A method and system for evaluating the photovoltaic carrying capacity of a power grid

Abstract

The present application relates to the field of grid-connected evaluation technology, in particular to a kind of power grid photovoltaic bearing capacity evaluation method and system, comprising the following steps, based on user node load fluctuation and mutation characteristics, sensitive sample is screened, coupling boundary is identified in combination with voltage and power flow variation characteristics, equipment overload is judged, capacity factor and sorting matching relationship are calculated and capacity allocation is adjusted, determine photovoltaic access upper limit.The present application can effectively identify the influence trend of load fluctuation on power grid structure by constructing dynamic sensitivity screening mechanism with load mutation rate and cycle difference as core, enhance the dynamic discrimination ability of node operating state by establishing the coupling analysis model between voltage state and power flow response, introduce the correlation rule of equipment operating state classification and capacity sorting factor, establish the multi-factor evaluation path covering load characteristics, electrical state and equipment pressure capacity, realize the synchronous matching of node response capability and voltage safety boundary in capacity allocation process.

Claims

1. A method for grid photovoltaic carrying capacity assessment, characterized in that, The method comprises the following steps: S1: Based on each user node in the power grid, analyze the load collection data and the peak-valley difference parameters, compare the periodic regularity and the fluctuation amplitude, screen the node curve with sudden change, eliminate the sample with weak fluctuation, and obtain the sensitive load feature set; S2: Based on the sensitive load feature set, analyze the voltage margin and the power flow data of the grid-connected node, calculate the voltage drop interval power flow difference, judge the node close to the safety boundary, and obtain the voltage power flow coupling feature; S3: Based on the voltage power flow coupling feature, judge the node device overload factor, analyze the operation pressure, compare the overload level and the operation benchmark, screen the node with overload risk, adjust the capacity allocation level thereof, and obtain the node constraint strength index; S4: Based on the node constraint strength index, compare the sorting relationship thereof with the sensitive load feature set, analyze the fluctuation amplitude and the constraint level order difference, calculate the capacity factor and the sorting matching condition, adjust the capacity allocation weight, and obtain the capacity sorting reference factor; S5: Based on the capacity sorting reference factor, compare the capacity factor and the voltage deviation trend direction, calculate the bearing range of the capacity factor under the voltage boundary, determine the access upper limit, and obtain the photovoltaic bearing capacity evaluation result.

2. The grid PV hosting capacity assessment method of claim 1, wherein, The sensitive load feature set comprises load fluctuation features, time period mutation features, and node classification identifiers; the voltage power flow coupling feature comprises voltage boundary parameters, power flow transfer features, and coupling gradient indexes; the node constraint strength index comprises device operation levels, overload state parameters, and constraint classification identifiers; the capacity sorting reference factor comprises allocation priority orders, node matching factors, and capacity proportion parameters; and the photovoltaic bearing capacity evaluation result comprises an access capacity upper limit, a bearing boundary range, and an allocation balance index.

3. The grid PV hosting capacity assessment method of claim 1, wherein, The acquisition step of the sensitive load feature set is specifically as follows: S111: Based on each user node in the power grid, analyze the load collection data, compare the load curve differences of days, weeks, and months, calculate the fluctuation ranges of the peak and the valley, screen the node curves with the front fluctuation change amplitude, judge the key segments in the curves that can reflect the periodic differences, and obtain the node periodic fluctuation features; S112: Based on the node periodic fluctuation features, analyze the direction and continuity of the sudden increase and sudden decrease segments, compare the corresponding relationship between the mutation segments and the fluctuation amplitudes, screen the curves with stable change direction, eliminate the node curves with inconspicuous mutation features, and obtain the mutation direction continuity factor; S113: Based on the mutation direction continuity factor, compare the coincidence of the mutation segments and the peak segments, analyze the mutation continuous direction and the periodic synchronization relationship, screen the node curves with continuous mutation and aligned with the key segments, adjust the redundant curves in the set, and obtain the sensitive load feature set.

4. The grid photovoltaic carrying capacity assessment method of claim 1, wherein, The acquisition step of the voltage power flow coupling feature is specifically as follows: S211: Based on the sensitivity load feature set, analyze the voltage margin sequence and power flow monitoring data of each grid-connected node, compare the power flow change direction in the continuous section of the voltage drop stage, judge whether the power flow remains a single trend or reverses in the drop process, filter the sections with correlation characteristics by comparing the corresponding trends of voltage and power flow in each section, and obtain the unidirectional power flow evolution section; S212: Based on the unidirectional power flow evolution section, locate the power flow transfer position, combine the short-circuit capacity parameters and load change slope of the grid-connected node, judge the synchronization between the two types of parameters, identify the nodes that have a relationship between power flow transfer and voltage drop, and obtain the power flow-voltage linkage relationship identifier; S213: Based on the power flow-voltage linkage relationship identifier, judge the response delay between each segment and the voltage change path, calculate the synchronization in change direction and amplitude, analyze the coupling degree of the voltage trend of each node under the influence of power flow change, filter the node group with consistent evolution direction and continuous distribution, and obtain the voltage-power coupling feature.

5. The grid photovoltaic carrying capacity assessment method of claim 1, wherein, The obtaining step of the node constraint strength index is specifically: S311: Based on the voltage-power coupling feature, analyze the change trend of the voltage boundary parameter and the coupling gradient index in the node operation process, compare the proportional fluctuation between the real-time load and the rated capacity of the equipment, judge the time correspondence of power flow mutation and load ratio change, identify the node operation characteristics matched with the mutation behavior, and obtain the equipment overload approaching degree; S312: Judge the change amplitude of the equipment overload approaching degree in the operation cycle, analyze the synchronization between the node temperature rise change trend and the historical load response trajectory, calculate the change offset range between the thermal stress trend and the load trajectory, compare the consistency of the change direction in the adjacent cycle, and obtain the operation pressure increment amplitude; S313: Based on the operation pressure increment amplitude, analyze the joint distribution characteristics of the node load fluctuation period, operation level and power flow response change, calculate the operation coupling amplitude factor, judge the distribution overlap between the operation coupling amplitude factor and the operation level parameter, and obtain the node constraint strength index.

6. The grid photovoltaic carrying capacity assessment method of claim 1, wherein, The obtaining step of the capacity sorting reference factor is specifically: S411: Based on the node constraint strength index, compare the sorting relationship between the node constraint strength index and the sensitivity load feature set, analyze the relative position of the node in the constraint level and the load fluctuation sequence, judge the offset direction and sorting difference between each sequence, calculate the relative change of the node in the two types of sequences, and obtain the sorting offset group; S412: Based on the sorting offset group, compare the consistency between the node in the capacity allocation sequence and the sorting offset group, analyze the node set with differences in sorting, judge the consistency level of each node in multiple sequences, and obtain the capacity allocation consistency mapping body; S413: Based on the capacity allocation consistency mapping body, optimize the priority of each node in capacity allocation, compare the corresponding relationship between the reconstructed allocation order and the original sorting, and identify the update position of the capacity factor in the allocation structure, and obtain the capacity sorting reference factor.

7. The grid photovoltaic carrying capacity assessment method of claim 1, wherein, The obtaining step of the photovoltaic carrying capacity evaluation result is specifically: S511: Based on the capacity ranking reference factor, the consistency of the node matching factor and the capacity factor direction with the voltage offset trend is compared, the nodes with the same direction are identified, and a direction corresponding node set is obtained; S512: Based on the direction corresponding node set, the capacity factor response process of each node under the voltage boundary condition is calculated, the capacity response characteristics of the nodes at each stage are sorted, and a stage capacity response set is obtained; S513: Based on the stage capacity response set, the voltage offset direction is analyzed by combining the node matching factor and the capacity proportion parameter, the joint mode of the capacity factor change and the voltage trend is compared, the capacity allocation proportion is adjusted and the bearing range is calculated, and a photovoltaic bearing capacity evaluation result is obtained.

8. The grid photovoltaic carrying capacity assessment method of claim 1, wherein, The node curve refers to the curve data of the load of the user access point changing with time, indicating the dynamic characteristics of the load of the node, the voltage margin refers to the safety margin between the voltage level of the node and the lower limit of the allowed voltage, and the node device overload factor refers to the ratio of the current load of the electrical equipment to its rated capacity.

9. A power grid photovoltaic carrying capacity assessment system, characterized in that, The system is used to implement the power grid photovoltaic bearing capacity evaluation method of any one of claims 1-8, and the system comprises: The load identification module analyzes the load collection data and the peak-valley difference parameter based on each user node in the power grid, compares the periodicity and fluctuation amplitude, screens the node curves with sudden fluctuations, eliminates samples with weak fluctuations, and obtains a sensitive load feature set; The coupling analysis module analyzes the voltage margin and power flow data of the grid-connected node based on the sensitive load feature set, calculates the power flow difference in the voltage drop interval, judges the nodes close to the safety boundary, and obtains voltage power flow coupling characteristics; The constraint evaluation module judges the node device overload factor based on the voltage power flow coupling characteristics, analyzes the operation pressure, compares the overload level with the operation benchmark, screens the nodes with overload risk, adjusts the capacity allocation level thereof, and obtains a node constraint strength index; The capacity ranking module compares the ordering relationship of the node constraint strength index with the sensitive load feature set, analyzes the difference between the fluctuation amplitude and the constraint level order, calculates the capacity factor and the matching condition, adjusts the capacity allocation weight, and obtains a capacity ranking reference factor; The bearing evaluation module compares the capacity factor and the voltage offset trend direction based on the capacity ranking reference factor, calculates the bearing range of the capacity factor under the voltage boundary, determines the upper limit of access, and obtains a photovoltaic bearing capacity evaluation result.

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