Energy storage real-time operation optimization method based on multi-time scale rolling and life perception

Abstract

The application relates to the technical field of energy storage operation optimization, and discloses an energy storage real-time operation optimization method based on multi-time scale rolling and life perception. The method comprises the following steps: evaluating a battery health state according to real-time battery parameter data to obtain a battery life loss cost; predicting the energy market power and the auxiliary service market power of the energy storage system at different moments according to a long-period optimization objective function to obtain an optimal state of charge reference trajectory; inputting the optimal state of charge reference trajectory into a short-period optimization objective function to solve the short-period optimization objective function and obtain power set values at different moments; inputting the power set values at different moments into a real-time optimization objective function to solve the real-time optimization objective function and obtain power control instructions; and issuing the power control instructions to an energy storage converter. Through multi-time scale rolling optimization and battery life perception, the energy storage system is efficiently operated in the whole life cycle.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage operation optimization, in particular to a real-time energy storage operation optimization method based on multi-time scale rolling and life perception. BACKGROUND

[0002] The existing energy storage operation optimization technology for new energy stations has obvious deficiencies in model construction and time scale design. Most of the existing technologies are based on initial fixed parameters such as battery rated capacity and efficiency to construct static optimization models, which cannot online perceive and adapt to the time-varying attenuation of battery performance. As the cycle number increases and the health state deteriorates, the model and the actual characteristics are mismatched, which leads to the deviation of the optimization instruction from the optimal working condition, and the decrease of control accuracy and system efficiency. At the same time, the traditional optimization strategy adopts a single time scale, which is difficult to coordinate different time dimension demands. For example, it is difficult to respond to minute-level or second-level power grid auxiliary services and power fluctuations when focusing on hour-level energy scheduling, and it is easy to deviate from the long-term economic optimization when focusing on short-time control, which causes the system to fail to balance real-time stability and global economic efficiency maximization.

[0003] In addition, the existing technology also has deficiencies in optimization target setting and anti-interference ability. The core target of the existing method is mostly the minimization of instantaneous operation cost or tracking error, and the marginal loss cost of battery life caused by charging and discharging strategy is not included in the decision. Although this optimization method improves short-term performance, it accelerates the irreversible aging of the battery and increases the life cycle maintenance and replacement cost, which damages the long-term economy. At the same time, the algorithm based on accurate mathematical model and ideal prediction data lacks effective online correction and fault tolerance mechanism when facing the uncertainty of battery nonlinearity, wind and light power prediction error and market fluctuation. It is sensitive to boundary condition changes and shows weakness in actual environment, and the stability and reliability of optimization effect are insufficient.

[0004] In summary, there is a need for an energy storage system operation optimization method that can meet the safe, economic and efficient operation requirements of the energy storage system in the whole life cycle. SUMMARY

[0005] The present application provides a real-time energy storage operation optimization method based on multi-time scale rolling and life perception to solve the problems of insufficient flexible optimization paradigm, lack of overall planning, neglect of long-term impact and weak anti-interference ability in related energy storage optimization technology.

[0006] In the first aspect, the present application provides a real-time energy storage operation optimization method based on multi-time scale rolling and life perception, which comprises:

[0007] The battery health state is evaluated according to environmental data of an environment where a new energy station energy storage system is located, real-time battery parameter data and state of charge data of the energy storage system, and a battery life loss cost is obtained according to the battery health state; the energy market power and the auxiliary service market power of the energy storage system at different times are predicted according to a long-period optimization objective function, and an optimal state of charge reference trajectory is obtained, and the long-period optimization objective function is established based on a sum of an energy quantity income item per unit time and an auxiliary service income item per unit time minus a battery life loss cost item; the optimal state of charge reference trajectory is input into a short-period optimization objective function, the short-period optimization objective function is solved, and power set values at different times are obtained, and the short-period optimization objective function is established based on a sum of a state of charge deviation item, a power smoothing item and the battery life loss cost item; the power set values at different times are input into a real-time optimization objective function, the real-time optimization objective function is solved, and a power control instruction is obtained, and the real-time optimization objective function is established based on a sum of a power tracking error item, a control quantity change item and the battery life loss cost item, and the time scales of the long-period optimization objective function, the short-period optimization objective function and the real-time optimization objective function are sequentially reduced; and the power control instruction is sent to an energy storage converter, so that the energy storage converter operates according to the power control instruction.

[0008] The multi-time scale rolling and life perception based energy storage real-time operation optimization method provided by the application evaluates the battery health state and quantifies the life loss cost, incorporates the long-term equipment life into the optimization decision, breaks the limitation of traditional optimization only pursuing short-term income, and realizes the preliminary trade-off between instant operation demand and whole life cycle economic benefit. Further, the long-period, short-period and real-time three-level optimization is constructed with sequentially reduced time scales, corresponds to market income planning, power accurate regulation and grid instruction tracking respectively, effectively coordinates the operation targets in different dimensions, and solves the problem that the single time scale optimization cannot consider both economy and stability. Finally, the optimal state of charge reference trajectory, the power set value and the power control instruction are sequentially output by each level of optimization, the compiled power control instruction is sent to the energy storage converter, the real-time and online optimal control of the energy storage system is realized in multiple application scenarios, the defect that the traditional static optimization model does not match the actual energy storage system is avoided, and the scientificity and effectiveness of operation optimization are improved.

[0009] In an optional implementation, the battery health state is evaluated according to environmental data of an environment where a new energy station energy storage system is located, real-time battery parameter data and state of charge data of the energy storage system, and a battery life loss cost is obtained according to the battery health state, including:

[0010] The battery actual available capacity is determined based on the battery ohmic internal resistance, polarization internal resistance and polarization capacitance in the real-time battery parameter data, the capacity type health state of the battery is calculated according to the battery actual available capacity and the battery initial capacity, the internal resistance type health state of the battery is calculated based on the battery ohmic internal resistance and polarization internal resistance in the real-time battery parameter data, in combination with the battery initial total internal resistance and the total internal resistance at the end of the battery life, the battery aging rate is calculated based on the environmental data, the capacity type health state and the internal resistance type health state of the battery, by using a battery aging model, and the battery life loss cost is calculated according to the battery aging rate, the unit battery cumulative energy throughput, the charge and discharge power data of the energy storage system and the energy storage system basic parameters.

[0011] The multi-time scale rolling and life perception based energy storage real-time operation optimization method provided by the application obtains real-time parameter data of the battery through an online parameter identification technology, further determines the actual available capacity, ensures that the battery capacity calculation is dynamically matched with the real-time attenuation state of the battery, and provides accurate data support for health state evaluation. Secondly, through the two-dimensional calculation of the capacity type health state and the internal resistance type health state, the battery attenuation characteristics are fully described, the one-sidedness of single index evaluation is avoided, and the scientificity and comprehensiveness of health state evaluation are greatly improved. Further, in combination with the environmental data and the two-dimensional health state, the battery aging model is used to quantify the comprehensive influence of multiple stress factors such as temperature and SOC interval and to calculate the aging rate, so that the aging evaluation is matched with the actual operation condition, and the problem that the existing technology ignores the influence of multi-factor coupling aging is solved. Finally, the abstract life loss is converted into quantifiable economic cost, and the subsequent optimization decision trade-off between immediate income and long-term life cost is realized.

[0012] In an optional implementation, in the long-period optimization objective function, the value of the electric energy income term is determined according to the product of the electric energy market power and the electric energy market price per unit time, the electric energy income term is determined according to the product of the ancillary service market power and the ancillary service market price, and the optimal state of charge reference trajectory is obtained by predicting the electric energy market power and the ancillary service market power of the energy storage system at different times according to the long-period optimization objective function, including:

[0013] The long-period optimization objective function is solved according to the constraint condition to obtain the optimal electric energy market power and the optimal ancillary service market power; and the optimal state of charge reference trajectory is calculated based on the optimal electric energy market power and the optimal ancillary service market power.

[0014] The energy storage real-time operation optimization method based on multi-time scale rolling and life perception provided by the application provides a clear basis for reasonable allocation of energy storage resources in different markets by defining the electrical energy income item as the product of the electrical energy market power and the corresponding price, and the auxiliary service income item as the product of the auxiliary service market power and the corresponding price. Secondly, by solving the long-period optimization objective function, the power distribution of the two markets is optimized synchronously, effectively coordinating the income demand of long-time scale energy transfer of the electrical energy market and the high compensation demand of the rapid response of the auxiliary service market, avoiding the income limitation or excessive battery wear caused by single market participation, and balancing the contradiction between short-term market income and long-term equipment health. Further, multiple constraints such as energy balance, SOC safety boundary, and charge and discharge power limit are integrated into the solving process to ensure the safety and feasibility of the optimal power distribution scheme and avoid the risk of breaking the equipment operation limit in pursuit of income. Finally, the optimal SOC reference trajectory calculated based on the optimal market power distribution provides a stable and scientific guidance benchmark for subsequent short-period optimization and real-time optimization, ensuring the consistency of the multi-time scale optimization system.

[0015] In an optional embodiment, in the short-period optimization objective function, the value of the state of charge deviation term is determined according to the real-time state of charge trajectory and the optimal state of charge reference trajectory, and the value of the power smoothing term is determined according to the fluctuation power of the energy storage system. The optimal state of charge reference trajectory is input into the short-period optimization objective function, and the short-period optimization objective function is solved to obtain the power set value at different times, including:

[0016] The short-period optimization objective function is solved according to the constraint conditions to obtain the power set value at different times, and the constraint conditions include the power balance constraint and the power change rate constraint.

[0017] The energy storage real-time operation optimization method based on multi-time scale rolling and life perception provided by the application ensures that the short-period optimization is always anchored to the goal of maximizing long-term economic benefits by defining the state of charge deviation term as the deviation of the real-time state of charge trajectory from the optimal state of charge reference trajectory output by the long-period layer, effectively connecting the hour-level and minute-level time scales, and solving the problem of the fragmentation of different time scale optimizations and the deviation of short-term control from long-term planning in the prior art. Further, through the combination of the power smoothing term and the power change rate constraint, both the instantaneous disturbance caused by wind and light power fluctuations can be filtered out to avoid the impact loss caused by the sudden change of charging and discharging power on the energy storage converter and the battery, and the stability of power output can be ensured to improve the stability of system operation and the service life of equipment. Further, the battery life loss cost term is included in the objective function, avoiding the short-sighted behavior of short-period optimization that only pursues immediate control accuracy while ignoring battery loss. Finally, the power balance constraint ensures that the power distribution of the energy storage system matches the new energy output and the grid demand, and the accurate power set value obtained by solving provides reliable data input for real-time layer second-level control.

[0018] In an optional embodiment, the power set values at different times are input into the real-time optimization objective function, and the real-time optimization objective function is solved to obtain a power control instruction, which includes:

[0019] The value of the control quantity change term is determined according to the control quantity change rate; and the value of the power tracking error term is determined according to the power set values at different times and the actual power values corresponding thereto.

[0020] The energy storage real-time operation optimization method based on multi-time scale rolling and life perception provided by the application ensures that the short-period optimization is always anchored to the goal of maximizing long-term economic benefits by defining the state of charge deviation term as the deviation of the real-time state of charge trajectory from the optimal state of charge reference trajectory output by the long-period layer, effectively connecting the hour-level and minute-level time scales, and solving the problem of the fragmentation of different time scale optimizations and the deviation of short-term control from long-term planning in the prior art. Further, through the combination of the power smoothing term and the power change rate constraint, both the instantaneous disturbance caused by wind and light power fluctuations can be filtered out to avoid the impact loss caused by the sudden change of charging and discharging power on the energy storage converter and the battery, and the stability of power output can be ensured to improve the stability of system operation and the service life of equipment. Further, the battery life loss cost term is included in the objective function, avoiding the short-sighted behavior of short-period optimization that only pursues immediate control accuracy while ignoring battery loss. Finally, the power balance constraint ensures that the power distribution of the energy storage system matches the new energy output and the grid demand, and the accurate power set value obtained by solving provides reliable data input for real-time layer second-level control.

[0021] In an alternative embodiment, the multi-time scale rolling and life-aware energy storage real-time operation optimization method further comprises:

[0022] The energy storage system of the new energy station is monitored in real time to obtain monitoring information of the energy storage system of the new energy station; a key performance indicator is calculated according to the monitoring information; and if the key performance indicator does not meet a preset condition, the target function is optimized according to the key performance indicator.

[0023] The multi-time scale rolling and life-aware energy storage real-time operation optimization method provided by the application calculates the key performance indicator of the energy storage system by using monitoring data, corrects the wind and light power and market price prediction coefficients according to the indicator, improves the accuracy of multi-time scale prediction, and reduces the interference of prediction errors on optimization decisions. Further, the pre-indicator factor, activation energy and other parameters in the life model are updated, so that the battery aging assessment and life loss quantification are more in line with the actual operating conditions, and the scientific nature of the whole life cycle optimization is ensured. Finally, through the dynamic updating of the performance indicator and the weight coefficient, the optimization strategy continuously adapts to the device aging, environmental changes and other conditions, and the synergy and robustness of the multi-time scale optimization system are strengthened, ensuring that the energy storage system maintains the optimal operating state for a long time, and a closed-loop optimization is formed.

[0024] In a second aspect, the application provides a multi-time scale rolling and life-aware energy storage real-time operation optimization device, which comprises:

[0025] A battery life loss cost calculation module is configured to evaluate the battery health state according to the environmental data of the environment in which the energy storage system of the new energy station is located, real-time battery parameter data and state of charge data of the energy storage system, and obtain the battery life loss cost according to the battery health state.

[0026] A long-period optimization module is configured to predict the energy market power and auxiliary service market power of the energy storage system at different times according to a long-period optimization target function, and obtain an optimal state of charge reference trajectory, wherein the long-period optimization target function is established based on the sum of the energy yield per unit time and the auxiliary service yield per unit time minus the battery life loss cost term.

[0027] A short-period optimization module is configured to input the optimal state of charge reference trajectory into a short-period optimization target function, solve the short-period optimization target function, and obtain power set values at different times, wherein the short-period optimization target function is established based on the sum of the state of charge deviation term, the power smoothing term and the battery life loss cost term.

[0028] The real-time optimization module is configured to input the power set values at different time points into a real-time optimization objective function, solve the real-time optimization objective function, and obtain a power control instruction, wherein the real-time optimization objective function is established based on a sum of a power tracking error term, a control quantity variation term, and a battery life loss cost term;

[0029] The optimization instruction issuing module is configured to issue the power control instruction to the energy storage converter, so that the energy storage converter operates according to the power control instruction.

[0030] In a third aspect, the present application provides an electronic device, comprising a memory and a processor, the memory and the processor are communicatively connected with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the energy storage real-time operation optimization method based on multi-time scale rolling and life perception of the first aspect or any of the corresponding embodiments thereof.

[0031] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to make a computer execute the energy storage real-time operation optimization method based on multi-time scale rolling and life perception of the first aspect or any of the corresponding embodiments thereof.

[0032] In a fifth aspect, the present application provides a computer program product, which comprises computer instructions, and the computer instructions are used to make a computer execute the energy storage real-time operation optimization method based on multi-time scale rolling and life perception of the first aspect or any of the corresponding embodiments thereof. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the specific embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0034] Figure 1 is a schematic diagram of an application scenario according to an embodiment of the present application;

[0035] Figure 2 is a flowchart of the energy storage real-time operation optimization method based on multi-time scale rolling and life perception according to an embodiment of the present application;

[0036] Figure 3 is a structural block diagram of the energy storage real-time operation optimization device based on multi-time scale rolling and life perception according to an embodiment of the present application;

[0037] Figure 4Fig. 1 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0039] It can be understood that, before using the technical solutions disclosed in the embodiments of the present application, the type of personal information, the range of use, the scene of use and the like involved in the present application should be informed to the user and the authorization of the user should be obtained through appropriate means according to relevant laws and regulations.

[0040] The terms "first", "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more than two, unless otherwise specifically limited.

[0041] As an optional application scenario of the embodiments of the present application, as shown in Figure 1 The multi-time scale rolling and life perception based energy storage real-time operation optimization method architecture can include at least one terminal device and at least one server, Figure 1 The system includes a computer 101, a mobile terminal 102 and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0042] The terminal device can be a smart phone, a tablet computer, a notebook computer, a palm computer, and can also be a desktop computer, a game console, a smart television, a smart wearable device, a vehicle-mounted terminal, a VR (Virtual Reality) device, an AR (Augmented Reality) device, etc. The server 103 can be a stand-alone physical server, a server cluster or a distributed system, or a cloud server providing cloud services. The network 110 can be a wired network or a wireless network, and examples thereof include but are not limited to the Internet, an intranet, a local area network, a wide area network, a mobile communication network and a combination thereof.

[0043] The embodiment of the application provides a kind of based on multi-time scale rolling and life perception energy storage real-time operation optimization method, by constructing multi-time scale hierarchical rolling optimization framework, embedding the dynamic model of life loss cost and the closed-loop optimization method of robust optimization, to reach the effect of collaborative promotion of energy storage system full life cycle economic benefit, equipment durability, operation adaptability and reliability.

[0044] According to the embodiment of the application, a multi-time scale rolling and life perception based energy storage real-time operation optimization method is provided.It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system, such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0045] In the present embodiment, a multi-time scale rolling and life perception based energy storage real-time operation optimization method is provided, which can be used in the mobile terminal mentioned above, such as mobile phone, tablet computer, etc. Figure 2 The flowchart of the multi-time scale rolling and life perception based energy storage real-time operation optimization method according to the embodiment of the application is shown in FIG. Figure 2 The flowchart includes the following steps:

[0046] In step S201, the battery health state is evaluated according to the environmental data of the environment where the energy storage system of the new energy station is located, the real-time battery parameter data and the state of charge data of the energy storage system, and the battery life loss cost is obtained according to the battery health state.

[0047] In an optional embodiment, the energy storage system collects the voltage V, current I, temperature T and state of charge SOC of the energy storage system through the battery management system with a period of 500ms through the sensor network deployed at each key node of the new energy station, and obtains meteorological data such as temperature, humidity, irradiance and wind speed through the environmental monitor. All data are time-synchronized to microsecond level through IEEE1588 precision clock protocol.

[0048] Further, an outlier detection algorithm based on 3σ criterion is used to automatically identify and eliminate abnormal data beyond the range. For missing data, time series linear interpolation combined with sliding window average method is used for repair. To further improve data quality, Kalman filter algorithm is applied to smooth the power fluctuation, effectively filter out measurement noise and improve data reliability.

[0049] In an optional embodiment, the battery end voltage response curve is collected by applying test pulse of specific frequency to the battery, and parameter identification is carried out based on battery equivalent circuit model, wherein the battery equivalent circuit model can be constructed by the following formula:

[0050]

[0051] In the formula, The open-circuit voltage function related to SOC. The internal resistance of the battery is in ohms. Polarization voltage, This is the terminal voltage.

[0052] In an optional embodiment, parameter identification specifically employs a recursive least squares method with a forgetting factor, the core formula of which includes:

[0053] Parameter vector definition formula: ;

[0054] formula for calculating gain matrix: ;

[0055] Parameter update formula: ;

[0056] Covariance matrix update formula: ,in The forgetting factor, ranging from 0.95 to 0.99, is used to balance the influence weights of historical data and fresh data.

[0057] The above recursive formula can be used to accurately estimate real-time battery parameters such as ohmic internal resistance, polarization internal resistance, and polarization capacitance.

[0058] Step S202: Based on the long-term optimization objective function, predict the power of the electric energy market and the power of the ancillary service market of the energy storage system at different times to obtain the optimal state of charge reference trajectory. The long-term optimization objective function is established based on the sum of the electric energy revenue item and the ancillary service revenue item per unit time minus the battery life loss cost item.

[0059] In one optional embodiment, the power in the electricity market refers to the charging and discharging power of the energy storage system participating in electricity trading in the electricity market. Its core function is to profit by transferring electricity through price differences at different times, adapting to long-term energy dispatch needs. The power in the ancillary services market refers to the response power of the energy storage system when providing ancillary services such as frequency regulation and backup to the grid. Its core function is to quickly respond to grid commands to ensure grid stability and obtain ancillary service compensation revenue.

[0060] In one optional embodiment, the energy storage system collects the three-phase active power of the wind turbines in real time at a sampling frequency of not less than 1Hz through a sensor network deployed at key nodes of the new energy power station. Photovoltaic inverter output power Power grid dispatch instructions Real-time node electricity price Key operating parameters. All data are time-synchronized to the microsecond level via IEEE 1588 precision clock protocol.

[0061] Further, an Attention-LSTM hybrid neural network model is used for 4-hour wind and light power ultra-short-term prediction. The model realizes effective extraction and prediction of wind and light output time series characteristics through a series of gating mechanisms such as forget gate, input gate, cell state update, and hidden state output. At the same time, the ARIMA time series analysis model is used to predict the day-ahead and real-time price fluctuations. The obtained wind and light output time series characteristics provide 4-hour accurate wind and light output data for long-period optimization, supporting the reasonable planning of energy storage charging and discharging time periods and power within 24 hours; the obtained day-ahead and real-time price fluctuation prediction provides price basis for long-period optimization, assisting in weighing power allocation in the energy market and the ancillary service market, accurately capturing price difference income and high compensation opportunities, and can support the formulation of optimal state of charge reference trajectory and market participation strategy.

[0062] In an optional embodiment, the long-period optimization objective function is optimized with 24 hours as the optimization time domain and 15 minutes as the time resolution, and the energy storage system can obtain income from the energy market and the ancillary service market respectively. The energy market income depends on the price difference and usually requires long-time-scale energy transfer, while the compensation unit price of the ancillary service market is high and requires fast response, which may increase battery loss. Frequent frequency modulation actions will accelerate battery aging. Therefore, it is necessary to weigh the power allocation of the energy storage power in the energy market and the ancillary service market, coordinate the participation strategies of the two markets, and balance the economy of high-frequency action income and battery life loss.

[0063] In step S203, the optimal state of charge reference trajectory is input into the short-period optimization objective function, the short-period optimization objective function is solved, and power set values at different times are obtained, wherein the short-period optimization objective function is established based on the sum of the state of charge deviation term, the power smoothing term, and the battery life loss cost term.

[0064] In an optional embodiment, the optimal state of charge reference trajectory is input into the short-period optimization objective function, the short-period optimization objective function is optimized, and power set values at different times are obtained, with 60 minutes as the optimization time domain and 5 minutes as the time resolution.

[0065] In step S204, the power set values at different times are input into the real-time optimization objective function, the real-time optimization objective function is solved, and power control instructions are obtained, wherein the real-time optimization objective function is established based on the sum of the power tracking error term, the control amount change term, and the battery life loss cost term, and the time scales of the long-period optimization objective function, the short-period optimization objective function, and the real-time optimization objective function are successively reduced.

[0066] In an optional embodiment, the power setting values at different time points are input into the real-time optimization objective function with 5 minutes as the optimization time domain and 1 second as the time resolution, and the real-time optimization objective function is optimized to obtain the power control instruction.

[0067] In step S205, the power control instruction is issued to the energy storage converter to enable the energy storage converter to operate according to the power control instruction.

[0068] In an optional embodiment, the obtained power control instruction is converted into an instruction set executable by the energy storage converter. To ensure safe operation, a power instruction limiting strategy is adopted to ensure that the issued instruction is always within the safe operation range of the device. The power instruction is issued to the energy storage converter at a period of 100 ms through the Modbus-TCP protocol.

[0069] In an optional embodiment, the power instruction limiting strategy is as follows:

[0070]

[0071] In the formula, is the final issued power instruction value, is the maximum power of the device, is the minimum power of the device, is the power control instruction value.

[0072] The energy storage real-time operation optimization method based on multi-time scale rolling and life perception provided in this embodiment incorporates long-term device life into optimization decisions by evaluating battery health status and quantifying life loss cost, breaking the limitation of traditional optimization only pursuing short-term benefits, and achieving a preliminary trade-off between immediate operation demand and life cycle economic benefits. Further, a three-level optimization of long cycle, short cycle and real-time layer is constructed with time scales successively narrowing, which respectively corresponds to market benefit planning, power precise regulation and grid instruction tracking, effectively coordinating operation targets in different dimensions and solving the problem that single time scale optimization cannot balance economy and stability. Finally, the optimal state of charge reference trajectory, power setting value and power control instruction are output successively through each level of optimization, and the compiled power control instruction is issued to the energy storage converter, ensuring that the energy storage system realizes real-time and online optimal control in multiple application scenarios, avoiding the defect that traditional static optimization model does not match the actual energy storage system, and improving the scientificity and effectiveness of operation optimization.

[0073] In some optional embodiments, the above step S201 specifically includes:

[0074] In step a1, the actual available capacity of the battery is determined based on the battery ohmic resistance, polarization resistance and polarization capacitance in the real-time battery parameter data.

[0075] In an optional embodiment, the battery ohmic internal resistance, polarization internal resistance and polarization capacitance in the real-time battery parameter data are taken as the core parameters to construct the battery equivalent circuit model, which accurately characterizes the dynamic correlation of voltage, current and charge during the battery charging and discharging process. Further, based on the above real-time parameter data, the current state of charge of the battery is estimated by a filtering algorithm. In combination with the battery cycle life data, the correlation model under different aging stages is established, the parameter changes are used to intuitively reflect the battery attenuation, and then the mapping coefficient of SOC and capacity is corrected. The real-time battery parameter data and the estimated SOC are substituted into the corrected correlation model, and the current actual available capacity of the battery can be calculated.

[0076] Step a2, the capacity-type health state of the battery is calculated according to the actual available capacity of the battery and the initial capacity of the battery.

[0077] In an optional embodiment, the capacity-type health state reflects the retention rate of the current actual available capacity of the battery relative to the initial capacity, and the capacity-type health state can be calculated by the following formula:

[0078]

[0079] In the formula, is the actual available capacity of the battery; is the initial battery capacity.

[0080] Step a3, the internal resistance-type health state of the battery is calculated based on the battery ohmic internal resistance and polarization internal resistance in the real-time battery parameter data, in combination with the initial total internal resistance of the battery and the total internal resistance at the end of the battery life.

[0081] In an optional embodiment, the internal resistance-type health state reflects the growth of the internal resistance of the battery, and the internal resistance-type health state can be calculated by the following formula:

[0082]

[0083] In the formula, is the total internal resistance at the end of the battery life; is the initial total internal resistance; is the current total internal resistance, which can be obtained by summing the battery ohmic internal resistance and the polarization internal resistance.

[0084] Step a4, the battery aging rate is calculated based on the environmental data, the capacity-type health state and the internal resistance-type health state of the battery, and the battery aging model.

[0085] In an optional embodiment, the energy storage system evaluates the comprehensive influence of multiple stress factors such as temperature, SOC interval and discharge depth on the battery aging based on the Arrhenius aging model.

[0086] Specifically, the core calculation formula of the Arrhenius aging model is:

[0087]

[0088] where A is a pre-exponential factor, Ea is the activation energy, R is the gas constant, T is the absolute temperature, f SOC and g DOD are stress functions corresponding to state of charge (SOC) and depth of discharge (DOD), respectively.

[0089] In step a5, the battery life loss cost is calculated according to the battery aging rate, the cumulative energy throughput per unit battery, the charge and discharge power data of the energy storage system and the basic parameters of the energy storage system.

[0090] In an optional embodiment, the battery life loss is converted into a quantifiable battery life loss cost, and the core calculation formula of the battery life loss cost is as follows:

[0091] ,

[0092] where C is the initial investment cost of the energy storage system, is the total expected number of cycles of the battery, is the rated capacity of the energy storage system, is the life attenuation rate caused by the cumulative energy throughput per unit battery, is the absolute value of the charge and discharge power at time t, is the time step.

[0093] The energy storage real-time operation optimization method based on multi-time scale rolling and life perception provided in the embodiment obtains battery real-time parameter data through online parameter identification technology, further determines the actual available capacity, ensures the dynamic matching of battery capacity calculation and battery real-time attenuation state, and provides accurate data support for health state assessment. Secondly, through the double-dimensional calculation of the capacity-type health state and the internal resistance-type health state, the battery attenuation characteristics are fully described, the one-sidedness of single index evaluation is avoided, and the scientificity and comprehensiveness of health state assessment are greatly improved. Further, combined with environmental data and double-dimensional health state, the battery aging model is used to quantify the comprehensive influence of multiple stress factors such as temperature and SOC interval and to calculate the aging rate, so that the aging assessment is adapted to the actual operation condition, and the problem that the existing technology ignores the influence of multi-factor coupling aging is solved. Finally, the abstract life loss is converted into a quantifiable economic cost, and the subsequent optimization decision trade-off between immediate income and long-term life cost is realized.

[0094] ​In some optional embodiments, in the long-period optimization objective function, the value of the electric energy revenue item is determined according to the product of the electric energy market power and the electric energy market price per unit time, the value of the ancillary service market revenue item is determined according to the product of the ancillary service market power and the ancillary service market price, the optimal state of charge reference trajectory is obtained by predicting the electric energy market power and the ancillary service market power of the energy storage system at different times according to the long-period optimization objective function, and the optimal state of charge reference trajectory includes:

[0095] solving the long-period optimization objective function according to the constraint condition to obtain the optimal electric energy market power and the optimal ancillary service market power;

[0096] calculating the optimal state of charge reference trajectory based on the optimal electric energy market power and the optimal ancillary service market power.

[0097] In an optional embodiment, the long-period optimization objective function is as follows:

[0098]

[0099] In the formula, is the electric energy market power, is the ancillary service market power, is the electric energy market price at time t, is the ancillary service market price at time t, is the time step, is the battery life loss cost.

[0100] In an optional embodiment, the constraint condition includes:

[0101] energy balance constraint:

[0102] SOC dynamic evolution equation:

[0103] SOC safety boundary:

[0104] charging and discharging power constraint: ,

[0105] electric energy market power constraint:

[0106] ancillary service market power constraint:

[0107] wherein, is the battery SOC at time t; and are the charging power and the discharging power of the battery, respectively; and respectively, are the charging efficiency and discharging efficiency of the battery; is the rated capacity of the battery; is the rated power of the battery; is a 0-1 integer variable for avoiding simultaneous charging and discharging of the energy storage system, subscript min and max are the lower limit and upper limit, respectively.

[0108] Further, the branch and bound method is used to solve the long-period optimization objective function, and the optimal energy market power and the optimal auxiliary service market power are output. Then, the optimal state of charge reference trajectory is calculated by using the energy balance constraint formula and the SOC dynamic evolution equation.

[0109] The energy storage real-time operation optimization method based on multi-time scale rolling and life perception provided by the embodiment provides a clear basis for the reasonable allocation of energy storage resources in different markets by defining the energy revenue item as the product of the energy market power and the corresponding price and the auxiliary service revenue item as the product of the auxiliary service market power and the corresponding price. Secondly, the power allocation in the two markets is simultaneously optimized by solving the long-period optimization objective function, effectively coordinating the revenue demand of long-time scale energy transfer in the energy market and the high compensation demand of rapid response in the auxiliary service market, avoiding the revenue limitation or excessive battery wear caused by single market participation, and balancing the contradiction between short-term market revenue and long-term equipment health. Further, multiple constraints such as energy balance, SOC safety boundary, and charging and discharging power limit are integrated into the solving process to ensure the safety and feasibility of the optimal power allocation scheme and avoid the risk of breaking the equipment operation limit in pursuit of revenue. Finally, based on the optimal market power and the optimal auxiliary service market power, the optimal SOC reference trajectory calculated by allocation is provided as a stable and scientific state of charge reference trajectory for subsequent short-period optimization and real-time optimization.

[0110] In some optional embodiments, in the short-period optimization objective function, the value of the state of charge deviation term is determined according to the real-time state of charge trajectory and the optimal state of charge reference trajectory, and the value of the power smoothing term is determined according to the fluctuation power of the energy storage system. The optimal state of charge reference trajectory is input into the short-period optimization objective function, and the short-period optimization objective function is solved to obtain power set values at different times, including:

[0111] The short-period optimization objective function is solved according to the constraint conditions to obtain power set values at different times. The constraint conditions include power balance constraints and power change rate constraints.

[0112] In an optional embodiment, the short-period optimization objective function is as follows:

[0113]

[0114] wherein, is a state of charge bias term, is a power smoothing term, is a battery life loss cost, , , is a weight coefficient. In the state of charge bias term, is a real-time state of charge, is an optimal state of charge, in the power smoothing term, is a power change.

[0115] Further, the constraint conditions of the short-period optimization objective function include:

[0116] a power balance constraint: ;

[0117] a power change rate constraint: .

[0118] wherein, is a total power of the energy storage system, is a maximum power change rate, is a time step.

[0119] In an optional embodiment, the short-period optimization objective function is solved by using an interior point method, and power set values at different times are output.

[0120] The energy storage real-time operation optimization method based on multi-time scale rolling and life perception provided in the embodiment ensures that the short-period optimization is always anchored to the goal of maximizing long-term economic benefits by defining the state of charge bias term as the deviation of the real-time state of charge trajectory from the optimal state of charge reference trajectory output by the long-period layer, effectively connects the hour-level and minute-level time scales, and solves the problem of the fragmentation of different time scale optimizations and the deviation of short-term control from long-term planning in the prior art. Further, through the combination of the power smoothing term and the power change rate constraint, both the instantaneous disturbance caused by wind and light power fluctuations can be filtered out, and the impact loss of the energy storage converter and the battery caused by the large abrupt change of the charge and discharge power can be avoided, and the stability of the power output can be ensured, and the stability and service life of the system operation can be improved. Further, the battery life loss cost term is included in the objective function, which avoids the short-sighted behavior of short-period optimization that only pursues immediate control accuracy while ignoring battery loss. Finally, the power balance constraint ensures that the power distribution of the energy storage system matches the new energy output and the grid demand, and the accurate power set value obtained by solving provides reliable data input for the real-time layer second-level control.

[0121] In some optional embodiments, the power set values at different times are input into the real-time optimization objective function, the real-time optimization objective function is solved, and a power control instruction is obtained, including:

[0122] The value of the control quantity change term is determined according to the control quantity change rate. The value of the power tracking error term is determined according to the power set value and the corresponding actual power value at different time points.

[0123] In an optional embodiment, the real-time optimization objective function is as follows:

[0124]

[0125] In the formula, is the power tracking error term, is the control quantity change term, is the battery life loss cost, , , is a weight coefficient.

[0126] In the power tracking error term, is the power difference, is the actual power value, is the power set value.

[0127] In the control quantity change term, is the control quantity change rate. The control quantity change rate refers to the maximum change amplitude of the core control quantity of the energy storage system, such as the charging and discharging power, allowed in a unit of time, which is used to measure the adjustment speed of the control quantity.

[0128] In an optional embodiment, when solving the real-time optimization objective function, the weight coefficient is adjusted by fuzzy logic, and the robust optimization method is used to process the prediction uncertainty, and finally the power control instruction is obtained.

[0129] The energy storage real-time operation optimization method based on multi-time scale rolling and life perception provided in the embodiment ensures that the real-time optimization always takes the accurate tracking of the power set value of the short-period layer output as the core target by defining the power tracking error term as the deviation of the power set value and the actual power value at different time points, effectively guaranteeing the fast response and execution accuracy of the grid second-level instruction. Secondly, the control quantity change term in the objective function is set based on the control quantity change rate, which further refines the rate constraint of power adjustment, better adapts to the fine control demand of the second-level time scale, can effectively avoid the sudden change of the charging and discharging power in the moment, reduce the intensification of the internal polarization effect of the battery and the impact on the energy storage converter, delay the equipment aging, and improve the safety and stability of real-time operation. Further, the battery life loss cost term is included in the real-time optimization objective function, which takes into account the short-term control performance and long-term equipment health. Finally, the power control instruction obtained by solving the real-time optimization objective function not only guarantees the real-time and accuracy of the grid instruction tracking under complex working conditions, but also improves the robustness of the energy storage system operation optimization through rate constraint and life cost trade-off.

[0130] In some optional embodiments, the multi-time scale rolling and life-aware based real-time operation optimization method for energy storage provided by the embodiments of the present application further comprises:

[0131] Step b1, real-time monitoring of the energy storage system of the new energy station to obtain monitoring information of the energy storage system of the new energy station.

[0132] In an optional embodiment, the key parameters of the battery are continuously monitored, including voltage deviation (required to be ≤2%) and temperature gradient (required to be ≤1℃ / min). At the same time, a multi-level protection strategy is implemented, for example, a SOC protection strategy is used to ensure that the battery always works in a safe SOC range, and the protection coefficient is dynamically adjusted according to the characteristics of the battery. The system also calculates the energy conversion efficiency in real time to monitor the energy loss of the system.

[0133] Step b2, calculating key performance indicators according to the monitoring information.

[0134] In an optional embodiment, the key performance indicators are calculated according to the obtained monitoring information, such as power tracking root mean square error, economic benefit, real-time energy conversion efficiency, etc.

[0135] Step b3, if the key performance indicators do not meet the preset conditions, the objective function is optimized according to the key performance indicators.

[0136] In an optional embodiment, based on the obtained key performance indicators, the degradation trend of the energy storage system, such as technical performance degradation, economic benefit decline and battery health deterioration, is comprehensively determined by using methods such as trend test, change point detection and correlation analysis. If the degradation trend appears, the preset conditions are not met, and the evaluation results are fed back to the long-period layer, the short-period layer and the real-time layer. The power distribution strategy of the long-period layer and the weight coefficient of the short-period layer and the real-time layer optimization are adjusted according to the key performance indicators to adapt to the changes of the operation strategy, forming a closed-loop optimization.

[0137] The multi-time scale rolling and life-aware based real-time operation optimization method for energy storage provided by the embodiments of the present application uses monitoring data to calculate the key performance indicators of the energy storage system, and corrects the wind and light power and market price prediction coefficients according to the indicators, which improves the accuracy of multi-time scale prediction and reduces the interference of prediction error on optimization decision. Further, the pre-indicator factor, activation energy and other parameters in the life model are updated, so that the battery aging evaluation and life loss quantification are more in line with the actual operation conditions, and the scientificity of the whole life cycle optimization is ensured. Finally, through the dynamic updating of the performance indicators and the weight coefficients, the optimization strategy continuously adapts to the equipment aging, environmental changes and other conditions, which strengthens the synergy and robustness of the multi-time scale optimization system, ensures that the energy storage system maintains the optimal operation state for a long time, and forms a closed-loop optimization.

[0138] There is also provided in the embodiment a multi-time scale rolling and life-aware based energy storage real-time operation optimization device for implementing the above embodiment and preferred embodiments, which has been described and will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiment is preferably implemented in software, hardware or a combination of software and hardware is also possible and contemplated.

[0139] The embodiment provides a multi-time scale rolling and life-aware based energy storage real-time operation optimization device, as shown in the accompanying drawings, comprising: Figure 3

[0140] A battery life loss cost calculation module 301 is configured to evaluate a battery health state according to environmental data of an environment in which a new energy station energy storage system is located, real-time battery parameter data and state of charge data of the energy storage system, and obtain a battery life loss cost according to the battery health state.

[0141] A long cycle optimization module 302 is configured to predict energy market power and auxiliary service market power of the energy storage system at different times according to a long cycle optimization objective function, and obtain an optimal state of charge reference trajectory, the long cycle optimization objective function being established based on a sum of an energy quantity income item per unit time and an auxiliary service income item per unit time minus a battery life loss cost item;

[0142] A short cycle optimization module 303 is configured to input the optimal state of charge reference trajectory into a short cycle optimization objective function, solve the short cycle optimization objective function, and obtain power set values at different times, wherein the short cycle optimization objective function is established based on a sum of a state of charge deviation item, a power smoothing item and a battery life loss cost item;

[0143] A real-time optimization module 304 is configured to input the power set values at different times into a real-time optimization objective function, solve the real-time optimization objective function, and obtain power control instructions, wherein the real-time optimization objective function is established based on a sum of a power tracking error item, a control quantity change item and a battery life loss cost item;

[0144] An optimization instruction issuing module 305 is configured to issue the power control instructions to an energy storage converter, so that the energy storage converter operates according to the power control instructions.

[0145] The multi-time scale rolling and life-aware based energy storage real-time operation optimization device provided in the embodiment can execute the multi-time scale rolling and life-aware based energy storage real-time operation optimization method provided in any embodiment of the present application, and has the corresponding function modules and beneficial effects of executing the method. The further function description of the above-described various modules and units is the same as that of the corresponding embodiment, and will not be repeated here.​

[0146] Figure 4 Fig. 1 shows a schematic diagram of an electronic device according to an embodiment of the application.

[0147] Reference will now be made in detail to Figure 4 Fig. 1 shows a schematic diagram of an electronic device according to an embodiment of the application. The electronic device can include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 401 that can perform various suitable actions and processes in accordance with programs stored in a read-only memory (ROM) 402 or loaded from a memory 408 into a random access memory (RAM) 403. Various programs and data required for operation of the electronic device are also stored in the RAM 403. The processor 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0148] Generally, the following devices can be connected to the I / O interface 405: input devices 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a memory 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 can allow the electronic device to communicate wirelessly or wired with other devices to exchange data. Although Figure 4 The electronic device is shown with various devices, but it should be understood that not all of the shown devices are required to be implemented or present, and more or fewer devices can be alternatively implemented or present.

[0149] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the application. For example, embodiments of the application include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through the communication device 409, or installed from the memory 408, or installed from the ROM 402. When the computer program is executed by the processor 401, the above-mentioned functions defined in the multi-time scale rolling and life-aware real-time operation optimization method for energy storage according to embodiments of the application are performed.

[0150] Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functions and scope of use of embodiments of the application.

[0151] The embodiments of the present application further provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded to a local storage medium through network and then stored in the local storage medium, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned memories. It can be understood that the computer, the processor, the microprocessor controller, or the programmable hardware includes a storage component that can store or receive software or computer code, when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the multi-time scale rolling and life perception based energy storage real-time operation optimization method shown in the above embodiments is implemented.

[0152] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, through the operation of the computer, the method and / or technical solutions according to the present application can be invoked or provided. Those skilled in the art should understand that the form of computer program instructions in a computer readable medium includes but is not limited to source files, executable files, installation package files, etc. Correspondingly, the way of computer program instructions executed by a computer includes but is not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer readable medium can be any available computer readable storage medium or communication medium accessible to the computer.

[0153] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A multi-time scale rolling and life-aware based energy storage real-time operation optimization method, characterized in that, The method comprises: According to the environmental data of the environment where the new energy station energy storage system is located, the real-time battery parameter data and the state of charge data of the energy storage system, the battery health state is evaluated, and the battery life loss cost is obtained according to the battery health state; According to the long-period optimization objective function, the energy market power and the auxiliary service market power of the energy storage system at different times are predicted, and the optimal state of charge reference trajectory is obtained, wherein the long-period optimization objective function is established based on the sum of the energy quantity income item per unit time and the auxiliary service income item per unit time minus the battery life loss cost item; The optimal state of charge reference trajectory is input into the short-period optimization objective function, the short-period optimization objective function is solved, and the power setting value at different times is obtained, wherein the short-period optimization objective function is established based on the sum of the state of charge deviation item, the power smoothing item and the battery life loss cost item; The power setting value at different times is input into the real-time optimization objective function, the real-time optimization objective function is solved, and the power control instruction is obtained, wherein the real-time optimization objective function is established based on the sum of the power tracking error item, the control amount change item and the battery life loss cost item, and the time scales of the long-period optimization objective function, the short-period optimization objective function and the real-time optimization objective function are sequentially reduced; The power control instruction is sent to the energy storage converter, so that the energy storage converter operates according to the power control instruction.

2. The method of claim 1, wherein, According to the environmental data of the environment where the new energy station energy storage system is located, the real-time battery parameter data and the state of charge data of the energy storage system, the battery health state is evaluated, and the battery life loss cost is obtained according to the battery health state, comprising: The actual available capacity of the battery is determined based on the battery ohmic internal resistance, polarization internal resistance and polarization capacitance in the real-time battery parameter data; The capacity type health state of the battery is calculated according to the actual available capacity of the battery and the initial capacity of the battery; The internal resistance type health state of the battery is calculated based on the battery ohmic internal resistance and polarization internal resistance in the real-time battery parameter data, combined with the initial total internal resistance of the battery and the total internal resistance at the end of the battery life; The battery aging rate is calculated based on the environmental data, the capacity type health state and the internal resistance type health state of the battery, and the battery aging model; The battery life loss cost is calculated according to the battery aging rate, the unit battery cumulative energy throughput, the charge and discharge power data of the energy storage system and the energy storage system basic parameters.

3. The method of claim 1, wherein, In the long-period optimization objective function, the value of the energy quantity income item is determined according to the product of the energy quantity market power per unit time and the energy quantity market price, and the energy quantity income item is determined according to the product of the auxiliary service market power and the auxiliary service market price, and the optimal state of charge reference trajectory is obtained by predicting the energy quantity market power and the auxiliary service market power of the energy storage system at different times according to the long-period optimization objective function, comprising: The long-period optimization objective function is solved according to the constraint condition, and the optimal energy quantity market power and the optimal auxiliary service market power are obtained; Based on the optimal energy market power and the optimal ancillary service market power, an optimal state of charge reference trajectory is calculated.

4. The method of claim 1, wherein, In the short-period optimization objective function, a value of the state of charge deviation term is determined according to a real-time state of charge trajectory and the optimal state of charge reference trajectory, and a value of the power smoothing term is determined according to the fluctuation power of the energy storage system, the optimal state of charge reference trajectory is input into the short-period optimization objective function, the short-period optimization objective function is solved, and power set values at different times are obtained, including: The short-period optimization objective function is solved according to a constraint condition to obtain the power set values at different times, and the constraint condition includes a power balance constraint and a power change rate constraint.

5. The method of claim 1, wherein, The power set values at different times are input into the real-time optimization objective function, the real-time optimization objective function is solved, and a power control instruction is obtained, including: A value of the control quantity change term is determined according to a control quantity change rate; A value of the power tracking error term is determined according to the power set values at different times and corresponding actual power values.

6. The method of claim 1, wherein, Including: The new energy station energy storage system is monitored in real time to obtain monitoring information of the new energy station energy storage system; A key performance indicator is calculated according to the monitoring information; If the key performance indicator does not meet a preset condition, a target function is optimized according to the key performance indicator.

7. A multi-time scale rolling and life-aware based energy storage real-time operation optimization apparatus, characterized in that, The device includes: A battery life loss cost calculation module is configured to evaluate a battery health state according to environmental data of an environment in which a new energy station energy storage system is located, real-time battery parameter data, and state of charge data of the energy storage system, and obtain a battery life loss cost according to the battery health state; A long-period optimization module is configured to predict energy market power and ancillary service market power of the energy storage system at different times according to a long-period optimization objective function to obtain an optimal state of charge reference trajectory, and the long-period optimization objective function is established based on a sum of an energy revenue term per unit time and an ancillary service revenue term per unit time minus a battery life loss cost term; A short-period optimization module is configured to input the optimal state of charge reference trajectory into a short-period optimization objective function, solve the short-period optimization objective function, and obtain power set values at different times, and the short-period optimization objective function is established based on a sum of a state of charge deviation term, a power smoothing term, and a battery life loss cost term; A real-time optimization module is configured to input the power set values at different times into a real-time optimization objective function, solve the real-time optimization objective function, and obtain a power control instruction, and the real-time optimization objective function is established based on a sum of a power tracking error term, a control quantity change term, and a battery life loss cost term; An optimization instruction issuing module is configured to issue the power control instruction to an energy storage converter, so that the energy storage converter operates according to the power control instruction.

8. An electronic device, comprising: Including: A memory and a processor, which are connected in communication with each other, the memory has stored computer instructions, and the processor executes the computer instructions to perform the method for real-time operation optimization of energy storage based on multi-time scale rolling and life perception according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium has stored computer instructions for causing a computer to perform the method for real-time operation optimization of energy storage based on multi-time scale rolling and life perception according to any one of claims 1 to 6.

10. A computer program product, characterised in that, The computer readable storage medium has stored computer instructions for causing a computer to perform the method for real-time operation optimization of energy storage based on multi-time scale rolling and life perception according to any one of claims 1 to 6.

Images