A real-time scheduling method for optical storage based on load-following strategy

Abstract

The present disclosure relates to the technical field of distributed energy systems and intelligent scheduling, and particularly relates to a real-time scheduling method for photovoltaic energy storage based on a load-following source dynamic strategy, comprising the following steps: step 1, load division; step 2, data acquisition; collecting operation data of the photovoltaic energy storage system according to a preset period, and the operation mode comprises a grid-connected mode and an off-grid mode; step 3, scheduling strategy; in the grid-connected mode, if real-time power generation is greater than real-time power consumption, the excess photovoltaic power is used to charge the energy storage battery first, and the excess power is sold to the grid; if real-time power generation is less than real-time power consumption, the power gap of the load is supplemented by discharging the energy storage battery, and when the energy storage of the energy storage battery is insufficient, the power is supplemented by purchasing from the grid; in the off-grid mode, the elastic load and the interruptible load are closed, and the rigid load is forced to be continuously powered. The present disclosure solves the problems of slow scheduling response, disordered load control, large energy storage life loss and strong communication dependency of the photovoltaic energy storage system in the prior art.

Description

Technical Field

[0001] This disclosure relates to the fields of distributed energy systems and intelligent scheduling technology, and in particular to a real-time scheduling method for photovoltaic energy storage based on a load-following-source-driven strategy. Background Technology

[0002] With the rapid development of distributed photovoltaic (PV) power generation, a large number of user-side PV-storage systems have been put into operation. However, PV power generation is intermittent and fluctuates. Traditional dispatching methods mostly adopt the "source follows load" mode, that is, adjusting power generation output according to load demand, but it is difficult to adapt to fluctuations in PV output. Some existing systems have attempted the "load follows source" strategy, that is, dynamically adjusting load power consumption according to PV power generation, but they lack real-time and precise dispatching methods, and mostly rely on host computer or cloud computing, resulting in slow response speed and strong dependence on communication.

[0003] Existing scheduling methods include: rule-based fixed threshold control (e.g., activating charging piles when photovoltaic power exceeds a certain value); prediction-based day-ahead scheduling (optimizing charging and discharging plans using weather forecasts); and some collaborative scheduling schemes employing centralized controllers. These methods are effective in single-user or simple scenarios, but they still have the following shortcomings: 1. Delayed dispatch response leads to curtailment of solar power or grid disruption.

[0004] 2. Lack of real-time awareness of load priority and user behavior can easily lead to power outages of critical loads or decreased user satisfaction, and it is impossible to achieve millisecond-level rapid adjustment.

[0005] 3. The energy storage charging and discharging strategy is not deeply coupled with the real-time power and load demand of photovoltaics, making it difficult to balance economic efficiency and battery life.

[0006] 4. Existing solutions mostly rely on external communication or cloud platforms, which have poor reliability in remote areas or when the network is unstable.

[0007] Based on the above, the existing technology needs further improvement. Summary of the Invention

[0008] The purpose of this invention is to solve the problems of slow scheduling response, disordered load management, large energy storage life loss, and strong communication dependence in existing photovoltaic energy storage systems.

[0009] To address the aforementioned technical problems, this disclosure provides a real-time scheduling method for optical storage based on a load-following-source-momentum strategy, comprising the following steps: Step 1, Load Allocation: Divide the load of the photovoltaic storage system into rigid loads, flexible loads, and interruptible loads according to priority. Step 2, Data Acquisition: Collect the operating data of the photovoltaic energy storage system according to the preset cycle. The operating data includes: real-time photovoltaic power generation, real-time load power consumption, and grid operation mode, including grid-connected mode and off-grid mode. Step 3, Scheduling Strategy: In grid-connected mode, if the real-time power generation is greater than the real-time power consumption, the surplus photovoltaic power is used first to charge the energy storage battery, and if there is still surplus power, it is sold to the grid; if the real-time power generation is less than the real-time power consumption, the load power gap is supplemented by the discharge of the energy storage battery, and if the energy storage battery is insufficient, it is purchased from the grid to supplement the energy. In off-grid mode, flexible loads and interruptible loads are shut down, and a continuous power supply is forcibly guaranteed for rigid loads.

[0010] In some embodiments, in step 1, Rigid loads include one or more types of lighting equipment, refrigerators, and medical support equipment; Flexible loads include one or more of air conditioners, charging stations, and water heaters; Interruptible loads include temporary electrical equipment.

[0011] In some embodiments, in step 2, the preset period is no more than 100 milliseconds.

[0012] In some embodiments, the operating data also includes the state of charge of the energy storage battery. In step 3, the charging start condition for the energy storage battery is a state of charge of less than 90%, and the discharging start condition for the energy storage battery is a state of charge of more than 20%.

[0013] In some embodiments, the operational data is collected by the energy router configured in the photovoltaic storage system.

[0014] In some embodiments, the energy router records the historical power consumption behavior of the photovoltaic energy storage system and uses a lightweight online learning algorithm to predict short-term power consumption trends of the load in order to adjust the scheduling strategy in advance.

[0015] In some embodiments, lightweight online learning algorithms include exponentially weighted moving averages.

[0016] In some embodiments, the operating data also includes the voltage, current, and temperature of the energy storage battery.

[0017] In some embodiments, the operating data also includes the voltage, frequency, and fault signals of the power grid.

[0018] In some embodiments, the energy router includes a control module for executing scheduling strategies. By adopting the above technical solution, this disclosure has at least the following beneficial effects: 1. The scheduling logic is embedded in the local energy router, with a data collection cycle of ≤100ms. It does not require cloud, host computer, or external communication network, and there is no communication delay. It can operate stably in remote areas without network, solving the problem of delayed response in traditional cloud scheduling. 2. Three-level load differentiation dispatch: rigid loads are given priority in power supply throughout the process, avoiding power outages for critical equipment; flexible loads are flexibly adjusted and interruptible loads are cut off as needed. While adapting to photovoltaic fluctuations, this system preserves users' daily electricity consumption habits to the greatest extent, significantly improving user satisfaction. 3. Combining upper and lower thresholds of SOC (State of Charge), and multi-parameter protection of battery voltage / current / temperature, it avoids various types of damage such as overcharging, over-discharging, overheating, and overcurrent, thus extending the service life of energy storage batteries. 4. By relying on lightweight self-learning algorithms to predict peak electricity demand, we can plan energy storage charging and discharging schedules in advance. Combined with self-consumption and surplus power grid connection strategies, we can reduce peak electricity purchase costs from the grid and increase revenue from selling surplus power to the grid. 5. It is compatible with both grid-connected and off-grid operating modes, and can be directly applied to residential single-node optical storage systems, or extended to multi-user cluster optical storage systems, making it highly versatile. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a real-time scheduling method for optical storage based on a load-following-source-move strategy according to an embodiment of the present disclosure. Detailed Implementation

[0021] The embodiments of this disclosure will be further described in detail below with reference to the accompanying drawings and examples. The detailed description of the embodiments and the accompanying drawings are used to illustrate the principles of this disclosure by way of example, but should not be used to limit the scope of this disclosure. This disclosure can be implemented in many different forms and is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

[0022] These embodiments are provided to make the disclosure thorough and complete, and to fully express the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specifically stated, the relative arrangement of components and steps, material composition, numerical expressions, and values ​​set forth in these embodiments should be interpreted as exemplary only and not as limiting.

[0023] It should be noted that, in the description of this disclosure, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicating orientation or positional relationship, are only for the convenience of describing this disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0024] Furthermore, the terms "first," "second," and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different parts. "Vertical" is not strictly vertical, but within the permissible margin of error. "Parallel" is not strictly parallel, but within the permissible margin of error. Terms such as "including" or "contains" mean that the element preceding the word encompasses the element listed after the word, and do not exclude the possibility of encompassing other elements as well.

[0025] It should also be noted that, in the description of this disclosure, unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this disclosure depending on the specific circumstances. When a particular device is described as being located between a first device and a second device, an intermediary device may or may not be present between the particular device and the first or second device.

[0026] All terms used in this disclosure have the same meaning as understood by one of ordinary skill in the art to which this disclosure pertains, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant art, and not as idealized or highly formalized, unless expressly defined herein.

[0027] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0028] As mentioned in the background section, photovoltaic (PV) power generation is intermittent and fluctuating. Traditional dispatching methods often employ a "source follows load" model, adjusting power output according to load demand, but this is difficult to adapt to fluctuations in PV output. Some existing systems attempt a "load follows source" strategy, dynamically adjusting load consumption based on PV power generation, but lack real-time and precise dispatching methods and rely heavily on host computers or cloud computing, resulting in slow response times and strong communication dependence. Based on these findings, this disclosure provides a real-time PV-storage-utilization dispatching method based on a load-following-source strategy. This method eliminates the need for cloud computing, host computers, and external communication networks, eliminating communication delays and enabling stable operation in remote areas without network access, thus solving the problem of delayed response in traditional cloud-based dispatching. Three-level load differentiation dispatching adapts to PV fluctuations while preserving users' daily electricity consumption habits to the greatest extent possible, significantly improving user satisfaction. This solves one or more problems in the existing technologies.

[0029] This disclosure provides a real-time scheduling method for optical storage based on a load-following-source-momentum strategy, such as... Figure 1 As shown, the process includes the following steps: Step 1, Load Allocation: The loads of the photovoltaic-storage system are allocated into rigid loads, flexible loads, and interruptible loads according to priority. Step 2, Data Acquisition: The operating data of the photovoltaic-storage system is collected according to a preset cycle. The operating data includes: real-time photovoltaic power generation, real-time load power consumption, and grid operating mode, including grid-connected mode and off-grid mode. Step 3, Dispatch Strategy: In grid-connected mode, if the real-time power generation is greater than the real-time power consumption, surplus photovoltaic power is used to charge the energy storage battery. If there is still surplus power, it is sold to the grid. If the real-time power generation is less than the real-time power consumption, the load power gap is supplemented by the discharge of the energy storage battery. If the energy storage battery is insufficient, power is purchased from the grid to supplement it. In off-grid mode, flexible loads and interruptible loads are shut down, and continuous power supply is forcibly guaranteed for rigid loads.

[0030] Specifically, this technical solution achieves reverse load-following-source logic through load classification, system operation data collection, and dual-mode (grid-connected and off-grid) differentiated load-following-source scheduling. It dynamically matches load power consumption and energy storage charging and discharging behavior based on real-time photovoltaic power generation. At the same time, it distinguishes between grid-connected and off-grid operating conditions. In grid-connected operating conditions, it prioritizes the consumption of surplus photovoltaic power and makes up for power shortages, while in off-grid operating conditions, it prioritizes the power supply to core loads, thus achieving adaptive energy scheduling under all operating conditions.

[0031] Compared with existing technologies, this disclosure proposes a real-time scheduling method for photovoltaic energy storage based on a load-following-source-momentum strategy, constructs a complete load-following-source-momentum scheduling framework, and solves the problem that traditional source-following-load scheduling cannot adapt to photovoltaic fluctuations.

[0032] In the above scheduling method, in step 1, rigid loads include one or more of lighting equipment, refrigerators, and medical support equipment; flexible loads include one or more of air conditioners, charging piles, and water heaters; and interruptible loads include temporary electrical equipment. Specifically, rigid loads are selected as essential living and medical equipment that cannot be interrupted, including lighting equipment, refrigerators, and medical support equipment; flexible loads are comfort electrical equipment that can operate with delayed or reduced power, including air conditioners, charging piles, and water heaters; and interruptible loads are temporary electrical equipment that does not require continuous power supply and can be disconnected at any time without impact. By specifically defining the three levels of loads, the actual electrical equipment corresponding to different levels of loads is clearly defined, enabling scheduling instructions to accurately match the actual electrical equipment in the system.

[0033] In the above scheduling method, in step 2, the preset period is no more than 100 milliseconds. Specifically, the system data acquisition and scheduling decision output period is set to no more than 100ms. Each cycle completes one data acquisition, power comparison, and scheduling command issuance, achieving a second-level rapid response after photovoltaic fluctuations.

[0034] In the above scheduling method, the operating data also includes the state of charge (SOC) of the energy storage battery. In step 3, the charging start condition for the energy storage battery is when the SOC is below 90%, and the discharging start condition is when the SOC is above 20%. By setting the charging and discharging conditions for the energy storage battery, overcharging and over-discharging damage to the energy storage battery are avoided, thus extending the battery's service life.

[0035] In the aforementioned scheduling method, operational data is collected by the energy routers configured within the photovoltaic-storage system. The system's data acquisition hardware is the energy router, eliminating the need for external acquisition modules and cloud-based acquisition links. All photovoltaic-storage operational data is collected, converted from analog to digital, and preprocessed locally by the energy routers deployed on-site. This achieves fully localized data acquisition, eliminating reliance on external networks and remote acquisition equipment, and improving system operational reliability.

[0036] In the aforementioned scheduling method, the energy router records the historical electricity consumption behavior of the photovoltaic-storage-utilization system and employs a lightweight online learning algorithm to predict short-term load electricity consumption trends, thereby adjusting the scheduling strategy in advance. Specifically, the energy router records daily time-of-use electricity consumption data and predicts short-term peak and off-peak electricity consumption based on historical electricity consumption patterns. This enables advance prediction of scheduling strategies, reduces the frequency of power grid purchases, and improves the overall economic efficiency of the system operation.

[0037] In the aforementioned scheduling method, the lightweight online learning algorithm includes the exponentially weighted moving average. Specifically, the exponentially weighted moving average algorithm is used to assign higher weights to recent electricity consumption data, weaken the impact of long-term failed data, ensure the accuracy of short-term load forecasting, and at the same time, the algorithm code is concise and does not consume excessive operating resources.

[0038] In the aforementioned scheduling method, the operational data also includes the voltage, current, and temperature of the energy storage battery. Specifically, the energy router synchronously collects the voltage, loop current, and surface temperature of the energy storage battery. Once overcurrent, overtemperature, or voltage abnormalities occur, the charging and discharging power of the energy storage battery is dynamically reduced directly. In cases of severe abnormalities, the energy storage battery can be directly locked, thus avoiding the risk of thermal runaway and overcurrent damage to the energy storage battery.

[0039] In the aforementioned scheduling method, operational data also includes grid voltage, frequency, and fault signals. Specifically, the system monitors grid voltage deviation, frequency fluctuations, and grid outage fault signals in real time. Once an abnormal grid fault is detected, the system immediately and automatically disconnects from the grid, preventing grid fault impacts from damaging photovoltaic and energy storage equipment.

[0040] In the aforementioned scheduling method, the energy router is equipped with a control module to execute the scheduling strategy. Specifically, the energy router has a built-in dedicated control module, which independently calculates and directly issues all load switching commands, energy storage battery charging and discharging commands, and grid interaction commands, without requiring any external control equipment.

[0041] The present technical solution will be further illustrated by the following examples.

[0042] Example 1: Sufficient daytime sunlight and surplus grid-connected photovoltaic power.

[0043] 1. Operating parameters Real-time photovoltaic power generation: 8kW; Real-time household power consumption: 2kW; Initial SOC of energy storage battery: 60%; Normal grid connection and operation; Energy storage voltage, current, and temperature are all within the normal range; No abnormalities in grid voltage and frequency.

[0044] 2. Execution Process The energy router collects all operational data at 50ms intervals; The system's built-in control module completes the power difference calculation: Photovoltaic power - real-time power consumption = 6kW (surplus power); In grid-connected mode, the surplus power dispatch logic is triggered: when the energy storage SOC is 60% < 90%, the energy storage battery is charged with 6kW of power. Continuously monitor the energy storage temperature, voltage, and current in real time, and limit the charging power throughout the process to protect the battery; When the energy storage SOC rises to 90%, charging automatically stops, and the remaining surplus energy is sold directly to the grid.

[0045] 3. Implementation Results There is no curtailment of solar power throughout the entire process, 100% of photovoltaic power is consumed locally, energy storage provides normal replenishment, there is no power surge from the grid, and rigid loads are supplied with stable power throughout the entire process.

[0046] Example 2: Insufficient sunlight in the evening + power outage.

[0047] 1. Operating parameters Real-time photovoltaic power generation: 1kW; Real-time household power consumption: 3kW; Rigid load: 1.5kW (refrigerator + lighting); Flexible load: 1.5kW (air conditioner); Initial SOC of energy storage: 40%; In the event of a sudden power outage and detection of a grid fault signal, the system automatically switches to off-grid mode.

[0048] 2. Execution Process The energy router collects grid fault signals every 50ms and automatically switches to off-grid mode.

[0049] Immediately disconnect the flexible load of air conditioning and cut off all temporary interruptible loads; keep the rigid loads of refrigerators and lighting continuously powered.

[0050] Currently, the photovoltaic power supply is 1kW, the rigid load shortfall is 0.5kW, and the SOC of the energy storage battery is higher than 20%, so the energy storage discharge is started to replenish the energy. Simultaneously monitor the temperature and electrical parameters of the energy storage battery to prevent overload discharge from damaging the battery; If continuous discharge causes the energy storage SOC to drop to 20%, the system will gradually reduce the secondary lighting load, retaining only the primary rigid load of the refrigerator.

[0051] 3. Implementation Results Rigid loads provide uninterrupted power supply, and energy storage always operates within a safe range, eliminating the risk of battery over-discharge damage.

[0052] Example 3: Pre-prediction scheduling based on historical electricity consumption self-learning.

[0053] 1. Operating parameters Historical data: For 7 consecutive days, the peak electricity consumption period was from 17:00 to 19:00, with an average load of 4kW and photovoltaic output approaching 0 during the same period; at the current time of 16:30, the real-time photovoltaic output is 6kW and the energy storage SOC is 55%.

[0054] 2. Execution Process The energy router stores historical electricity consumption data locally and uses an exponentially weighted moving average algorithm to perform short-term load forecasting. Anticipating a peak in power consumption with no photovoltaic output in half an hour, the system issues a pre-dispatch instruction in advance; it uses the current surplus photovoltaic power to replenish the energy storage in advance, increasing the energy storage SOC to 80% and reserving peak discharge capacity; after the peak arrives, the photovoltaic output drops to zero, and the load is directly supported by the energy storage discharge, without the need to purchase electricity from the grid; It monitors grid parameters, energy storage electrical and temperature parameters throughout the process, and autonomously completes all scheduling based on the energy router control module, without the need for cloud intervention.

[0055] 3. Implementation Results By proactively avoiding power shortages during evening peak hours, peak-hour power purchases from the grid are avoided, reducing electricity costs for users. The high accuracy of dispatch prediction also improves the economic efficiency of system operation.

[0056] The embodiments of this disclosure have now been described in detail. To avoid obscuring the concept of this disclosure, some details known in the art have not been described. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein based on the above description.

[0057] While specific embodiments of this disclosure have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments or equivalent substitutions can be made to some technical features without departing from the scope and spirit of this disclosure. In particular, as long as there is no structural conflict, the technical features mentioned in the various embodiments can be combined in any manner.

Claims

1. A real-time scheduling method for optical storage based on a load-following-source-momentum strategy, characterized in that, Includes the following steps: Step 1, Load Allocation: Divide the load of the photovoltaic storage system into rigid loads, flexible loads, and interruptible loads according to priority. Step 2, Data Acquisition: Collect the operating data of the photovoltaic energy storage system according to a preset cycle. The operating data includes: real-time photovoltaic power generation, real-time load power consumption, and grid operation mode, including grid-connected mode and off-grid mode. Step 3, Scheduling Strategy: In the grid-connected mode, if the real-time power generation is greater than the real-time power consumption, the surplus photovoltaic power is used first to charge the energy storage battery, and if there is still surplus power, it is sold to the grid; if the real-time power generation is less than the real-time power consumption, the load power gap is supplemented by the discharge of the energy storage battery, and if the energy storage battery is insufficient, it purchases power from the grid to supplement the energy. In the off-grid mode, the resilient load and the interruptible load are shut down, and the rigid load is forced to continue to be powered.

2. The real-time scheduling method for optical storage and utilization based on a load-following-source-momentum strategy according to claim 1, characterized in that, In step 1, The rigid load includes one or more of lighting equipment, refrigerators, and medical support equipment; The flexible load includes one or more of air conditioners, charging piles, and water heaters; The interruptible loads include temporary electrical equipment.

3. The real-time scheduling method for optical storage based on a load-following-source-momentum strategy according to claim 1, characterized in that, In step 2, the preset period is no more than 100 milliseconds.

4. The real-time scheduling method for optical storage and utilization based on a load-following-source-momentum strategy according to claim 1, characterized in that, The operating data also includes the state of charge of the energy storage battery. In step 3, the charging start condition of the energy storage battery is that the state of charge is below 90%, and the discharging start condition of the energy storage battery is that the state of charge is above 20%.

5. The real-time scheduling method for optical storage and utilization based on a load-following-source-momentum strategy according to claim 1, characterized in that, The operational data is collected by the energy router configured in the photovoltaic energy storage system.

6. The real-time scheduling method for optical storage based on a load-following-source-momentum strategy according to claim 5, characterized in that, The energy router is configured to record the historical power consumption behavior of the photovoltaic energy storage system and use a lightweight online learning algorithm to predict the short-term power consumption trend of the load in order to adjust the scheduling strategy in advance.

7. The real-time scheduling method for optical storage and utilization based on a load-following-source-momentum strategy according to claim 6, characterized in that, The lightweight online learning algorithm includes an exponentially weighted moving average.

8. The real-time scheduling method for optical storage and utilization based on the load-following-source-momentum strategy according to claim 1, characterized in that, The operational data also includes the voltage, current, and temperature of the energy storage battery.

9. The real-time scheduling method for optical storage and utilization based on a load-following-source-momentum strategy according to claim 1, characterized in that, The operational data also includes the voltage, frequency, and fault signals of the power grid.

10. The real-time scheduling method for optical storage based on a load-following-source-momentum strategy according to claim 5, characterized in that, The energy router is equipped with a control module for executing the scheduling strategy.

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