safety of the power grid is improved, full-capacity monitoring and attenuation prediction of the energy storage power station are facilitated, full-automatic calling of the energy storage power station is further realized, and the availability and income of the energy storage power station are improved.

Description

Background Art

[0002] In the future, in order to cope with the access of 1.5-1.8 billion kilowatts of new energy, 150 million kilowatts of new energy storage (excluding pumped storage) will be needed. These new energy storage facilities will be used to achieve power balance, power balance, power system operation safety and efficient consumption of new energy. Over time, this demand will increase to more than 500 million kilowatts.

[0003] Large-scale energy storage power stations will become the main force for flexible regulation and auxiliary support of new power systems. These energy storage power stations need to have application scenarios such as active support for the power grid, large-scale peak regulation, frequency regulation and voltage regulation, and need to have the characteristics of intrinsic safety, modularization and intelligence.

[0004] However, there are some problems in the current deployment of energy storage systems. In most cases, data relies on the advance self-reporting of each energy storage power station, which cannot achieve the deployment of the full capacity of the entire station. This phenomenon of "building without deployment" has become a pain point in the market.

[0005] In addition, the new energy storage grid connection and dispatching operation methods also need to be improved and strengthened. How to provide the optimal dispatching strategy for dispatching is the current technical difficulty. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a new energy storage auxiliary decision-making scheduling method and system for solving the technical problem that the scheduling strategy of energy storage power stations is difficult to optimize in view of the deficiencies in the above-mentioned prior art.

[0007] The present invention adopts the following technical solutions: A novel energy storage auxiliary decision-making and scheduling method includes the following steps: Obtain energy storage information of the energy storage power station, including historical operation information, dispatch response information, reliability information, battery charging and discharging information, operating conditions and battery characteristic data of the energy storage power station; Determine the battery information of the energy storage batteries in the energy storage power station based on the energy storage information, and build an operation profile of each energy storage power station; Predict the capacity decay of energy storage power stations based on battery information and operation profiles of each energy storage power station; Carry out grid control and evaluate the response capability of energy storage power stations based on battery information and operation profiles, and obtain evaluation results including response speed. Grid control includes primary frequency regulation, automatic generation control and automatic voltage control; Based on the evaluation results, a response weight is assigned to each energy storage power station. Based on the response weight and the predicted power demand of the power grid, a power command is issued to the energy storage power station. The charging and discharging strategy of the energy storage batteries in each energy storage power station is determined according to the power demand, charge state and capacity attenuation of the energy storage power station.

[0008] Preferably, the battery information of the energy storage battery in the energy storage power station is determined specifically as follows: Perform data processing on the energy storage information of the energy storage power station to obtain multiple sections of historical energy storage information and current energy storage information; Feature extraction is performed on multiple segments of historical energy storage information and current energy storage information to obtain multiple historical battery features and current battery features, and the historical battery health status corresponding to each historical battery feature is determined; An initial battery health state determination model is constructed, and the initial battery health state determination model is trained through multiple historical battery features and corresponding historical battery health states to obtain a battery health state determination model; The current battery characteristics are input into a battery health state determination model to obtain battery information including the current battery health state and the predicted battery health state.

[0009] Preferably, the battery characteristics include battery charging and discharging characteristics and battery characteristic characteristics; the battery health state determination model includes a multi-scale pooling module, a gating mechanism, a long short-term memory network module, an attention mechanism and a gated residual network; The battery charge and discharge characteristics and the battery characteristic characteristics are respectively input into the multi-scale pooling module to obtain the first operation characteristics and the first parameter characteristics; the first operation characteristics and the first parameter characteristics are respectively screened through the gating mechanism to obtain the second operation characteristics and the second parameter characteristics; the second operation characteristics and the two parameter characteristics are input into the long short-term memory network module to obtain battery characteristics at multiple time scales; the battery characteristics at multiple time scales are input into the attention mechanism to obtain multi-scale battery characteristics; the multi-scale battery characteristics are input into the gated residual network to obtain the current battery health status.

[0010] Preferably, the long short-term memory network module includes multiple long short-term memory networks, and different long short-term memory networks are used to process second operating characteristics of different time lengths and their corresponding second parameter characteristics.

[0011] Preferably, the loss function of the battery health status determination model is L for:

[0012] Where A represents the total number of training samples. Represents the training sample variable; represents the adjustment coefficient; Indicates the length of the interval of charge state that the current battery health state falls into; represents the L2 norm; Indicates the actual battery health status of the battery, which is a range value. Indicates the maximum value of the actual battery health status; Indicates the minimum value of the actual battery health status.

[0013] Preferably, the current battery health status SOC Specifically:

[0014] in, Represents the sigmoid activation function; , , and They represent the first parameter, the second parameter, the third parameter and the fourth parameter respectively, which are obtained through model training; An intermediate variable representing the battery health status; Represent multi-scale battery characteristics; represents normalization; Represents bit-wise multiplication of elements.

[0015] Preferably, the battery health status is predicted as follows: Based on the historical operation information, the future operation information of the future time period is predicted; based on the future operation information, the future battery characteristics are constructed; the future battery characteristics are input into the battery health status determination model to obtain the predicted battery health status.

[0016] Preferably, the operation profile of each energy storage power station is constructed as follows: Obtain power grid information and new energy information; Extract energy storage information based on power grid information to obtain the time periods for peak load shaving and valley filling and the time periods for power regulation of each energy storage power station; Extract energy storage information based on new energy information and grid load data to obtain the time period for each energy storage power station to consume new energy; Calculate and analyze the charging and discharging efficiency, command response speed and charging and discharging power curve of each energy storage power station in each operation period, and build a control operation portrait; Extract the sub-storage information of each energy storage power station respectively to obtain the charging and discharging status of each energy storage power station; Based on the charging and discharging conditions of each energy storage power station, the charge state changes, charging and discharging capacity, and charging and discharging power curves in each charging and discharging process are determined, and a charging and discharging operation profile is constructed.

[0017] Preferably, the power grid information includes power grid load data and power grid frequency data; the new energy information includes new energy power generation data.

[0018] Preferably, the action period includes a time period for peak shaving and valley filling, a time period for power regulation and a time period for new energy consumption.

[0019] Preferably, the capacity attenuation of the energy storage power station is predicted based on the battery information and the operation profile of each energy storage power station as follows: For each energy storage battery: Determine the maximum state of charge and minimum state of charge during each charge and discharge process; Determine the discharge amount of the energy storage battery during the current charge and discharge process based on the difference between the maximum charge state and the minimum charge state; When the difference between the maximum state of charge and the minimum state of charge is greater than or equal to a preset state of charge difference threshold, the charge and discharge is used as a reference charge and discharge, and the reference battery capacity decay is determined by a capacity decay model; When the difference between the maximum state of charge and the minimum state of charge is less than a preset state of charge difference threshold, the battery capacity decay is determined based on the previous reference battery capacity decay and the discharge amount, charge state change, ambient temperature and discharge current during the current charge and discharge process; For each energy storage plant: The average value of the battery capacity attenuation of the energy storage batteries in the energy storage power station is taken as the capacity attenuation of the energy storage power station.

[0020] Preferably, the capacity decay model is used to process the historical reference battery capacity decay and charge and discharge conditions of the energy storage battery to obtain the current reference battery capacity decay.

[0021] Preferably, the battery capacity attenuation Dc for:

[0022] in, Indicates the capacity attenuation of the reference battery; represents the charge state parameter; Indicates the current discharge amount of the energy storage battery; Indicates the current ambient temperature Capacity correction factor; Represents the reference discharge current; k represents the Peukert index; Represents the state of charge correction factor.

[0023] Preferably, the objective function of the power demand of the power station is:

[0024] in, Indicates taking the minimum value; n indicates the variable of the energy storage power station; N indicates the total number of energy storage power stations; represents the response speed of the energy storage power station n; t represents the time variable; T represents the predicted total grid dispatch time; represents the power demand of energy storage power station n at time t; represents the power demand of energy storage power station n at time t-1; Indicates taking the absolute value.

[0025] Preferably, the constraints of the power demand of the power station are:

[0026] in, represents the predicted grid power demand at time t;

[0027] in, Indicates the maximum power change rate of energy storage station n;

[0028] in, represents the minimum output power of energy storage power station n; represents the maximum output power of energy storage station n;

[0029]

[0030] in, represents the energy state of energy storage station n at time t; represents the energy state of energy storage station n at time t-1; Indicates the discharge time interval; Indicates the charge and discharge efficiency; Indicates the lowest energy state of energy storage station n; Indicates the highest energy state of energy storage station n.

[0031] Preferably, the objective function of the charging and discharging strategy of the energy storage batteries in each energy storage power station is:

[0032] in, Indicates finding the minimum value; m indicates the energy storage battery variable; M indicates the total number of energy storage batteries in the energy storage power station; Indicates the current battery capacity attenuation; Indicates the rated capacity of the energy storage battery; represents the charge state of the energy storage battery m at time t; Represents the energy state of the energy storage battery m at time t.

[0033] Preferably, the constraints of the charging and discharging strategy of the energy storage batteries in each energy storage power station are:

[0034] in, It represents the battery charging and discharging power of the energy storage battery m in the energy storage power station n at time t; represents the power demand of energy storage power station n at time t;

[0035] in, represents the battery charging and discharging power of the energy storage battery m in the energy storage power station n at time t-1; Indicates the maximum power change rate of the energy storage battery m;

[0036] in, Represents the minimum output power of the energy storage battery m; Indicates the maximum output power of the energy storage battery m;

[0037] in, Indicates the lower limit of the charge state of the energy storage battery; Indicates the upper limit of the charge state of the energy storage battery.

[0038] In a second aspect, an embodiment of the present invention provides a novel energy storage auxiliary decision-making and scheduling system, including: An acquisition module is used to acquire energy storage information of the energy storage power station, including historical operation information, dispatch response information, reliability information, battery charge and discharge information, operating conditions and battery characteristic data of the energy storage power station; An information module determines the battery information of the energy storage batteries in the energy storage power station based on the energy storage information, and constructs an operation profile of each energy storage power station; The prediction module predicts the capacity decay of the energy storage power station based on battery information and the operation profile of each energy storage power station; The control module conducts grid control and evaluates the response capability of the energy storage power station based on battery information and operation profiles to obtain evaluation results including response speed. Grid control includes primary frequency regulation, automatic generation control, and automatic voltage control. The output module assigns a response weight to each energy storage power station based on the evaluation results, and issues power instructions to the energy storage power station based on the response weight and the predicted power demand of the power grid; and determines the charging and discharging strategy of the energy storage batteries in each energy storage power station according to the power demand, charge state and capacity attenuation of the energy storage power station.

[0039] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned novel energy storage auxiliary decision-making and scheduling method when executing the computer program.

[0040] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned new energy storage auxiliary decision-making and scheduling method.

[0041] In a fifth aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned novel energy storage auxiliary decision-making and scheduling method when executing the computer program.

[0042] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned new energy storage auxiliary decision-making and scheduling method.

[0043] Compared with the prior art, the present invention has at least the following beneficial effects: A new energy storage auxiliary decision-making and scheduling method can more accurately predict the capacity decay of energy storage power stations and evaluate the response capacity of energy storage power stations by collecting detailed energy storage information of energy storage power stations; optimize the scheduling strategy of energy storage power stations and improve the operation efficiency of energy storage power stations; determine the charging and discharging strategy of energy storage batteries in each energy storage power station according to the power station power demand, charge state and capacity decay of energy storage power stations, so that energy storage power stations can better adapt to the needs of the power grid and improve the reliability and stability of the power grid; by assigning response weights to each energy storage power station and issuing power instructions to the energy storage power station based on the response weights and predicted power demand of the power grid, the utilization efficiency of the energy storage power station is maximized, the operating cost of the energy storage power station can be reduced, and the economic benefits can be improved; by carrying out power grid regulation, including primary frequency regulation, automatic power generation control and automatic voltage control, the operation of the power grid can be improved. Improve the stability and power supply quality of the power grid and provide better services to users; better adapt to the volatility characteristics of new energy and improve the absorption capacity of new energy. This helps to reduce energy waste and promote energy transformation and sustainable development.

[0044] Furthermore, by collecting and processing the energy storage information of the energy storage power station, using historical data for feature extraction and model training, a battery health status determination model is obtained, which fully utilizes the advantages of data-driven and can more accurately predict the battery health status; by inputting the current battery characteristics into the battery health status determination model, the battery information of the current battery health status and the predicted battery health status is obtained; so that the energy storage power station operator can timely understand the health status of the battery, through the prediction of the battery health status, discover potential problems in advance, issue early warnings and formulate corresponding maintenance plans, which helps to reduce the probability of failure, extend battery life, and reduce the operating cost of the energy storage power station; by obtaining the battery health status information, the charging and discharging strategy is adjusted according to the actual situation of the battery, the operation of the energy storage power station is optimized, and the operating efficiency and reliability of the energy storage power station are improved; through the analysis of energy storage information and the prediction of battery health status, a reference basis is provided for the planning and design of energy storage power stations.

[0045] Furthermore, through the multi-scale pooling module, battery features of different time scales are extracted from the battery charging and discharging features and battery characteristic features, so as to better capture the dynamic changes and long-term trends of the battery; through the gating mechanism, the extracted features are screened, redundancy and noise are removed, and the features useful for battery health status prediction are retained, thereby improving the prediction accuracy of the model; through the long short-term memory network module, the time dependence of battery features can be captured, so as to better predict the battery health status; through the attention mechanism, the more important features for battery health status prediction at different time scales can be highlighted, further improving the prediction accuracy of the model; through the gated residual network, the battery health status can be effectively predicted, providing strong support for the operation and maintenance of energy storage power stations; by accurately predicting the battery health status, potential problems can be discovered in advance, early warnings can be issued, and corresponding maintenance plans can be formulated, thereby reducing maintenance costs and reducing the probability of failures.

[0046] Furthermore, by utilizing historical operating information, the health status of batteries in future time periods can be predicted, so as to discover potential problems in advance, issue early warnings and formulate corresponding maintenance plans; by predicting the future health status of batteries, maintenance personnel and equipment can be arranged in advance to avoid losses caused by emergency repairs and downtime, and improve maintenance efficiency; by predicting the future health status of batteries, operation plans for energy storage power stations can be formulated in advance, charging and discharging strategies can be optimized, and the operating efficiency and reliability of energy storage power stations can be improved; by predicting the future health status of batteries, problems such as battery aging and performance degradation can be discovered in a timely manner, and corresponding measures can be taken to extend battery life and reduce the operating costs of energy storage power stations; by predicting the future health status of batteries, potential safety hazards can be discovered in advance, and corresponding measures can be taken to ensure the safe operation of energy storage power stations and reduce accident risks.

[0047] Furthermore, by obtaining grid information and new energy information, we can better understand the operating conditions of the grid and new energy, so as to more accurately determine the time periods for energy storage power stations to perform peak shaving and valley filling and new energy consumption, and optimize the scheduling strategy of energy storage power stations; by extracting and analyzing energy storage information, we can better understand indicators such as the charging and discharging efficiency, command response speed, and charging and discharging power curve of energy storage power stations, so as to better play the role of energy storage power stations and improve the utilization rate of energy storage power stations; by constructing control operation portraits and charging and discharging operation portraits, we can accurately characterize the operating characteristics of energy storage power stations and provide strong support for the operation and maintenance of energy storage power stations; by analyzing the charging and discharging conditions of energy storage power stations, we can better understand the use of batteries, so as to better formulate battery maintenance plans and reduce the operating costs of energy storage power stations; by optimizing the scheduling strategy of energy storage power stations and improving the utilization rate of energy storage power stations, we can improve the reliability and stability of the power grid and provide better services to users.

[0048] Furthermore, by considering the difference between the maximum state of charge and the minimum state of charge during the charging and discharging process, the battery capacity decay can be predicted more accurately, thereby better formulating battery maintenance plans and energy storage power station operation strategies; when determining the battery capacity decay, factors such as the discharge amount, charge state change, ambient temperature, and discharge current are comprehensively considered, so as to more comprehensively evaluate the battery capacity decay; by taking the mean of the capacity decay of the energy storage battery as the capacity decay of the energy storage power station, the calculation process can be simplified and the calculation complexity can be reduced; by evaluating the capacity decay of the energy storage power station, the operating status of the energy storage power station can be better understood, providing strong support for the operation and maintenance of the energy storage power station; by accurately predicting the battery capacity decay, the operation strategy of the energy storage power station can be better formulated, the operating efficiency and reliability of the energy storage power station can be improved, and strong support can be provided for the stable and reliable operation of the power grid.

[0049] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0050] In summary, the present invention optimizes the dispatching strategy of the energy storage power station according to the response capability of the energy storage power station to the grid dispatching, improves the safety of the grid, helps to realize the full capacity monitoring and attenuation prediction of the energy storage station, and then realizes the fully automated energy storage power station calling, thereby improving the availability and revenue of the energy storage power station.

[0051] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 An exemplary flow chart of a novel energy storage auxiliary decision-making and scheduling method provided by the present invention; Figure 2A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 3 The block diagram of a chip provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0055] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0056] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0057] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0058] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0059] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0060] The present invention provides a novel energy storage auxiliary decision-making and scheduling method, which obtains energy storage information of an energy storage power station; determines battery information of energy storage batteries in the energy storage power station based on the energy storage information, and constructs an operation profile of each energy storage power station; predicts the capacity attenuation of the energy storage power station based on the battery information and the operation profile; carries out power grid regulation, and evaluates the response capability of the energy storage power station based on the battery information and the operation profile; determines the scheduling strategy of the power grid for the energy storage power station based on the evaluation result; so as to more accurately predict and optimize the performance of each power station while optimizing the maintenance plan and extending the battery life.

[0061] See also Figure 1 The present invention provides a novel energy storage auxiliary decision-making and scheduling method, comprising the following steps: S1. Obtain energy storage information of the energy storage power station; Energy storage information refers to various information related to energy storage in energy storage power stations. Energy storage information includes historical operation information, dispatch response information, reliability information, battery charging and discharging information, operating conditions and battery characteristic data of energy storage power stations.

[0062] Historical operation information refers to data such as the operating status and performance of the energy storage battery over a period of time in the past; historical operation information includes power station charging and discharging data, grid interaction data, environmental data, fault and alarm records, etc.

[0063] Degree response information refers to the reaction and execution of the energy storage system according to the grid dispatch instructions; dispatch response information can include response time, ramp rate, accuracy (the degree of match between actual output and instruction requirements), duration (the time that the specified output can be maintained) and availability (the proportion of time that can respond to dispatch instructions), etc.

[0064] Reliability information is used to evaluate the performance, stability and availability of energy storage power stations; reliability evaluation indicators may include availability, mean time between failures, mean time to repair, reliability and responsiveness, etc.

[0065] Battery charging and discharging information refers to information related to the charging and discharging of energy storage batteries in energy storage power stations; battery charging and discharging information includes total charging and discharging power, system voltage and current, overall state of charge, charging and discharging efficiency, available capacity, response time, temperature distribution and battery pack balance status, etc.

[0066] Operating conditions refer to the operating status and mode of the energy storage power station under different conditions and demands; operating conditions include peak-valley filling, frequency regulation, voltage support, renewable energy smoothing, spare capacity and demand response.

[0067] Battery characteristic data refers to a series of quantitative indicators that describe battery performance, behavior and characteristics. Battery characteristic data includes capacity characteristics, charge and discharge characteristics, cycle life characteristics, temperature characteristics, self-discharge characteristics, internal resistance characteristics, power characteristics and efficiency characteristics.

[0068] S2. Determine the battery information of the energy storage battery in the energy storage power station based on the energy storage information; Battery information refers to information related to the battery, including the current battery health state and the predicted battery health state.

[0069] The current battery health status refers to the health status of the energy storage battery at the current time; Predicted battery health status refers to the predicted health status of the energy storage battery in the future.

[0070] Determine the current battery health status of the energy storage batteries in the energy storage plant, including: S201, performing data processing on energy storage information to obtain multiple sections of historical energy storage information and current energy storage information; Historical energy storage information refers to the operating information of energy storage power stations acquired historically, and multiple segments of historical energy storage information are obtained by dividing the historical time into time intervals; current energy storage information refers to the operating information of the current energy storage power station.

[0071] The current energy storage information includes the information of the energy storage power station at the current moment. By splicing the information of the energy storage power station at the current moment with the information at the historical moment, the current energy storage information with a time interval consistent with the length of the historical energy storage information is obtained. Of course, the current energy storage information may not overlap with the historical energy storage information, and only the most recent energy storage information with a time interval consistent with the historical energy storage information is used as the current energy storage information.

[0072] S202, extracting features from multiple sections of historical energy storage information and current energy storage information respectively to obtain multiple historical battery features and current battery features; Battery characteristics include battery charge and discharge information and battery characteristic data. The historical battery characteristics are obtained by extracting the battery charge and discharge information and battery characteristic data at historical moments. Similarly, the current battery charge and discharge information and battery characteristics are extracted to obtain the current battery characteristics.

[0073] S203, determining a historical battery health status corresponding to each historical battery feature; The historical battery health status refers to the battery health status of the battery in the past, and the historical battery health status is calculated by testing the battery.

[0074] S204, constructing an initial battery health state determination model, and training the initial battery health state determination model through multiple historical battery features and corresponding historical battery health states to obtain a battery health state determination model; The initial battery health state determination model is used to generate a battery health state determination model through machine model training.

[0075] The battery health state determination model is used to determine the current health state of the battery through the current battery characteristics.

[0076] Battery characteristics include battery charging and discharging characteristics and battery characteristic characteristics. The battery health status determination model includes a multi-scale pooling module, a gating mechanism, a long short-term memory network module, an attention mechanism and a gated residual network.

[0077] S204, inputting the battery charge and discharge characteristics and the battery characteristic characteristics into the multi-scale pooling module respectively to obtain the first operation characteristics and the first parameter characteristics; filtering the logarithmic characteristics respectively through the gating mechanism to obtain the second operation characteristics and the second parameter characteristics; inputting the second operation characteristics and the second parameter characteristics into the long short-term memory network module to obtain the battery characteristics of multiple time scales; inputting the battery characteristics of multiple time scales into the attention mechanism to obtain the multi-scale battery characteristics; inputting the multi-scale battery characteristics into the gated residual network to obtain the current battery health status; The long short-term memory network module includes multiple long short-term memory networks, and different long short-term memory networks are used to process second operating characteristics of different time lengths and their corresponding second parameter characteristics.

[0078] The expression of the current battery health status is:

[0079]

[0080] The loss function of the battery health status determination model is:

[0081] in, Indicates the current battery health status; Represents the sigmoid activation function; , , and They represent the first parameter, the second parameter, the third parameter and the fourth parameter respectively, which are obtained through model training; An intermediate variable representing the battery health status; Represent multi-scale battery characteristics; represents normalization; Indicates bit-by-bit multiplication of elements; represents the loss function; A represents the total number of training samples, Represents the training sample variable; represents the adjustment coefficient; Indicates the length of the interval of charge state that the current battery health state falls into; represents the L2 norm; Indicates the actual battery health status of the battery, which is a range value. Indicates the maximum value of the actual battery health status; Indicates the minimum value of the actual battery health status.

[0082] S205, inputting the current battery characteristics into a battery health state determination model to obtain the current battery health state; S206, determining the predicted battery health state of the energy storage battery in the energy storage power station, including: predicting future operation information in a future time period based on historical operation information; constructing future battery characteristics based on the future operation information; and inputting the future battery characteristics into a battery health state determination model to obtain a predicted battery health state.

[0083] The future operation information refers to the operation information of the energy storage power station in the future time period. The future battery characteristics may at least include the predicted battery charging and discharging characteristics and battery characteristic characteristics of the energy storage power station in the future time period.

[0084] S3. Based on the energy storage information, build an operation profile of each energy storage power station; Operation profiles are used to describe and analyze the operation characteristics of energy storage power stations, including grid information, new energy information, and energy storage information. Operation profiles of each energy storage power station are constructed, including: S301, obtaining power grid information and new energy information; The power grid information includes power grid load data and power grid frequency data; the new energy information includes new energy power generation data.

[0085] S302, extracting energy storage information based on power grid information to obtain the time period for each energy storage power station to perform peak shaving and valley filling and the time period for adjusting power; extracting energy storage information based on new energy information and power grid load data to obtain the time period for each energy storage power station to consume new energy; S303, calculating and analyzing the charging and discharging efficiency, command response speed and charging and discharging power curve of each energy storage power station in each operation period, and constructing a control operation portrait; The action periods include the time periods for peak shaving and valley filling, the time periods for power regulation and the time periods for new energy consumption.

[0086] S304, extracting the sub-energy storage information of each energy storage power station respectively, and obtaining the charging and discharging conditions of each energy storage power station; Sub-energy storage information refers to the energy storage information of a single energy storage power station.

[0087] S305: Based on the charging and discharging conditions of each energy storage power station, determine the charge state change, charging and discharging capacity and charging and discharging power curve in each charging and discharging process, and construct a charging and discharging operation portrait.

[0088] S4. Predict the capacity decay of the energy storage power station based on battery information and operation profiles; Capacity decay refers to the situation where the actual available capacity of the energy storage power station gradually decreases over time and with the increase in usage. Capacity decay can be expressed as a percentage, for example, 90%, 80% of the initial capacity, etc. Predicting the capacity decay of the energy storage power station includes: For each energy storage battery: S401, determining the maximum charge state and the minimum charge state in each charging and discharging process; The maximum state of charge refers to the minimum value between the charge state at the beginning of discharge and the charge state at the end of charge; the minimum state of charge refers to the maximum value between the charge state at the beginning of charge and the charge state at the end of discharge.

[0089] S402, determining the discharge amount of the energy storage battery in the current charge and discharge process based on the difference between the maximum charge state and the minimum charge state; S403, when the difference between the maximum state of charge and the minimum state of charge is greater than or equal to a preset state of charge difference threshold, use the charge and discharge as a reference charge and discharge, and determine the reference battery capacity decay through a capacity decay model; The capacity decay model is used to process the historical reference battery capacity decay and charge and discharge conditions of the energy storage battery to obtain the current reference battery capacity decay.

[0090] The preset charge state difference threshold refers to a preset charge state change difference of the energy storage battery.

[0091] S4031, when the charge state change is greater than the preset charge state difference threshold, the current charge and discharge is regarded as a complete charge and discharge, which has a certain reference value, and a capacity decay prediction is performed; S4032, when the charge state change is less than the preset charge state difference threshold, it means that the current discharge is partial discharge, the error may be serious and not very useful for reference; Reference charge and discharge refers to the reference standard used for subsequent partial discharges.

[0092] The capacity decay model is trained by the experimental data of the battery. Since the actual capacity of the battery decays during use, directly using the rated capacity to estimate the current capacity of the battery will cause a large error. Therefore, using a relatively accurate reference battery capacity decay to predict the battery capacity decay at a time closer to the current time can improve the accuracy of subsequent calculations. Therefore, the reference battery capacity decay refers to the battery capacity decay of the reference standard used for subsequent partial charging and discharging.

[0093] S404: When the difference between the maximum state of charge and the minimum state of charge is less than a preset state of charge difference threshold, the battery capacity decay is determined based on the previous reference battery capacity decay and the discharge amount, charge state change, ambient temperature, and discharge current during the current charge and discharge process.

[0094] The expression for capacity attenuation is:

[0095]

[0096] in, Indicates the current battery capacity attenuation; Indicates the capacity attenuation of the reference battery; represents the charge state parameter; Indicates the current discharge amount of the energy storage battery; Indicates the current ambient temperature Capacity correction factor; represents the reference discharge current; k represents the Peukert index; represents the charge state correction factor; Indicates the reference charge state change value; Indicates the current charge state change value.

[0097] For each energy storage plant: The average value of the battery capacity attenuation of the energy storage batteries in the energy storage power station is taken as the capacity attenuation of the energy storage power station.

[0098] S5. Carry out grid control and evaluate the response capability of the energy storage power station based on battery information and operation profiles to obtain the response speed; Grid regulation refers to the method of adjusting and controlling the system within the grid to ensure stable operation of the grid, including primary frequency regulation, automatic generation control (AGC) and automatic voltage control (AVC).

[0099] Responsiveness refers to the speed and contribution of energy storage batteries in grid regulation, including response speed, contribution to new energy consumption, contribution to peak regulation, contribution to frequency regulation and contribution to grid stability.

[0100] Response speed refers to the time interval from when the energy storage battery receives the control command to when it reaches the required output; The contribution of new energy consumption refers to the change in the amount of electricity generated by new energy accepted and used through the energy storage battery power system.

[0101] Frequency regulation contribution refers to the ability of energy storage power stations to participate in grid frequency regulation.

[0102] Grid stability contribution can be used to indicate the contribution of energy storage power stations to grid stability (e.g., peak shaving and valley filling).

[0103] Evaluate the response capability of energy storage power stations: S501, constructing a response curve of an energy storage power station, a grid load curve, and a new energy power generation curve; S502, determining the response speed of the energy storage power station and the contribution of new energy consumption based on the energy storage power station response curve, the grid load curve and the new energy power generation curve; S503, determining the peak load contribution based on the difference between the output peak after the energy storage power station is added and the original peak; S504, determining the frequency regulation contribution based on the regulation speed and difference between the actual grid frequency after the energy storage power station is added and the original grid frequency; S505 . Determine a grid stability contribution based on a difference between the actual grid response curve and the original grid response curve.

[0104] S6. Based on the response speed, a response weight is assigned to each energy storage power station. Based on the response weight and the predicted power demand of the power grid, a power instruction is issued to the energy storage power station, and the power instruction includes the power demand of each power station. According to the power demand, charge state and capacity decay of the energy storage power station, the charging and discharging strategy of the energy storage batteries in each energy storage power station is determined, and the response capability of the energy storage power station is evaluated through battery information and operation portraits. Based on the evaluation results, the strategy of the power grid to adjust the energy storage power station is determined, while optimizing the maintenance plan and extending the battery life, the performance of each power station is accurately predicted and optimized. The capacity decay prediction of batteries and power stations can ensure the reliability and efficiency of the system, facilitate maintenance, and reduce the risk of failure and downtime.

[0105] S601, assigning a response weight to each energy storage power station based on the evaluation result; The evaluation results include response speed. The response weight refers to the weight obtained based on the response speed of the energy storage power station. The faster the power station responds, the more it can improve the flexibility of the power grid and accelerate the regulation of the power grid.

[0106] S602: Based on the response weight and the predicted power demand of the power grid, a power instruction is issued to the energy storage power station; The power instruction includes the power demand of each power station.

[0107] The predicted grid power demand refers to the grid power demand in the future time period obtained based on the historical grid load forecast.

[0108] The power command is used to instruct the energy storage power station to operate, including the power demand of the power station, the charging and discharging power, and the charging and discharging time.

[0109] The objective function of the power demand of the power station is:

[0110] in, Indicates taking the minimum value; n indicates the variable of the energy storage power station; N indicates the total number of energy storage power stations; represents the response speed of the energy storage power station n; t represents the time variable; T represents the predicted total grid dispatch time; represents the power demand of energy storage power station n at time t; represents the power demand of energy storage power station n at time t-1; Indicates taking the absolute value; The constraints on the power demand of the power station are:

[0111] in, represents the predicted grid power demand at time t;

[0112] in, Indicates the maximum power change rate of energy storage station n;

[0113] in, represents the minimum output power of energy storage power station n; represents the maximum output power of energy storage station n;

[0114]

[0115] in, represents the energy state of energy storage station n at time t; represents the energy state of energy storage station n at time t-1; Indicates the discharge time interval; Indicates the charge and discharge efficiency; Indicates the lowest energy state of energy storage station n; Indicates the highest energy state of energy storage station n.

[0116] S603: Determine the charging and discharging strategy of the energy storage batteries in each energy storage power station according to the power station power demand, charge state and capacity attenuation of the energy storage power station.

[0117] The objective function of the charging and discharging strategy of the energy storage batteries in each energy storage power station is:

[0118] in, Indicates finding the minimum value; m indicates the energy storage battery variable; M indicates the total number of energy storage batteries in the energy storage power station; Indicates the current battery capacity attenuation; Indicates the rated capacity of the energy storage battery; represents the charge state of the energy storage battery m at time t; Represents the energy state of the energy storage battery m at time t; The constraints of the charging and discharging strategy of the energy storage batteries in each energy storage power station are:

[0119] in, It represents the battery charging and discharging power of the energy storage battery m in the energy storage power station n at time t; represents the power demand of energy storage power station n at time t;

[0120] in, represents the battery charging and discharging power of the energy storage battery m in the energy storage power station n at time t-1; Indicates the maximum power change rate of the energy storage battery m;

[0121] in, Represents the minimum output power of the energy storage battery m; Indicates the maximum output power of the energy storage battery m;

[0122] in, Indicates the lower limit of the charge state of the energy storage battery; Indicates the upper limit of the charge state of the energy storage battery.

[0123] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0124] In another embodiment of the present invention, a new energy storage auxiliary decision-making and scheduling system is provided, which can be used to implement the above-mentioned new energy storage auxiliary decision-making and scheduling method. Specifically, the new energy storage auxiliary decision-making and scheduling system includes an acquisition module, an information module, a prediction module, a control module and an output module.

[0125] Among them, the acquisition module acquires the energy storage information of the energy storage power station, and the energy storage information includes the historical operation information, dispatch response information, reliability information, battery charging and discharging information, operating conditions and battery characteristic data of the energy storage power station; An information module determines the battery information of the energy storage batteries in the energy storage power station based on the energy storage information, and constructs an operation profile of each energy storage power station; The prediction module predicts the capacity decay of the energy storage power station based on battery information and the operation profile of each energy storage power station; The control module conducts grid control and evaluates the response capability of the energy storage power station based on battery information and operation profiles to obtain evaluation results including response speed. Grid control includes primary frequency regulation, automatic generation control, and automatic voltage control. The output module assigns a response weight to each energy storage power station based on the evaluation results, and issues power instructions to the energy storage power station based on the response weight and the predicted power demand of the power grid; and determines the charging and discharging strategy of the energy storage batteries in each energy storage power station according to the power demand, charge state and capacity attenuation of the energy storage power station.

[0126] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), other general-purpose processors, graphics processors (GPU), tensor processors (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the new energy storage auxiliary decision-making and scheduling method, including: Obtain energy storage information of the energy storage power station, which includes historical operation information, dispatch response information, reliability information, battery charging and discharging information, operating conditions and battery characteristic data of the energy storage power station; determine the battery information of the energy storage batteries in the energy storage power station based on the energy storage information, and construct an operation profile of each energy storage power station; predict the capacity decay of the energy storage power station based on the battery information and the operation profile of each energy storage power station; carry out grid regulation, and evaluate the response capability of the energy storage power station based on the battery information and the operation profile, and obtain an evaluation result including the response speed, the grid regulation includes primary frequency regulation, automatic power generation control and automatic voltage control; assign a response weight to each energy storage power station based on the evaluation result, and issue a power command to the energy storage power station based on the response weight and the predicted power demand of the power grid; determine the charging and discharging strategy of the energy storage batteries in each energy storage power station according to the power demand, charge state and capacity decay of the energy storage power station.

[0127] See also Figure 2, the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the new energy storage auxiliary decision-making scheduling method in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the new energy storage auxiliary decision-making scheduling system in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0128] The computer device 60 includes a desktop computer, a notebook, a PDA, a cloud server, and other computing devices. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 2 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0129] The processor 61 is a central processing unit (CPU), and also includes other general-purpose processors, graphics processing units (GPU), tensor processing units (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor, a microprocessor or the processor is also any conventional processor, etc.

[0130] The memory 62 is an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 is also an external storage device of the computer device 60, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device 60.

[0131] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0132] See also Figure 3 , the terminal device is a chip, and the chip 600 of this embodiment includes a processor 622, which may be one or more, and a memory 632 for storing a computer program executable by the processor 622. The computer program stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. In addition, the processor 622 may be configured to execute the computer program to execute the above-mentioned novel energy storage auxiliary decision-making and scheduling method.

[0133] In addition, the chip 600 may further include a power supply component 626 and a communication component 650, wherein the power supply component 626 may be configured to perform power management of the chip 600, and the communication component 650 may be configured to implement communication, for example, wired or wireless communication, of the chip 600. In addition, the chip 600 may further include an input / output interface 658. The chip 600 may operate based on an operating system stored in the memory 632.

[0134] In another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions are one or more computer programs. It should be noted that the computer-readable storage medium here is a high-speed RAM memory, and also a non-volatile memory (Non-Volatile Memory), such as at least one disk memory.

[0135] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the novel energy storage auxiliary decision-making and scheduling method in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows: Obtain energy storage information of the energy storage power station, which includes historical operation information, dispatch response information, reliability information, battery charging and discharging information, operating conditions and battery characteristic data of the energy storage power station; determine the battery information of the energy storage batteries in the energy storage power station based on the energy storage information, and construct an operation profile of each energy storage power station; predict the capacity decay of the energy storage power station based on the battery information and the operation profile of each energy storage power station; carry out grid regulation, and evaluate the response capability of the energy storage power station based on the battery information and the operation profile, and obtain an evaluation result including the response speed, the grid regulation includes primary frequency regulation, automatic power generation control and automatic voltage control; assign a response weight to each energy storage power station based on the evaluation result, and issue a power command to the energy storage power station based on the response weight and the predicted power demand of the power grid; determine the charging and discharging strategy of the energy storage batteries in each energy storage power station according to the power demand, charge state and capacity decay of the energy storage power station.

[0136] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0137] In order to verify the effectiveness of the new energy storage auxiliary decision-making and scheduling method, a simulation experiment was carried out.

[0138] The experimental data comes from the actual operation data of a certain energy storage power station, including the historical operation information, dispatch response information, reliability information, battery charging and discharging information, operating conditions and battery characteristic data of the energy storage power station. Using Python programming language and PyTorch deep learning framework, a storage battery health status prediction model was built, including multi-scale pooling module, gating mechanism, long short-term memory network module, attention mechanism and gated residual network.

[0139] The experimental results show that the new energy storage assisted decision-making and scheduling method has obvious advantages in the operating efficiency and reliability of energy storage power stations compared with the traditional scheduling method.

[0140] Specifically, the new energy storage decision-making and scheduling method has shown good performance in the prediction of capacity decay of energy storage power stations, the response capability of energy storage power stations, and the formulation of scheduling strategies. For example, in the prediction of capacity decay of energy storage power stations, the prediction error of the new energy storage decision-making and scheduling method is about 30% lower than that of the traditional scheduling method, which shows that the new energy storage decision-making and scheduling method can more accurately predict the capacity decay of energy storage batteries, thereby more reasonably arranging the charging and discharging strategies of energy storage power stations and improving the operating efficiency and reliability of energy storage power stations.

[0141] In addition, the new energy storage auxiliary decision-making scheduling method has also achieved good results in practical applications. For example, in the actual operation of a certain energy storage power station, the new energy storage auxiliary decision-making scheduling method was used to schedule it. The results showed that the operation efficiency and reliability of the energy storage power station were significantly improved. The average charging efficiency of the energy storage power station increased by about 10%, the average discharge efficiency increased by about 8%, and the average availability increased by about 5%.

[0142] In summary, the novel energy storage auxiliary decision-making and scheduling method and system of the present invention can, on the one hand, improve the project revenue of the power generation group and the enthusiasm of the power generation group to participate in the construction of energy storage resources, and indirectly improve the construction quality and operation and maintenance level of the energy storage station; on the other hand, it can enrich the regional power grid balance adjustment means, support the safe and stable operation of the power grid, and assist in the construction of a new power system.

[0143] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit, and the above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0144] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0145] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0146] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed is through some interfaces, indirect coupling or communication connection of devices or units, electrical, mechanical or other forms.

[0147] The units described as separate components may not be physically separated, and the components shown as units may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0149] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0150] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0151] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0152] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0153] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A new energy storage auxiliary decision-making and scheduling method, characterized in that: The following steps are involved: Obtain energy storage information of the energy storage power station, including historical operation information, dispatch response information, reliability information, battery charging and discharging information, operating conditions and battery characteristic data of the energy storage power station; Determine the battery information of the energy storage batteries in the energy storage power station based on the energy storage information, and build an operation profile of each energy storage power station; Predict the capacity decay of energy storage power stations based on battery information and operation profiles of each energy storage power station; Carry out grid control and evaluate the response capability of energy storage power stations based on battery information and operation profiles, and obtain evaluation results including response speed. Grid control includes primary frequency regulation, automatic generation control and automatic voltage control; Based on the evaluation results, a response weight is assigned to each energy storage power station. Based on the response weight and the predicted power demand of the power grid, a power command is issued to the energy storage power station. The charging and discharging strategy of the energy storage batteries in each energy storage power station is determined according to the power demand, charge state and capacity attenuation of the energy storage power station.

2. The novel energy storage auxiliary decision-making and scheduling method according to claim 1 is characterized in that: Determine the battery information of the energy storage battery in the energy storage power station as follows: Perform data processing on the energy storage information of the energy storage power station to obtain multiple sections of historical energy storage information and current energy storage information; Feature extraction is performed on multiple segments of historical energy storage information and current energy storage information to obtain multiple historical battery features and current battery features, and the historical battery health status corresponding to each historical battery feature is determined; An initial battery health state determination model is constructed, and the initial battery health state determination model is trained through multiple historical battery features and corresponding historical battery health states to obtain a battery health state determination model; The current battery characteristics are input into a battery health state determination model to obtain battery information including the current battery health state and the predicted battery health state.

3. The novel energy storage auxiliary decision-making and scheduling method according to claim 2 is characterized in that: Battery characteristics include battery charging and discharging characteristics and battery characteristic characteristics; the battery health status determination model includes a multi-scale pooling module, a gating mechanism, a long short-term memory network module, an attention mechanism, and a gated residual network; Inputting the battery charge and discharge characteristics and the battery characteristic characteristics into the multi-scale pooling module respectively to obtain a first operation characteristic and a first parameter characteristic; The first operation characteristic and the first parameter characteristic are respectively screened by a gating mechanism to obtain a second operation characteristic and a second parameter characteristic; Inputting the second operation characteristic and the second parameter characteristic into the long short-term memory network module to obtain battery characteristics at multiple time scales; The battery features of multiple time scales are input into the attention mechanism to obtain multi-scale battery features; the multi-scale battery features are input into the gated residual network to obtain the current battery health status.

4. The novel energy storage auxiliary decision-making and scheduling method according to claim 3 is characterized in that: The long short-term memory network module includes multiple long short-term memory networks, and different long short-term memory networks are used to process second operating characteristics of different time lengths and their corresponding second parameter characteristics.

5. The novel energy storage auxiliary decision-making and scheduling method according to claim 3 is characterized in that: Loss function of the battery health status determination model L for: Where A represents the total number of training samples. Represents the training sample variable; represents the adjustment coefficient; Indicates the length of the interval of charge state that the current battery health state falls into; represents the L2 norm; Indicates the actual battery health status of the battery, which is a range value. Indicates the maximum value of the actual battery health status; Indicates the minimum value of the actual battery health status.

6. The novel energy storage auxiliary decision-making and scheduling method according to claim 2 is characterized in that: Current battery health status SOC Specifically: in, Represents the sigmoid activation function; , , and They represent the first parameter, the second parameter, the third parameter and the fourth parameter respectively, which are obtained through model training; An intermediate variable representing the battery health status; Represent multi-scale battery characteristics; represents normalization; Represents element-wise multiplication.

7. The novel energy storage auxiliary decision-making and scheduling method according to claim 2 is characterized in that: The predicted battery health status is as follows: Based on the historical operation information, the future operation information of the future time period is predicted; based on the future operation information, the future battery characteristics are constructed; the future battery characteristics are input into the battery health status determination model to obtain the predicted battery health status.

8. The novel energy storage auxiliary decision-making and scheduling method according to claim 1 is characterized in that: The operation profiles of each energy storage power station are as follows: Obtain power grid information and new energy information; Extract energy storage information based on power grid information to obtain the time periods for peak load shaving and valley filling and the time periods for power regulation of each energy storage power station; Extract energy storage information based on new energy information and grid load data to obtain the time period for each energy storage power station to consume new energy; Calculate and analyze the charging and discharging efficiency, command response speed and charging and discharging power curve of each energy storage power station in each operation period, and build a control operation portrait; Extract the sub-storage information of each energy storage power station respectively to obtain the charging and discharging status of each energy storage power station; Based on the charging and discharging conditions of each energy storage power station, the charge state changes, charging and discharging capacity, and charging and discharging power curves in each charging and discharging process are determined, and a charging and discharging operation profile is constructed.

9. The novel energy storage auxiliary decision-making and scheduling method according to claim 8 is characterized in that: The power grid information includes power grid load data and power grid frequency data; the new energy information includes new energy power generation data.

10. The novel energy storage auxiliary decision-making and scheduling method according to claim 8 is characterized in that: The action periods include the time periods for peak shaving and valley filling, the time periods for power regulation and the time periods for new energy consumption.

11. The novel energy storage auxiliary decision-making and scheduling method according to claim 1 is characterized in that: Based on the battery information and the operation profiles of each energy storage power station, the capacity attenuation of the energy storage power station is predicted as follows: For each energy storage battery: Determine the maximum state of charge and minimum state of charge during each charge and discharge process; Determine the discharge amount of the energy storage battery during the charge and discharge process based on the difference between the maximum charge state and the minimum charge state; When the difference between the maximum state of charge and the minimum state of charge is greater than or equal to a preset state of charge difference threshold, the charge and discharge is used as a reference charge and discharge, and the reference battery capacity decay is determined by a capacity decay model; When the difference between the maximum state of charge and the minimum state of charge is less than a preset state of charge difference threshold, the battery capacity decay is determined based on the previous reference battery capacity decay and the discharge amount, charge state change, ambient temperature and discharge current during the current charge and discharge process; For each energy storage plant: The average value of the battery capacity attenuation of the energy storage batteries in the energy storage power station is taken as the capacity attenuation of the energy storage power station.

12. The novel energy storage auxiliary decision-making and scheduling method according to claim 11 is characterized in that: The capacity decay model is used to process the historical reference battery capacity decay and charge and discharge conditions of the energy storage battery to obtain the current reference battery capacity decay.

13. The novel energy storage auxiliary decision-making and scheduling method according to claim 11 is characterized in that: Battery capacity attenuation Dc for: in, Indicates the capacity attenuation of the reference battery; represents the charge state parameter; Indicates the current discharge amount of the energy storage battery; Indicates the current ambient temperature Capacity correction factor; Represents the reference discharge current; k represents the Peukert index; Represents the state of charge correction factor.

14. The novel energy storage auxiliary decision-making and scheduling method according to claim 1 is characterized in that: The objective function of the power demand of the power station is: in, Indicates taking the minimum value; n indicates the variable of the energy storage power station; N indicates the total number of energy storage power stations; represents the response speed of the energy storage power station n; t represents the time variable; T represents the predicted total grid dispatch time; represents the power demand of energy storage power station n at time t; represents the power demand of energy storage power station n at time t-1; Indicates taking the absolute value.

15. The novel energy storage auxiliary decision-making and scheduling method according to claim 14 is characterized in that: The constraints on the power demand of the power station are: in, represents the predicted grid power demand at time t; in, Indicates the maximum power change rate of energy storage station n; in, represents the minimum output power of energy storage power station n; represents the maximum output power of energy storage station n; in, represents the energy state of energy storage station n at time t; represents the energy state of energy storage station n at time t-1; Indicates the discharge time interval; Indicates the charge and discharge efficiency; Indicates the lowest energy state of energy storage station n; Indicates the highest energy state of energy storage station n.

16. The novel energy storage auxiliary decision-making and scheduling method according to claim 1 is characterized in that: The objective function of the charging and discharging strategy of the energy storage batteries in each energy storage power station is: in, Indicates finding the minimum value; m indicates the energy storage battery variable; M indicates the total number of energy storage batteries in the energy storage power station; Indicates the current battery capacity attenuation; Indicates the rated capacity of the energy storage battery; represents the charge state of the energy storage battery m at time t; Represents the energy state of the energy storage battery m at time t.

17. The novel energy storage auxiliary decision-making and scheduling method according to claim 16 is characterized in that: The constraints of the charging and discharging strategy of the energy storage batteries in each energy storage power station are: in, It represents the battery charging and discharging power of the energy storage battery m in the energy storage power station n at time t; represents the power demand of energy storage power station n at time t; in, represents the battery charging and discharging power of the energy storage battery m in the energy storage power station n at time t-1; Indicates the maximum power change rate of the energy storage battery m; in, Represents the minimum output power of the energy storage battery m; Indicates the maximum output power of the energy storage battery m; in, Indicates the lower limit of the charge state of the energy storage battery; Indicates the upper limit of the charge state of the energy storage battery.

18. A new energy storage auxiliary decision-making and dispatching system, characterized in that: include: An acquisition module is used to acquire energy storage information of the energy storage power station, including historical operation information, dispatch response information, reliability information, battery charge and discharge information, operating conditions and battery characteristic data of the energy storage power station; An information module determines the battery information of the energy storage batteries in the energy storage power station based on the energy storage information, and constructs an operation profile of each energy storage power station; The prediction module predicts the capacity decay of the energy storage power station based on battery information and the operation profile of each energy storage power station; The control module conducts grid control and evaluates the response capability of the energy storage power station based on battery information and operation profiles to obtain evaluation results including response speed. Grid control includes primary frequency regulation, automatic generation control, and automatic voltage control. The output module assigns a response weight to each energy storage power station based on the evaluation results, and issues power instructions to the energy storage power station based on the response weight and the predicted power demand of the power grid; and determines the charging and discharging strategy of the energy storage batteries in each energy storage power station according to the power demand, charge state and capacity attenuation of the energy storage power station.

19. A computer-readable storage medium storing one or more programs, characterized in that: The one or more programs include instructions, which, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 17.

20. A computing device, characterized in that include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include steps for executing the method of any one of claims 1 to 17.

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