Multi-objective optimization scheduling control strategy of photovoltaic energy storage system

Abstract

The invention discloses a multi-objective optimization scheduling control strategy for a photovoltaic energy storage system, relates to the technical field of energy storage optimization scheduling, and solves the technical problems that a real-time monitoring and feedback mechanism is lacked, the strategy cannot be dynamically adjusted according to an energy storage state and distribution network constraint, and risks such as over-charging and over-discharging, excessive light abandoning rate and the like are easily caused. According to the invention, on the basis of the excess / deficient power, the energy storage state, the distribution network bearing capacity and the duration, the excess / deficient power is divided into small / medium / large three levels, so that strategy extensions, such as priority flexible charging and load response during small excess, activation of hydrogen production during large excess, adjacent network support and other standby paths, are avoided, the absorption efficiency is improved, and the energy consumption is reduced. Energy storage charging, demand side response, distribution network interaction, standby consumption and the like are integrated in case of excess, energy storage flexible discharging, load adjustment and an emergency power supply are combined in case of insufficient, economy and environmental protection cooperation is achieved, and over-charging and over-discharging are avoided by monitoring the energy storage SOC and the distribution network state in real time and dynamically adjusting the charging and discharging power.

Description

Technical Field

[0001] This invention relates to the field of energy storage optimization scheduling technology, specifically a multi-objective optimization scheduling control strategy for photovoltaic energy storage systems. Background Technology

[0002] A photovoltaic-storage-charging integrated system is typically a microgrid system consisting of a power supply and distribution system, a charging system, a monitoring system, a photovoltaic system, and an energy storage system.

[0003] According to patent application CN117040028A, a method and system for optimizing the control strategy of a photovoltaic-storage-charging microgrid in an industrial and commercial park is disclosed. The method includes: acquiring the historical photovoltaic power output of the target industrial and commercial park; screening factors influencing photovoltaic power output to determine output prediction indicators; constructing a photovoltaic power output prediction model; predicting the photovoltaic power output data of the target industrial and commercial park using the output prediction indicators and the output prediction model; acquiring multi-source charging data of users in the target industrial and commercial park within a preset time period; analyzing user charging behavior to construct a charging load prediction model; simulating the total charging load at each time point; obtaining the optimal solution based on the photovoltaic power output data and the total charging load at each time point using a particle swarm optimization algorithm; and scheduling charging for the current period based on the optimal solution.

[0004] Existing photovoltaic energy storage dispatch technologies have the following limitations: First, the prediction methods are singular, relying solely on physical or statistical models, making it difficult to balance prediction accuracy under different scenarios; second, the classification of photovoltaic surplus / deficit is coarse, lacking fine-grained grading based on power, duration, energy storage status, and distribution network constraints, resulting in a "one-size-fits-all" dispatch strategy; third, there is insufficient coordination among multiple objectives, making it difficult to balance economy, safety, and environmental protection; and fourth, the dynamic response capability is weak, lacking real-time feedback and closed-loop adjustment mechanisms, making it difficult to cope with sudden weather or load fluctuations. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a multi-objective optimized scheduling and control strategy for photovoltaic energy storage systems. This strategy solves the problems of lacking real-time monitoring and feedback mechanisms, being unable to dynamically adjust strategies based on energy storage status and distribution network constraints, and being prone to risks such as overcharging, over-discharging, and exceeding curtailment rates.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-objective optimization scheduling and control strategy for a photovoltaic energy storage system, which specifically includes the following steps:

[0007] Predict short-term photovoltaic processing and load demand, compare the two values, and generate photovoltaic over-dispatch signals or photovoltaic under-dispatch signals;

[0008] The system analyzes the photovoltaic overcapacity dispatch signal, calculates and matches the overcapacity, determines the specific overcapacity situation, performs dispatch according to different overcapacity situations, and generates dispatch information.

[0009] The photovoltaic power shortage dispatch signal is analyzed, the power shortage is calculated and matched, the specific situation of the shortage is determined, and dispatch is carried out according to different specific situations of the shortage to generate dispatch information;

[0010] The photovoltaic energy storage system is scheduled and controlled based on the scheduling information obtained from the analysis of photovoltaic surplus and photovoltaic shortage.

[0011] As a further aspect of the present invention, the specific method for generating the photovoltaic surplus dispatch signal or the photovoltaic shortage dispatch signal is as follows:

[0012] The short-term photovoltaic output is predicted by combining physical and statistical models, while the short-term load demand is predicted by analyzing historical data, and the magnitudes of the two are compared.

[0013] If the photovoltaic output exceeds the load demand, it indicates a photovoltaic surplus, and a photovoltaic surplus dispatch signal is generated. Conversely, if the photovoltaic output is less than the load demand, it indicates a photovoltaic shortage, and a photovoltaic shortage dispatch signal is generated.

[0014] As a further aspect of the present invention, the specific method for analyzing the photovoltaic excess dispatch signal is as follows:

[0015] The difference between photovoltaic power output and load demand is denoted as excess power P. 剩 Simultaneously, obtain the remaining rechargeable energy storage capacity C. ESS剩 And C ESS剩 =CESS 额 ×(SOC 上限 -SOC 当前 SOC 上限 State of Charge (SOC) indicates the upper limit of energy storage in an energy storage system. 当前 This indicates the current energy storage capacity of the energy storage system, CESS. 额 This represents the total energy storage capacity, where the excess power P 剩 Remaining rechargeable capacity C ESS剩 If the total energy storage capacity of CESS is slightly excessive, it is classified as slightly excessive. 额 <P 剩 ×t 持续 <P 允许 And P 允许 If P represents the allowable power for the distribution network to access the internet, then it is classified as medium excess. 剩 ×t 持续 >P 允许 If the surplus is classified as a large surplus, then scheduling is carried out on the different surplus situations after classification.

[0016] As a further aspect of the present invention, the specific method for scheduling different excess situations after classification is as follows:

[0017] For situations with a small surplus, the excess power is used as the upper limit of the charging power. Devices with "flexible time and power matching" are selected, and the load clustering algorithm is used to trigger the charging power precisely. If the energy storage SOC is close to the warning value, the charging power is automatically reduced, the charging time is extended, the battery life is protected, and scheduling information is generated.

[0018] As a further aspect of the present invention, the specific method for scheduling different excess situations after classification is as follows:

[0019] In response to the surplus situation, the energy storage is charged at its rated power until the SOC reaches its limit. Adjustable and interruptible loads are then called upon to absorb the surplus. The remaining power that needs to be connected to the grid avoids the flat price period and is prioritized to be connected to the grid during the peak price period. Time series forecasting is used to select the optimal grid connection point and generate scheduling and processing information.

[0020] As a further aspect of the present invention, the specific method for scheduling different excess situations after classification is as follows:

[0021] In the event of a large overcapacity, the system charges the photovoltaic (PV) modules to their rated power limit, activates all interruptible, transferable, and adjustable loads, triggers hydrogen production and neighboring grid support, and prioritizes cutting off inefficient PV strings to control the curtailment rate to ≤5%, generating dispatch information.

[0022] As a further aspect of the present invention, the specific method for analyzing the photovoltaic under-scheduling signal is as follows:

[0023] The power deficit P is obtained by calculating the difference between photovoltaic processing capacity and load demand. 缺 Simultaneously, obtain the remaining available storage capacity C. ESS放 And C ESS剩 =CESS 额 ×(SOC 当前 -SOC 下限 ), and SOC 下限 This represents the minimum energy storage capacity of the energy storage system; if the power P is insufficient... 缺 <Remaining available energy storage capacity C ESS放 If it is classified as a minor deficiency, then the total energy storage capacity C is considered to be insufficient. ESS放 ×(SOC 当前 -SOC 下限 )< Lack of power P 缺 ×t 持续 <Grid purchase capacity P 网购 If it is insufficient, then it is classified as moderately deficient; if it lacks power P... 缺 ×t 持续 >Purchasable grid capacity P 网购If the deficiency is classified as a major deficiency, then scheduling and processing will be carried out for each different deficiency situation.

[0024] As a further aspect of the present invention, the specific method for scheduling different deficiency situations is as follows:

[0025] For minor shortages, the discharge power is dynamically matched according to the real-time load gap, flexible loads are called up, and unnecessary electricity consumption is reduced through fuzzy control algorithms. If there is still a remaining gap after energy storage and load adjustment, electricity is purchased from the grid at the real-time electricity price to generate dispatch information.

[0026] As a further aspect of the present invention, the specific method for scheduling different deficiency situations is as follows:

[0027] In response to insufficient power, the energy storage is discharged at its rated power to prioritize critical loads until the State of Charge (SOC) drops to a safe threshold. Loads are then transferred and delayed until the off-peak electricity price period. Interruptible loads are shut down according to priority. The remaining shortfall is dynamically compensated for at the peak-valley electricity price. Power is directly replenished during flat periods and the remaining capacity of the energy storage is prioritized during peak periods, generating dispatch information.

[0028] As a further aspect of the present invention, the specific method for scheduling different deficiency situations is as follows:

[0029] In the event of a severe power shortage, the energy storage system discharges to the lower limit of the State of Charge (SOC) at its rated power to fully guarantee critical loads, shuts down interruptible loads, remotely disconnects unnecessary circuits via smart circuit breakers, activates emergency power supplies, and purchases electricity from the main grid according to grid dispatch instructions for any remaining shortfall. At the same time, it reports demand response to the dispatch center and generates dispatch information.

[0030] This invention provides a multi-objective optimized scheduling and control strategy for photovoltaic energy storage systems. Compared with existing technologies, it has the following advantages:

[0031] This invention categorizes excess / deficient power into three levels: "small," "medium," and "large," based on excess / deficient power, energy storage status, distribution network capacity, and duration. This avoids a crude strategy. For example, in cases of small excess, flexible charging and load response are prioritized; in cases of large excess, backup paths such as hydrogen production and neighboring grid support are activated to improve absorption efficiency; in cases of excess, multiple paths such as energy storage charging, demand-side response, distribution network interaction, and backup absorption are integrated; and in cases of deficiency, flexible energy storage discharge, load regulation, and emergency power are combined to achieve a synergy between economic efficiency and environmental protection. By monitoring the energy storage SOC and distribution network status in real time, the charging and discharging power is dynamically adjusted to avoid overcharging and over-discharging. Attached Figure Description

[0032] Figure 1 This is a flowchart of the control strategy of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Please see Figure 1 This application provides a multi-objective optimization scheduling and control strategy for a photovoltaic energy storage system, which specifically includes the following steps:

[0035] Step S1: Obtain the corresponding light intensity, ambient temperature and other parameters of the photovoltaic energy storage system, and obtain its corresponding historical data. Use a combination of physical and statistical models to predict short-term photovoltaic output, and analyze the historical data to predict short-term load demand. Compare the two values. If the photovoltaic output is greater than the load demand, it indicates photovoltaic surplus, and a photovoltaic surplus dispatch signal is generated. Conversely, if the photovoltaic output is less than the load demand, it indicates photovoltaic shortage, and a photovoltaic shortage dispatch signal is generated.

[0036] Photovoltaic side: Smart sensors (accuracy ±2%) are used to collect data on light intensity (radiometer), module temperature (infrared thermometer), and inverter DC side voltage / current.

[0037] Load side: Real-time power, voltage, and current are collected using smart meters (accuracy class 0.5);

[0038] On the environmental side: Deploy micro-weather stations to collect data on wind speed, humidity, and cloud cover (to help correct forecast errors).

[0039] The data is stored in categories based on "weather type + season" (e.g., "sunny at 12 noon in summer" and "cloudy at 6 pm in winter"), creating a scenario-based database (solving the error problem of traditional "one-size-fits-all" historical data).

[0040] Solar power output forecast (short-term 1-72h):

[0041] Physical model: Calculate theoretical output using PVsyst (inputs: light intensity, module temperature, tilt angle, efficiency degradation rate);

[0042] Statistical model: Use LSTM to correct the physical model results (input: historical prediction error, cloud cover rate of change);

[0043] Dynamic switching: On sunny days, the physical model weight is 0.8 (high accuracy), and on cloudy days, the LSTM weight is 0.7 (adapts to random fluctuations).

[0044] Load demand forecast (short-term 1-72h):

[0045] User profile modeling: Classified by "residential / industrial / commercial", residential load uses the Prophet model (to capture peak and valley patterns), and industrial load uses the production plan correlation model (e.g., factories with three shifts, where load is strongly correlated with order volume);

[0046] Real-time correction: The forecast is updated every 15 minutes using the latest load data (such as when residents have just turned on their air conditioners) to resolve the error caused by "sudden changes in user behavior".

[0047] Step S2: Analyze the generated photovoltaic surplus dispatch signal and calculate the difference between photovoltaic output and load demand, denoted as surplus power P. 剩 Simultaneously, obtain the remaining rechargeable energy storage capacity C. ESS剩 And C ESS剩 = CESS limit × (SOC upper limit - SOC current), where SOC upper limit represents the maximum energy storage capacity of the energy storage system, and SOC current represents the current energy storage capacity of the energy storage system. 额 This represents the total energy storage capacity, where the excess power P 剩 Remaining rechargeable capacity C ESS剩 If the total energy storage capacity is CESS, it indicates a slight surplus. 额 <P 剩 ×t 持续 <P 允许 And P 允许 If P represents the allowable power for the distribution network to access the internet, then it is classified as medium excess. 剩 ×t 持续 >P 允许 If the surplus is classified as a large surplus, then scheduling is carried out on the different surplus situations after classification.

[0048] In response to situations of slight overcapacity, the charging process employs a "dynamic power matching" mechanism, using the real-time overcapacity as the upper limit threshold for charging power, rather than mechanically applying the rated power of the energy storage.

[0049] The load side activates a "precise response" strategy, relying on load clustering algorithms to deeply mine users' electricity consumption habits and select objects with high time flexibility and excellent power matching from a large number of adjustable devices—such as electric water heaters whose set temperature has not reached the threshold and charging piles that are idle.

[0050] Simultaneously, a full-time safety protection network is constructed to monitor changes in the energy storage's State of Charge (SOC) in real time. When the SOC approaches a warning threshold (e.g., 80%), a tiered power reduction command is triggered, for example, smoothly reducing the power from 1.5kW to 0.5kW, thus avoiding overcharging risks by extending the charging cycle. This closed-loop control logic not only ensures the efficient absorption of excess power but also builds a defense line for the safe operation of the battery through refined charge and discharge management, generating scheduling and processing information.

[0051] Scenario: During the midday hours of 12-2 PM in a household, the photovoltaic output is 3kW, the load is 1kW (2kW surplus); the energy storage capacity is 5kWh (rated power 2kW), and the current SOC is 30% (remaining rechargeable capacity: 5kWh x (90% - 30%) = 3kWh, which means it can be fully charged in 1.5 hours).

[0052] The excess power of 2kW is less than the remaining rechargeable power of the energy storage of 2kW, so it is in a state of slight excess. Then the energy storage is charged at full power of 2kW (the SOC reaches 90% after 1.5h, storing 3kWh), and the transferable load is triggered at the same time: the scheduled washing machine is started in advance (originally planned to start at 16:00, adjusted to 12:00, increasing the load by 0.5kW, the excess power is reduced to 1.5kW, the energy storage charging time is shortened to 1h, and overcharging is avoided).

[0053] To address excess power, the energy storage system employs a "full-power sprint" strategy, charging at its rated power until the State of Charge (SOC) reaches a safe upper limit (e.g., 90%), quickly locking in the core portion of the excess power. During this process, real-time monitoring data from the Battery Management System (BMS) dynamically adjusts the charging curve—when the SOC approaches 85%, it automatically switches from constant current charging to constant voltage charging to prevent battery damage caused by sudden voltage spikes, ensuring both efficient energy storage and equipment safety.

[0054] The load side initiates a "deep response" mechanism, which calls for adjustable and interruptible loads in a tiered manner: for adjustable equipment such as central air conditioning, the operating parameters are finely adjusted through fuzzy control algorithms (such as raising the set temperature by 1°C), increasing the power consumption by 10%-15% without making a significant difference to the user; for interruptible loads such as non-essential lighting and backup motors, they are automatically put into operation in a "priority reverse order" (such as turning on the workshop auxiliary lighting first, and then starting the backup cooling pump), further absorbing the remaining excess power.

[0055] A "price arbitrage" strategy is implemented during grid connection, relying on time series forecasting models (such as the ARIMA and LSTM fusion algorithm) to predict future electricity price fluctuations 2-4 hours in advance. For surplus power that still needs to be connected to the grid after energy storage and load absorption, the grid connection is strictly avoided during the flat price period of 0.5 yuan / kWh, and the peak price window of 0.8 yuan / kWh is prioritized. If the predicted peak price for the next day is higher than that day (e.g., the morning peak reaches 0.9 yuan / kWh), the power is temporarily stored through a virtual power plant aggregation platform to maximize the benefits of "off-peak grid connection". Throughout the process, the voltage and frequency deviations at the grid connection point are monitored in real time to ensure that the grid-connected power fluctuations are controlled within ±5%, meeting the requirements of power grid dispatching specifications and generating dispatching processing information.

[0056] For example, a factory's photovoltaic output is 500kW at midday, with a load of 300kW (200kW surplus); energy storage capacity is 1MWh (rated power 200kW, SOC 20%, total rechargeable capacity: 1MWh x (90% - 20%) = 700kWh, rechargeable for 3.5 hours); the distribution network allows a grid connection power of 150kW, and the following dispatching is implemented:

[0057] Energy storage is charged at 200kW (reaching 90% SOC after 3.5 hours, storing 700kWh);

[0058] Demand-side response:

[0059] Transferable load: 10 forklift charging stations (3kW each) are started in advance, increasing the load by 30kW (excess load is reduced to 170kW);

[0060] Adjustable load: When the central air conditioning set temperature is adjusted from 26℃ to 27℃, the compressor power is reduced by 10kW (if excessive, it is further reduced to 160kW);

[0061] Excess capacity: 160kW-200kW, energy storage charging. With 200kW of energy storage charging and an additional 40kW of load on the demand side, the actual excess capacity is 200kW - 40kW = 160kW. At this point:

[0062] If the distribution network allows 150kW of power to be connected to the grid, then 150kW will be connected to the grid, and the remaining 10kW will be used to trigger interruptible loads (such as non-essential lighting, which will be temporarily turned off to absorb 10kW).

[0063] In response to the situation of large excess capacity, the energy storage segment starts the "saturation energy storage" mode, charging at full speed with the system's rated power until the SOC reaches the safe upper limit (such as 90%). During the charging process, the current slope at the end of the charging period is dynamically adjusted in combination with the battery cycle life model. When the SOC is close to 85%, it smoothly transitions from constant current mode to trickle charging, which ensures energy storage efficiency and reduces battery polarization damage.

[0064] The load side activates the "full response" mechanism, calling up all types of adjustable resources according to priority: transferable loads (such as early start-up of irrigation pumps in industrial parks), adjustable loads (such as increasing the electrolysis power of hydrogen production equipment to 120% of the rated value), and interruptible loads (such as temporary access to backup heating devices in non-core production workshops), forming a multi-dimensional power consumption network.

[0065] At the cross-regional coordination level, a "grid-load interaction" backup plan is activated: on the one hand, excess electricity is transmitted to surrounding load centers (such as commercial complexes 30 kilometers away) through microgrid interconnection systems, utilizing regional load differences to achieve mutual power sharing; on the other hand, physical energy storage backup devices such as hydrogen energy and thermal energy storage are activated to convert electrical energy into storable secondary energy.

[0066] If there is still surplus power after the above measures, the "intelligent curtailment" mechanism will be activated: based on AI visual recognition and string efficiency assessment model, priority will be given to cutting off shaded areas and aging strings of modules (inefficient units with power generation efficiency of less than 70%), and the curtailment rate will be strictly controlled to ≤5% through "precise cutting" rather than "overall curtailment".

[0067] Throughout the dispatching process, relying on the real-time power balance monitoring platform, the absorption data of each link is updated every 5 minutes. By dynamically adjusting the resource input ratio, the dual objectives of maximizing the utilization of excess power and minimizing curtailment losses are achieved under the premise of meeting the grid security constraints (such as power fluctuation at the grid connection point ≤ ±10%), and dispatching information is generated.

[0068] For example, a photovoltaic power station outputs 100MW at midday, but the load is only 10MW (90MW surplus); the energy storage capacity is 10MWh (rated power 30MW, SOC 10%, total rechargeable capacity: 10MWh x 80% = 8MWh, which can be charged for 0.27h, far less than the surplus duration of 6h); the power allowed to be fed into the grid is 50MW.

[0069] action:

[0070] Energy storage is charged at 30MW (reaching 90% SOC after 0.27h, storing 8MWh);

[0071] Maximizing demand-side response:

[0072] Transferable load: All surrounding agricultural irrigation pumps (total 20MW) are started (excess capacity reduced to 70MW);

[0073] Adjustable load: Increase the power of oxygen generators and air compressor stations (total 15MW) within the park (reduce excess capacity to 55MW);

[0074] Interruptible load: Temporary shutdown of non-essential production equipment (5MW) (excess capacity reduced to 50MW);

[0075] Activate standby power consumption:

[0076] The electrolyzer hydrogen production system (20MW) is started up, consuming 20MW (excess capacity reduced to 30MW);

[0077] Neighboring network support: Through microgrid interconnection, 10MW can be delivered to an industrial park 30km away (excess capacity reduced to 20MW);

[0078] Final curtailment: Only 20MW - Distribution network allows 50MW to be connected to the grid. At this point, the excess has been reduced to 20MW, and all of it is connected to the grid (because the distribution network allows 50MW, only 20MW is actually needed, which meets the target of limiting the curtailment rate to <5%).

[0079] Step S3: Analyze the generated photovoltaic power shortage dispatch signal and calculate the difference between photovoltaic processing and load demand to obtain the power shortage P. 缺 Simultaneously, obtain the remaining available storage capacity C. ESS放 And C ESS剩 = CESS Amount × (Current SOC - Lower SOC Limit), where the lower SOC limit represents the minimum energy storage capacity of the energy storage system. If the power shortage Pdeficient < the remaining available energy storage capacity Cstorage ESS放 If it is classified as a minor deficiency, then the total energy storage capacity C is considered to be insufficient. ESS放 ×(SOC 当 If the current-to-current (SOC) limit is less than the power deficit Pdeficient × t, and the grid-available capacity Pgrid-available is less than the grid-available capacity, then it is classified as moderately deficient. If the power deficit Pdeficient 缺 ×t 持续 >Purchasable grid capacity P 网购 If the deficiency is classified as a major deficiency, then scheduling and processing will be carried out for each different deficiency situation.

[0080] For minor load shortages, the energy storage dynamic discharge matching adopts a "gap tracking" mode, precisely setting the discharge power according to the real-time load gap. For example, when the detected actual load gap is 1.2kW, the energy storage system outputs 1.2kW of power instead of mechanically applying the rated power of 2kW, avoiding excessive energy release through "on-demand discharge" (experimental data shows that unnecessary over-discharge can shorten battery cycle life by 3%-5%). Simultaneously, it monitors changes in the energy storage SOC in real time. When the SOC falls below 30%, a "power buffer" mechanism is automatically activated to control discharge power fluctuations within ±0.1kW, reducing battery degradation losses from frequent charging and discharging.

[0081] Flexible load coordination and regulation achieves refined energy control through "intelligent algorithms + scenario adaptation": When the air conditioner is activated, the set temperature is finely adjusted from 26℃ to 25℃ using a fuzzy control algorithm (with no significant difference in human perception), reducing the load by 0.3kW; for electric water heaters, a "step-down power reduction" strategy is adopted, reducing the heating setting from 2kW to the heat preservation setting, further reducing unnecessary energy consumption by 0.2kW. The linkage of these two types of loads can stably reduce electricity consumption by 0.3-0.5kW, equivalent to reducing the original deficit by nearly 40%.

[0082] The grid purchase of electricity is optimized by implementing a "micro-scale real-time supplement" strategy: after energy storage and load regulation, if there is still a small gap such as 0.2kW, electricity is purchased from the grid at the real-time electricity price. Through the "small batch, high frequency" energy supplementation method, large electricity purchase expenditures caused by accumulated gaps are avoided, and dispatch information is generated.

[0083] For example, in a household between 5 PM and 7 PM, the photovoltaic output is 0.5kW (sunset), the load is 2kW (a shortfall of 1.5kW); the energy storage capacity is 10kWh (rated power 2kW), the current SOC is 70% (remaining discharge capacity: 10kWh x (70% - 20%) = 5kWh, which can be discharged for 2.5 hours), the shortfall of 1.5kW is less than the remaining discharge capacity of the energy storage of 2kW, so a small deficit strategy is implemented;

[0084] The energy storage can discharge at 1.5kW (prioritizing to fill the gap), and can discharge for 3.3 hours (covering 5-7 PM + the following 1 hour);

[0085] Trigger adjustable load: When the air conditioner set temperature is adjusted from 26℃ to 28℃ (reducing compressor power by 0.3kW), the actual power deficit is reduced to 1.2kW, and the energy storage discharge time is extended to 4.2h (for greater safety).

[0086] To address insufficient power, the energy storage-guided discharge employs a "power adaptation + threshold protection" strategy: The rated power of the energy storage is used as the upper limit, and the discharge power is dynamically adjusted according to the real-time demand of critical loads (e.g., if a factory production line requires 200kW, then precisely output 200kW, rather than discharging at full power), until the SOC drops to a safe threshold (e.g., 20%). During this process, a dedicated power supply channel is configured for primary loads such as hospital ICUs and chip manufacturing cleanrooms. The BMS system locks 20% of the energy storage capacity as a "safety net," ensuring continuous power supply to critical loads for more than 30 minutes even if the overall SOC drops to the threshold, thus preventing damage to battery cycle life from deep discharge (experimental data shows that a drop in SOC from 20% to 15% shortens battery cycle life by 8%-12%).

[0087] Flexible load reduction is implemented through "timing optimization + priority control":

[0088] Transferable loads are delayed until off-peak electricity prices through a "peak-shaving scheduling algorithm";

[0089] Interruptible loads are shut down in a three-tier priority table: Level 1 (non-essential lighting, advertising screens) are cut off first, reducing the load by 10%; Level 2 (office air conditioners, water dispensers) are shut down next, reducing the load by another 15%; Level 3 (auxiliary production equipment) is shut down last, with the total reduction controlled in the range of 20%-30%, ensuring that core production is not affected.

[0090] The intelligent power grid purchase and replenishment system relies on a "peak-valley arbitrage + dynamic balance" model: the remaining gap is replenished in stages according to real-time electricity prices—power is purchased directly from the grid during flat periods; during peak periods, if the energy storage SOC still has redundancy (e.g., 20%), 5% of the capacity is temporarily released (discharged to 15%) to fill the gap.

[0091] The entire scheduling process is dynamically adjusted through a closed-loop system of "real-time monitoring - algorithm decision-making - execution feedback". Load data and electricity price information are updated every 15 minutes to ensure that the strategy always adapts to the actual operating conditions. While ensuring the continuous operation of critical loads, the overall electricity cost is controlled at the optimal level, and scheduling information is generated.

[0092] For example, at 6-8 PM, the factory's photovoltaic output is 100kW, and the load is 500kW (with a 400kW shortfall); the energy storage capacity is 2MWh (rated power 200kW), and the current SOC is 60% (remaining dischargeable capacity: 2MWh x 40% = 0.8MWh, which can be discharged for 4 hours); the grid's available capacity is 300kW, and the energy storage's dischargeable capacity of 0.8MWh is less than the shortfall of 400kW × 2 hours (800MWh) less than the grid's available capacity of 300kW × 2 hours (600MWh), thus implementing a shortfall strategy.

[0093] Energy storage discharged at 200kW (to ensure critical production lines: 300kW load), leaving a 100kW shortfall;

[0094] Transferable load: The charging of forklifts for the next morning shift (20kW) will be delayed until after 8 pm (the gap is reduced to 80kW);

[0095] Interruptible loads: Non-critical lighting and office equipment (20kW) will be shut down (the gap will be reduced to 60kW);

[0096] Electricity purchased from the grid: Electricity is purchased at the flat electricity price.

[0097] In the event of a severe power shortage, the energy storage system discharges to the lower limit of the State of Charge (SOC) at its rated power to fully guarantee the operation of primary loads such as operating rooms and data centers. All interruptible loads (except for critical systems) are shut down. Unnecessary circuits are remotely cut off through smart circuit breakers to minimize the gap. Emergency power sources such as diesel generators and hydrogen fuel cells are started to form a double insurance of "energy storage + backup". The remaining gap is filled by purchasing electricity from the main grid according to the grid dispatch instructions. At the same time, the demand response is reported to the dispatch center to generate dispatch information.

[0098] The energy storage emergency supply guarantee mechanism employs a "directional discharge + threshold protection" approach: it continuously discharges at the rated power of the energy storage until the State of Charge (SOC) reaches the emergency lower limit (e.g., 10%, this threshold is only activated during large shortages and is maintained above 20% under normal circumstances). During the discharge process, the primary load (operating room life support equipment, data center server clusters, etc.) is locked in through a load priority matrix.

[0099] Rigid load reduction implements "circuit-level precise disconnection": relying on intelligent circuit breakers and load topology maps, non-critical circuits (such as general ward lighting and office air conditioning) are remotely disconnected, while essential loads such as fire protection and monitoring are preserved. Through a preset "load reduction priority table," 80% of non-essential power consumption can be disconnected within 15 seconds.

[0100] The backup power supply is linked to build a "multi-energy complementary emergency network": the backup power supply is activated according to the principle of "start-up speed priority". The diesel generator (response time < 5 minutes) is given priority to make up for the high frequency fluctuation gap, and the hydrogen fuel cell (endurance > 8 hours) undertakes the base load supplementation, forming a double insurance of "energy storage buffer + backup power supply continuous power supply".

[0101] The power grid coordinates and fills gaps by implementing a dual-track strategy of "dispatch response + economic optimization": the remaining gap is strictly filled by purchasing electricity from the main grid according to the grid dispatch instructions, while at the same time, the "adjustable load capacity" (such as promising to increase electricity consumption by 100kW during off-peak hours) is declared to the dispatch center through the demand response platform in exchange for priority power supply quotas during peak hours.

[0102] The entire process incorporates "safety redundancy monitoring": real-time tracking of individual battery cell voltage differences (power reduction immediately when exceeding 50mV), backup power supply exhaust temperature (early warning triggered when exceeding 60℃), and grid connection point frequency deviation (forced frequency stabilization within ±0.5Hz). Through multi-level protection, emergency dispatch is ensured to be both efficient and safe.

[0103] For example, during a midday downpour at the hospital, the photovoltaic output plummeted from 500kW to 50kW, while the load was 1000kW (a shortfall of 950kW). The energy storage capacity was 5MWh (rated power 500kW), with a current SOC of 80% (remaining discharge capacity: 5MWh x 60% = 3MWh, enough for 6 hours of discharge). The grid's available capacity was 400kW (but due to a distribution network fault, only 400kW could be supported). The shortfall of 950kW x 6 hours (5700MWh) exceeded the grid's available capacity of 400kW x 6 hours (2400MWh), thus implementing a major shortfall strategy.

[0104] Energy storage discharges at 500kW (to ensure critical loads: 600kW for operating rooms and ICUs), leaving a remaining shortfall of 350kW;

[0105] Interruptible loads: Non-critical lighting and general ward air conditioning (200kW) are all shut down (the shortfall is reduced to 150kW);

[0106] Power purchase from the grid: 400kW (covering a 150kW power shortage + reserved for energy storage replenishment);

[0107] Start backup power: Start the diesel generator (200kW) to supplement 100kW (the shortfall is reduced to 50kW);

[0108] Ultimately: 600kW of critical loads are guaranteed by energy storage (500kW) + grid (100kW), while only 50kW of non-critical loads are subject to power curtailment (meeting the "ensuring critical loads" target).

[0109] Step S4: Perform scheduling control on the photovoltaic energy storage system based on the scheduling information obtained from the analysis of photovoltaic surplus and photovoltaic shortage.

[0110] The data in the above formulas are all calculated using numerical values, without substituting the units of the parameters. In addition, the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0111] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A multi-objective optimization scheduling and control strategy for a photovoltaic energy storage system, characterized in that, The strategy specifically includes the following steps: Predict short-term photovoltaic processing and load demand, compare the two values, and generate photovoltaic over-dispatch signals or photovoltaic under-dispatch signals; The system analyzes the photovoltaic overcapacity dispatch signal, calculates and matches the overcapacity, determines the specific overcapacity situation, performs dispatch according to different overcapacity situations, and generates dispatch information. The photovoltaic power shortage dispatch signal is analyzed, the power shortage is calculated and matched, the specific situation of the shortage is determined, and dispatch is carried out according to different specific situations of the shortage to generate dispatch information; The photovoltaic energy storage system is scheduled and controlled based on the scheduling information obtained from the analysis of photovoltaic surplus and photovoltaic shortage.

2. The multi-objective optimization scheduling and control strategy for a photovoltaic energy storage system according to claim 1, characterized in that, The specific method for generating photovoltaic surplus dispatch signals or photovoltaic shortage dispatch signals is as follows: The short-term photovoltaic output is predicted by combining physical and statistical models, while the short-term load demand is predicted by analyzing historical data, and the magnitudes of the two are compared. If the photovoltaic output exceeds the load demand, it indicates a photovoltaic surplus, and a photovoltaic surplus dispatch signal is generated. Conversely, if the photovoltaic output is less than the load demand, it indicates a photovoltaic shortage, and a photovoltaic shortage dispatch signal is generated.

3. The multi-objective optimization scheduling and control strategy for a photovoltaic energy storage system according to claim 1, characterized in that, The specific method for analyzing the photovoltaic surplus dispatch signal is as follows: The difference between photovoltaic power output and load demand is denoted as excess power P. 剩 Simultaneously, obtain the remaining rechargeable energy storage capacity C. ESS剩 And C ESS剩 =CESS 额 ×(SOC 上限 -SOC 当前 SOC 上限 State of Charge (SOC) indicates the upper limit of energy storage in an energy storage system. 当前 This indicates the current energy storage capacity of the energy storage system, CESS. 额 This represents the total energy storage capacity, where the excess power P 剩 Remaining rechargeable capacity C ESS剩 If the total energy storage capacity of CESS is slightly excessive, it is classified as slightly excessive. 额 <P 剩 ×t 持续 <P 允许 And P 允许 If P represents the allowable power for the distribution network to access the internet, then it is classified as medium excess. 剩 ×t 持续 If P is allowed, it is classified as a large surplus, and then different surplus situations after classification are scheduled and processed.

4. The multi-objective optimization scheduling and control strategy for a photovoltaic energy storage system according to claim 3, characterized in that, The specific method for scheduling different excess situations after classification is as follows: For situations with a small surplus, the excess power is used as the upper limit of the charging power. Devices with "flexible time and power matching" are selected, and the load clustering algorithm is used to trigger the charging power precisely. If the energy storage SOC is close to the warning value, the charging power is automatically reduced, the charging time is extended, the battery life is protected, and scheduling information is generated.

5. The multi-objective optimization scheduling and control strategy for a photovoltaic energy storage system according to claim 3, characterized in that, The specific method for scheduling different excess situations after classification is as follows: In response to the surplus situation, the energy storage is charged at its rated power until the SOC reaches its limit. Adjustable and interruptible loads are then called upon to absorb the surplus. The remaining power that needs to be connected to the grid avoids the flat price period and is prioritized to be connected to the grid during the peak price period. Time series forecasting is used to select the optimal grid connection point and generate scheduling and processing information.

6. The multi-objective optimization scheduling and control strategy for a photovoltaic energy storage system according to claim 3, characterized in that, The specific method for scheduling different excess situations after classification is as follows: In the event of a large overcapacity, the system charges the photovoltaic (PV) modules to their rated power limit, activates all interruptible, transferable, and adjustable loads, triggers hydrogen production and neighboring grid support, and prioritizes cutting off inefficient PV strings to control the curtailment rate to ≤5%, generating dispatch information.

7. The multi-objective optimization scheduling and control strategy for a photovoltaic energy storage system according to claim 1, characterized in that, The specific method for analyzing the photovoltaic under-scheduling signal is as follows: The power deficit P is obtained by calculating the difference between photovoltaic processing capacity and load demand. 缺 Simultaneously, obtain the remaining available storage capacity C. ESS放 And C ESS剩 =CESS 额 ×(SOC 当前 -SOC 下限 ), and SOC 下限 This represents the minimum energy storage capacity of the energy storage system; if the power P is insufficient... 缺 <Remaining available energy storage capacity C ESS放 If it is classified as a minor deficiency, then the total energy storage capacity C is considered to be insufficient. ESS放 ×(SOC 当前 -SOC 下限 )< Lack of power P 缺 ×t 持续 If the available grid capacity P is purchased from the grid, it is classified as a moderate shortage. If the power shortage P is continuous for t seconds, it is classified as a severe shortage. At the same time, different scheduling processes are carried out for different shortage situations.

8. The multi-objective optimization scheduling and control strategy for a photovoltaic energy storage system according to claim 7, characterized in that, The specific method for scheduling different deficiency situations is as follows: For minor shortages, the discharge power is dynamically matched according to the real-time load gap, flexible loads are called up, and unnecessary electricity consumption is reduced through fuzzy control algorithms. If there is still a remaining gap after energy storage and load adjustment, electricity is purchased from the grid at the real-time electricity price to generate dispatch information.

9. The multi-objective optimization scheduling and control strategy for a photovoltaic energy storage system according to claim 7, characterized in that, The specific method for scheduling different deficiency situations is as follows: In response to insufficient power, the energy storage is discharged at its rated power to prioritize critical loads until the State of Charge (SOC) drops to a safe threshold. Loads are then transferred and delayed until the off-peak electricity price period. Interruptible loads are shut down according to priority. The remaining shortfall is dynamically compensated for at the peak-valley electricity price. Power is directly replenished during flat periods and the remaining capacity of the energy storage is prioritized during peak periods, generating dispatch information.

10. The multi-objective optimization scheduling and control strategy for a photovoltaic energy storage system according to claim 7, characterized in that, The specific method for scheduling different deficiency situations is as follows: In the event of a severe power shortage, the energy storage system discharges to the lower limit of the State of Charge (SOC) at its rated power to fully guarantee critical loads, shuts down interruptible loads, remotely disconnects unnecessary circuits via smart circuit breakers, activates emergency power supplies, and purchases electricity from the main grid according to grid dispatch instructions for any remaining shortfall. At the same time, it reports demand response to the dispatch center and generates dispatch information.

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