Solar energy storage control method and system

Abstract

The invention is suitable for the field of solar energy storage control, and provides a solar energy storage control method and system, and the method comprises the steps: constructing a multi-dimensional monitoring system, monitoring the state parameters of at least two energy storage modes at the same time, generating a power generation power prediction value and a load demand prediction value in a future time period based on a time demand prediction model, calculating a full-time-domain charging and discharging demand function of the energy storage system by combining the instantaneous power generation power and the dynamic demand power of the load side, dividing a dynamic time period based on the time domain distribution characteristics of the full-time-domain charging and discharging demand function, and constructing a time-sharing cooperation decision matrix according to the characteristic parameters of each energy storage mode; a time-sharing cooperation decision matrix is optimized through a multi-objective optimization model, a multi-dimensional monitoring system and a full-time-domain charging and discharging demand function are constructed, dynamic time period division and the time-sharing cooperation decision matrix are combined, energy storage advantages of electrochemistry, machinery, heat, chemistry and the like are cooperated, the comprehensive efficiency of the system is improved through accurate power distribution and multi-objective optimization, and the system performance is improved. And the defect of a single energy storage mode is effectively overcome.

Description

Technical Field

[0001] This invention belongs to the field of solar energy storage control, and particularly relates to a solar energy storage control method and system. Background Technology

[0002] As the global energy structure shifts towards clean energy, solar energy, as a renewable energy source, has received widespread attention for its development and utilization. However, solar energy is affected by natural factors such as the alternation of day and night and weather changes, resulting in intermittency and instability. This leads to significant fluctuations in the output power of solar power systems, making it difficult to directly meet the stable electricity demands of loads. To address this issue, solar energy storage technology has emerged, storing surplus solar power and releasing it when power generation is insufficient to achieve a stable electricity supply. Currently, common solar energy storage methods include electrochemical energy storage, mechanical energy storage, thermal energy storage, and chemical energy storage, each with its own advantages and disadvantages in terms of energy density, conversion efficiency, response speed, cost, and applicable scenarios. A single energy storage method cannot simultaneously meet the high-efficiency energy storage requirements under different time periods and load demands, resulting in low overall efficiency of solar energy storage systems and limiting the large-scale application of solar energy. Therefore, how to rationally coordinate various energy storage methods to compensate for the shortcomings of a single method and improve the overall performance of solar energy storage systems has become an urgent technical problem to be solved. Summary of the Invention

[0003] The purpose of this invention is to provide a solar energy storage control method and system, which aims to solve the problems mentioned in the background art.

[0004] This invention is implemented as follows: On one hand, a solar energy storage control method, the method comprising: Construct a multi-dimensional monitoring system to collect real-time instantaneous power generation data of solar power systems. Dynamic power demand on the load side Simultaneously monitor the state parameters of at least two energy storage methods, including the remaining capacity of each energy storage unit. Energy conversion efficiency Health and ambient temperature ; Generate the future based on a time-to-demand forecasting model. Forecast value of power generation during the period and load demand forecast Combined with instantaneous power generation Dynamic power demand on the load side Calculate the full-time-domain charge and discharge demand function of the energy storage system; Based on the time-domain distribution characteristics of the full-time-domain charge and discharge demand function, dynamic time periods are divided. And construct a time-sharing coordination decision matrix based on the characteristic parameters of each energy storage method; The specific characteristic parameters of each energy storage method include response speed. , capacity upper limit Energy conversion efficiency Energy exchange loss coefficient ; The time-sharing decision matrix is ​​optimized using a multi-objective optimization model.

[0005] As a further aspect of the present invention, the energy storage method specifically includes: Electrochemical energy storage includes lithium-ion batteries, sodium-ion batteries, lead-acid batteries, or flow batteries; Mechanical energy storage includes pumped hydro storage, compressed air storage, or flywheel energy storage. Thermal energy storage includes molten salt energy storage, sensible heat energy storage, or phase change material energy storage. Chemical energy storage: including hydrogen energy storage or synthetic fuel energy storage.

[0006] As a further aspect of the present invention, the dynamic time periods are divided based on the time-domain distribution characteristics of the full-time-domain charge-discharge demand function. And based on the characteristic parameters of each energy storage method, a time-sharing coordination decision matrix is ​​constructed, specifically including: Based on charging and discharging power requirements The size and duration of the day are used to divide the day into several time periods. ; Based on several time periods, allocate the charging and discharging power ratios of different energy storage methods. ; In the formula, Indicates the first Various energy storage methods, and meet the requirements ; Control each energy storage method according to the charging and discharging power ratio Perform charging and discharging operations; The charge / discharge power ratio The calculation process is as follows: ; For the first Energy conversion efficiency of various energy storage methods For the first The percentage of remaining capacity for each type of energy storage method. For the first The response speed coefficient of this type of energy storage method For the first The time period of this type of energy storage The weighting coefficients, For the index variable of energy storage method, , The total number of energy storage methods involved in the cooperation.

[0007] As a further aspect of the present invention, the optimization of the time-sharing decision matrix through a multi-objective optimization model specifically includes: Calculate the overall efficiency index of the energy storage system ; The comprehensive efficiency index The calculation process is as follows: ; In the formula, For the first The input energy for this type of energy storage method For the first The output energy of this type of energy storage method For the first Energy conversion efficiency of various energy storage methods For the first For the first Non-energy conversion losses of various energy storage methods Calculate the economic indicators of energy storage systems ; The economic indicators The calculation process is as follows: ; In the formula, For the first The amount of electricity saved through energy storage over a given period of time. For the first Electricity price difference revenue over time period For the first The initial investment cost of this type of energy storage method For the first The operation and maintenance costs of various energy storage methods.

[0008] As a further aspect of the present invention, the optimization of the time-sharing decision matrix through a multi-objective optimization model specifically includes: Comprehensive efficiency index based on energy storage system Economic indicators And the optimal objective function, optimize the time-sharing decision matrix; The calculation process of the optimal objective function is as follows: ; In the formula, For comprehensive efficiency indicators and economic indicators Corresponding weighting coefficients; Real-time monitoring of the overall efficiency indicators of energy storage systems and economic indicators When the comprehensive efficiency index and economic indicators When the value falls below the corresponding threshold, the time-sharing decision matrix is ​​automatically adjusted back to the preset optimization range.

[0009] As a further aspect of the present invention, another option is a solar energy storage control system, the system comprising: The multi-dimensional monitoring system module is used to construct a multi-dimensional monitoring system and collect the instantaneous power generation of the solar power system in real time. Dynamic power demand on the load side Simultaneously monitor the state parameters of at least two energy storage methods; The generation module is used to generate future forecasts based on a real-time forecasting model. Forecast value of power generation during the period and load demand forecast ; The calculation module is used to combine instantaneous power generation. Dynamic power demand on the load side Calculate the full-time-domain charge and discharge demand function of the energy storage system; The time-sharing decision matrix module is used to divide dynamic time periods based on the time-domain distribution characteristics of the full-time-domain charging and discharging demand function. And construct a time-sharing coordination decision matrix based on the characteristic parameters of each energy storage method; The optimization module is used to optimize the time-sharing decision matrix through a multi-objective optimization model.

[0010] As a further aspect of the present invention, the time-sharing decision matrix module specifically includes: Dividing units, used to determine the charging and discharging power requirements. The size and duration of the day are used to divide the day into several time periods; The allocation unit is used to allocate the charging and discharging power ratios of different energy storage methods based on several time periods. ; The control unit is used to control each energy storage method according to the charging and discharging power ratio. Perform charging and discharging operations.

[0011] As a further aspect of the present invention, the optimization module specifically includes: The first calculation unit is used to calculate the overall efficiency index of the energy storage system. ; The second calculation unit is used to calculate the economic indicators of the energy storage system. ; Optimization unit, used for the overall efficiency index of energy storage system Economic indicators And the optimal objective function, optimize the time-sharing decision matrix; The real-time monitoring unit is used to monitor the overall efficiency indicators of the energy storage system in real time. and economic indicators ; The recovery unit is used when the overall efficiency index and economic indicators When the value falls below the corresponding threshold, the time-sharing decision matrix is ​​automatically adjusted back to the preset optimization range.

[0012] This invention provides a solar energy storage control method and system. By constructing a multi-dimensional monitoring system and a full-time-domain charge and discharge demand function, combined with dynamic time period division and time-sharing decision matrix, it leverages the advantages of electrochemical, mechanical, thermal, and chemical energy storage methods. Through precise power allocation and multi-objective optimization, it improves the overall system efficiency, balances cost and performance, adapts to different scenario requirements, and effectively compensates for the shortcomings of single energy storage methods. Attached Figure Description

[0013] Figure 1 This is the main flowchart of a solar energy storage control method.

[0014] Figure 2 This is a flowchart of a solar energy storage control method that uses the time-domain distribution characteristics of the full-time-domain charge and discharge demand function to divide dynamic time periods and construct a time-sharing coordination decision matrix based on the characteristic parameters of each energy storage method.

[0015] Figure 3 This is a flowchart of the first embodiment of a solar energy storage control method that optimizes the time-sharing decision matrix using a multi-objective optimization model.

[0016] Figure 4 This is a flowchart of the first embodiment of a solar energy storage control method that optimizes the time-sharing decision matrix using a multi-objective optimization model.

[0017] Figure 5 This is a main structure diagram of a solar energy storage control system.

[0018] Figure 6 This is a structural block diagram of a time-sharing decision matrix module in a solar energy storage control system.

[0019] Figure 7 This is a structural block diagram of an optimized module in a solar energy storage control system. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0022] The present invention provides a solar energy storage control method and system, which solves the technical problems in the background art.

[0023] like Figure 1 The diagram shown is a main flowchart of a solar energy storage control method according to an embodiment of the present invention. The solar energy storage control method includes: Step S100: Construct a multi-dimensional monitoring system to collect real-time instantaneous power generation of the solar power system. Dynamic power demand on the load side Simultaneously monitor the state parameters of at least two energy storage methods; Status parameters include the remaining capacity of each energy storage unit. Energy conversion efficiency Health and ambient temperature ; Step S200: Generate future forecasts based on the time-to-demand prediction model. Forecast value of power generation during the period and load demand forecast Combined with instantaneous power generation Dynamic power demand on the load side Calculate the full-time-domain charge and discharge demand function of the energy storage system; The calculation process of the full-time-domain charge and discharge demand function is as follows: ; In the formula, To predict the weighting coefficients, , To predict duration, ; Step S300: Based on the time-domain distribution characteristics of the full-time-domain charge and discharge demand function, divide the dynamic time periods. And construct a time-sharing coordination decision matrix based on the characteristic parameters of each energy storage method; The specific characteristic parameters of each energy storage method include response speed. , capacity upper limit Energy conversion efficiency Energy exchange loss coefficient ; Step S400: Optimize the time-sharing decision matrix using a multi-objective optimization model; In this embodiment, multi-dimensional collaborative optimization is used to address the intermittency of solar energy and low energy storage efficiency. This includes constructing a monitoring system, calculating full-time charging and discharging demand, dividing dynamic time periods, building a decision matrix, and generating the optimal strategy. The multi-dimensional monitoring system requires the deployment of high-precision sensors to collect the instantaneous power output of the photovoltaic panels. Dynamic power demand on the load side At the same time, a status monitoring module is configured for each energy storage unit to record the remaining capacity in real time. Energy conversion efficiency Exchange loss coefficient The exchange loss coefficient It can measure the loss rate during energy interaction between different energy storage methods. When multiple energy storage methods need to be charged and discharged in tandem, the combination with the highest energy interaction loss coefficient is selected first to ensure real-time and accurate data. The time-based forecasting model uses a hybrid algorithm that integrates LSTM and attention mechanisms. It takes at least three months of historical power generation data, weather forecasts, and load curves as input to predict the time period. It can be dynamically adjusted according to the scenario; it identifies peak / flat / valley and sudden fluctuation periods based on the rate of change and duration of charging and discharging power. The purpose of this setting is to overcome the limitations of a single energy storage method through full-time monitoring and prediction, realize dynamic collaboration of multiple energy storage systems, construct a decision matrix to provide a quantitative basis for power allocation, and avoid efficiency losses caused by subjective experience.

[0024] As a preferred embodiment of the present invention, the energy storage method specifically includes: Electrochemical energy storage includes lithium-ion batteries, sodium-ion batteries, lead-acid batteries, or flow batteries; Mechanical energy storage includes pumped hydro storage, compressed air storage, or flywheel energy storage. Thermal energy storage includes molten salt energy storage, sensible heat energy storage, or phase change material energy storage. Chemical energy storage: including hydrogen energy storage or synthetic fuel energy storage In this embodiment, when applied, in electrochemical energy storage, lithium-ion batteries are suitable for residential scenarios with high response speed requirements and medium capacity, sodium batteries are suitable for low-temperature environments and cost-sensitive industrial and commercial scenarios, and flow batteries are used in large power plants with high cycle life requirements; in mechanical energy storage, pumped hydro storage requires high and low level reservoirs and is suitable for long-term energy storage in power plants of 100MW or above, compressed air energy storage preferentially uses salt cavern geology and is suitable for power plants of 50-500MW, and flywheel energy storage is used for power smoothing at the second to minute level; in thermal energy storage, molten salt energy storage is paired with parabolic trough solar thermal power plants, sensible heat energy storage is suitable for distributed heating, and phase change materials are used in small scenarios with temperature differences ≤50℃; in chemical energy storage, hydrogen energy storage requires a proton exchange membrane electrolyzer and hydrogen storage tank, and synthetic fuels are used in remote areas that require cross-seasonal energy storage, providing precise energy storage combination solutions for different scenarios.

[0025] like Figure 2 As shown, in a preferred embodiment of the present invention, the dynamic time periods are divided based on the time-domain distribution characteristics of the full-time-domain charge-discharge demand function. And based on the characteristic parameters of each energy storage method, a time-sharing coordination decision matrix is ​​constructed, specifically including: Step S301: Based on the charging and discharging power requirements The size and duration of the day are used to divide the day into several time periods. ; Step S302: Based on several time periods, allocate the charging and discharging power ratios of different energy storage methods. ; In the formula, Indicates the first Various energy storage methods, and meet the requirements ; Step S303: Control each energy storage method according to the charging and discharging power ratio Perform charging and discharging operations; The charge / discharge power ratio The calculation process is as follows: ; In the formula, For the first Energy conversion efficiency of various energy storage methods For the first The percentage of remaining capacity for each type of energy storage method. For the first The response speed coefficient of this type of energy storage method For the first The time period of this type of energy storage The weighting coefficients, For the index variable of energy storage method, , The total number of energy storage methods participating in the coordination; In this embodiment, dynamic time period segmentation requires setting a quantitative threshold: when the fluctuation range of charging and discharging power is ≥10% and the duration is ≥15 minutes, time period segmentation is triggered; the number of time periods per day... Dynamically adjust according to the scenario to ensure consistent power demand characteristics for each time period. Charge / discharge power ratio. In the calculation, the weighting coefficient Dynamic adjustments are made based on time-of-day characteristics: during peak charging periods (9:00-15:00), the focus is on capacity parameters; during peak discharging periods (18:00-22:00), the focus is on response speed; remaining capacity percentage. When the discharge rate falls below 20%, the power ratio is automatically reduced by more than 30% to avoid over-discharge damage. Power allocation also needs to meet constraints: the actual charging and discharging power of each energy storage method should not exceed 90% of its rated power to protect equipment; the instantaneous power change rate should be ≤5% / s to avoid grid impact. Through refined time-sharing and power allocation, each energy storage method can maximize its efficiency within its advantageous range, reducing resource waste. Time-sharing coordination is transformed into executable quantitative commands, achieving a balance between response speed and stability.

[0026] like Figure 3 As shown, in a preferred embodiment of the present invention, the optimization of the time-sharing decision matrix using a multi-objective optimization model specifically includes: Step S401: Calculate the overall efficiency index of the energy storage system. ; The comprehensive efficiency index The calculation process is as follows: ; In the formula, For the first The input energy for this type of energy storage method For the first The output energy of this type of energy storage method For the first Energy conversion efficiency of various energy storage methods For the first For the first Non-energy conversion losses of this type of energy storage method; Step S402: Calculate the economic indicators of the energy storage system ; The economic indicators The calculation process is as follows: ; In the formula, For the first The amount of electricity saved through energy storage over a given period of time. For the first Electricity price difference revenue over time period For the first The initial investment cost of this type of energy storage method For the first The operation and maintenance costs of various energy storage methods; It should be understood that, through comprehensive efficiency indicators Economic indicators The quantification of this provides an evaluation standard for the optimal strategy. Comprehensive efficiency index In the calculation, non-energy conversion losses This includes: self-discharge losses in electrochemical energy storage, friction losses in mechanical energy storage, heat dissipation losses in thermal energy storage, and hydrogen leakage losses in chemical energy storage, all of which need to be collected and accumulated in real time using sensors. Economic indicators. In the calculation, the initial investment cost needs to be amortized over the entire life cycle, and the operation and maintenance costs include consumable replacement, manual inspection, and energy consumption. Electricity price difference revenue is differentiated according to peak and off-peak periods. The calculation cycle is set to 1 hour, and the indicator values ​​are updated every hour to ensure timely feedback on the system status. It breaks through the limitations of a single efficiency evaluation and achieves a balance between technical feasibility and economic rationality through efficiency-cost dual-dimensional quantification.

[0027] like Figure 4 As shown, in a preferred embodiment of the present invention, the optimization of the time-sharing decision matrix through a multi-objective optimization model further includes: Step S403: Comprehensive efficiency index based on energy storage system Economic indicators And the optimal objective function, optimize the time-sharing decision matrix; The calculation process of the optimal objective function is as follows: ; In the formula, For comprehensive efficiency indicators and economic indicators Corresponding weighting coefficients; Step S404: Real-time monitoring of the overall efficiency indicators of the energy storage system and economic indicators When the comprehensive efficiency index and economic indicators When the value falls below the corresponding threshold, the time-sharing decision matrix will be automatically adjusted back to the preset optimization range. In this embodiment, the optimal objective function is... Weighting coefficients Dynamic adjustments are required: Adjustments should be made upwards when the power grid is under strain. In cost-sensitive scenarios, increase The adjustment cycle is one day. The overall efficiency threshold is set based on industry standards, and the economic threshold must ensure an investment payback period of ≤10 years. When the indicator falls below the threshold, a three-level adjustment mechanism is triggered, automatically restoring the time-sharing decision matrix to the preset optimization range, prioritizing power allocation adjustment, followed by time period division adjustment, and finally switching the energy storage mode combination. Real-time monitoring uses edge computing terminals. When the indicator fails to meet the standard for three consecutive cycles, an early warning is automatically sent to the operation and maintenance platform, prompting manual intervention. This can address the performance degradation caused by dynamic changes in the energy storage system during operation, and the closed-loop adjustment mechanism ensures the adaptability and robustness of the strategy.

[0028] like Figure 5As shown, in another preferred embodiment of the present invention, a solar energy storage control system is provided, the system comprising: Multi-dimensional monitoring system module 100 is used to construct a multi-dimensional monitoring system and collect the instantaneous power generation of the solar power generation system in real time. Dynamic power demand on the load side Simultaneously monitor the state parameters of at least two energy storage methods; Generation module 200 is used to generate future forecasts based on a time-to-time prediction model. Forecast value of power generation during the period and load demand forecast ; Calculation module 300 is used to combine instantaneous power generation. Dynamic power demand on the load side Calculate the full-time-domain charge and discharge demand function of the energy storage system; The time-sharing decision matrix module 400 is used to divide dynamic time periods based on the time-domain distribution characteristics of the full-time-domain charging and discharging demand function. And construct a time-sharing coordination decision matrix based on the characteristic parameters of each energy storage method; The optimization module 500 is used to optimize the time-sharing decision matrix through a multi-objective optimization model.

[0029] In this embodiment, the multi-dimensional monitoring system module 100 constructs a multi-dimensional monitoring system to collect real-time data on the instantaneous power generation of the solar power system. Dynamic power demand on the load side Simultaneously monitoring the state parameters of at least two energy storage methods, and based on a real-time prediction model, the generation module 200 generates future... Forecast value of power generation during the period and load demand forecast Combined with instantaneous power generation Dynamic power demand on the load side The calculation module 300 calculates the full-time-domain charge and discharge demand function of the energy storage system, while the decision matrix module 400, based on the time-domain distribution characteristics of the full-time-domain charge and discharge demand function, divides the system into dynamic time periods. Based on the characteristic parameters of each energy storage method, a time-sharing coordination decision matrix is ​​constructed. Through a multi-objective optimization model, the optimization module 500 optimizes the time-sharing coordination decision matrix.

[0030] like Figure 6 As shown, in another preferred embodiment of the present invention, the time-sharing decision matrix module 400 specifically includes: Division unit 401 is used to divide the power according to the charging and discharging demand. The size and duration of the day are used to divide the day into several time periods; Allocation unit 402 is used to allocate the charging and discharging power ratios of different energy storage methods based on several time periods. ; Control unit 403 is used to control each energy storage method according to the charging and discharging power ratio. Perform charging and discharging operations.

[0031] In this embodiment, the power required for charging and discharging is determined during application. Based on the size and duration, the division unit 401 divides a single day into several time periods. Based on these time periods, the allocation unit 402 allocates the charging and discharging power ratios for different energy storage methods. The control unit 403 controls each energy storage mode according to the charging and discharging power ratio. Perform charging and discharging operations.

[0032] like Figure 7 As shown, in another preferred embodiment of the present invention, the optimization module 500 specifically includes: The first calculation unit 501 is used to calculate the overall efficiency index of the energy storage system. ; The second calculation unit 502 is used to calculate the economic indicators of the energy storage system. ; Optimization unit 503 is used for the overall efficiency index of energy storage systems. Economic indicators And the optimal objective function, optimize the time-sharing decision matrix; Real-time monitoring unit 504 is used to monitor the overall efficiency index of the energy storage system in real time. and economic indicators ; Recovery unit 505 is used when the overall efficiency index and economic indicators When the value falls below the corresponding threshold, the time-sharing decision matrix is ​​automatically adjusted back to the preset optimization range.

[0033] In this embodiment, the first calculation unit 501 calculates the overall efficiency index of the energy storage system. The second calculation unit 502 calculates the economic indicators of the energy storage system. Based on the comprehensive efficiency index of energy storage systems Economic indicators The optimal objective function is used, and the optimization unit 503 optimizes the time-sharing coordination decision matrix. The real-time monitoring unit 504 monitors the comprehensive efficiency index of the energy storage system in real time. and economic indicators When the comprehensive efficiency index and economic indicators When the value is below the corresponding threshold, the recovery unit 505 automatically restores and adjusts the time-sharing decision matrix to the preset optimization range.

[0034] The above embodiments of the present invention provide a solar energy storage control method and a solar energy storage control system, which solves the problems of solar intermittency and low energy storage efficiency through multi-dimensional collaborative optimization. This includes constructing a monitoring system, calculating full-time charging and discharging demand, dividing dynamic time periods, constructing a decision matrix, and generating the optimal strategy. The multi-dimensional monitoring system requires the deployment of high-precision sensors to collect the instantaneous power output of the photovoltaic panels. Dynamic power demand on the load side At the same time, a status monitoring module is configured for each energy storage unit to record the remaining capacity in real time. Energy conversion efficiency Health and ambient temperature To ensure real-time and accurate data, the forecasting model employs a hybrid algorithm combining LSTM and attention mechanisms. Inputs include at least three months of historical power generation data, weather forecasts, and load curves. The forecast period is... It can be dynamically adjusted according to the scenario; using the rate of change of charging and discharging power and its duration as characteristics, it identifies peak / flat / valley and sudden fluctuation periods. The purpose of this setup is to overcome the limitations of a single energy storage method through full-time monitoring and prediction, achieve dynamic collaboration of multiple energy storage systems, construct a decision matrix to provide a quantitative basis for power allocation, avoid efficiency losses caused by subjective experience, and build a multi-dimensional monitoring system and a full-time charging and discharging demand function. Combined with dynamic time period division and time-sharing coordination decision matrix, it leverages the advantages of electrochemical, mechanical, thermal, and chemical energy storage methods, improves the overall system efficiency through precise power allocation and multi-objective optimization, balances cost and performance, adapts to different scenario requirements, and effectively compensates for the shortcomings of a single energy storage method.

[0035] In order for the above methods and systems to operate smoothly, the system may include more or fewer components than those described above, or combine certain components, or different components, in addition to the various modules mentioned above. For example, it may include input / output devices, network access devices, buses, processors, and memory.

[0036] The processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (OPGs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the system, connecting various parts via various interfaces and lines.

[0037] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0038] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0039] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A solar energy storage control method, characterized in that, The method includes: Construct a multi-dimensional monitoring system to collect real-time instantaneous power generation data of solar power systems. Dynamic power demand on the load side Simultaneously monitor the state parameters of at least two energy storage methods, including the remaining capacity of each energy storage unit. Energy conversion efficiency Health and ambient temperature ; Generate the future based on a time-to-demand forecasting model. Forecast value of power generation during the period and load demand forecast Combined with instantaneous power generation Dynamic power demand on the load side Calculate the full-time-domain charge and discharge demand function of the energy storage system; Based on the time-domain distribution characteristics of the full-time-domain charge and discharge demand function, dynamic time periods are divided. And construct a time-sharing coordination decision matrix based on the characteristic parameters of each energy storage method; The specific characteristic parameters of each energy storage method include response speed. , capacity upper limit Energy conversion efficiency Energy exchange loss coefficient ; The time-sharing decision matrix is ​​optimized using a multi-objective optimization model.

2. The solar energy storage control method according to claim 1, characterized in that, The energy storage methods specifically include: Electrochemical energy storage includes lithium-ion batteries, sodium-ion batteries, lead-acid batteries, or flow batteries; Mechanical energy storage includes pumped hydro storage, compressed air storage, or flywheel energy storage. Thermal energy storage includes molten salt energy storage, sensible heat energy storage, or phase change material energy storage. Chemical energy storage: including hydrogen energy storage or synthetic fuel energy storage.

3. The solar energy storage control method according to claim 1, characterized in that, The dynamic time periods are divided based on the time-domain distribution characteristics of the full-time-domain charge and discharge demand function. And based on the characteristic parameters of each energy storage method, a time-sharing coordination decision matrix is ​​constructed, specifically including: Based on charging and discharging power requirements The size and duration of the day are used to divide the day into several time periods. ; Based on several time periods, allocate the charging and discharging power ratios of different energy storage methods. ; In the formula, Indicates the first Various energy storage methods, and meet the requirements ; Control each energy storage method according to the charging and discharging power ratio Perform charging and discharging operations; The charge / discharge power ratio The calculation process is as follows: ; In the formula, For the first Energy conversion efficiency of various energy storage methods For the first The percentage of remaining capacity for each type of energy storage method. For the first The response speed coefficient of this type of energy storage method For the first The time period of this type of energy storage The weighting coefficients, For the index variable of energy storage method, , The total number of energy storage methods involved in the cooperation.

4. The solar energy storage control method according to claim 1, characterized in that, The optimization of the time-sharing decision matrix using a multi-objective optimization model specifically includes: Calculate the overall efficiency index of the energy storage system ; The comprehensive efficiency index The calculation process is as follows: ; In the formula, For the first The input energy for this type of energy storage method For the first The output energy of this type of energy storage method For the first Energy conversion efficiency of various energy storage methods For the first For the first Non-energy conversion losses of this type of energy storage method; Calculate the economic indicators of energy storage systems ; The economic indicators The calculation process is as follows: ; In the formula, For the first The amount of electricity saved through energy storage over a given period of time. For the first Electricity price difference revenue over time period For the first The initial investment cost of this type of energy storage method For the first The operation and maintenance costs of various energy storage methods.

5. The solar energy storage control method according to claim 4, characterized in that, The optimization of the time-sharing decision matrix through a multi-objective optimization model specifically includes: Comprehensive efficiency index based on energy storage system Economic indicators And the optimal objective function, optimize the time-sharing decision matrix; The calculation process of the optimal objective function is as follows: ; In the formula, For comprehensive efficiency indicators and economic indicators Corresponding weighting coefficients; Real-time monitoring of the overall efficiency indicators of energy storage systems and economic indicators When the comprehensive efficiency index and economic indicators When the value falls below the corresponding threshold, the time-sharing decision matrix is ​​automatically adjusted back to the preset optimization range.

6. A solar energy storage control system, characterized in that, The system, employing the solar energy storage control method as described in any one of claims 1-5, comprises: The multi-dimensional monitoring system module is used to construct a multi-dimensional monitoring system and collect the instantaneous power generation of the solar power system in real time. Dynamic power demand on the load side Simultaneously monitor the state parameters of at least two energy storage methods; The generation module is used to generate future forecasts based on a real-time forecasting model. Forecast value of power generation during the period and load demand forecast ; The calculation module is used to combine instantaneous power generation. Dynamic power demand on the load side Calculate the full-time-domain charge and discharge demand function of the energy storage system; The time-sharing decision matrix module is used to divide dynamic time periods based on the time-domain distribution characteristics of the full-time-domain charging and discharging demand function. And construct a time-sharing coordination decision matrix based on the characteristic parameters of each energy storage method; The optimization module is used to optimize the time-sharing decision matrix through a multi-objective optimization model.

7. The solar energy storage control system according to claim 6, characterized in that, The time-sharing decision matrix module specifically includes: Dividing units, used to determine the charging and discharging power requirements. The size and duration of the day are used to divide the day into several time periods; The allocation unit is used to allocate the charging and discharging power ratios of different energy storage methods based on several time periods. ; The control unit is used to control each energy storage method according to the charging and discharging power ratio. Perform charging and discharging operations.

8. The solar energy storage control system according to claim 6, characterized in that, The optimization module specifically includes: The first calculation unit is used to calculate the overall efficiency index of the energy storage system. ; The second calculation unit is used to calculate the economic indicators of the energy storage system. ; Optimization unit, used for the overall efficiency index of energy storage system Economic indicators And the optimal objective function, optimize the time-sharing decision matrix; The real-time monitoring unit is used to monitor the overall efficiency indicators of the energy storage system in real time. and economic indicators ; The recovery unit is used when the overall efficiency index and economic indicators When the value falls below the corresponding threshold, the time-sharing decision matrix is ​​automatically adjusted back to the preset optimization range.

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