Distributed photovoltaic output flexible regulation method and system

Abstract

The application discloses a kind of distributed photovoltaic output flexible regulation method and system, it is related to grid-connected regulation technical field, determine the regulation priority of each photovoltaic system, for each received regulation request, calculate the current power output condition of each target photovoltaic system, assess the feasibility of output adjustment, according to the data and preset regulation target of real-time acquisition, judge the actual execution difficulty and timeliness of each request, for effective regulation request, according to the current output condition of photovoltaic system and power demand timely regulation, after regulation operation is completed, regulation result is fed back to service end, and optimization suggestion is provided, if there are multiple effective requests, according to the priority of each request, regulation feasibility and power grid stability requirement scheduling resource.The regulation system reduces resource waste through dynamic scheduling and priority management, optimizes power grid load distribution, improves power grid stability and power resource utilization efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of grid-connected regulation, and particularly relates to a distributed photovoltaic output flexible regulation method and system. BACKGROUND

[0002] With the rapid development of renewable energy technology, distributed photovoltaic systems, as an important form of green energy, have gradually been applied worldwide. Such systems change power production from centralized generation to decentralized generation, which can reduce dependence on traditional energy and reduce carbon emissions. However, the power output of distributed photovoltaic systems has strong volatility and intermittency, which poses a challenge to the stability and reliability of the power grid. Therefore, how to efficiently connect distributed photovoltaic systems with the power grid has become a problem to be solved. Grid-connected technology enables distributed photovoltaic systems to deliver generated power to the power grid and transmit it to consumers through the power grid. However, due to the unpredictability of photovoltaic output, how to achieve stable scheduling of the power grid is a key problem. For this reason, a flexible regulation system has emerged.

[0003] The prior art has the following defects:

[0004] The existing regulation system lacks real-time dynamic adjustment of actual power demand and system state. This approach often cannot cope with the complex changes of photovoltaic systems. Since the output of photovoltaic systems is greatly affected by external factors such as light and temperature, traditional control methods usually only operate within a set range and cannot flexibly respond to sudden changes in demand or load fluctuations, resulting in waste of power resources or overload of the power grid. Not only does this fail to efficiently utilize the potential of distributed photovoltaic systems, but it also fails to effectively ensure the stability of the power grid. SUMMARY

[0005] The purpose of the present application is to provide a distributed photovoltaic output flexible regulation method and system, which reduces resource waste, optimizes power grid load distribution, and improves power grid stability and power resource utilization efficiency through dynamic scheduling and priority management.

[0006] In order to achieve the above-mentioned purpose, the present application provides the following technical solution: a distributed photovoltaic output flexible regulation method, the regulation method comprising the following steps:

[0007] S1: The intelligent gateway receives a plurality of distributed photovoltaic output regulation requests sent by the service end, extracts the relevant photovoltaic system configuration and its location according to each item of content in the regulation request, evaluates the power demand and current power supply capacity of the region, and determines the regulation priority of each photovoltaic system;

[0008] S2: For each received regulation request, calculate the current power output status of each target photovoltaic system, evaluate the feasibility of output adjustment, judge the actual execution difficulty and timeliness of each request according to the real-time acquired data and the preset regulation target, if the power output of the target photovoltaic system reaches the expected value and is not limited by other factors within the adjustment range, the request is considered valid, otherwise, the intelligent gateway marks the request as invalid request and feeds back the regulation result to the business end;

[0009] S3: For the valid regulation request, timely regulation is carried out according to the current output status of the photovoltaic system and the power demand, after the regulation operation is completed, the regulation result is fed back to the business end, and optimization suggestions are provided, if there are multiple valid requests, the resources are dispatched according to the priority of each request, the regulation feasibility and the grid stability requirement.

[0010] Preferably, for each received regulation request, the current power output status of each target photovoltaic system is calculated, and the feasibility of output adjustment is evaluated, including the following steps:

[0011] The current power output status of each target photovoltaic system is extracted from the real-time monitoring database, at the same time, the intelligent gateway synchronously acquires the external environment data and system performance parameters related to the target photovoltaic system;

[0012] The technical feasibility of the target photovoltaic system to execute the regulation request is judged by multi-factor coupling analysis, and the evaluation dimensions include environmental adaptability, equipment physical limitation and adjustment ability matching;

[0013] The theoretical maximum output power of the photovoltaic system under the premise of no human intervention is calculated according to the current environmental parameters;

[0014] The component operating temperature is calculated according to the measured environmental air temperature T_air and the nominal operating temperature of the photovoltaic module;

[0015] The comprehensive theoretical power is calculated, after obtaining the comprehensive theoretical power, it is judged whether the target photovoltaic system can adjust its real-time active power to the requested target output value.

[0016] Preferably, the actual execution difficulty and timeliness of each request are judged according to the real-time acquired data and the preset regulation target, including the following steps:

[0017] The intelligent gateway performs validity determination on each regulation request;

[0018] Valid request: the request is considered valid only when all the following conditions are met simultaneously:

[0019] The current state of the target photovoltaic system is normal operation;

[0020] The target output value is within the physical limit of the device and does not exceed the theoretical limit of the environment;

[0021] The required power difference is adjusted within the current adjustment margin of the system;

[0022] No dynamic limiting factors hinder the adjustment execution;

[0023] Invalid request: if any condition is not met, the request is marked as invalid.

[0024] Preferably, the current power output status of each target photovoltaic system is extracted from the real-time monitoring database, including real-time active output, current working state, minimum technical output, maximum technical output, and current adjustment margin.

[0025] Preferably, for valid regulation requests, timely regulation is performed according to the current output status of the photovoltaic system and the power demand, including the following steps:

[0026] Select the current edge redundant unit, send start-stop instructions through the inverter communication interface, calculate the total capacity of the panel units to be turned off or the capacity of the standby units to be started according to the requested target output difference;

[0027] According to the real-time light intensity and component temperature, dynamically optimize the scanning interval of the maximum power point tracking parameter, set the active power upper limit of the inverter or remove the amplitude limiting, and cooperate with the power regulation response of the inverter itself;

[0028] If the target photovoltaic system is associated with a supporting battery energy storage, the intelligent gateway will include the energy storage charging and discharging strategy in the joint optimization, and match the demand through the combination of photovoltaic output regulation and energy storage energy translation.

[0029] Preferably, the demand is matched through the combination of photovoltaic output regulation and energy storage energy translation, including the following steps:

[0030] If the real-time active output is lower than the target output value and the energy storage is in a charging state, start discharging the energy storage to preferentially make up for the gap that cannot be covered by photovoltaic;

[0031] If the real-time active output is higher than the target output value, the excess power is preferentially stored in the energy storage;

[0032] The energy storage regulation meets its physical limitations and cycle life constraints.

[0033] Preferably, if there are multiple valid requests, resources are dispatched according to the priority of each request, the regulation feasibility, and the grid stability requirements, including the following steps:

[0034] According to the calculated request priority score, combined with the current real-time condition to update the dynamic weight, a final execution sequence queue is generated, and if the priority is the same, the request with high adjustment feasibility or small impact on the power grid is preferentially processed;

[0035] Each request is processed in turn according to the priority order, and at each step, it is necessary to check whether the remaining adjustable resources meet the current request demand:

[0036] If the resources are sufficient, the regulation and control is directly executed and the resource state is updated;

[0037] If the resources are insufficient, the conflict resolution mechanism is triggered;

[0038] After each regulation and control operation, it is checked through the real-time simulation model whether the key indicators of the power grid are out of limit, and if the check fails, the regulation and control operation of the current request is rolled back, and the resource allocation scheme of the subsequent request is re-adjusted.

[0039] Preferably, the intelligent gateway receives multiple distributed photovoltaic output regulation requests sent by the business end, including real-time or predicted power demand of the target area, user expected photovoltaic power generation equipment type, equipment geographic location information and additional constraint conditions.

[0040] Preferably, in step S1, the power demand and current power supply capacity of the region are evaluated to determine the regulation priority of each photovoltaic system, including the following steps:

[0041] A set of basic weight parameters is initialized for each candidate photovoltaic system, including type matching weight, location correlation weight and capacity adaptation weight;

[0042] The actual adjustable potential of each candidate system is calculated, including available adjustment margin, adjustment rate limit and comprehensive priority calculation;

[0043] The intelligent gateway outputs a priority ranking list of all candidate photovoltaic systems, wherein the system with the highest score will be preferentially selected as the regulation and control execution object.

[0044] The application also provides a distributed photovoltaic output flexible regulation system, comprising a request receiving module, a request analysis module and a regulation and control module;

[0045] The request receiving module receives multiple distributed photovoltaic output regulation requests sent by the business end, extracts the related photovoltaic system configuration and its location according to each content in the regulation request, evaluates the power demand and current power supply capacity of the region, and determines the regulation priority of each photovoltaic system;

[0046] Request Analysis Module: For each received control request, calculate the current power output status of each target photovoltaic system, assess the feasibility of power output adjustment, and judge the actual execution difficulty and timeliness of each request based on real-time acquired data and preset control targets. If the power output of the target photovoltaic system reaches the expected value and is not limited by other factors within the adjustment range, the request is considered valid; otherwise, the request is marked as invalid and the control result is fed back to the business end.

[0047] Control module: For valid control requests, timely control is carried out based on the current output status and power demand of the photovoltaic system. After the control operation is completed, the control results are fed back to the business side, and optimization suggestions are provided. If there are multiple valid requests, resources are scheduled according to the priority of each request, control feasibility, and grid stability requirements.

[0048] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0049] This invention addresses the problems of lagging regulation and unreasonable resource allocation in existing technologies by introducing real-time data analysis and intelligent scheduling mechanisms. First, by receiving multiple distributed photovoltaic (PV) output regulation requests from the business end, the intelligent gateway can comprehensively evaluate the configuration, location, power demand, and power supply capacity of different PV systems, and dynamically adjust the regulation priority of each PV system based on these factors. Second, the system calculates the output status of the target PV system in real time and assesses the feasibility of regulation based on real-time meteorological data and power demand forecasts. This allows for flexible responses to actual conditions, avoiding resource waste and power shortages common in traditional methods. Finally, through a priority scheduling mechanism, the intelligent gateway ensures timely responses to high-priority requests while optimizing grid load distribution and guaranteeing power supply stability. Furthermore, the system provides optimization suggestions to help the business end further adjust scheduling strategies based on historical data, achieving long-term efficient utilization of power resources. Overall, this invention improves the regulation accuracy and flexibility of distributed PV systems, thereby effectively enhancing grid operating efficiency and stability and promoting the efficient utilization of green energy. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0051] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Example: Please refer to Figure 1 As shown in the figure, this embodiment provides a method for flexible regulation of distributed photovoltaic power output. The regulation method includes the following steps:

[0054] S1: The smart gateway receives multiple distributed photovoltaic (PV) output control requests from the service provider. These requests include information such as the target area's power demand, the type and location of the PV power generation equipment the user wishes to control, and more. Based on each item in the control request, the smart gateway extracts the relevant PV system configuration and its location, while simultaneously assessing the area's power demand and current power supply capacity. By analyzing the service provider's requests, the smart gateway can preliminarily determine the control priority for each PV system.

[0055] S2: For each received control request, the smart gateway first calculates the current power output status of each target photovoltaic system and, in conjunction with its geographical location, weather data (such as sunlight intensity and temperature), and system performance parameters, assesses the feasibility of power output adjustment. Based on real-time acquired data and preset control targets, the smart gateway determines the actual execution difficulty and timeliness of each request. If the power output of the target system reaches or can be adjusted to the expected value, and the adjustment range is not limited by other factors, the request is considered valid; otherwise, the smart gateway marks the request as invalid and feeds back the control result to the business end.

[0056] S3: For valid control requests, the smart gateway will make timely adjustments based on the current output of the photovoltaic system and power demand. Specific operations include starting or stopping certain photovoltaic panel units, adjusting operating parameters, or optimizing the charging and discharging strategies of the battery storage system to ensure that the final power output meets demand. After the control operation is completed, the smart gateway will promptly feed back the control results to the business end and provide system optimization suggestions to help users further adjust their operating strategies according to power demand. If multiple valid requests exist, the smart gateway will rationally allocate resources based on the priority of each request, control feasibility, and grid stability requirements to ensure overall system stability.

[0057] This application addresses the problems of lagging regulation and unreasonable resource allocation in existing technologies by introducing real-time data analysis and intelligent scheduling mechanisms. First, by receiving multiple distributed photovoltaic (PV) output regulation requests from the business side, the intelligent gateway can comprehensively assess the configuration, location, power demand, and power supply capacity of different PV systems, and dynamically adjust the regulation priority of each PV system based on these factors. Second, the system calculates the output status of the target PV system in real time and assesses the feasibility of regulation based on real-time meteorological data and power demand forecasts. This allows for flexible responses to actual conditions, avoiding resource waste and power shortages inherent in traditional methods. Finally, through a priority scheduling mechanism, the intelligent gateway ensures timely responses to high-priority requests while optimizing grid load distribution and guaranteeing power supply stability. Furthermore, the system provides optimization suggestions to help the business side further adjust scheduling strategies based on historical data, achieving long-term efficient utilization of power resources. Overall, this invention improves the regulation accuracy and flexibility of distributed PV systems, thereby effectively enhancing grid operating efficiency and stability and promoting the efficient utilization of green energy.

[0058] This embodiment provides a distributed photovoltaic power output flexible control system, including a request receiving module, a request analysis module, and a control module;

[0059] Request receiving module: Receives multiple distributed photovoltaic output control requests sent by the business terminal, extracts the relevant photovoltaic system configuration and its location based on each item in the control request, assesses the power demand and current power supply capacity of the area, determines the control priority of each photovoltaic system, and sends the control priority and control request to the request analysis module.

[0060] Request Analysis Module: For each received control request, calculate the current power output status of each target photovoltaic system, assess the feasibility of power output adjustment, and judge the actual execution difficulty and timeliness of each request based on real-time acquired data and preset control targets. If the power output of the target photovoltaic system reaches the expected value and is not limited by other factors within the adjustment range, the request is considered valid. Otherwise, mark the request as invalid and feed back the control result to the business end. Valid control requests are sent to the control module.

[0061] Control module: For valid control requests, timely control is carried out based on the current output status and power demand of the photovoltaic system. After the control operation is completed, the control results are fed back to the business side, and optimization suggestions are provided. If there are multiple valid requests, resources are scheduled according to the priority of each request, control feasibility, and grid stability requirements.

[0062] S1: The smart gateway receives multiple distributed photovoltaic (PV) output control requests from the service provider. These requests include information such as the target area's power demand, the type and location of the PV power generation equipment the user wishes to control, and more. Based on each item in the control request, the smart gateway extracts the relevant PV system configuration and its location, while simultaneously assessing the area's power demand and current power supply capacity. By analyzing the service provider's requests, the smart gateway can preliminarily determine the control priority for each PV system.

[0063] When business units (such as regional power grid dispatch centers, aggregator platforms, or user-side energy management systems) send multiple distributed photovoltaic (PV) output control requests to the smart gateway, the smart gateway first needs to perform structured parsing and information extraction on the request content. These requests typically contain multi-dimensional key information, such as real-time or predicted power demand in the target area (which may be presented as active power deficit / surplus, load curve characteristics, or supply-demand ratio), the type of PV power generation equipment that the user expects to control (such as rooftop PV arrays, industrial and commercial PV systems, agricultural-PV hybrid power stations, etc., which may be identified by equipment coding rules or classification labels), equipment geographical location information (accurate to latitude and longitude coordinates, administrative region grid codes, or substation power supply zone numbers), and possible additional constraints (such as control time windows, equipment maximum / minimum output limits, user preference priorities, etc.).

[0064] Based on the aforementioned request, the smart gateway extracts the photovoltaic system configuration information and its physical location distribution directly related to the request from the local or cloud-based photovoltaic system registration database through a multi-source data association and indexing mechanism. Specifically, the database pre-stores metadata for all distributed photovoltaic systems connected to the control network, including but not limited to: unique device identifiers (such as UUIDs or serial numbers), device type classification tags (such as "residential <10kW", "commercial <100kW", "ground-mounted centralized >1MW"), installation geographical location (latitude and longitude + altitude / administrative region code), rated capacity parameters (peak power generation, inverter capacity), historical operating data (typical daily / monthly output curves, attenuation coefficient), real-time status information (current output value, available adjustment margin, grid-connected node number), and control permission configuration (whether participation in demand response is allowed, maximum adjustment rate limit, etc.). The smart gateway performs fuzzy matching between the "Photovoltaic Power Generation Equipment Type" field in the request and the classification tags in the database (for example, if the request specifies "commercial and industrial photovoltaic", then it filters equipment with the type tags "commercial <100kW" or "industrial <1MW"). It further filters by combining the "Location Information" field (such as the target area being "XX City XX District" or the latitude and longitude range "116.3°E~116.5°E, 39.9°N~40.1°N"), and finally generates a set of candidate photovoltaic systems that meet the request conditions.

[0065] After extracting candidate photovoltaic systems, the smart gateway needs to simultaneously assess the power demand characteristics and current power supply capacity of the target area to determine the urgency and feasibility of regulation. Power demand assessment includes two levels: first, static demand analysis, which involves parsing the explicitly provided "target area power demand" value in the request (e.g., "current time-limited power shortage is 5MW" or "predicted power supply increase of 3MW in the next 2 hours"). If the request does not directly provide a value, it is dynamically calculated using a related regional load forecasting model (e.g., time-series forecasts based on historical data, weather conditions, and economic activity indices). Second, dynamic power supply capacity analysis, which involves obtaining the real-time total output of all online power sources in the target area (including conventional generators, other connected distributed power sources, and current candidate photovoltaic systems), and combining this with the power supply path constraints in the grid topology (e.g., substation remaining capacity, line transmission limits) to calculate the current actual available power supply redundancy (i.e., the remaining value after subtracting the current load from the total power supply capacity). By comparing power demand and power supply redundancy, the smart gateway can quantify the size of the demand gap (e.g., "demand gap is 2MW, current power supply redundancy is only 1MW, requiring an additional 1MW adjustment").

[0066] Based on the above information, the smart gateway further determines the control priority of each candidate photovoltaic system through a multi-dimensional priority evaluation algorithm, as follows:

[0067] Initialize a set of basic weight parameters for each candidate photovoltaic system, including:

[0068] Type matching weight (W_type): Assigned a value based on the degree of matching between the expected device type specified in the request and the actual type label of the candidate system (e.g., 1.0 for a complete match, 0.7 for a partial match, and 0.3 for no match).

[0069] Location relevance weight (W_location): Calculated based on the distance between the candidate system and the geographical boundary of the target area (e.g., using Euclidean distance or administrative region hierarchy difference; the closer the distance / the more matched the hierarchy, the higher the weight, such as 1.0 for systems within the same region, 0.8 for adjacent regions, and 0.5 for systems across cities).

[0070] Capacity fit weight (W_capacity): Evaluates the fit between the rated capacity of the candidate system and the demand gap (e.g., when the demand gap is 2MW, a system with a capacity of 3MW gets 0.9 (close and with margin), and a system with a capacity of 0.5MW gets 0.4 (too small and requires multiple devices to work together)).

[0071] Calculate the actual adjustable potential of each candidate system, including:

[0072] Available adjustment margin (P_avail): The difference between the current real-time output of the candidate system and the minimum allowable output (such as the minimum operating power of the inverter or the safety threshold of the equipment) (for example, if the current output is 2kW and the minimum output is 0.5kW, then P_avail=1.5kW).

[0073] Adjustment rate limit (R_rate): Based on the equipment parameters, obtain its maximum allowable power change rate (e.g., ±0.2MW per minute) for feasibility assessment of subsequent dynamic control scenarios;

[0074] Comprehensive Priority Calculation: The above parameters are integrated through a weighted logic function to generate a control priority score (Priority_Score) for each photovoltaic system: First, the basic comprehensive weight Base_Score = α × W_type + β × W_location + ×W_capacity (where α, β, Preset weighting coefficients, for example, α=0.4, β=0.3, =0.3, reflecting the importance of different factors); then, the adjustment capacity correction factor Capacity_Adjustment=min(1.0, P_avail / demand gap) is introduced (if a system can fully cover the demand gap with adjustment margin, then it gets 1.0, otherwise it is proportionally reduced); the final priority score Priority_Score=Base_Score×Capacity_Adjustment× ( To adjust the rate compliance coefficient, if the system adjustment rate meets the power change requirements within the demand period, then... =1.0, otherwise reduce proportionally);

[0075] The smart gateway outputs a priority ranking list of all candidate photovoltaic systems (e.g., ranked from highest to lowest by Priority_Score). The system with the highest score will be prioritized for regulation (e.g., prioritizing industrial and commercial photovoltaic systems with suitable capacity and type within the region due to their strong regulation capabilities and fast response speed). This priority result will serve as the core basis for subsequent regulation instruction generation and resource allocation, ensuring that the power demand of the target area is met while achieving optimized utilization of distributed photovoltaic resources and stable grid operation.

[0076] S2: For each received control request, the smart gateway first calculates the current power output status of each target photovoltaic system and, in conjunction with its geographical location, weather data (such as sunlight intensity and temperature), and system performance parameters, assesses the feasibility of power output adjustment. Based on real-time acquired data and preset control targets, the smart gateway determines the actual execution difficulty and timeliness of each request. If the power output of the target system reaches or can be adjusted to the expected value, and the adjustment range is not limited by other factors, the request is considered valid; otherwise, the smart gateway marks the request as invalid and feeds back the control result to the business end.

[0077] Upon receiving a distributed photovoltaic (PV) output control request from the business unit, the smart gateway needs to conduct a detailed technical feasibility assessment for each target PV system associated with the request to determine whether it has the actual capability to adjust its output according to the request requirements. This process is a crucial preliminary step before the control instruction is generated, directly affecting the final execution status (valid / invalid) of the request and the accuracy of subsequent feedback.

[0078] The smart gateway first extracts the current power output status of each target photovoltaic system from the real-time monitoring database, including parameters such as:

[0079] Real-time active power output (P_real): The actual power generation of the system at the current moment (unit: kW / MW), which is collected in real time through the inverter communication module or smart meter;

[0080] Current operating status (Status): Indicates whether the system is in normal operating mode (such as "grid-connected power generation", "fault shutdown", "maintenance mode", etc.). Systems in abnormal states (such as faults or offline) are directly excluded from further evaluation.

[0081] Minimum technical output (P_min) and maximum technical output (P_max): These are safe operating boundaries determined by equipment hardware parameters (such as inverter capacity and the number of photovoltaic modules connected in series), and are usually stored in the equipment registration database (for example, for a 50kW photovoltaic system, P_min=5kW and P_max=50kW).

[0082] Current adjustment margin (ΔP_avail): defined as P_max-P_real (upward adjustment potential) or P_real-P_min (downward adjustment potential), used to quantify the adjustable space of the system without violating technical limitations.

[0083] Simultaneously, the smart gateway needs to acquire external environmental data and system performance parameters that are strongly correlated with the photovoltaic system, including:

[0084] Geographic location data association: The latitude and longitude coordinates (or administrative region codes) in the system registration information are associated with the corresponding geographic grid (such as a 1km×1km meteorological grid) to accurately match local meteorological conditions;

[0085] Real-time weather data: Obtain parameters such as solar irradiance (unit: W / m², usually divided into total irradiance GHI and direct irradiance DNI), ambient temperature (T_air, unit: ℃), and cloud cover (cloud_Cover, percentage) of the target area at the current moment from meteorological service API or local weather station. These factors directly affect the photoelectric conversion efficiency of photovoltaic modules.

[0086] System performance parameters: including the nominal conversion efficiency of photovoltaic modules (… _nominal, such as monocrystalline silicon approximately 20%–22%), temperature coefficient (α_temp, typically -0.35% / ℃ to -0.45% / ℃, representing the percentage decrease in efficiency for every 1℃ increase in temperature), inverter conversion efficiency ( _inverter (typically 95%–98%), shading factor (affected by surrounding buildings / trees, ranging from 0 to 1, with 1 indicating no shading), and other parameters—these parameters together determine the theoretical maximum output power of the system under given environmental conditions.

[0087] Based on the aforementioned real-time data, the smart gateway uses multi-factor coupling analysis to determine whether the target photovoltaic system possesses the technical feasibility to execute control requests. The core evaluation dimensions include environmental adaptability and the matching of equipment physical limitations with control capabilities.

[0088] First, the theoretical maximum possible output power (P_theoretical) of the photovoltaic system under the premise of no human intervention needs to be calculated based on the current environmental parameters. This value reflects the limit of power generation capacity that the system hardware can achieve under given conditions such as sunlight intensity and temperature. The processing logic is as follows:

[0089] Multiply the measured total irradiance GHI (unit: W / m²) by the tilt surface irradiance correction factor (K_tilt, which is pre-calculated by photovoltaic design software or obtained by looking up a table, with the default conversion relationship between horizontal and tilt surface irradiance) to obtain the effective irradiance GHI_effective actually received by the system; if the request involves a specific photovoltaic array (such as a module tilted 20° to the south of the roof), the customized K_tilt value of the array must be used.

[0090] Based on the measured ambient temperature \(T_{air}\) and the nominal operating temperature of the photovoltaic module (usually 25 °C), calculate the module operating temperature \(T_{cell}=T_{air}+\Delta T\) (\(\Delta T\) is the empirical correction value, typical values are 30 °C - 40 °C, depending on the installation method - \(\Delta T\) is higher for rooftop installations and lower for ground-mounted brackets); based on the temperature coefficient \(\alpha_{temp}\), correct the actual conversion efficiency of the module _actual = _nominal×(1 + \(\alpha_{temp}\)×(\(T_{cell}\) - 25)).

[0091] Comprehensive theoretical power calculation: \(P_{theoretical}=GHI_{effective}\times\) total photovoltaic module area (\(A_{module}\))× _actual× _inverter×Shading_Factor.

[0092] Example: For a commercial and industrial photovoltaic system (\(P_{max}=100kW\)), the currently measured \(GHI = 800W / m²\) (\(GHI_{effective}=720W / m²\) after tilt-plane correction), \(T_{air}=28°C\) (calculated \(T_{cell}=68°C\), _actual = 21%×(1% - 0.4%×(68 - 25))≈19.3%), _inverter = 97%, Shading_Factor = 0.95 (slight shading), total module area \(A_{module}=500m²\), then \(P_{theoretical}≈720×500×19.3%×97%×0.95≈64.2kW\) (lower than \(P_{max}=100kW\), indicating environmental limitations).

[0093] After obtaining \(P_{theoretical}\), the intelligent gateway needs to further determine whether the target photovoltaic system can adjust (increase or decrease) its real-time active power output \(P_{real}\) to the requested desired target output value (\(P_{target}\)) (this value is clearly specified by the business side in the request, such as "increase the output of a 50kW photovoltaic system to 30kW" or "decrease it to 10kW"). The verification logic includes the following sub-steps:

[0094] Determination of the adjustment direction: If \(P_{target}>P_{real}\), it is necessary to check the feasibility of increasing (i.e., \(P_{target}≤P_{theoretical}\) and \(P_{target}≤P_{max}\)); if \(P_{target}<P_{real}\), it is necessary to check the feasibility of decreasing (i.e., \(P_{target}≥P_{min}\));

[0095] Hard constraint check: Ensure that P_target simultaneously meets both the physical limits of the equipment (P_min≤P_target≤P_max) and the theoretical limits of the environment (P_target≤P_theoretical) — for example, if a request requires the system with P_real=40kW to be increased to P_target=70kW, but P_theoretical is only 65kW and P_max=60kW, it is deemed infeasible because it exceeds the double constraints;

[0096] Adjustment margin verification: Calculate whether the current adjustment margin covers the target difference (when adjusting upward, ΔP_avail_up=P_max-P_real≥(P_target-P_real) is required; when adjusting downward, ΔP_avail_down=P_real-P_min≥(P_real-P_target) is required).

[0097] In addition to the static technical parameters mentioned above, dynamic constraints that may affect the implementation of the adjustment should also be considered, such as:

[0098] Grid connection constraints: Whether the current voltage / frequency of the grid connection node where the target photovoltaic system is located is within the allowable adjustment range (e.g., if the voltage deviation exceeds ±5%, the output should not be significantly reduced); Real-time equipment status: Whether the inverter has an over-temperature alarm, and whether the communication module is interrupted (if the real-time communication delay exceeds the threshold, it is considered that the adjustment is uncontrollable); Coordinated control conflict: If the system has been locked by other higher priority control requests (e.g., participating in the day-ahead demand response plan), the current request must be avoided.

[0099] Based on the above technical assessment, the smart gateway performs a validity determination on each control request:

[0100] A valid request is considered valid if and only if all of the following conditions are met:

[0101] (1) The target photovoltaic system is currently in normal operation (Status = "grid-connected power generation");

[0102] (2) The target output value P_target is within the physical limits of the equipment (P_min≤P_target≤P_max) and does not exceed the theoretical limits of the environment (P_target≤P_theoretical).

[0103] (3) The required power difference (|P_target-P_real|) is within the current adjustment margin of the system (when adjusting upward, P_max-P_real≥(P_target-P_real), when adjusting downward, P_real-P_min≥(P_real-P_target));

[0104] (4) No dynamic limiting factors (such as grid constraints, equipment alarms or coordination conflicts) hinder regulation execution.

[0105] Invalid Request: If any condition is not met (e.g., P_target exceeds P_theoretical, the system is in a fault state, or the adjustment margin is insufficient), the request is marked as invalid.

[0106] For invalid requests, the smart gateway needs to generate detailed feedback information on the control results and push it to the business side. The feedback content should include at least the following:

[0107] Request identifier (such as request ID, associated target photovoltaic system ID);

[0108] Classification of invalidation reasons (e.g., "target output exceeds environmental theoretical limits", "equipment is currently in maintenance mode", "insufficient adjustment margin", etc.);

[0109] Optional alternative suggestions (e.g., "The current maximum adjustable output is XkW, it is recommended to adjust the target value to this range" or "Please wait for environmental conditions to improve and then try again").

[0110] For valid requests, the smart gateway will proceed to the subsequent process of generating and issuing control instructions (such as calculating the specific power regulation rate and generating inverter control signals). This feasibility assessment mechanism, through the coupled analysis of multi-dimensional technical parameters, ensures the reliability of distributed photovoltaic control and grid security, while providing transparent execution status feedback to the business side.

[0111] S3: For valid control requests, the smart gateway will make timely adjustments based on the current output of the photovoltaic system and power demand. Specific operations include starting or stopping certain photovoltaic panel units, adjusting operating parameters, or optimizing the charging and discharging strategies of the battery storage system to ensure that the final power output meets demand. After the control operation is completed, the smart gateway will promptly feed back the control results to the business end and provide system optimization suggestions to help users further adjust their operating strategies according to power demand. If multiple valid requests exist, the smart gateway will rationally allocate resources based on the priority of each request, control feasibility, and grid stability requirements to ensure overall system stability.

[0112] For control requests deemed valid by S2, the smart gateway must execute refined control operations. This involves dynamically adjusting the photovoltaic system's operating parameters, coordinating the energy storage system's charging and discharging strategies, and considering grid stability constraints to ensure that the final power output accurately matches the target demand. This process involves multi-system collaborative control, real-time dynamic optimization, and result feedback, and is the core execution link of distributed photovoltaic control. The operations fall into three categories: operating parameter optimization, energy storage system collaborative control, and other related operations.

[0113] For each valid request, the smart gateway considers the current output status of the photovoltaic system (such as real-time active power output and the operating status of each photovoltaic panel unit) and the power demand gap (such as the target area needing to replenish electricity △P_demand or reduce electricity △P_cut). The system generates a hierarchical control strategy, specifically divided into photovoltaic panel unit-level control. When a request is made to quickly adjust the output and the system has segmented control capabilities (such as using string inverters or photovoltaic arrays supporting single-panel MPPT), the smart gateway achieves coarse-grained power regulation by selectively starting and stopping photovoltaic panel units. The operation logic is as follows:

[0114] Prioritize selecting board units that are currently partially obstructed, operating inefficiently (e.g., board units whose actual conversion efficiency is lower than the system average due to dust or orientation differences), or edge redundant units (e.g., strings in parallel branches with large output power fluctuations), and send start / stop commands through the inverter communication interface (e.g., Modbus TCP / IP or PLC); based on the requested target output difference △P=P_target-P_real, where P_target is the target output value and P_real is the real-time active power output, calculate the total capacity △P_down of the board units to be shut down (e.g., when △P_down=P_real-P_minallowed, P_minallowed is the minimum allowed output, shutting down inefficient units with a capacity of △P_down), or the capacity of the standby units to be started (e.g., activating idle board units in extended mode). Example: A 50kW photovoltaic system is currently outputting 45kW (with a target of reducing to 30kW). Monitoring reveals that the string on the west side (capacity 10kW, efficiency only 15% due to afternoon shading) can be safely shut down. The smart gateway then sends a shutdown command to its inverter, causing the output to drop by 10kW to 35kW. The remaining 5kW is compensated through parameter optimization.

[0115] For centralized photovoltaic systems (such as large ground-mounted power plants) that lack panel-level control capabilities, the smart gateway achieves fine-grained adjustment by modifying the inverter's maximum power point tracking (MPPT) parameters, power factor settings, or output limiting values. Operations include:

[0116] Based on real-time light intensity and component temperature, dynamically optimize the MPPT scanning range (e.g., reduce the voltage search range to near the component's current optimal operating voltage when light intensity decreases, reducing ineffective scanning losses); directly set the inverter's active power limit P_limit=P_target (downward adjustment scenario) or remove the limit (upward adjustment scenario), in conjunction with the inverter's own power regulation response (typical response time <100ms); if the requirement includes reactive power support (e.g., increasing the power factor to 0.98), adjust the inverter's reactive power output mode (e.g., switch from "unity power factor" to "constant reactive power mode").

[0117] If the target photovoltaic system is associated with a supporting battery energy storage system (such as a lithium-ion battery energy storage system, BESS), the smart gateway incorporates the energy storage charging and discharging strategy into joint optimization, accurately matching demand through a combination of "photovoltaic output adjustment + energy storage energy transfer". The control logic is as follows:

[0118] Upgrade scenario (output increased): If the real-time active power output is lower than the target output value and the energy storage is in a charging state (SOC>20%), then start energy storage discharge (discharge power). ,in For photovoltaic adjustable margin, For upward power changes, priority is given to filling the gaps that photovoltaic power cannot cover; for downward scenarios (reduced output required): if the real-time active power output is higher than the target output value, the excess electricity will be stored in energy storage (charging power) first. , To ensure sufficient excess power, the system must meet the capacity limit of SOC < 90% to reduce direct curtailment losses; dynamic constraints: energy storage regulation must simultaneously meet its physical limitations (such as maximum charge and discharge power). SOC upper and lower limits And cycle life constraints (avoiding frequent deep charge and discharge).

[0119] When multiple valid requests exist (e.g., overlapping requests from multiple terminals within the same area), the smart gateway needs to rationally allocate control resources (photovoltaic systems, energy storage systems) based on global optimization objectives. The core logic is dynamic scheduling through a three-dimensional evaluation model of priority, feasibility, and grid constraints. The steps are as follows:

[0120] Based on the calculated request priority score (Priority_Score), dynamic weights are updated according to current real-time conditions (e.g., timeliness weight is increased during periods of urgent demand), generating a final execution order queue. If priorities are the same, requests with higher adjustment feasibility (e.g., greater margin, more sufficient energy storage support) or less impact on the grid are processed first (e.g., photovoltaic systems near substations are prioritized for adjustment to reduce line losses). Each request is processed sequentially according to priority, and at each step, it is necessary to check whether the remaining adjustable resources (e.g., unused photovoltaic capacity, energy storage SOC margin) meet the current request requirements: if resources are sufficient (e.g., remaining photovoltaic margin ≥ request difference and energy storage SOC allows discharge), the adjustment is executed directly and the resource status is updated (e.g., marked occupied photovoltaic capacity, deducted energy storage discharge); if resources are insufficient (e.g., multiple high-priority requests compete for the same energy storage system), a conflict resolution mechanism is triggered: priority is given to ensuring requests related to critical infrastructure (e.g., photovoltaic adjustment in the power supply areas of hospitals and data centers), or resources are allocated proportionally (e.g., the total gap is split according to the priority weight of each request, and each request performs partial adjustment).

[0121] After each control operation, the smart gateway needs to verify whether key power grid indicators have exceeded limits using a real-time simulation model (such as a simplified model based on power flow calculation or a digital twin system), including:

[0122] Node voltage deviation: Whether the voltage amplitude of the grid-connected nodes in the target area remains within ±5% of the rated value after adjustment;

[0123] Line transmission capacity: Whether the power change caused by regulation exceeds the thermal stability limit of the transmission line connecting the area;

[0124] Frequency stability: Whether a large-scale photovoltaic power output surge causes a system frequency shift (e.g., a deviation of 50Hz ± 0.2Hz).

[0125] If the verification fails, the current request's adjustment operation is rolled back (restoring the state before adjustment), and the resource allocation scheme for subsequent requests is readjusted. After the adjustment operation is completed, the smart gateway needs to push structured feedback information to the business end and generate optimization suggestions based on historical data and real-time operating status. Specific content includes:

[0126] Clearly indicate whether the request was successfully completed (e.g., "Target output of 30kW has been achieved"), partially completed (e.g., "Real-time active power output of 28kW, achievement rate of 93%), or completely failed (e.g., "Reduced to 35kW only due to insufficient energy storage SOC"); Actual adjustment amount: Provide the actual change value of key parameters (e.g., photovoltaic output adjustment amount). Energy storage discharge capacity ); Power grid impact indicators: Measured voltage / frequency values ​​of the target area after adjustment (e.g., node voltage 380V (nominal 400V, deviation -5%), frequency 49.98Hz). Measured voltage / frequency values ​​of the target area after adjustment (e.g., node voltage 380V (nominal 400V, deviation -5%), frequency 49.98Hz).

[0127] Based on the operational data from this adjustment, the smart gateway provides end users with policy-level improvement suggestions, such as:

[0128] On the photovoltaic side: it is recommended to clean inefficient panel units (such as string efficiency on the west side that is consistently below average), adjust string layout (reduce shading), or upgrade the inverter's MPPT algorithm (to improve low light response capability).

[0129] On the energy storage side: If the SOC boundary limit is frequently triggered (such as reaching the 90% upper limit multiple times), it is recommended to expand the energy storage capacity or optimize the charging and discharging strategy (such as setting a more conservative SOC threshold).

[0130] Synergistic Strategy: We recommend participating in the power grid ancillary services market (such as frequency regulation reserves) to balance short-term regulation costs with long-term benefits.

[0131] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0132] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for flexible regulation of distributed photovoltaic power output, characterized in that: The control method includes the following steps: S1: The smart gateway receives multiple distributed photovoltaic output control requests sent by the business terminal. Based on each item in the control request, it extracts the relevant photovoltaic system configuration and its location. At the same time, it assesses the regional power demand and current power supply capacity to determine the control priority of each photovoltaic system. S2: For each received control request, calculate the current power output status of each target photovoltaic system, assess the feasibility of power output adjustment, and judge the actual execution difficulty and timeliness of each request based on the real-time data and the preset control target. If the power output of the target photovoltaic system reaches the expected value and is not limited by other factors within the adjustment range, the request is considered valid. Otherwise, the smart gateway marks the request as invalid and feeds back the control result to the business end. S3: For valid control requests, timely control is carried out based on the current output status of the photovoltaic system and power demand. After the control operation is completed, the control results are fed back to the business side, and optimization suggestions are provided. If there are multiple valid requests, resources are scheduled according to the priority of each request, control feasibility, and grid stability requirements.

2. The method for flexible control of distributed photovoltaic power output according to claim 1, characterized in that: For each received regulation request, calculate the current power output status of each target photovoltaic system and assess the feasibility of power output regulation, including the following steps: The current power output status of each target photovoltaic system is extracted from the real-time monitoring database. At the same time, the smart gateway synchronously acquires external environmental data and system performance parameters related to the target photovoltaic system. Multi-factor coupling analysis is used to determine whether the target photovoltaic system has the technical feasibility to execute the control request. The evaluation dimensions include environmental adaptability, matching of equipment physical limitations and control capabilities. Calculate the theoretical maximum output power of the photovoltaic system under the premise of no human intervention based on current environmental parameters; The module operating temperature is calculated based on the measured ambient air temperature T_air and the nominal operating temperature of the photovoltaic module. Calculate the comprehensive theoretical power, and after obtaining the comprehensive theoretical power, determine whether the target photovoltaic system can adjust its real-time active power output to the requested target output value through adjustment.

3. The method for flexible control of distributed photovoltaic power output according to claim 2, characterized in that: Based on real-time data and preset control targets, the actual execution difficulty and timeliness of each request are determined, including the following steps: The smart gateway performs a validity check on each control request; A valid request is considered valid if and only if all of the following conditions are met: The target photovoltaic system is currently operating normally. The target output value is within the physical limits of the equipment and does not exceed the theoretical limits of the environment; The required power difference for adjustment is within the current system adjustment margin; The absence of dynamic limiting factors hinders the implementation of regulation; Invalid request: If any condition is not met, the request is marked as invalid.

4. A method for flexible regulation of distributed photovoltaic power output according to claim 2 or 3, characterized in that: The current power output status of each target photovoltaic system is extracted from the real-time monitoring database. The parameters include real-time active power output, current operating status, minimum technical output, maximum technical output, and current regulation margin.

5. The method for flexible regulation of distributed photovoltaic power output according to claim 1, characterized in that: For valid control requests, timely adjustments are made based on the current output of the photovoltaic system and electricity demand, including the following steps: Select the current edge redundant unit, send start / stop command through the inverter communication interface, and calculate the total capacity of the board units to be shut down or the capacity of the standby units to be started based on the requested target output difference. Based on real-time light intensity and component temperature, the scanning range of maximum power point tracking parameters is dynamically optimized, and the upper limit of active power of the inverter is set or the limiting is lifted, in conjunction with the inverter's own power regulation response. If the target photovoltaic system is associated with a battery energy storage system, the smart gateway will incorporate the energy storage charging and discharging strategy into the joint optimization, and match the demand through a combination of photovoltaic output adjustment and energy storage energy transfer.

6. The method for flexible regulation of distributed photovoltaic power output according to claim 5, characterized in that: Matching demand through a combination of photovoltaic power output regulation and energy storage energy transfer includes the following steps: If the real-time active power output is lower than the target output value and the energy storage is in a charging state, the energy storage discharge will be activated to make up for the gap that photovoltaic cannot cover. If the real-time active power output is higher than the target output value, the excess power will be stored in energy storage first. Energy storage regulation simultaneously meets its physical limitations and cycle life constraints.

7. The method for flexible regulation of distributed photovoltaic power output according to claim 6, characterized in that: If multiple valid requests exist, resources are scheduled according to the priority of each request, the feasibility of regulation, and the requirements for grid stability, including the following steps: Based on the calculated request priority score, the dynamic weight is updated in combination with the current real-time conditions to generate the final execution order queue. If the priorities are the same, requests with high adjustment feasibility or low impact on the power grid are processed first. Each request is processed sequentially according to priority, and at each step, it is necessary to check whether the remaining adjustable resources can meet the current request's requirements: If resources are sufficient, then directly implement adjustments and update the resource status; If resources are insufficient, the conflict resolution mechanism will be triggered; After each control operation, the key indicators of the power grid are checked through a real-time simulation model to see if they exceed the limits. If the check fails, the current control operation is rolled back and the resource allocation scheme for subsequent requests is readjusted.

8. The method for flexible regulation of distributed photovoltaic power output according to claim 1, characterized in that: The smart gateway receives multiple distributed photovoltaic power output control requests sent by the business terminal, including the real-time or predicted power demand of the target area, the type of photovoltaic power generation equipment that the user expects to control, the geographical location information of the equipment, and additional constraints.

9. A method for flexible regulation of distributed photovoltaic power output according to claim 8, characterized in that: In step S1, the region's electricity demand and current power supply capacity are assessed, and the control priority for each photovoltaic system is determined, including the following steps: A set of basic weight parameters is initialized for each candidate photovoltaic system, including type matching weight, location relevance weight, and capacity adaptation weight; Calculate the actual adjustable potential of each candidate system, including available adjustment margin, adjustment rate limit, and overall priority calculation; The smart gateway outputs a priority ranking list of all candidate photovoltaic systems, and the system with the highest score will be selected as the target for regulation.

10. A distributed photovoltaic power output flexible control system, used to implement the control method according to any one of claims 1-9, characterized in that: It includes a request receiving module, a request analysis module, and a control module; Request receiving module: Receives multiple distributed photovoltaic output control requests sent by the business terminal, extracts the relevant photovoltaic system configuration and its location based on each item in the control request, and assesses the power demand and current power supply capacity of the area to determine the control priority of each photovoltaic system; Request Analysis Module: For each received control request, calculate the current power output status of each target photovoltaic system, assess the feasibility of power output adjustment, and judge the actual execution difficulty and timeliness of each request based on real-time acquired data and preset control targets. If the power output of the target photovoltaic system reaches the expected value and is not limited by other factors within the adjustment range, the request is considered valid; otherwise, the request is marked as invalid and the control result is fed back to the business end. Control module: For valid control requests, timely control is carried out based on the current output status and power demand of the photovoltaic system. After the control operation is completed, the control results are fed back to the business side, and optimization suggestions are provided. If there are multiple valid requests, resources are scheduled according to the priority of each request, control feasibility, and grid stability requirements.

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