Photovoltaic energy storage scheduling management system

Abstract

The invention provides a photovoltaic energy storage scheduling management system. The photovoltaic energy storage scheduling management system comprises a data acquisition module for collecting real-time data; the operation simulation module simulates a future working state based on the data; the decision making module generates a charging and discharging scheduling strategy; the execution module controls the photovoltaic module to interact with the energy storage battery according to the strategy; according to the invention, through real-time data acquisition and accurate simulation and prediction of a working state, an optimal charging and discharging strategy is formulated, and the energy utilization efficiency is improved; an intelligent scheduling mechanism automatically adjusts strategies according to meteorological conditions and load consumption, and the response speed, flexibility and reliability of the system are improved; the automatic execution module reduces human intervention, simplifies the maintenance process and improves the stability and availability of the system; the efficiency and economical efficiency of a photovoltaic system are improved, application of renewable energy sources is promoted, and sustainable development of the environment is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation, and in particular to a photovoltaic energy storage scheduling and management system. Background Art

[0002] As the global demand for renewable energy grows, solar power generation has attracted widespread attention as a clean and sustainable form of energy. The construction scale of photovoltaic power stations continues to expand. In order to make full use of solar energy resources and smooth power output, photovoltaic energy storage systems have emerged. Photovoltaic energy storage systems improve the reliability and flexibility of the power system by storing excess solar power for use when there is insufficient sunlight.

[0003] However, existing photovoltaic energy storage systems usually lack precise energy scheduling mechanisms, resulting in energy storage and release that cannot match actual demand well, causing energy waste. In addition, due to the high cost of energy storage batteries, if the energy storage system cannot be effectively used to balance the fluctuations in grid electricity prices, the overall operating costs will increase. At the same time, many systems fail to automatically adjust the charging and discharging strategies according to factors such as real-time meteorological conditions and changes in grid electricity prices, which limits the flexibility and economy of the system. Summary of the invention

[0004] The present invention provides a photovoltaic energy storage system that can effectively improve the efficiency and economy of the system and realize a more intelligent, flexible and reliable photovoltaic energy storage scheduling and management system.

[0005] The present invention provides a photovoltaic energy storage scheduling and management system, comprising:

[0006] A data acquisition module is used to collect real-time data of the photovoltaic power station, including photovoltaic module parameters, energy storage battery parameters and working environment prediction parameters;

[0007] A running simulation module is connected to the data acquisition module, and simulates the working status of the photovoltaic components and the energy storage battery at a future time based on the real-time data to generate a simulation result;

[0008] A decision-making module, which is connected to the operation simulation module, balances the supply deviation of the photovoltaic components and the energy storage battery at the future time based on the simulation results, and generates a charge and discharge scheduling strategy;

[0009] An execution module is connected to the decision-making module and controls the interaction between the photovoltaic assembly and the energy storage battery according to the charge and discharge scheduling strategy to achieve balanced energy scheduling.

[0010] According to a photovoltaic energy storage scheduling and management system provided by the present invention, the data acquisition module includes:

[0011] A weather forecasting unit, which is used to obtain weather information of the area where the photovoltaic power station is located at a future time;

[0012] A component-side acquisition unit connected to the photovoltaic component and the weather forecast unit, used to obtain the short-circuit current, open-circuit voltage, temperature and light intensity under reference conditions, the short-circuit current temperature coefficient and the open-circuit voltage temperature coefficient, obtain the predicted operating temperature and predicted light intensity at each future moment based on the weather information at the future moment, and obtain the conversion efficiency of the output power based on the short-circuit current and the open-circuit voltage;

[0013] The energy storage end acquisition unit is connected to the energy storage battery and is used to obtain the maximum charging power, maximum discharging power, remaining storage capacity, maximum storage capacity, charging efficiency and discharging efficiency of the energy storage battery at the current moment, as well as the power consumption of the photovoltaic array load in each period.

[0014] According to a photovoltaic energy storage scheduling and management system provided by the present invention, the photovoltaic array load power consumption in each time period is the load power consumption under an ideal state, which is obtained by combining a machine learning algorithm with historical load records.

[0015] According to a photovoltaic energy storage scheduling and management system provided by the present invention, the operation simulation module includes:

[0016] A photovoltaic module simulation unit is connected to the module-end acquisition unit, and a power simulation model is pre-established, and the data collected by the module-end acquisition unit is correspondingly substituted into the model to generate the output power of the photovoltaic module at each future moment;

[0017] The energy storage system simulation unit is connected to the energy storage end collection unit and the photovoltaic module simulation unit, and establishes an energy storage simulation model in advance. The data collected by the energy storage end collection unit is combined with the output power of the photovoltaic module at each future time and substituted into the model to generate the ideal charging power, the ideal discharging power and the remaining power of the energy storage battery at the future time.

[0018] According to a photovoltaic energy storage dispatching and management system provided by the present invention, the power simulation model is:

[0019] P outi =I sci *V oci *μ

[0020]

[0021] V oci =V ocref *{1+β oc *(T cei -T ref )}

[0022] Among them, Pouti is the output power of the photovoltaic module at the future time i, I sci is the short-circuit current at future time i, V oci is the open circuit voltage at the future time i, μ is the conversion efficiency; I scref is the short-circuit current under reference conditions, V ocref is the open circuit voltage under reference conditions, α sc is the short-circuit current temperature coefficient, β oc is the open circuit voltage temperature coefficient, T cei is the predicted operating temperature at future time i, G i is the predicted light intensity at future time i, T ref is the temperature under reference conditions, T ref is the light intensity under reference conditions.

[0023] According to a photovoltaic energy storage scheduling and management system provided by the present invention, the energy storage simulation model is:

[0024] P ci =min(P outi -P loadi , P cmax )

[0025] P di =min(P loadi -P outi , E t , P dmax )

[0026] E i =E t +(η c *P ci *Δt)-(P di *η d *Δt)

[0027] Among them, E i is the remaining power of the energy storage battery at the future time i, P ci is the ideal charging power of the energy storage battery at future time i, P di P is the ideal discharge power of the energy storage battery at future time i; loadi is the load power consumed by the photovoltaic array at the future time i, P cmax is the maximum charging power of the energy storage battery, P dmax is the maximum discharge power of the energy storage battery, E t is the remaining storage capacity of the energy storage battery at the current moment; η c is the charging efficiency, η d is the discharge efficiency; Δt is the time step, representing the duration of charge / discharge.

[0028] According to a photovoltaic energy storage scheduling and management system provided by the present invention, the decision-making module includes:

[0029] A charge and discharge determination unit is connected to the photovoltaic module simulation unit and the energy storage end collection unit to compare the output power P outi and load power consumption P loadi , and get the time step Δt;

[0030] When P outi >P loadi When the charging process is detected,

[0031] When P outi ≤P loadi When , it is judged as the discharge process.

[0032] The time interval from the charging process to the discharging process or from the discharging process to the charging process is the time step Δt;

[0033] A decision generating unit, connected to the charge and discharge determination unit and the energy storage system simulation unit, for generating the charge and discharge scheduling strategy;

[0034] When in the charging process, the ideal charging power P is calculated through the energy storage simulation model. ci ; At the same time, define the ideal discharge power P di = 0, calculate the remaining power E of the energy storage battery at the future time i i ; When the remaining power E i ≥ Maximum storage capacity E max When the charging process is completed, the charging completion time T1 is recorded;

[0035] When in the discharge process, the ideal discharge power P is calculated through the energy storage simulation model. di ; At the same time, define the ideal charging power P ci = 0, calculate the remaining power E of the energy storage battery at the future time i i ; When the remaining power E i When ≤0, the discharge process is completed and the discharge completion time T2 is recorded;

[0036] The charging and discharging scheduling strategy includes a charging strategy and a discharging strategy. The charging strategy includes an ideal charging power P ci and charging completion time T1; the discharge strategy includes the ideal discharge power P di And discharge completion time T2.

[0037] According to a photovoltaic energy storage scheduling and management system provided by the present invention, the execution module includes:

[0038] The charging execution unit is connected to the decision generation unit and is used to obtain a charging strategy and control the photovoltaic components to charge at an ideal power P based on the charging strategy. ci Supply power to the energy storage battery and stop when the charging completion time T1 is reached;

[0039] The discharge execution unit is connected to the decision generation unit and is used to obtain a discharge strategy and control the energy storage battery to discharge at an ideal power P based on the discharge strategy. di It supplies power to the grid load and stops when the discharge completion time T2 is reached, and then obtains electrical energy from the grid.

[0040] According to a photovoltaic energy storage dispatching and management system provided by the present invention, the method further includes: adjusting the ideal charging power P based on the grid price ci And the ideal discharge power P di .

[0041] Compared with the prior art, the beneficial effects of this application are:

[0042] Through real-time data collection and precise operation simulation, this application can accurately predict the working status of photovoltaic modules and energy storage batteries, so as to formulate the optimal charging and discharging scheduling strategy; this helps to improve energy utilization efficiency and ensure that the photovoltaic system can respond more effectively to changing load demands;

[0043] By adjusting the ideal charging power and the ideal discharging power based on the grid price, the present application can increase the charging power when the electricity price is low, and increase the discharging power when the electricity price is high; this can not only reduce the cost of purchasing high-priced electricity, but also save electricity expenses by storing electricity when the electricity price is low and using it when the electricity price is high;

[0044] Through the intelligent scheduling mechanism, the present application can automatically adjust the charging and discharging strategy according to the actual meteorological conditions and load consumption; this not only improves the response speed of the system, but also enhances the flexibility and reliability of the system, ensuring that energy can be balanced and scheduled in all situations;

[0045] This application reduces the need for human intervention through automated execution modules, simplifies the system maintenance process, reduces problems caused by human errors, and improves the overall stability and availability of the system;

[0046] This application improves the intelligence of the system by combining machine learning algorithms with historical load records to predict the power consumption of the photovoltaic array load in each period; this enables the system to more accurately predict future load demand and optimize the charging and discharging strategy;

[0047] Through precise charge and discharge control, this application helps reduce fluctuations in the power grid and improve the overall stability of the power grid; this is conducive to the smooth operation of the power system and reduces potential risks caused by unstable power supply;

[0048] By improving the efficiency and economy of photovoltaic systems, this application helps promote the popularization and application of renewable energy, reduce dependence on fossil fuels, and thus promote environmental sustainable development;

[0049] In summary, the present application achieves the effects of improving energy utilization efficiency, reducing costs, and enhancing system flexibility and reliability.

[0050] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

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

[0052] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0053] Figure 1 It is a structural schematic diagram of a photovoltaic energy storage scheduling and management system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0055] Embodiment 1:

[0056] The present invention provides a photovoltaic energy storage scheduling and management system. Figure 1 ,include:

[0057] A data acquisition module is used to collect real-time data of the photovoltaic power station, including photovoltaic module parameters, energy storage battery parameters and working environment prediction parameters;

[0058] Running the simulation module, which is connected to the data acquisition module, simulates the working status of the photovoltaic components and energy storage batteries at future times based on the real-time data and generates simulation results;

[0059] A decision-making module, which is connected to the operation simulation module, balances the supply deviation of photovoltaic components and energy storage batteries at the future time based on the simulation results, and generates a charging and discharging scheduling strategy;

[0060] The execution module is connected to the decision-making module, and controls the interaction between the photovoltaic components and the energy storage battery according to the charging and discharging scheduling strategy to achieve balanced energy scheduling.

[0061] The principle of the above embodiment is that the data acquisition module collects real-time data of the photovoltaic power station, such as environmental factors such as light intensity, temperature, and humidity; monitors the working parameters of the photovoltaic components (such as output voltage and current); monitors the status of the energy storage battery (such as battery SOC (state of charge), temperature, voltage, etc.); the operation simulation module analyzes and predicts the power generation and electricity demand in the future based on real-time data and historical data; simulates the working status of photovoltaic components and energy storage batteries under different conditions in the future; calculates the best charging and discharging strategy through algorithms to cope with possible changes in supply and demand; the decision-making module analyzes the simulation results provided by the operation simulation module; formulates a charging and discharging scheduling strategy to minimize the power supply mismatch; considers factors such as price signals and policy regulations of the power grid to optimize the scheduling strategy; the execution module performs corresponding operations according to the charging and discharging scheduling strategy generated by the decision-making module; controls the energy flow between photovoltaic components and energy storage batteries to ensure the stable operation of the system.

[0062] The beneficial effects of the above embodiments are: reducing energy waste and increasing overall system efficiency through accurate prediction and scheduling; reasonable planning of the use of energy storage batteries can reduce unnecessary charging and discharging times, extend battery life and reduce maintenance costs; by dynamically adjusting the charging and discharging strategy, the power generation fluctuation problem caused by weather changes can be effectively alleviated, and the stability and reliability of the power grid can be improved; the operating strategy can be flexibly adjusted according to the government's energy policy, such as participating in power grid peak regulation.

[0063] In order to further optimize the above embodiment, the data acquisition module includes:

[0064] A weather forecasting unit, which is used to obtain weather information of the area where the photovoltaic power station is located at a future time;

[0065] The component-side acquisition unit is connected to the photovoltaic component and the weather forecast unit, and is used to obtain the short-circuit current, open-circuit voltage, temperature and light intensity under reference conditions, the short-circuit current temperature coefficient and the open-circuit voltage temperature coefficient, obtain the predicted operating temperature and predicted light intensity at each future moment based on the weather information at the future moment, and obtain the conversion efficiency of the output power based on the short-circuit current and the open-circuit voltage;

[0066] The energy storage end acquisition unit is connected to the energy storage battery and is used to obtain the maximum charging power, maximum discharging power, remaining storage capacity, maximum storage capacity, charging efficiency and discharging efficiency of the energy storage battery at the current moment, as well as the power consumption of the photovoltaic array load in each period.

[0067] It should be noted that the meteorological forecast unit is responsible for obtaining meteorological information in the future specific time period in the area where the photovoltaic power station is located, including but not limited to temperature, humidity, wind speed, light intensity, etc.; meteorological information is crucial for predicting the performance of photovoltaic modules, because the output power of photovoltaic modules is affected by factors such as light intensity and temperature; through accurate meteorological forecasts, the power generation capacity of photovoltaic power stations can be better planned;

[0068] The module-side acquisition unit collects relevant data from the photovoltaic modules, and combines the data from the meteorological forecast unit to calculate the working temperature and light intensity of the photovoltaic modules at various moments in the future, and then calculates the conversion efficiency and output power; the short-circuit current and open-circuit voltage reflect the performance indicators of the photovoltaic modules under specific conditions; the short-circuit current temperature coefficient and the open-circuit voltage temperature coefficient are used to evaluate the impact of temperature changes on the performance of photovoltaic modules; the working temperature and light intensity are used to predict the performance of photovoltaic modules under different environmental conditions; the conversion efficiency is the efficiency of the photovoltaic module in converting solar energy into electrical energy; the output power is the actual power generation capacity of the photovoltaic module under given conditions;

[0069] The energy storage end acquisition unit collects relevant information from the energy storage battery, including the current state and performance parameters of the energy storage battery; the maximum charging power and maximum discharging power are the maximum charging and discharging rates that the energy storage battery can withstand under ideal conditions; the remaining storage capacity and maximum storage capacity at the current moment reflect the current state of charge of the energy storage battery and its total capacity; charging efficiency and discharging efficiency: the proportion of energy loss during the charging and discharging process of the energy storage battery; photovoltaic array load power consumption: the power demand of the photovoltaic system to supply power to the load at different time periods;

[0070] This embodiment can more accurately grasp the operating status of the photovoltaic power station so as to make more accurate predictions and decisions; by adding these detailed data collection points, the real-time status and future trends of the photovoltaic power station can be more comprehensively understood, which helps to improve the accuracy and response speed of the entire photovoltaic energy storage scheduling and management system.

[0071] In order to further optimize the above embodiment, the load power consumption of the photovoltaic array in each time period is the load power consumption under an ideal state, which is obtained by combining a machine learning algorithm with historical load records.

[0072] It should be noted that machine learning algorithms improve prediction accuracy and ensure that charging and discharging strategies are more in line with actual conditions. Machine learning algorithms can learn from a large amount of historical data the trend of load power consumption over time, including daily and seasonal changes. For example, it can identify the load differences between weekdays and weekends, and between summer and winter, and make predictions based on this.

[0073] Historical load records usually contain load consumption data for the past few years, including hourly or minute-by-minute load changes;

[0074] Machine learning algorithms can capture subtle changes in load patterns, thereby providing more accurate prediction results; as new data is continuously added, the algorithm will continue to learn and optimize, improving the applicability and robustness of the prediction model; even if the load pattern changes, the algorithm can gradually adapt to the new pattern through continuous training.

[0075] In order to further optimize the above embodiment, running the simulation module includes:

[0076] A photovoltaic module simulation unit is connected to the module-side acquisition unit, and a power simulation model is established in advance. The data collected by the module-side acquisition unit is correspondingly substituted into the model to generate the output power of the photovoltaic module at each future moment.

[0077] The power simulation model is:

[0078] P outi =I sci *V oci *μ

[0079]

[0080] V oci =V ocref *{1+β oc *(T cei -T ref )}

[0081] Among them, P outi is the output power of the photovoltaic module at the future time i, I sci is the short-circuit current at future time i, V oci is the open circuit voltage at the future time i, μ is the conversion efficiency; I scref is the short-circuit current under reference conditions, V ocref is the open circuit voltage under reference conditions, α sc is the short-circuit current temperature coefficient, β oc is the open circuit voltage temperature coefficient, T cei is the predicted operating temperature at future time i, G i is the predicted light intensity at future time i, Tref is the temperature under reference conditions, T ref is the light intensity under reference conditions;

[0082] It should be noted that, under normal circumstances, the temperature and light intensity under reference conditions are set at 25°C and 1000W / m 2 ; These values ​​are typical under standard test conditions;

[0083] Conversion efficiency is the ratio of the sunlight received by a PV panel to usable electricity; this value is provided by the PV panel manufacturer and depends on the type of PV technology (such as monocrystalline silicon, multicrystalline silicon, etc.);

[0084] The short-circuit current temperature coefficient and the open-circuit voltage temperature coefficient are also provided by the manufacturer and describe the effect of temperature changes on the performance of PV modules; generally, the short-circuit current increases with increasing temperature, while the open-circuit voltage decreases with increasing temperature;

[0085] The operating temperature of PV modules can be predicted through weather forecasts, historical data or thermodynamic models; the light intensity at the location of PV modules can also be predicted through weather forecasts, historical data or satellite images;

[0086] Through such a model, the present embodiment can more accurately predict the output power of the photovoltaic module at a future time, which is crucial for optimizing the operation control of the photovoltaic system and improving the system efficiency; in addition, such a model can also be used to evaluate the performance of the photovoltaic system under different climatic conditions, helping designers make better design decisions.

[0087] The energy storage system simulation unit is connected to the energy storage end collection unit and the photovoltaic module simulation unit, and establishes an energy storage simulation model in advance. The data collected by the energy storage end collection unit is combined with the output power of the photovoltaic module at each future time and substituted into the model to generate the ideal charging power, the ideal discharging power and the remaining power of the energy storage battery at the future time;

[0088] The energy storage simulation model is:

[0089] P ci =min(P outi -P loadi , P cmax )

[0090] P di =min(P loadi -P outi , E t , P dmax )

[0091] E i =E t +(ηc *P ci *Δt)-(P di *η d *Δt)

[0092] Among them, E i is the remaining power of the energy storage battery at the future time i, P ci is the ideal charging power of the energy storage battery at future time i, P di P is the ideal discharge power of the energy storage battery at future time i; loadi is the load power consumed by the photovoltaic array at the future time i, P cmax is the maximum charging power of the energy storage battery, P dmax is the maximum discharge power of the energy storage battery, E t is the remaining storage capacity of the energy storage battery at the current moment; η c is the charging efficiency, η d is the discharge efficiency; Δt is the time step, representing the duration of charge / discharge.

[0093] It should be noted that when the power generated by the photovoltaic module is greater than the power consumed by the load, the excess power can be used to charge the energy storage battery; the ideal charging power is the smaller value between the additional power generated by the photovoltaic module and the maximum charging power of the energy storage battery;

[0094] When the power generated by the photovoltaic module is less than the power consumed by the load, the energy storage battery will discharge to make up for the shortfall; the ideal discharge power is the minimum of the difference between the load power consumption and the photovoltaic module output power, the current discharge capacity of the battery, and the maximum discharge power;

[0095] The remaining capacity of the energy storage battery is obtained by adding the remaining capacity at the previous moment to the ideal charging capacity after adjusting the charging efficiency, and then subtracting the ideal discharging capacity after adjusting the discharging efficiency;

[0096] The charging efficiency and discharging efficiency describe the energy loss during the charging and discharging process; these values ​​are generally provided by the energy storage battery manufacturer;

[0097] The time step represents the length of time for each simulation step, which determines the level of detail of the simulation; usually, the choice of the time step requires a balance between accuracy and computational complexity;

[0098] Through such a model, the present embodiment is able to simulate the behavior of the energy storage system and predict its future state, which is essential to ensure the stable operation of the entire photovoltaic system; for example, when photovoltaic power generation is insufficient, the stored energy can be released by the energy storage system to meet the load demand, and when photovoltaic power generation is in excess, the energy storage system can be used to store excess energy.

[0099] To further optimize the above embodiment, the decision making module includes:

[0100] The charge and discharge determination unit is connected to the photovoltaic module simulation unit and the energy storage end collection unit to compare the output power P outi and load power consumption P loadi , and get the time step Δt;

[0101] When P outi >P loadi When the charging process is detected,

[0102] When P outi ≤P loadi When , it is judged as the discharge process.

[0103] The time interval from the charging process to the discharging process or from the discharging process to the charging process is the time step Δt;

[0104] A decision generation unit, which is connected to the charge and discharge determination unit and the energy storage system simulation unit, and is used to generate a charge and discharge scheduling strategy;

[0105] When in the charging process, the ideal charging power P is calculated through the energy storage simulation model. ci ; At the same time, define the ideal discharge power P di = 0, calculate the remaining power E of the energy storage battery at the future time i i ; When the remaining power E i ≥ Maximum storage capacity E max When the charging process is completed, the charging completion time T1 is recorded;

[0106] When in the discharge process, the ideal discharge power P is calculated through the energy storage simulation model. di ; At the same time, define the ideal charging power P ci = 0, calculate the remaining power E of the energy storage battery at the future time i i ; When the remaining power E i When ≤0, the discharge process is completed and the discharge completion time T2 is recorded;

[0107] The charging and discharging scheduling strategy includes charging strategy and discharging strategy. The charging strategy includes the ideal charging power P di and charging completion time T1; the discharge strategy includes the ideal discharge power P di And discharge completion time T2.

[0108] It should be noted that in actual operation, the time step should be small enough to accurately capture the changes in the charge and discharge state; however, too small a time step will increase the amount of calculation, so it is necessary to find a suitable balance between accuracy and computational efficiency;

[0109] The switching of charging and discharging status is automatically carried out according to the real-time output power of the photovoltaic module and the power consumed by the load; this means that the system may experience multiple charging and discharging cycles at different times of the day;

[0110] When calculating the remaining power, it is necessary to consider the effect of the charging and discharging efficiency on energy conversion; the efficiency is usually less than 100%, which means that there will be a certain amount of energy loss during the charging and discharging process.

[0111] In order to further optimize the above embodiment, the execution module includes:

[0112] The charging execution unit is connected to the decision generation unit and is used to obtain a charging strategy and control the photovoltaic components to charge at an ideal power P based on the charging strategy. ci Supply power to the energy storage battery and stop when the charging completion time T1 is reached;

[0113] The discharge execution unit is connected to the decision generation unit and is used to obtain the discharge strategy and control the energy storage battery to discharge at the ideal power P based on the discharge strategy. di It supplies power to the grid load and stops when the discharge completion time T2 is reached, and then obtains electrical energy from the grid.

[0114] It should be noted that the charging execution unit and the discharging execution unit need to have precise control capabilities to ensure that the photovoltaic modules or energy storage batteries can operate according to the set ideal power; this usually involves precise power electronic equipment such as inverters and controllers;

[0115] When switching from discharge mode to charge mode or vice versa, the system needs to transition smoothly to avoid impact on the grid or energy storage system; this switching usually requires appropriate buffer mechanisms or preset logic rules to achieve;

[0116] Both the charging execution unit and the discharging execution unit should have safety measures such as overload protection and short circuit protection to prevent damage caused by system failure or abnormal conditions;

[0117] The execution unit needs to maintain good communication with the decision-making unit to ensure the accuracy and timeliness of data transmission; this usually involves the use of industrial standard protocols such as Modbus, EtherCAT, etc.

[0118] Since the execution unit directly controls the physical devices, it needs to have a high real-time response capability to ensure the stable operation of the system; the operation of the execution unit is usually coordinated by the energy management system (EMS) software, which can monitor the status of the entire system and adjust the charging and discharging strategy according to the instructions of the decision generation unit.

[0119] In order to further optimize the above embodiment, it also includes: adjusting the ideal charging power P based on the grid price ci And the ideal discharge power P di .

[0120] It should be noted that the steps include:

[0121] Collect price data from the power grid, including real-time and forecast electricity prices;

[0122] Monitor the amount of electricity generated by photovoltaic panels and the status of energy storage batteries (such as remaining capacity);

[0123] Develop strategies based on the collected data to determine when to charge and discharge and at what power levels;

[0124] Use algorithms (such as machine learning models or optimization algorithms) to predict electricity price trends over a period of time in the future and formulate charging and discharging strategies accordingly;

[0125] When it is detected that the electricity price is low, the ideal charging power is increased to fully utilize the low-price period for charging;

[0126] When it is detected that the electricity price is high, the ideal discharge power is increased to discharge to the grid during the high-price period to earn revenue or meet local load demand;

[0127] The charging execution unit controls the photovoltaic module to charge the energy storage battery according to the adjusted ideal charging power;

[0128] The discharge execution unit controls the energy storage battery to supply power to the grid load according to the adjusted ideal discharge power;

[0129] It should be noted that increasing charging power when grid prices are low and increasing discharging power when prices are high can effectively use price differences to reduce costs; establishing a reliable forecasting model to predict future electricity price changes so that charging and discharging plans can be made in advance; the system needs to be flexible enough to adapt to fluctuations in grid prices and be able to quickly adjust charging and discharging strategies; by analyzing the impact of charging and discharging strategies on costs, the economic benefits of the system can be evaluated to ensure a return on investment; when adjusting the charging and discharging power, it is also necessary to ensure that the safe operating range of photovoltaic modules, energy storage batteries and power electronic equipment is not exceeded.

[0130] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A photovoltaic energy storage dispatching and management system, characterized in that: include: A data acquisition module is used to collect real-time data of the photovoltaic power station, including photovoltaic module parameters, energy storage battery parameters and working environment prediction parameters; A running simulation module is connected to the data acquisition module, and simulates the working status of the photovoltaic components and the energy storage battery at a future time based on the real-time data to generate a simulation result; A decision-making module, which is connected to the operation simulation module, balances the supply deviation of the photovoltaic components and the energy storage battery at the future time based on the simulation results, and generates a charge and discharge scheduling strategy; An execution module is connected to the decision-making module and controls the interaction between the photovoltaic assembly and the energy storage battery according to the charge and discharge scheduling strategy to achieve balanced energy scheduling.

2. A photovoltaic energy storage dispatching and management system according to claim 1, characterized in that: The data acquisition module comprises: A weather forecasting unit, which is used to obtain weather information of the area where the photovoltaic power station is located at a future time; A component-side acquisition unit connected to the photovoltaic component and the weather forecast unit, used to obtain the short-circuit current, open-circuit voltage, temperature and light intensity under reference conditions, the short-circuit current temperature coefficient and the open-circuit voltage temperature coefficient, obtain the predicted operating temperature and predicted light intensity at each future moment based on the weather information at the future moment, and obtain the conversion efficiency of the output power based on the short-circuit current and the open-circuit voltage; The energy storage end acquisition unit is connected to the energy storage battery and is used to obtain the maximum charging power, maximum discharging power, remaining storage capacity, maximum storage capacity, charging efficiency and discharging efficiency of the energy storage battery at the current moment, as well as the power consumption of the photovoltaic array load in each period.

3. A photovoltaic energy storage dispatching and management system according to claim 2, characterized in that: The photovoltaic array load power consumption in each time period is the load power consumption under an ideal state, which is obtained by combining a machine learning algorithm with historical load records.

4. A photovoltaic energy storage dispatching and management system according to claim 2, characterized in that: The operation simulation module comprises: A photovoltaic module simulation unit is connected to the module-end acquisition unit, and a power simulation model is pre-established, and the data collected by the module-end acquisition unit is correspondingly substituted into the model to generate the output power of the photovoltaic module at each future moment; The energy storage system simulation unit is connected to the energy storage end collection unit and the photovoltaic module simulation unit, and establishes an energy storage simulation model in advance. The data collected by the energy storage end collection unit is combined with the output power of the photovoltaic module at each future time and substituted into the model to generate the ideal charging power, the ideal discharging power and the remaining power of the energy storage battery at the future time.

5. A photovoltaic energy storage dispatching and management system according to claim 4, characterized in that: The power simulation model is: P outi =I sci *V oci *µ V oci =V ocref *{1+β oc *(T cei -T ref )} Among them, P outi is the output power of the photovoltaic module at the future time i, I sci is the short-circuit current at future time i, V oci is the open circuit voltage at the future time i, μ is the conversion efficiency; I scref is the short-circuit current under reference conditions, V ocref is the open circuit voltage under reference conditions, α sc is the short-circuit current temperature coefficient, β oc is the open circuit voltage temperature coefficient, T cei is the predicted operating temperature at future time i, G i is the predicted light intensity at future time i, T ref is the temperature under reference conditions, T ref is the light intensity under reference conditions.

6. A photovoltaic energy storage dispatching and management system according to claim 5, characterized in that: The energy storage simulation model is: P ci =min(P outi -P loadi ,P cmax ) P di =min(P loadi -P outi ,E t ,P dmax ) E i =E t +(the c *P ci *Δt)-(P di *or d *Δt) Among them, E i is the remaining power of the energy storage battery at the future time i, P ci is the ideal charging power of the energy storage battery at future time i, P di is the ideal discharge power of the energy storage battery at future time i; P loadi is the load power consumed by the photovoltaic array at the future time i, P cmax is the maximum charging power of the energy storage battery, P dmax is the maximum discharge power of the energy storage battery, E t is the remaining storage capacity of the energy storage battery at the current moment; η c is the charging efficiency, η d is the discharge efficiency; Δt is the time step, representing the duration of charge / discharge.

7. A photovoltaic energy storage dispatching and management system according to claim 6, characterized in that: The decision making module includes: A charge and discharge determination unit is connected to the photovoltaic module simulation unit and the energy storage end collection unit to compare the output power P outi and load power consumption P loadi , and get the time step Δt; When P outi >P loadi When the charging process is detected, When P outi ≤P loadi When , it is judged as the discharge process. The time interval from the charging process to the discharging process or from the discharging process to the charging process is the time step Δt; A decision generating unit, connected to the charge and discharge determination unit and the energy storage system simulation unit, for generating the charge and discharge scheduling strategy; When in the charging process, the ideal charging power P is calculated through the energy storage simulation model. ci ; At the same time, define the ideal discharge power P di = 0, calculate the remaining power E of the energy storage battery at the future time i i ; When the remaining power E i ≥ Maximum storage capacity E max When the charging process is completed, the charging completion time T1 is recorded; When in the discharge process, the ideal discharge power P is calculated through the energy storage simulation model. di ; At the same time, define the ideal charging power P ci = 0, calculate the remaining power E of the energy storage battery at the future time i i ; When the remaining power E i When ≤0, the discharge process is completed and the discharge completion time T2 is recorded; The charging and discharging scheduling strategy includes a charging strategy and a discharging strategy. The charging strategy includes an ideal charging power P ci and charging completion time T1; the discharge strategy includes the ideal discharge power P di And discharge completion time T2.

8. A photovoltaic energy storage dispatching and management system according to claim 7, characterized in that: The execution module includes: The charging execution unit is connected to the decision generation unit and is used to obtain a charging strategy and control the photovoltaic components to charge at an ideal power P based on the charging strategy. ci Supply power to the energy storage battery and stop when the charging completion time T1 is reached; The discharge execution unit is connected to the decision generation unit and is used to obtain a discharge strategy and control the energy storage battery to discharge at an ideal power P based on the discharge strategy. di It supplies power to the grid load and stops when the discharge completion time T2 is reached, and then obtains electrical energy from the grid.

9. A photovoltaic energy storage dispatching and management system according to claim 8, characterized in that: Also includes: Adjusting the ideal charging power P based on grid price ci And the ideal discharge power P di .

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