A photovoltaic power operation and dispatching method and system based on smart grid

Abstract

This invention provides a photovoltaic power operation and scheduling method and system based on a smart grid, relating to the field of smart grid technology. The method includes the following steps: acquiring real-time operating data of preset monitoring nodes in the distribution network; when there is an abnormal risk of voltage deviation in the area where the preset monitoring node belongs; determining power regulation needs and generating power control commands for the power-adjustable loads based on the output trend of distributed power sources and the current operating status of power-adjustable loads; and sending the power control commands to the power-adjustable loads to bring the voltage value of the preset monitoring nodes back to the voltage safety threshold range. This invention aims to solve the problems of untimely and inaccurate response, insufficient utilization of regulation potential, and lack of multi-objective dynamic coordination capabilities in existing technologies. It fully utilizes the regulation potential of power-adjustable loads, effectively compensates for voltage deviations, brings the grid voltage back to the safe range, and improves the operating efficiency and reliability of the smart grid.

Description

Technical Field

[0001] This invention relates to the field of smart grid technology, and more specifically, to a method and system for photovoltaic power operation and scheduling based on a smart grid. Background Technology

[0002] In modern power systems, with the large-scale integration of renewable energy sources such as photovoltaic power generation, distribution network operation faces the dual challenges of high distributed generation penetration and rapid growth of new loads. Traditional centralized dispatching methods have three significant drawbacks: First, it has a lagging response to local voltage anomalies and can only rely on substation-level aggregated data for rough control, making it unable to accurately identify voltage deviations at key nodes such as feeder ends. Secondly, the adjustment potential of adjustable loads (such as electric vehicle charging piles) is not fully utilized. Existing technologies mostly use fixed thresholds to trigger power curtailment or grid disconnection protection, resulting in a large waste of photovoltaic power generation. Third, it lacks the ability to coordinate multiple objectives dynamically. When there is a directional conflict between the demand for voltage stability and the demand for photovoltaic power consumption, it is difficult to adjust and optimize strategies in a timely manner.

[0003] Specifically, in areas with high photovoltaic penetration, the reverse power flow generated during peak midday power generation often causes voltage rises at the grid's end. Traditional solutions can only address this by passively limiting inverter power generation, leading to a decrease in clean energy utilization. Simultaneously, the random connection of new loads such as electric vehicle charging stations causes instantaneous voltage drops, and current technologies cannot achieve rapid and flexible power adjustment while ensuring user charging needs. A more significant contradiction lies in the fact that when the grid needs to simultaneously meet voltage stability and photovoltaic absorption targets, existing dispatch systems lack the ability to quantitatively analyze key factors such as electrical distance and network topology, making it difficult to scientifically allocate adjustment responsibilities for each adjustable load, resulting in uneven utilization of local adjustment resources. These problems collectively constrain the safe and economical operation of smart grids in scenarios with a high proportion of renewable energy.

[0004] There is currently no effective technical solution to the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a photovoltaic power operation and scheduling method and system based on smart grids, which aims to solve the problems of untimely and inaccurate response, insufficient utilization of regulation potential, and lack of multi-objective dynamic coordination capabilities in the existing technology. It fully utilizes the regulation potential of power adjustable loads, effectively compensates for voltage deviations, brings the grid voltage back to a safe range, and improves the operating efficiency and reliability of smart grids.

[0006] In a first aspect, the present invention provides a photovoltaic power operation and dispatching method based on a smart grid, comprising the following steps: S1. Obtain real-time operating data of preset monitoring nodes in the power distribution network; the real-time operating data includes the voltage value of each preset monitoring node; S2. Compare the voltage value of the preset monitoring node with the preset voltage safety threshold range to determine whether there is an abnormal risk of voltage deviation in the area to which the preset monitoring node belongs; S3. When it is determined that there is an abnormal risk of voltage deviation in the area to which the preset monitoring node belongs, the output trend of distributed power sources and the current operating status of power adjustable loads in the area to which the preset monitoring node belongs are obtained; S4. Determine the power regulation requirement based on the output trend of the distributed power source and the current operating status of the power adjustable load; S5. Based on the voltage deviation and the power adjustment requirement, generate a power control command for the power adjustable load; the power control command is used to instruct the power adjustable load to increase or decrease its operating power; S6. Send the power control command to the power adjustable load to compensate for the voltage deviation of the preset monitoring node by adjusting the electrical energy absorbed by the power adjustable load from the distribution network, so that the voltage value of the preset monitoring node returns to the voltage safety threshold range.

[0007] The photovoltaic power operation and dispatching method based on smart grid provided by this invention can monitor the real-time voltage of preset monitoring nodes in the distribution network, and generate precise power control commands based on voltage deviation risk, combined with the output trend of distributed power sources and the operating status of power adjustable loads. This effectively compensates for voltage deviation, brings the grid voltage back to a safe range, solves the problems of slow response and inability to accurately identify local voltage anomalies in traditional dispatching methods, and fully utilizes the adjustment potential of power adjustable loads.

[0008] Secondly, the present invention provides a photovoltaic power operation and dispatching system based on a smart grid, comprising: The first acquisition module is used to acquire real-time operating data of preset monitoring nodes in the power distribution network; the real-time operating data includes the voltage value of each preset monitoring node; The comparison and judgment module is used to compare the voltage value of the preset monitoring node with the preset voltage safety threshold range to determine whether there is an abnormal risk of voltage deviation in the area to which the preset monitoring node belongs. The second acquisition module is used to acquire the output trend of distributed power sources and the current operating status of power-adjustable loads in the area where the preset monitoring node is located when it is determined that there is an abnormal risk of voltage deviation in the area where the preset monitoring node is located. The status determination module is used to determine the power adjustment requirements based on the output trend of the distributed power source and the current operating status of the power adjustable load. The control generation module is used to generate power control commands for the power adjustable load based on the voltage deviation and the power adjustment requirements; the power control commands are used to instruct the power adjustable load to increase or decrease its operating power. The control adjustment module is used to send the power control command to the power adjustable load, so as to compensate the voltage deviation of the preset monitoring node by adjusting the power adjustable load from the distribution network, so that the voltage value of the preset monitoring node returns to the voltage safety threshold range.

[0009] As can be seen from the above, the photovoltaic power operation and scheduling method based on the smart grid provided by this invention fully utilizes the adjustment potential of power-adjustable loads (such as electric vehicle charging piles), avoiding the waste of photovoltaic power generation caused by using only fixed threshold triggers for power curtailment or grid disconnection protection in traditional schemes, and achieving effective absorption of clean energy. Simultaneously, this method can flexibly adjust according to the real-time operating status of the grid, solving the problem that existing technologies cannot achieve rapid and flexible adjustment while ensuring user charging needs when instantaneous voltage drops are caused by the random access of new loads such as electric vehicle charging piles. Therefore, the method of this application can accurately identify voltage deviations, effectively utilize the adjustment potential of power-adjustable loads, significantly improve the safe and economical operation level of the smart grid in scenarios with a high proportion of new energy, and solve the problems of untimely and inaccurate response, insufficient utilization of adjustment potential, and lack of multi-objective dynamic coordination capabilities in existing technologies, demonstrating significant and superior technical effects.

[0010] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0011] Figure 1 This is a flowchart of a photovoltaic power operation and scheduling method based on a smart grid, provided in an embodiment of the present invention.

[0012] Figure 2 This is a schematic diagram of a photovoltaic power operation and dispatching system based on a smart grid, provided in an embodiment of the present invention.

[0013] Label Explanation: 100. First acquisition module; 200. Comparison and judgment module; 300. Second acquisition module; 400. Status determination module; 500. Control generation module; 600. Control adjustment module. Detailed Implementation

[0014] 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0015] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0016] In traditional smart grid dispatching methods, the increased penetration rate of distributed photovoltaic (PV) systems leads to reverse power flow in the distribution network, causing voltage rises at local nodes that exceed the voltage safety threshold. Simultaneously, changes in electricity load, such as electric vehicle charging, cause voltage fluctuations. Existing methods rely on regional aggregated data and historical forecasts, lacking real-time voltage monitoring and proactive adjustment capabilities for local nodes within the distribution network. This makes it impossible to promptly identify voltage deviation risks, leading to passive power generation limiting or grid disconnection of distributed sources, reducing renewable energy utilization efficiency, and impacting grid voltage stability and reliability.

[0017] For example, in a residential urban area, a rooftop photovoltaic (PV) system is installed at the end of a feeder. During midday, the PV output exceeds local load demand, generating reverse power flow and causing a voltage rise at the feeder end. Simultaneously, electric vehicles start charging, and the instantaneous power demand causes a voltage drop. The regional power grid dispatch center only obtains aggregated power data from the substation outlet and cannot monitor the voltage value at the feeder end in real time, thus preventing proactive intervention before voltage anomalies occur. When the voltage exceeds the safe range, the PV inverter triggers overvoltage protection, reducing output power or disconnecting from the grid. However, the regulating equipment responds lagly during voltage drops, causing localized voltage instability.

[0018] If the above problems are not resolved, local voltage deviations will occur, which may damage users' electrical equipment and increase the risk of grid failures; passive protection actions of distributed power sources will occur, reducing the effective utilization of clean electricity; the grid's voltage regulation capability will be limited, affecting overall operating efficiency and power quality, and hindering the reliable access of renewable energy.

[0019] For reference, see the appendix. Figure 1This invention provides a photovoltaic power operation and dispatching method based on a smart grid, comprising the following steps: S1. Obtain real-time operating data of preset monitoring nodes in the distribution network; the real-time operating data includes the voltage value of each preset monitoring node; S2. Compare the voltage value of the preset monitoring node with the preset voltage safety threshold range to determine whether there is an abnormal risk of voltage deviation in the area to which the preset monitoring node belongs; S3. When it is determined that there is an abnormal risk of voltage deviation in the area where the preset monitoring node belongs, obtain the output trend of distributed power sources and the current operating status of power adjustable loads in the area where the preset monitoring node belongs; S4. Determine the power regulation requirements based on the output trend of distributed power sources and the current operating status of power-adjustable loads; S5. Generate power control commands for power-adjustable loads based on voltage deviation and power regulation requirements; the power control commands are used to instruct the power-adjustable loads to increase or decrease their operating power. S6. Send power control commands to the power adjustable load to compensate for the voltage deviation of the preset monitoring node by adjusting the electrical energy absorbed by the power adjustable load from the distribution network, so that the voltage value of the preset monitoring node returns to the voltage safety threshold range.

[0020] For ease of understanding, the following explains some key terms in this embodiment: Distribution network: refers to the power network that distributes electrical energy from the transmission system to the user end, and typically includes substations, feeders, distribution transformers, and lines connecting users.

[0021] Pre-defined monitoring nodes: These refer to key locations in the distribution network that are pre-set for real-time data collection, such as high-penetration photovoltaic access points, feeder ends, or near electric vehicle charging stations. Data from these nodes is crucial for assessing the grid's operational status.

[0022] Real-time operating data refers to data collected from preset monitoring nodes at a specific point in time that reflects the current operating status of the power grid, including but not limited to voltage values, active power flow, and reactive power flow.

[0023] Voltage safety threshold range: This refers to the permissible voltage fluctuation range specified in the power grid operation specifications to ensure power quality and equipment safety. For example, for a 220V low-voltage distribution network, the voltage safety range may be set between 200V and 240V.

[0024] Voltage deviation risk: refers to the potential risk that the voltage value of the preset monitoring node exceeds the voltage safety threshold range, or the voltage change rate is too fast, which may affect the stable operation of the power grid and user equipment.

[0025] Distributed power generation output trend: This refers to the tendency of distributed power sources (such as photovoltaic power generation) to change their power generation over a future period within a specific region. This is usually determined through meteorological data, historical output data, and forecasting models.

[0026] Adjustable power loads refer to electrical equipment that can adjust its power absorption or output according to instructions, such as electric vehicle charging stations. These loads have a certain degree of flexibility and can be used for power balancing and voltage regulation of the power grid.

[0027] Power regulation requirements: refers to the magnitude and direction of power adjustment required to compensate for voltage deviations and bring the grid voltage back to a safe range.

[0028] Power control command: refers to the command generated by the dispatching system and sent to power-adjustable loads to instruct the load to increase or decrease its operating power in order to meet power regulation requirements.

[0029] This application proposes a photovoltaic power operation and dispatching method based on smart grids, which realizes active management and adjustment of voltage deviation in distribution networks through a series of steps.

[0030] In step S1, real-time operating data of preset monitoring nodes in the distribution network is acquired. This real-time operating data includes the voltage values ​​of each preset monitoring node. This step is fundamental to the entire dispatching method, providing real-time data for subsequent risk assessment and power regulation by continuously monitoring the operating status of key nodes in the power grid. For example, the dispatching platform can continuously receive real-time operating data from various key nodes in the distribution network (e.g., high-penetration photovoltaic access points, feeder ends, and areas near electric vehicle charging stations). This data can include the voltage values, active power flow, reactive power flow, and actual output of distributed photovoltaic systems at each node. This data can be collected through smart meters, sensors, or remote terminal units (RTUs) installed in the distribution network and transmitted to the central dispatching platform via communication networks (such as fiber optic, wireless, or power line carrier communication).

[0031] In step S2, the voltage values ​​of preset monitoring nodes are compared with preset voltage safety threshold ranges to determine whether there is an abnormal risk of voltage deviation in the area where the preset monitoring nodes belong. This step aims to proactively identify potential voltage problems in the power grid and avoid reactive responses. For example, after receiving real-time data, the dispatch platform immediately compares these voltage values ​​with preset voltage safety operating ranges. For a 220V low-voltage distribution network, the voltage safety range may be set between 200V and 240V. If the voltage value of a node continuously exceeds this range, or the voltage change rate (e.g., voltage change exceeding 2% within 1 second) is too fast, the platform will identify it as an area with a local voltage anomaly risk. This comparison and judgment can be automatically completed by the voltage monitoring module inside the dispatch platform, which uses preset algorithms and logic to assess the voltage status in real time.

[0032] In step S3, when it is determined that there is an abnormal risk of voltage deviation in the area where the preset monitoring node belongs, the output trend of distributed power sources and the current operating status of power-adjustable loads (such as electric vehicle charging piles) in the area where the preset monitoring node belongs are obtained. After identifying the voltage risk, this step further collects key dynamic factor data related to voltage regulation to provide a basis for subsequent power regulation. For example, when the dispatch platform determines that there is a risk of local voltage rise in a certain area of ​​the distribution network, it will immediately obtain the output trend of distributed power sources (such as photovoltaic systems) in that area. This can be done by analyzing meteorological forecast data (such as solar irradiance and temperature), historical output data, and real-time output data, and using predictive models to determine the changes in photovoltaic power generation in the future. At the same time, the platform will identify electric vehicle charging piles in the area that are currently in standby or low-power charging state and obtain their current operating status, such as battery state of charge (SOC), user-preset charging parameters, charging power, etc. This information can be obtained through data exchange with the charging pile management system.

[0033] In step S4, power regulation requirements are determined based on the output trend of distributed power sources and the current operating status of adjustable loads. This step, based on dynamic analysis of power sources and loads, calculates precise power regulation amounts to ensure timely and adaptable adjustments. For example, the platform combines real-time photovoltaic output data with current electricity load conditions to predict photovoltaic power generation trends and electric vehicle charging demand in the near future. Specifically, the platform obtains real-time meteorological data such as solar irradiance and temperature from meteorological service providers and combines this data with the actual output data of the current photovoltaic system. Through its internal short-term forecasting module, it predicts the trend of photovoltaic power generation in the region over the next 15 minutes to 1 hour. If there is sufficient sunshine and sunny weather is predicted for the near future, the platform will determine that photovoltaic output will remain high or continue to rise. Simultaneously, the platform exchanges data with the electric vehicle charging pile management system to obtain information such as the occupancy of charging piles, the number of charging vehicles, average charging power, and user-reserved charging information in the current area. Combining historical charging patterns with the current time, the platform predicts the electric vehicle charging load trend in the near future. For example, during rush hour, the platform may predict a significant increase in electric vehicle charging demand. When the platform determines that during periods of abundant sunshine, the photovoltaic output in a certain area may far exceed local electricity demand, thereby triggering reverse power flow and voltage rise risks (e.g., predicting that photovoltaic output will cause the voltage at the end of the feeder to exceed 235V), or when it determines that during a specific period, a large number of electric vehicles may be charging simultaneously, causing the risk of local voltage drops (e.g., predicting that concentrated charging will cause the voltage at the end of the feeder to fall below 205V), the platform will calculate, based on this information, how much power regulation is needed to restore the voltage to a safe range.

[0034] In step S5, based on voltage deviation and power regulation requirements, a power control command is generated for the power-adjustable load. This command instructs the power-adjustable load to increase or decrease its operating power. This step combines the severity of the problem with the solution to generate specific adjustment commands, ensuring their relevance and effectiveness. For example, if an overvoltage risk is identified and a voltage reduction is necessary, the platform will generate a command instructing the power-adjustable load (such as an electric vehicle charging station) to increase its operating power to draw more energy from the grid. Conversely, if an undervoltage risk exists, the load may be instructed to reduce its operating power. The command may include a target charging power (e.g., increasing from 5kW to 10kW), duration (e.g., 30 minutes), or priority flags. The platform will prioritize charging stations connected to vehicles with lower battery levels, where users are less sensitive to charging time, or who are willing to accept incentives.

[0035] In step S6, a power control command is sent to the adjustable load to compensate for the voltage deviation of the preset monitoring node by adjusting the electrical energy absorbed from the distribution network by the adjustable load, so that the voltage value of the preset monitoring node returns to the voltage safety threshold range. This step is a direct execution of the adjustment action. Through the response of the load, the grid voltage is restored to normal, ensuring the stable operation of the grid. For example, the platform sends control commands to the selected charging pile through the communication interface with the charging pile (e.g., MQTT or HTTP communication based on the OCPP protocol). After receiving the command, the charging pile immediately adjusts its internal power conversion circuit to increase the electrical energy absorbed from the grid. For example, a vehicle that was originally charging at 5kW will now be charging at 8kW; a charging pile that was originally in standby will prioritize charging at 6kW if a vehicle is connected. These additional electrical loads are like suddenly adding a "sponge" to absorb the excess power of the photovoltaic system. The dispatching platform continuously monitors the voltage at the end of the feeder. As the charging pile responds, the voltage begins to drop slowly and eventually stabilizes within a safe range. In this way, photovoltaic inverters no longer need to be restricted or disconnected from the grid, and clean energy is fully utilized.

[0036] The core concept of this solution lies in transforming electric vehicle charging stations from traditional passive electricity loads into actively controllable auxiliary resources within the smart grid. By establishing a coordinated dispatch platform, this platform can monitor the voltage status and photovoltaic (PV) power generation trends at each node of the distribution network in real time. Based on this information, it proactively issues "accelerated charging" or "delayed charging" commands to the electric vehicle charging stations. This two-way intelligent coordination, based on the actual operating status of the grid, enables electric vehicle charging stations to dynamically cooperate with distributed PV power generation. When PV output is excessive, it absorbs excess energy to suppress voltage rise; when the grid load is too high, it reduces power consumption to support voltage, thereby achieving refined management and stable operation of the voltage within the distribution network.

[0037] The following example will provide a more detailed explanation of the above technical solution: Suppose that in a certain area of ​​a city's power distribution network, due to the connection of a large number of rooftop photovoltaic systems, the output of distributed power sources in this area far exceeds the local electricity demand during the midday hours when the sun is strong. This causes the voltage at the preset monitoring node at the end of the power distribution feeder to rise continuously, reaching 245V, which exceeds the upper limit of the voltage safety threshold of 240V, posing a risk of overvoltage anomaly.

[0038] At this point, the dispatch platform obtains the real-time voltage data of the preset monitoring node through step S1, and determines in step S2 that there is a risk of abnormal voltage deviation. Subsequently, in step S3, the dispatch platform obtains the output trend of distributed power sources (photovoltaics) in the area and finds that it will remain at a high level for some time to come. At the same time, the platform identifies that multiple electric vehicle charging piles in the area are in standby or low-power charging state, for example, there are 10 charging piles, each with a current average charging power of 2kW.

[0039] In step S4, the dispatching platform calculates, based on the photovoltaic output trend and the current operating status of the charging pile, that approximately 30kW of additional power absorption is needed to bring the voltage at the end of the feeder back to a safe range of around 230V.

[0040] Next, in step S5, the scheduling platform generates power control instructions for the 10 electric vehicle charging stations based on the 30kW power adjustment requirement. The instructions instruct each charging station to increase its charging power from 2kW to 5kW for 30 minutes. The platform will prioritize charging stations connected to vehicles with lower battery levels, where users are less sensitive to charging time, or who are willing to accept the incentive.

[0041] Finally, in step S6, the dispatch platform sends these power control commands to the selected charging piles. Upon receiving the commands, the charging piles immediately adjust their operating power to absorb more electricity from the grid. For example, a vehicle that was originally charging at 2kW will now be charging at 5kW. With the increased power of these charging piles, a total of 30kW of electrical load is added, effectively absorbing the excess power from the photovoltaic system. The dispatch platform continuously monitors the voltage at the end of the feeder and finds that the voltage begins to slowly decrease, eventually stabilizing at a safe range of around 230V.

[0042] As can be seen from the above examples, the scheduling method of this application can proactively detect voltage anomaly risks in the power grid and dynamically determine power regulation needs by combining the output trends of distributed power sources and the operating status of power-adjustable loads. By generating and sending precise power control commands, this method can effectively utilize the flexibility of power-adjustable loads to proactively compensate for voltage deviations and bring the grid voltage back to a safe range.

[0043] Compared to traditional grid dispatching methods, the technical solution of this application has significant technological contributions. Traditional methods often rely on post-event analysis or passive protection actions of inverters to deal with voltage anomalies. For example, in the above example, if the traditional method is used, when the voltage at the end of the feeder reaches 245V, the photovoltaic inverter may automatically limit power generation or disconnect from the grid, resulting in the waste of clean energy. The method of this application, however, adjusts the voltage before it reaches the critical value through real-time monitoring and proactive intervention, avoiding photovoltaic power limiting or grid disconnection, thereby improving the utilization efficiency of renewable energy. Furthermore, traditional methods have relatively limited ability to perceive and control localized, rapidly changing voltage fluctuations and power flow reversals within the distribution network, making refined management difficult. This application, by acquiring real-time operating data from preset monitoring nodes and combining it with the output trend of distributed power sources and the current operating status of adjustable loads, can achieve refined and predictive intervention for localized voltage problems, ensuring stable grid operation and power quality. This proactive and refined dispatching strategy effectively solves the challenges of voltage stability and energy utilization efficiency in high-penetration photovoltaic grids, improving the operational efficiency and reliability of smart grids.

[0044] In some embodiments, the specific steps in step S4 include: S41. Obtain user-preset charging parameters for adjustable power load; S42. Calculate the actual adjustable power range of the adjustable power load based on the user-preset charging parameters and the current operating status of the adjustable power load; S43. Within the actual adjustable power range, determine the power regulation requirements based on the output trend of distributed power sources and the current operating status of power-adjustable loads.

[0045] In some of the above embodiments, step S41 aims to collect users' personalized preferences for the operation of power-adjustable loads (e.g., electric vehicle charging stations). The user-preset charging parameters may include the user's desired charging completion time, maximum charging power, minimum charging power, target charging capacity, and sensitivity to electricity prices, set through a mobile application, in-vehicle infotainment system, or the charging station's local operating interface. These parameters provide important user preference boundaries for subsequent power adjustments, ensuring that dispatch instructions meet grid demands while also considering user experience.

[0046] Step S42 aims to comprehensively consider user preferences and equipment physical limitations to determine the range within which the power adjustable load can safely and effectively adjust its power at the current moment. The user-preset charging parameters, such as user-defined charging time windows and maximum / minimum charging power limits, directly define the acceptable adjustment boundaries for the user. The current operating state of the power adjustable load may include its battery's state of charge (SoC), battery temperature, current charging power, equipment health status, and grid connection status. Based on this information, the system can use a preset algorithm model (e.g., a power limitation model based on battery management system (BMS) data analysis or a constraint model based on equipment rated parameters) to calculate the actual range within which the load can increase or decrease its power without damaging the equipment or violating user preferences. For example, if the user sets the maximum charging power to 10kW and the battery's current SoC is high, the system may limit the upper limit of the actual adjustable power range to 8kW to protect battery life.

[0047] Step S43, after clarifying the actual regulation capacity of the power-adjustable load, combines the actual demand of the power grid with the regulation potential of the load to generate the final power regulation command. The output trend of the distributed generation, such as predicted changes in photovoltaic power generation, indicates the direction and extent of potential voltage deviations in the power grid. The current operating state of the power-adjustable load, in addition to the parameters mentioned in S42, may also include its sensitivity to grid voltage deviations. The system can employ multi-objective optimization algorithms or rule-based decision logic to balance multiple objectives such as ensuring voltage stability, maximizing photovoltaic absorption, minimizing grid losses, and meeting user satisfaction. For example, when there is an overvoltage risk in the power grid and the output trend of distributed generation is increasing, the system will prioritize increasing the operating power of the power-adjustable load within the actual adjustable power range to absorb excess power. Conversely, when there is an undervoltage risk in the power grid, it may instruct a reduction in operating power.

[0048] The solution proposed in this application proactively acquires and integrates user-preset charging parameters and the current operating status of adjustable loads before determining power regulation needs. The system can then calculate the actual adjustable power range of each adjustable load at the current moment. Based on this, the dispatch platform determines the final power regulation needs according to the grid voltage deviation and distributed power output trends, ensuring that the actual adjustable power range is not exceeded. Specifically, after acquiring real-time operating data from preset monitoring nodes in the distribution network and determining that there is an abnormal risk of voltage deviation in the area where the preset monitoring nodes belong, the dispatch platform combines real-time photovoltaic output data and current electricity load conditions to judge the photovoltaic power generation trend and electric vehicle charging demand in the near future. For example, the platform acquires real-time meteorological data such as solar irradiance and temperature from meteorological service providers and combines this with the actual output data of the current photovoltaic system. Through its internal short-term prediction module, it judges the trend of photovoltaic power generation in the area over the next 15 minutes to 1 hour. Simultaneously, the platform exchanges data with the electric vehicle charging pile management system to obtain information such as the occupancy of charging piles, the number of charging vehicles, average charging power, and user-reserved charging information in the current area, thereby judging the electric vehicle charging load trend in the near future. When the platform determines that during periods of abundant sunshine, the photovoltaic output in a certain area may far exceed local electricity demand, potentially leading to reverse power flow and voltage rise risks, or when it determines that a large number of electric vehicles may be charging simultaneously during a specific period, causing localized voltage drops, steps S41 to S43 will be initiated. This mechanism ensures that the determined power regulation demand meets both the grid's voltage stability target and respects user wishes and equipment physical limitations, thereby significantly improving the success rate of dispatch command execution and the reliability of grid voltage control.

[0049] As a specific implementation method, when the dispatch platform determines that there is a risk of local voltage rise in a certain area of ​​the distribution network, for example, if it predicts that photovoltaic output will cause the voltage at the end of the feeder to exceed 235V, requiring adjustment through adjustable loads, the platform first obtains the user-preset charging parameters of the electric vehicle charging piles in that area. For example, user A sets the maximum charging power of their electric vehicle to 10kW via a mobile application, expecting to fully charge before 8 pm, and allowing power adjustment without affecting the charging completion time. Simultaneously, the platform obtains the current operating status of the charging pile, including a battery state of charge of 30%, a battery temperature of 25°C, and a current charging power of 5kW. Based on this information, the system calculates the actual adjustable power range of the charging pile. Considering battery health and user settings, the system determines that the acceptable charging power range for the charging pile at the current moment is 3kW to 8kW. Subsequently, combining the predicted distributed power output trend (e.g., photovoltaic output will continue to rise in the next 15 minutes) and the current operating status of the charging pile, the system determines the power adjustment requirement within this actual adjustable power range of 3kW to 8kW. For example, to mitigate overvoltage, the system might determine to increase the charging power of the charging station to 7kW. This requirement is then translated into a power control command and sent to the charging station, ensuring that the command effectively alleviates grid voltage issues while meeting user expectations and equipment capabilities.

[0050] Through the above technical solution, this application fully considers the user-preset charging parameters and current operating status of the power-adjustable load when determining power regulation requirements, thereby calculating the accurate actual adjustable power range. Based on this, the power regulation requirements are further determined according to the distributed power output trend of the power grid and the current operating status of the power-adjustable load. This ensures that the generated power control commands can effectively respond to voltage deviations in the power grid while respecting user wishes and equipment physical limitations. This significantly improves the success rate of dispatch command execution, avoids user dissatisfaction or system refusal to execute due to unreasonable commands, and thus enhances the effectiveness of compensating for voltage deviations through power-adjustable loads, ensuring the stable operation of the power grid and the full utilization of clean energy.

[0051] In some embodiments, the specific steps in step S42 include: S421. Obtain the battery state of charge and battery temperature for the power adjustable load; S422. Determine the safe operating power boundary of the battery for adjustable load based on the battery state of charge and battery temperature; S423. Based on the user's preset charging parameters, determine the user's preferred power range for adjustable load within the battery's safe operating power boundary; S424. Combining the voltage deviation of the distribution network and the output trend of distributed power sources, within the user's preferred power range, the actual adjustable power range of the adjustable load is obtained by adjusting the regulating power of the adjustable load.

[0052] Specifically, acquiring the battery state of charge (SOC) and battery temperature of the adjustable power load aims to obtain real-time key operating parameters of the battery connected to the adjustable power load (e.g., an electric vehicle charging station). SOC reflects the remaining charge of the battery, while battery temperature directly affects battery performance, lifespan, and safety. Obtaining these parameters is fundamental for safe and efficient power regulation, avoiding blind operation without understanding the battery's internal state, which could lead to safety hazards or accelerate battery aging. This process can involve data exchange between the vehicle's battery management system (BMS) and the charging station's communication interface (e.g., CAN bus, Ethernet, or wireless communication module), with the charging station uploading the acquired SOC and temperature information to the dispatch platform. Alternatively, sensors integrated within the charging station can directly measure charging current and voltage, and combine this with a battery model to estimate the battery SOC; simultaneously, temperature sensors such as thermistors can directly measure the temperature of the battery pack or key cells, and this data can be transmitted to the dispatch platform in real time.

[0053] Based on the battery's state of charge (SOC) and temperature, the safe operating power boundary for the adjustable load is determined. The core of this method is to set a hard constraint on the battery's charge and discharge power, ensuring that the battery will not be damaged by overcharging, over-discharging, overcurrent, or overheating during any adjustment operation. The safe operating power boundary defines the maximum acceptable charging power and minimum acceptable discharging power (if discharging is supported) of the battery at the current SOC and temperature. This boundary can be determined based on pre-stored battery characteristic curves and safe operating zone diagrams provided by the battery manufacturer. These curves typically provide maximum charge / discharge current or power limits at different SOCs and temperatures. The scheduling platform can determine the current power boundary by looking up tables or using interpolation algorithms based on the acquired real-time SOC and temperature values. Alternatively, real-time calculation methods based on battery equivalent circuit models or electrochemical models can be employed. By inputting the battery's real-time SOC, temperature, and historical operating data, the model can dynamically predict the battery's internal state (such as internal resistance and polarization voltage) and calculate the maximum instantaneous charge / discharge power that meets battery life and safety requirements.

[0054] Based on the user-preset charging parameters, within the battery's safe operating power boundary, the user-preferred power range for the adjustable load is determined. This aims to integrate the user's personalized needs into the power adjustment strategy, ensuring that user charging preferences are respected as much as possible while meeting battery safety requirements. The user-preset charging parameters reflect the user's expectations regarding charging speed, completion time, cost sensitivity, etc. The user-preferred power range is the acceptable power range for the user within the battery's safe operating power boundary. Users can set their charging preferences through a mobile app or the charging station's human-machine interface (HMI), such as "charge as soon as possible," "charge before point X," "prioritize low-cost charging," or "maximum charging power not exceeding YkW." After receiving these parameters, the scheduling platform can define an acceptable power range for the user within the battery's safe operating power boundary based on these preferences. For example, if the user selects "charge as soon as possible," the preferred power range will tend towards the maximum power allowed by the battery's safe operating power boundary; if the user sets "charge before point X," the system will calculate the minimum average power required to meet this time requirement based on the remaining time, current SOC, and battery capacity, and use this as the lower limit and the battery's safe operating power boundary as the upper limit to form the preferred power range. Furthermore, machine learning algorithms can be used to analyze users' historical charging behavior patterns and responses to different electricity price signals, automatically learning and predicting users' charging preferences. For example, if a user frequently charges during off-peak electricity hours at night, the system can infer that the user is price-sensitive, and thus prioritize low-power charging options when determining the user's preferred power range to reduce charging costs.

[0055] Combining the voltage deviation of the distribution network and the output trend of the distributed generation, the actual adjustable power range of the adjustable load is obtained by adjusting its regulation power within the user's preferred power range. This is crucial for achieving multi-objective coordination between the power grid, users, and batteries. It uses the real-time operational demands of the power grid (voltage deviation, distributed generation output trend) as external constraints, further refining the final actual power regulation range usable for grid dispatch within the already defined range of battery safety and user preferences. By dynamically adjusting the operating power of the adjustable load, the voltage stability requirements of the power grid can be effectively responded to. The dispatch platform can calculate the total power regulation required by the current power grid based on the determined direction and degree of voltage deviation (e.g., overvoltage requires a reduction in net power injection, undervoltage requires an increase in net power injection) and the obtained distributed generation output trend (e.g., excessive photovoltaic output leading to overvoltage). Then, within the determined user preferred power range, the total regulation is allocated to each adjustable load using optimization algorithms (such as linear programming, quadratic programming, or heuristic algorithms), thereby determining the actual adjustable power range of each load. For example, when there is an overvoltage risk in the power grid, the system tends to adjust the regulating power of adjustable loads to a higher value within the user's preferred power range to absorb more energy; conversely, when there is an undervoltage risk, it tends to adjust to a lower value (or stop charging) to reduce energy absorption. Simultaneously, decision-making mechanisms based on fuzzy logic or expert systems can also be employed. The system presets a series of rules, such as "when the voltage deviation exceeds X% and the photovoltaic output trend is upward, prioritize increasing the electric vehicle charging power to 80% of the upper limit of the user's preferred range." These rules, combined with real-time grid conditions and user preferences, dynamically select an optimal regulating power point or range within the user's preferred power range as the actual adjustable power range.

[0056] This application's solution employs a hierarchical, progressive, and gradually converging strategy to balance multiple objectives. This layered constraint, from the inside out and from hard constraints to soft constraints, ensures effective trade-offs and coordination among multiple objectives. First, by acquiring the battery's state of charge (SOC) and temperature for the adjustable power load, crucial information about the battery's current state is provided, laying the foundation for determining subsequent safety boundaries and avoiding adjustment risks due to unknown states. Second, based on the battery's SOC and temperature, a safe operating power boundary for the battery is determined, ensuring that power adjustment is strictly limited within the battery's physical tolerance range, preventing equipment damage caused by overcharging or over-discharging. On this basis, based on user-preset charging parameters within the safe operating power boundary, a user-preferred power range is determined. This, combined with user needs such as charging time or power limitations, ensures that the adjustment scheme conforms to user habits, improving satisfaction. Finally, considering the voltage deviation of the distribution network and the output trend of distributed power sources, the actual adjustable power range is obtained by adjusting the adjustment power within the user-preferred power range. This allows the adjustment scheme to dynamically respond to grid demands while taking into account user preferences and battery safety, achieving more intelligent and coordinated scheduling. This multi-level, phased power range determination mechanism allows for comprehensive consideration of battery physical limitations, user-specific needs, and real-time grid operation when determining power regulation requirements, thereby ensuring that the generated power control commands are both safe, effective, and executable.

[0057] The following is a concrete example. Assume there is an electric vehicle charging station within a distribution network area, and an electric vehicle is charging. The dispatch platform first obtains the real-time state of charge (SOC) of the electric vehicle's battery as 40% and the battery temperature as 25°C. Based on the characteristic curve of the battery model, the system determines that the safe operating power boundary of the battery under the current conditions is 5kW to 150kW. Simultaneously, the user sets charging preferences via a mobile application, requesting that the battery be charged to 80% before 6 PM and indicating a willingness to accept lower power to save costs. Based on these parameters, the system calculates the user's preferred power range within the safe boundary as 20kW to 60kW. At this point, the dispatch platform detects a local overvoltage risk in the distribution network due to excess photovoltaic (PV) output, and the distributed power output trend shows that PV power generation is still increasing. To alleviate the overvoltage, the grid needs to absorb more energy. The system, combining the grid voltage deviation and PV output trend, dynamically adjusts the electric vehicle's charging power within the user's preferred power range (20kW-60kW). For example, the system can instruct the charging station to increase the charging power to 50kW. In this way, the actual adjustable power range of the electric vehicle is determined to be [20kW, 50kW] (or with 50kW as the target power), thereby effectively responding to the voltage stability requirements of the power grid while ensuring battery safety and user preferences.

[0058] Through the above technical solution, this application provides a more refined and intelligent method for determining the actual adjustable power range of power-adjustable loads. This method, through a hierarchical and progressively converging strategy, effectively resolves potential conflicts between the three objectives of battery safety, user preference, and grid stability when determining power adjustment needs. First, by acquiring the battery's state of charge and temperature and determining the battery's safe operating power boundary, the operational safety and lifespan of power-adjustable loads (such as electric vehicle batteries) are fundamentally guaranteed, avoiding equipment damage caused by blind adjustments. Second, by considering user-preset charging parameters and determining the user's preferred power range, user acceptance and satisfaction with grid dispatch strategies are significantly improved, thereby encouraging users to actively participate in grid interaction. Finally, based on balancing battery safety and user preference, and combined with the voltage deviation of the distribution network and the output trend of distributed power sources, the adjustment power of power-adjustable loads is dynamically adjusted, ensuring that the determined actual adjustable power range can accurately respond to the real-time needs of the grid and effectively compensate for voltage deviations. This strategy, which comprehensively considers multiple constraints, makes the subsequently generated power control commands more reasonable, feasible, and efficient, greatly improving the overall performance and practicality of the aforementioned photovoltaic power operation and dispatching method based on smart grids, and ensuring the efficient utilization of clean energy and the stable operation of the power grid.

[0059] In some embodiments, the specific steps in step S424 include: S4241. Calculate the regional total power regulation demand of the distribution network; the regional total power regulation demand is determined based on the voltage deviation of the distribution network and the output trend of distributed power sources. S4242. Obtain the user-preferred power range for each of multiple power-adjustable loads; S4243. Based on the total regional power regulation demand and the user preference power range of each power adjustable load, and according to the response sensitivity of each power adjustable load to voltage deviation, determine the power allocation scheme for each power adjustable load; wherein, the response sensitivity is determined according to the following steps: based on the electrical distance between the power adjustable load and the location where the voltage deviation occurs and the network topology, determine the response sensitivity of each power adjustable load to voltage deviation. S4244. Each power adjustable load adjusts its operating power within its respective user-preferred power range according to the power allocation scheme, thereby obtaining the actual adjustable power range of each power adjustable load.

[0060] The total regional power regulation demand refers to the total power adjustment required to restore the voltage values ​​of preset monitoring nodes in the distribution network to within the voltage safety threshold range and effectively cope with changes in the output trend of distributed generation. This demand can be calculated comprehensively based on the current voltage deviation of the distribution network (e.g., the magnitude of overvoltage or undervoltage) and the predicted output trend of distributed generation (such as photovoltaics) over a future period (e.g., an expected increase in output leading to voltage rise, or a decrease in output leading to voltage drop). For example, when overvoltage exists, the total regional power regulation demand is negative, indicating that the load power absorption needs to be increased; when undervoltage exists, the total regional power regulation demand is positive, indicating that the load power absorption needs to be reduced. Calculation methods can include sensitivity analysis based on power flow calculations, or solving for the minimum power regulation amount under voltage constraints using optimization algorithms.

[0061] User-preferred power range refers to the range of power adjustments allowed for each adjustable power load (such as an electric vehicle charging station) while meeting user charging needs, battery safety, and user experience. This range is typically preset by the user in the charging management interface; for example, the user might set "charge as quickly as possible" or "charge within a specific time," or set a maximum and minimum charging power. Furthermore, this range may also be limited by factors such as battery state of charge (SOC) and battery temperature to ensure safe battery operation. It can be obtained through interaction with the user's application or by reading from the charging station management system database.

[0062] A power allocation scheme refers to how to rationally distribute the total power demand to various adjustable loads while meeting the overall regional power regulation requirements, enabling them to regulate power within their respective user-preferred power ranges. Determining this scheme requires comprehensive consideration of multiple factors. Among these, response sensitivity is an indicator that measures the degree to which the power change of a single adjustable load affects voltage deviation. Response sensitivity can be calculated based on the electrical distance between the load and the location where the voltage deviation occurs (e.g., line impedance, line length) and the network topology of the distribution network (e.g., radial, ring network). For example, loads closer to the location of the voltage deviation and with tighter electrical connections have higher response sensitivity, meaning their power regulation has a more significant effect on compensating for the voltage deviation. The allocation scheme can be determined using optimization algorithms, such as linear programming, quadratic programming, or heuristic algorithms, to achieve optimal power allocation while satisfying total demand, user preferences, and sensitivity constraints.

[0063] The actual adjustable power range refers to the range of power adjustments each adjustable load can actually make within its user-preferred power range, based on the allocated adjustment amount, after receiving the power allocation plan. This means that even if the allocation plan provides a suggested adjustment amount, the load must ensure that its final operating power does not exceed the user's preset preferred range. For example, if the allocation plan requires a charging station to increase its power by 5kW, but its user-preferred maximum charging power is only allowed to increase by 3kW, then the actual adjustment amount for that charging station will be 3kW. This step ensures a balance between the effectiveness of power adjustment and user satisfaction.

[0064] This application's solution addresses the complex issue of coordinated regulation of multiple adjustable-power loads in a distribution network through a refined multi-load coordination mechanism. When an abnormal voltage value is detected at a pre-set monitoring node in the distribution network, the system first calculates the total power regulation demand for the entire area based on the voltage deviation and distributed power generation output trends. This total demand is global, providing a macro-level target for subsequent refined allocation. Simultaneously, the system acquires the user-preferred power range for each adjustable-power load. This ensures that user-specific needs and battery safety limitations are fully respected during power regulation, avoiding decreased user satisfaction or equipment damage due to forced adjustments. Building upon this, the core of this solution lies in introducing the key indicator of response sensitivity. By analyzing the electrical distance and network topology between each adjustable-power load and the location of the voltage deviation, the system can accurately assess the compensation effect of each load on the voltage deviation. Loads closer to the voltage anomaly point and with stronger electrical coupling exhibit a more significant improvement in voltage through power regulation, thus exhibiting higher response sensitivity. When determining the power allocation scheme, the system comprehensively considers the total regional power regulation demand, the user-preferred power range of each load, and its response sensitivity. For example, the system can employ optimization algorithms to prioritize allocating regulation tasks to loads with high response sensitivity that remain within their user-preferred power range, thereby minimizing the total regulation or maximizing voltage improvement. Ultimately, each adjustable load adjusts its operating power within its respective user-preferred power range according to this optimized regulation power allocation scheme. This coordinated control mechanism ensures that the regulation behavior of each load is locally optimal and globally coordinated, thus collectively achieving effective compensation for the overall voltage deviation of the distribution network. In this way, this solution not only accurately addresses local voltage anomalies but also maximizes user experience and equipment safety while achieving voltage stability, avoiding problems such as unreasonable regulation and low efficiency that may occur in traditional methods.

[0065] The following is a concrete example to illustrate this. Suppose that in a certain distribution network area, due to excessive photovoltaic power output at midday, the voltage value at the preset monitoring node at the end of the feeder remains continuously higher than the upper limit of the voltage safety threshold, requiring a total reduction of 100kW of power absorption to restore the voltage to normal. There are three electric vehicle charging stations A, B, and C in this area, all of which are identified as power-adjustable loads. First, the system calculates the total power regulation demand for the area as -100kW (indicating a need to increase the load absorption power by 100kW). Next, the system obtains the user's preferred power range for these three charging stations: Charging station A: user's preferred power increase range is 0kW to 40kW; Charging station B: user's preferred power increase range is 0kW to 60kW; Charging station C: user's preferred power increase range is 0kW to 30kW. Simultaneously, based on the electrical distance of charging stations A, B, and C from the location where the voltage deviation occurs and the network topology, the system calculates their response sensitivity to the voltage deviation. Suppose calculations show that charging station A is closest to the voltage anomaly point and has the highest response sensitivity; charging station B is next; and charging station C is the farthest and has the lowest response sensitivity. Based on this information, the system will determine a power allocation scheme. For example, the system might use an optimization model to allocate the total regulation demand of 100kW as follows: charging station A is allocated an additional 40kW of power absorption (reaching its user preference limit, due to its highest sensitivity); charging station B is allocated an additional 50kW of power absorption (within its user preference range and relatively high sensitivity); and charging station C is allocated an additional 10kW of power absorption (within its user preference range). This results in a total increase of 40kW + 50kW + 10kW = 100kW of power absorption, meeting the region's total power regulation demand. Finally, each charging station adjusts its operating power within its respective user preference power range according to this allocation scheme. Charging station A increases its operating power by 40kW, charging station B by 50kW, and charging station C by 10kW. Through this coordinated adjustment, the voltage deviation of the distribution network is effectively compensated, the voltage value of the preset monitoring nodes returns to the voltage safety threshold range, and at the same time, the charging needs of users and battery safety of each charging station are respected.

[0066] Through the above technical solution, this application can effectively solve the complex problem of coordinated regulation of multiple adjustable loads in a distribution network. This solution provides a clear global objective for multi-load coordination by calculating the total regional power regulation demand. Simultaneously, it fully considers the user-preferred power range of each adjustable load, ensuring that user experience and equipment safety are balanced during power regulation. Crucially, by introducing and determining the regulation power allocation scheme based on the response sensitivity of each adjustable load to voltage deviation, regulation resources can be used precisely and efficiently, prioritizing the regulation of loads with the most significant voltage improvement effect, thereby achieving the best voltage compensation effect with minimal regulation cost. This refined and intelligent allocation mechanism avoids problems such as wasted regulation resources, over-regulation of some loads, or poor regulation effects that may occur in traditional methods, significantly improving the voltage stability and operating efficiency of the distribution network and promoting the effective absorption of distributed power sources.

[0067] In some embodiments, the specific steps in step S43 include: S431. Determine the grid's demand level for voltage stability based on the degree of voltage deviation; S432. Determine the grid's demand level for photovoltaic power consumption based on the output trend of distributed power sources; S433. Based on the voltage stability demand level and the photovoltaic absorption demand level, dynamically adjust the weight of the preset target parameters; the preset target parameters include voltage stability target, photovoltaic absorption maximization target, grid loss minimization target, and user satisfaction target; S434. Within the actual adjustable power range, determine the power adjustment requirements based on the current operating status of the power-adjustable load and the dynamically adjusted weights.

[0068] Specifically, when determining the grid's demand level for voltage stability, the degree of voltage deviation refers to the extent to which the real-time voltage value of a preset monitoring node in the distribution network deviates from the preset voltage safety threshold range. This degree can be expressed as an absolute value deviation, a relative percentage deviation, or the length of time the voltage exceeds the safe range. For example, it can be quantified by calculating the difference between the real-time voltage value and the upper or lower limit of the voltage safety threshold. When the voltage is higher than the upper limit, the deviation degree is (real-time voltage - upper limit voltage); when the voltage is lower than the lower limit, the deviation degree is (lower limit voltage - real-time voltage). Alternatively, multiple voltage deviation intervals can be defined, each corresponding to a deviation level, such as slight deviation, moderate deviation, and severe deviation. The grid's demand level for voltage stability reflects the urgency and importance of maintaining voltage stability in the current grid. The higher the level, the more serious the voltage stability problem, requiring priority. This demand level can be set as a discrete level, such as "low," "medium," "high," or "urgent," divided according to different thresholds for the degree of voltage deviation; or it can be a continuous value, mapped to a demand intensity value between 0 and 1 through a function.

[0069] When determining the grid's demand level for photovoltaic (PV) power absorption, the distributed generation output trend refers to the trend of PV power generation (such as PV) in the distribution network over a future period. This can be a predicted increase, decrease, or stabilization, along with the rate and magnitude of change. This trend can be predicted using short-term load forecasting models and meteorological forecast data (such as solar irradiance and temperature) combined with historical output data, or by real-time monitoring of the current output of distributed PV power sources and inferring it in conjunction with their maximum output capacity and weather changes. The grid's demand level for PV power absorption reflects the urgency and importance of absorbing and utilizing distributed PV power generation. A higher level indicates a more prominent PV absorption problem that needs to be addressed first. This demand level can be set as a discrete level, such as "low," "medium," or "high," based on the predicted degree of PV power surplus or deficit; or it can be a continuous value, mapping the PV output trend to a demand intensity value between 0 and 1 using a function. For example, a higher demand level is indicated when the predicted PV output will significantly exceed the local load.

[0070] When dynamically adjusting the weights of preset target parameters, the weight refers to an importance coefficient used to balance multiple optimization objectives. A larger value indicates a higher priority assigned to the corresponding objective during the optimization process. Dynamic adjustment means flexibly changing the importance of different scheduling objectives in optimization decisions based on the real-time operating status and demand of the power grid. This can be achieved by using a preset rule table or fuzzy logic system to find or calculate the weights of each target parameter based on a combination of voltage stability demand levels and photovoltaic (PV) absorption demand levels; or by using adaptive algorithms, such as reinforcement learning or genetic algorithms, to iteratively optimize weight allocation based on historical scheduling effects and the current power grid state. The preset target parameters refer to multiple performance indicators or optimization directions that need to be considered simultaneously during power grid scheduling optimization. These parameters represent different dimensions of power grid operation and require trade-offs to achieve overall optimization. In addition to voltage stability objectives, PV absorption maximization objectives, power grid loss minimization objectives, and user satisfaction objectives, they can also include equipment lifespan maximization objectives, operating cost minimization objectives, etc. These target parameters can be quantified into mathematical expressions. For example, the voltage stability objective can be expressed as minimizing the sum of squares of voltage deviations, and the PV absorption maximization objective can be expressed as minimizing curtailment.

[0071] When determining the power adjustment requirement, the actual adjustable power range refers to the upper and lower limits of the power adjustable load (such as an electric vehicle charging station) under its current operating state, enabling safe and effective power adjustment. This range is limited by various factors such as battery state of charge, battery temperature, and user-preset charging parameters. It can be dynamically calculated by real-time monitoring of the load's internal state (such as battery SOC and temperature) and user settings (such as desired charging time and maximum charging power), or by communicating with the load management system to obtain its reported current adjustable power range. The current operating state of the power adjustable load refers to the real-time operating status information of the power adjustable load when making power adjustment decisions. For example, the current charging power, battery state of charge (SOC), battery temperature, user presence, and charging reservation information of the electric vehicle charging station can be directly measured by sensors or obtained from the load equipment through a communication interface. Alternatively, the operating state in the near future can be estimated using historical data and predictive models. Determining the power adjustment requirement means calculating the specific value at which the power adjustable load needs to increase or decrease its power after considering various grid demands and the actual capacity of the load. This requirement is a solution to a multi-objective optimization problem, which can be achieved by solving a multi-objective optimization model that uses dynamically adjusted weights as coefficients and comprehensively considers objectives such as voltage stability, photovoltaic absorption, grid losses, and user satisfaction. Alternatively, it can be achieved by using a rule-based expert system or lookup table to directly provide recommended power regulation values ​​based on the current grid status and load operating status.

[0072] This application's solution introduces a demand level judgment and dynamic weight adjustment mechanism, enabling intelligent trade-offs among multiple objectives such as voltage stability, photovoltaic absorption, grid losses, and user satisfaction when determining power regulation demand, based on the real-time operating status and priorities of the power grid. This significantly improves the precision and adaptability of scheduling compared to the basic solution that determines power regulation demand solely based on the output trend of distributed power sources and the current operating status of adjustable loads. In this way, the system can more effectively cope with the complex operating environment of the power grid, avoiding suboptimal solutions caused by optimizing a single objective, thereby achieving smarter and more adaptive grid scheduling. It ensures that the determined power regulation demand can be prioritized according to the real-time "pain points" of the power grid, thus finding a comprehensive and optimal balance point among multiple conflicting objectives, achieving smarter and more adaptive grid scheduling.

[0073] The following is a concrete example. The solution proposed in this application can be deployed in a smart grid dispatch center. This dispatch center is equipped with an Advanced Meter Infrastructure (AMI) and an Energy Management System (EMS). The dispatch center continuously receives real-time voltage data from preset monitoring nodes in the distribution network. For example, if the voltage at the end of a feeder remains above 240V (the upper limit of the preset voltage safety threshold) for 5 minutes, or if the voltage rapidly increases from 230V to 245V within 1 minute, the system will determine the voltage deviation as "severe overvoltage." At this time, the grid's demand level for voltage stability can be set to "urgent" and assigned a high value, such as 0.9 (range 0-1). Simultaneously, the dispatch center obtains the solar irradiance forecast for the next hour and the output forecast of distributed photovoltaic power plants in the region through data interfaces with meteorological service providers and photovoltaic power plant management systems. For example, if it is predicted that solar irradiance will continue to increase in the next hour, and the total photovoltaic output in the region will increase from the current 1MW to 2MW, while the local load forecast is only 0.8MW, the system will determine that the distributed power output trend is "significantly increasing, with a risk of overcapacity." At this point, the grid's demand level for photovoltaic power consumption can be set to "high" and given a moderately high value, such as 0.7.

[0074] Based on this, the dispatch center dynamically adjusts the weights of preset target parameters according to the determined "urgent" voltage stability demand level (0.9) and "high" photovoltaic (PV) absorption demand level (0.7). These preset target parameters include voltage stability targets, PV absorption maximization targets, grid loss minimization targets, and user satisfaction targets. For example, the system can employ a weight adjustment module based on fuzzy logic. When the voltage stability demand level is "urgent," the weight of the voltage stability target can be increased from the initial 0.4 to 0.7; simultaneously, due to excess PV output, the weight of the PV absorption maximization target can be increased from the initial 0.3 to 0.5. To balance this, the weights of the grid loss minimization target and the user satisfaction target may be slightly reduced accordingly, for example, from 0.15 to 0.1 respectively. Ultimately, the dispatch center adjusts the weights within the calculated actual adjustable power range of the adjustable load, for example, the actual adjustable power range of a certain electric vehicle charging station is 2kW to 10kW. Based on the charging station's current operating status (e.g., current charging power of 5kW, battery state of charge of 60%, and user-set expected charging completion time of 2 hours) and dynamically adjusted weights, the dispatch center determines the power regulation requirement by solving a multi-objective optimization problem. This optimization problem aims to maximize the weighted overall objective function. For example, under the aforementioned weights, the optimization algorithm will prioritize addressing overvoltage issues and maximizing photovoltaic power absorption. Therefore, the system might determine the charging station's power regulation requirement as "increase by 3kW," i.e., from 5kW to 8kW. This regulation requirement helps absorb excess photovoltaic power to reduce voltage, remains within the user's acceptable range, and considers the safe operation of the battery.

[0075] Through the above technical solution, this application effectively addresses the shortcomings of traditional scheduling methods in multi-objective trade-offs. While some of the aforementioned embodiments propose methods for determining power regulation needs within an adjustable power range to optimize power regulation, they fail to systematically balance multiple objectives. This may lead to inconsistent power regulation needs, resulting in an inability to achieve optimal overall scheduling and impacting the overall operating efficiency and stability of the power grid. This application, by introducing the judgment of voltage stability demand levels and photovoltaic (PV) absorption demand levels, enables the scheduling system to accurately identify the "pain points" and priorities of the power grid in real time. Based on this, the weights of preset target parameters such as voltage stability, PV absorption maximization, grid loss minimization, and user satisfaction are dynamically adjusted. This ensures that when determining power regulation needs, intelligent and flexible trade-offs can be made based on the real-time operating status and urgency of the power grid. For example, when the power grid faces severe voltage anomalies, the voltage stability target is given a higher weight, thus prioritizing grid safety; when PV output is significantly excessive, the weight of the PV absorption maximization target increases, effectively promoting the utilization of clean energy. This dynamic trade-off mechanism avoids suboptimal solutions caused by single-objective optimization, enabling the determined power regulation demand to better adapt to the complex operating environment of the power grid and achieve more intelligent and adaptive grid dispatch. Ultimately, within the actual adjustable power range, the power regulation demand is determined by combining the current operating status of the adjustable load and the dynamically adjusted weights, ensuring the effectiveness and feasibility of the regulation commands. This significantly improves the overall operating efficiency and stability of the power grid, maximizes the utilization rate of renewable energy, and also takes into account user satisfaction.

[0076] In some embodiments, the specific steps in step S433 include: S4331. Obtain the rate of change of voltage stability demand level and the rate of change of photovoltaic consumption demand level, and determine whether there is a conflict between voltage stability demand level and photovoltaic consumption demand level, and obtain the judgment result; specifically, compare the power adjustment direction indicated by voltage stability demand level and photovoltaic consumption demand level, and when the power adjustment directions of the two are opposite, determine that there is a conflict between voltage stability demand level and photovoltaic consumption demand level. S4332. Based on the judgment results, determine the weight adjustment range of each preset target parameter; S4333. Based on the preset weight adjustment rules and weight adjustment range, the weights of each preset target parameter are adjusted step by step and limited to obtain the dynamically adjusted weights.

[0077] The purpose of acquiring the rates of change for voltage stability demand levels and photovoltaic power consumption demand levels is to capture the dynamic trends of these demand levels over time. The rate of change can be understood as the amount of change in demand levels per unit time, and its function is to provide crucial time-dimensional information for subsequent conflict assessment and weight adjustment. Specifically, this can be achieved by performing differential calculations or slope analysis on historical or real-time demand level data. For example, the system can record demand level values ​​over several past time steps and then calculate their linear regression slope or simple difference to characterize the rate of change. Alternatively, predictive models can be used to forecast demand levels in the near future, and the changing trend or rate can be calculated based on the forecast results.

[0078] Determining whether there is a conflict between the voltage stability demand level and the photovoltaic (PV) consumption demand level hinges on identifying whether the two demands contradict each other in terms of power regulation direction—that is, one demand requires an increase in power, while the other requires a decrease. This step aims to identify key scenarios in grid operation that could lead to conflicting dispatch decisions. Specifically, this involves comparing the power regulation direction indicated by the voltage stability demand level (e.g., reducing power when voltage is too high, increasing power when voltage is too low) with the power regulation direction indicated by the PV consumption demand level (e.g., increasing power consumption when PV output is excessive, decreasing power consumption when PV output is insufficient). A conflict is identified when the two directions are opposite. Alternatively, a conflict threshold can be set; a conflict is only identified when the regulation directions of the two demand levels are opposite and their intensity reaches a certain level, thus avoiding processing minor, insignificant directional differences.

[0079] Based on the judgment results, the weight adjustment range of each preset target parameter is determined. Here, the weight adjustment range refers to the amount by which the weights of each preset target parameter (e.g., voltage stability target, photovoltaic absorption maximization target, grid loss minimization target, and user satisfaction target) are adjusted after a conflict is detected. Its function is to quantify the intensity of the weight adjustment according to the severity and nature of the conflict, ensuring the targeting and effectiveness of the adjustment. Specifically, an adjustment range lookup table can be preset. Based on the type of conflict (e.g., conflict between excessive voltage and photovoltaic absorption, conflict between excessive voltage and photovoltaic absorption) and the severity of the conflict (e.g., voltage deviation magnitude, degree of photovoltaic overcapacity), the corresponding weight adjustment range can be directly obtained by looking up the table. Alternatively, fuzzy logic or an expert system can be used to dynamically calculate the weight adjustment range based on the judgment results (conflict type, severity) and preset rules.

[0080] Based on preset weight adjustment rules and the weight adjustment range, the weights of each preset target parameter are adjusted stepwise and limited to obtain dynamically adjusted weights. Stepwise adjustment means that the weights are not adjusted to the target value all at once, but gradually approach the target value through multiple small steps; limiting the weight adjustment means restricting the weight adjustment to a preset reasonable range to prevent the weights from being too large or too small. The purpose of this step is to ensure the smoothness, controllability, and stability of the weight adjustment process, and to avoid system oscillations or scheduling decision imbalances caused by sudden weight changes. Stepwise adjustment can use an iterative algorithm, adjusting a small portion of the adjustment range in each iteration until the target weight is reached or the conflict is resolved. Limiting the weights involves checking whether the weights exceed preset upper and lower limits after each adjustment; if they do, they are truncated to the boundary value. As another implementation method, a smoothing function or filter can be designed to apply the calculated adjustment range to the current weights, causing them to change gradually over a certain period of time. At the same time, hard boundary conditions are set to ensure that the weights always remain within a reasonable range.

[0081] This application's solution, by acquiring the rate of change of voltage stability demand levels and photovoltaic (PV) absorption demand levels, can capture the dynamic changing trends of the power grid's operating status in real time, providing a more timely and accurate basis for subsequent decision-making. By determining whether these two demands conflict, particularly by comparing the power regulation directions they indicate, it can effectively identify contradictory dispatching objectives in the power grid, avoiding the exacerbation of system instability due to blindly adjusting weights. Based on the conflict judgment results, the system can determine the weight adjustment range of each preset target parameter in a targeted manner, making weight adjustment more adaptable. Furthermore, by adjusting and limiting the weights step by step according to preset weight adjustment rules and determined adjustment ranges, the gradual and controllable nature of the weight adjustment process is ensured, effectively avoiding system oscillations that may be caused by sudden weight changes. Overall, these technical means work together to enable a more intelligent and stable balance between voltage stability objectives, PV absorption maximization objectives, power grid loss minimization objectives, and user satisfaction objectives when determining power regulation demands. Especially in complex scenarios with rapidly changing demands and conflicting objectives, it significantly improves the robustness and optimization effect of dispatching decisions. This enables the entire smart grid-based photovoltaic power operation and dispatch method to provide more refined and stable control strategies when dealing with voltage fluctuations and curtailment issues caused by high-penetration photovoltaic access.

[0082] The following is a concrete example. Suppose that on a sunny afternoon, the voltage stability demand level in a certain area of ​​the distribution network shows a continuous rise in voltage, requiring a reduction in power to maintain stability. Simultaneously, the photovoltaic (PV) consumption demand level shows strong PV output, requiring increased power consumption to avoid curtailment. At this point, the system first obtains the rate of change of the voltage stability demand level, for example, a voltage increase of 0.5V / min; and simultaneously obtains the rate of change of the PV consumption demand level, for example, an increase in PV output of 1MW / min. By comparison, it is found that the voltage stability demand indicates a reduction in power, while the PV consumption demand indicates an increase in power; the two power adjustments are opposite, thus indicating a conflict. Based on this conflict determination, the system, according to a preset strategy, determines the adjustment range of the weights for the voltage stability target and the PV consumption maximization target. For example, due to the high risk of voltage rise, the system might decide to increase the weight of the voltage stability target by 0.1, while decreasing the weight of the PV consumption maximization target by 0.05. Based on preset weight adjustment rules (e.g., 0.01 per second) and a determined adjustment range, the system adjusts these weights step by step, ensuring that the adjusted weights remain within the range of [0,1]. For example, the weight of the voltage stability target gradually increases from 0.4 to 0.5, while the weight of the photovoltaic power consumption maximization target gradually decreases from 0.3 to 0.25. This gradual adjustment avoids abrupt weight changes and ensures a smooth transition in scheduling decisions.

[0083] Through the above technical solution, this application can effectively address the challenges of voltage stability and rapid changes and conflicts in photovoltaic (PV) consumption demand during grid operation. By sensing the rate of change and conflict status of demand levels in real time and adopting a step-by-step, limited weight adjustment strategy, the stability and responsiveness of dispatch decisions are significantly improved, avoiding system oscillations caused by sudden weight changes. This makes the grid more flexible and efficient in balancing multiple objectives, thereby maximizing the utilization efficiency of renewable energy and reducing the occurrence of curtailment while ensuring the safe and stable operation of the grid.

[0084] Reference Appendix Figure 2 This invention provides a photovoltaic power operation and dispatching system based on a smart grid (this system adopts the photovoltaic power operation and dispatching method based on a smart grid as described in the above embodiments, and the specific process is described in the corresponding steps above), comprising: The first acquisition module 100 is used to acquire real-time operating data of preset monitoring nodes in the distribution network; the real-time operating data includes the voltage value of each preset monitoring node; The comparison and judgment module 200 is used to compare the voltage value of the preset monitoring node with the preset voltage safety threshold range to determine whether there is an abnormal risk of voltage deviation in the area to which the preset monitoring node belongs. The second acquisition module 300 is used to acquire the output trend of distributed power sources and the current operating status of power adjustable loads in the area where the preset monitoring node is located when it is determined that there is an abnormal risk of voltage deviation in the area where the preset monitoring node is located. The status determination module 400 is used to determine the power regulation requirements based on the output trend of the distributed power source and the current operating status of the power adjustable load. The control generation module 500 is used to generate power control commands for power-adjustable loads based on voltage deviation and power adjustment requirements; the power control commands are used to instruct the power-adjustable loads to increase or decrease their operating power. The control adjustment module 600 is used to send power control commands to the power adjustable load so as to compensate for the voltage deviation of the preset monitoring node by adjusting the power adjustable load to absorb electrical energy from the distribution network, so that the voltage value of the preset monitoring node returns to the voltage safety threshold range.

[0085] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0086] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A photovoltaic power operation and dispatching method based on a smart grid, characterized in that, Includes the following steps: S1. Obtain real-time operating data of preset monitoring nodes in the power distribution network; The real-time operating data includes the voltage values ​​of each of the preset monitoring nodes; S2. Compare the voltage value of the preset monitoring node with the preset voltage safety threshold range to determine whether there is an abnormal risk of voltage deviation in the area to which the preset monitoring node belongs; S3. When it is determined that there is an abnormal risk of voltage deviation in the area to which the preset monitoring node belongs, the output trend of distributed power sources and the current operating status of power adjustable loads in the area to which the preset monitoring node belongs are obtained; S4. Determine the power regulation requirement based on the output trend of the distributed power source and the current operating status of the power adjustable load; S5. Based on the voltage deviation and the power adjustment requirement, generate a power control command for the power adjustable load; the power control command is used to instruct the power adjustable load to increase or decrease its operating power; S6. Send the power control command to the power adjustable load to compensate for the voltage deviation of the preset monitoring node by adjusting the electrical energy absorbed by the power adjustable load from the distribution network, so that the voltage value of the preset monitoring node returns to the voltage safety threshold range.

2. The photovoltaic power operation and dispatching method based on smart grid according to claim 1, characterized in that, The specific steps in step S4 include: S41. Obtain the user-preset charging parameters of the power adjustable load; S42. Calculate the actual adjustable power range of the adjustable power load based on the user-preset charging parameters and the current operating status of the adjustable power load; S43. Within the actual adjustable power range, the power adjustment requirement is determined based on the output trend of the distributed power source and the current operating status of the power adjustable load.

3. The photovoltaic power operation and dispatching method based on smart grid according to claim 2, characterized in that, The specific steps in step S42 include: S421. Obtain the battery state of charge and battery temperature of the power adjustable load; S422. Determine the safe operating power boundary of the battery for the adjustable power load based on the battery state of charge and the battery temperature; S423. Based on the user-preset charging parameters, determine the user-preferred power range of the adjustable power load within the battery safe operating power boundary; S424. Combining the voltage deviation of the distribution network and the output trend of the distributed power source, within the user's preferred power range, the actual adjustable power range of the adjustable load is obtained by adjusting the regulating power of the adjustable load.

4. The photovoltaic power operation and dispatching method based on a smart grid according to claim 3, characterized in that, The specific steps in step S424 include: S4241. Calculate the regional total power regulation demand of the distribution network; the regional total power regulation demand is determined based on the voltage deviation of the distribution network and the output trend of the distributed power source; S4242. Obtain the user-preferred power range for each of multiple power-adjustable loads; S4243. Based on the total power regulation demand of the area and the user preference power range of each of the power adjustable loads, and according to the response sensitivity of each of the power adjustable loads to the voltage deviation, determine the power regulation allocation scheme of each of the power adjustable loads; S4244. Each of the power adjustable loads adjusts its operating power within its respective user-preferred power range according to the power adjustment allocation scheme, thereby obtaining the actual adjustable power range of each of the power adjustable loads.

5. The photovoltaic power operation and dispatching method based on a smart grid according to claim 4, characterized in that, The response sensitivity is determined according to the following steps: Based on the electrical distance between the power adjustable load and the location where the voltage deviation occurs, and the network topology, the response sensitivity of each power adjustable load to the voltage deviation is determined.

6. The photovoltaic power operation and dispatching method based on a smart grid according to claim 2, characterized in that, The specific steps in step S43 include: S431. Based on the degree of voltage deviation, determine the grid's demand level for voltage stability; S432. Based on the output trend of the distributed power sources, determine the grid's demand level for photovoltaic power consumption; S433. Based on the voltage stability requirement level and the photovoltaic absorption requirement level, dynamically adjust the weight of the preset target parameters; S434. Within the actual adjustable power range, the power adjustment requirement is determined based on the current operating status of the power adjustable load and the dynamically adjusted weight.

7. The photovoltaic power operation and dispatching method based on a smart grid according to claim 6, characterized in that, The preset target parameters include voltage stability target, photovoltaic absorption maximization target, grid loss minimization target, and user satisfaction target.

8. The photovoltaic power operation and dispatching method based on a smart grid according to claim 6, characterized in that, The specific steps in step S433 include: S4331. Obtain the rate of change of the voltage stability demand level and the rate of change of the photovoltaic absorption demand level, and determine whether there is a conflict between the voltage stability demand level and the photovoltaic absorption demand level, and obtain the determination result; S4332. Based on the judgment result, determine the weight adjustment range of each of the preset target parameters; S4333. Based on the preset weight adjustment rules and the weight adjustment range, the weights of each preset target parameter are adjusted step by step and limited to obtain the dynamically adjusted weights.

9. The photovoltaic power operation and dispatching method based on a smart grid according to claim 8, characterized in that, In step S4331, the specific steps for determining whether there is a conflict between the voltage stability requirement level and the photovoltaic power consumption requirement level, and obtaining the determination result, include: By comparing the power regulation directions indicated by the voltage stability demand level and the photovoltaic absorption demand level, if the power regulation directions of the two are opposite, it is determined that there is a conflict between the voltage stability demand level and the photovoltaic absorption demand level.

10. A photovoltaic power operation and dispatching system based on a smart grid, characterized in that, include: The first acquisition module is used to acquire real-time operating data of preset monitoring nodes in the power distribution network; The real-time operating data includes the voltage values ​​of each of the preset monitoring nodes; The comparison and judgment module is used to compare the voltage value of the preset monitoring node with the preset voltage safety threshold range to determine whether there is an abnormal risk of voltage deviation in the area to which the preset monitoring node belongs. The second acquisition module is used to acquire the output trend of distributed power sources and the current operating status of power-adjustable loads in the area where the preset monitoring node is located when it is determined that there is an abnormal risk of voltage deviation in the area where the preset monitoring node is located. The status determination module is used to determine the power adjustment requirements based on the output trend of the distributed power source and the current operating status of the power adjustable load. The control generation module is used to generate power control commands for the power adjustable load based on the voltage deviation and the power adjustment requirements; the power control commands are used to instruct the power adjustable load to increase or decrease its operating power. The control adjustment module is used to send the power control command to the power adjustable load, so as to compensate the voltage deviation of the preset monitoring node by adjusting the power adjustable load from the distribution network, so that the voltage value of the preset monitoring node returns to the voltage safety threshold range.

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