Micro-grid power supply recovery method based on island division and network reconstruction

Abstract

The invention relates to the technical field of micro-grid power supply recovery, and discloses a micro-grid power supply recovery method based on island division and network reconstruction, and the method comprises the steps: dividing a micro-grid based on obtained fault information and distributed power supply information, and generating a plurality of first-level networks; constructing a connected graph based on nodes in the first-level network, calculating circuit loss of the connected graph, and generating various first-level network reconstruction schemes; evaluating the first-level network reconstruction scheme to generate an evaluation value set of the first-level network reconstruction scheme; selecting a primary network reconstruction scheme based on the evaluation value set, and generating a secondary network based on the selected primary network reconstruction scheme; and establishing an energy storage model based on the secondary network to formulate a control strategy to recover power supply of the micro-grid. According to the invention, through fault analysis, network reconstruction and energy storage control, the power supply reliability of the micro-grid after the fault is improved, and the power failure time and the influence range are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid power supply restoration, and in particular to a microgrid power supply restoration method based on island division and network reconstruction. Background Art

[0002] Currently, in traditional centralized power supply systems, failures can cause widespread blackouts, disrupting people's lives and work. Microgrids, as distributed power supply systems, can achieve localized power self-sufficiency, improving power supply reliability and stability. Furthermore, with the development of renewable energy and the increasing use of distributed power sources, the power recovery capabilities of microgrids are becoming increasingly important.

[0003] Due to the vulnerability of traditional centralized power supply systems in the face of natural disasters or other emergencies, how to achieve the best power restoration effect has become an urgent problem that needs to be solved. Summary of the Invention

[0004] The purpose of this invention is to generate multiple first-level network reconstruction schemes by constructing a connectivity diagram and calculating circuit losses, and to evaluate and select them to achieve the best power restoration effect; to establish an energy storage model, formulate a control strategy, and control the microgrid power restoration through the charge and discharge power of the energy storage system.

[0005] To achieve the above objectives, the present invention provides a microgrid power supply restoration method based on island partitioning and network reconstruction, comprising:

[0006] Based on the acquired fault information and distributed power information, the microgrid is divided into multiple first-level networks;

[0007] Construct a connectivity graph based on the nodes in the first-level network, calculate the circuit loss of the connectivity graph, and generate multiple first-level network reconstruction schemes;

[0008] Evaluating the first-level network reconstruction scheme to generate an evaluation value set of the first-level network reconstruction scheme;

[0009] Selecting a first-level network reconstruction scheme based on the evaluation value set, and generating a second-level network based on the selected first-level network reconstruction scheme;

[0010] Formulate control strategies based on the energy storage model established in the secondary network to restore power to the microgrid;

[0011] Among them, the nodes in the first-level network include: distributed power sources and power loads.

[0012] In some embodiments of the present invention, the generating of multiple primary networks includes:

[0013] Generate the area where the current microgrid needs to be shut down based on the acquired fault information;

[0014] Among them, the fault information includes: the fault type and the location where the fault occurs;

[0015] Obtain the line connection relationship, distributed power sources and power consumption loads of the area that needs to be powered off, and construct a topological model of the current area;

[0016] Based on historical data, set the priority A for the power consumption loads inside the topological model, A = {A1, A2... Ai... An};

[0017] Among them, Ai represents the priority of the i-th power consumption load, and n represents the number of power consumption loads in the current topological model;

[0018] Formulate a first-level network division strategy based on the priorities of the power consumption loads, and generate multiple first-level networks by combining the first-level network division strategy and the topological model.

[0019] In some embodiments of the present invention, when formulating the first-level network division strategy based on the priorities of the power consumption loads, it includes:

[0020] Set the priority preset value a1 based on historical data;

[0021] By comparing the priority of the power consumption load with the priority preset value, formulate a first-level network division strategy, and the first-level network division strategy includes:

[0022] If a1 < Ai, then the current power consumption load is the first type of power consumption load;

[0023] If Ai ≤ a1, then the current power consumption load is the second type of power consumption load;

[0024] Successively obtain the first type of power consumption loads in the current topological model and the distributed power sources connected to the first type of power consumption loads;

[0025] And calculate the remaining output power P of the distributed power source , , , , B1 , , B , , B1 , ,

[0027] , B1 ,

[0022] ,

[0026] , B1 ,

[0025] , B ,

[0024] , B1 ,

[0023] ,

[0028] , P B1 = P B - P1;

[0026] Among them, P B is the output power of the current distributed power source, and P1 is the predicted demand power of the first type of power consumption load connected to the current distributed power source;

[0027] If the remaining output power P of the distributed power source B1 < 0, then generate an optimization strategy for the power consumption behavior of the current first type of power consumption load;

[0028] If P<00000​​​

[0029] In some embodiments of the present invention, when generating multiple first-level network reconstruction schemes, it includes:

[0030] Obtain the characteristic information of the current first-level network, including: the magnitude of the current and the magnitude of the resistance on each line;

[0031] Calculate the power loss P of the j-th line in the current first-level network j =I j 2 R j ;

[0032] Where, I j is the current on line j, and R j is the resistance of line j;

[0033] Calculate the total line loss P under different line combinations M ,

[0034] M is the total number of lines in the first-level network under the current line combination;

[0035] And set the preset value c1 of the line loss value based on historical data, and select the line combination with P M <c1 to generate multiple first-level network reconstruction schemes.

[0036] In some embodiments of the present invention, when evaluating the set of evaluation values of the generated first-level network reconstruction scheme, it includes:

[0037] Generate a reliability evaluation value Q1 and a power quality evaluation value Q2 based on the operation data of each node in the current first-level network reconstruction scheme;

[0038] Generate an evaluation value Q of the current first-level network reconstruction scheme based on the reliability evaluation value Q1 and the power quality evaluation value Q2 Z ;

[0039] Q Z =w1*Q1+w2*Q2;

[0040] Where, w1 is the weight of the reliability evaluation value Q1, and w2 is the weight of the power quality evaluation value Q2.

[0041] In some embodiments of the present invention, when generating the reliability evaluation value Q1, it includes:

[0042] Obtain the historical reliability statistical parameters of each node in the current first-level network reconstruction scheme, including the historical equipment failure rate and the historical fault repair time;

[0043] Based on the historical equipment failure rate and historical fault repair time, generate the equipment failure rate evaluation value and fault repair time evaluation value of each node respectively;

[0044] Calculate the reliability evaluation value Q1 of the current first-level network reconstruction plan;

[0045]

[0046] Among them, q represents the number of nodes in the current first-level network, Q ak represents the equipment failure evaluation value of the kth node, d1 represents the weight of the equipment failure evaluation value of the kth node, Q bk represents the fault repair time evaluation value of the kth node, d2 represents the weight of the fault repair time evaluation value of the kth node, and d1+d2=1, Q ak and Q bk The value range is the same.

[0047] In some embodiments of the present invention, generating the power quality evaluation value Q2 includes:

[0048] Obtain voltage and frequency data of each node in the current first-level network reconstruction plan;

[0049] Evaluate the voltage data, generate the voltage evaluation value of each node in the current first-level network, and calculate the variance σ of the voltage evaluation value in the current first-level network a , generating the voltage stability value e v , e v =r1*σ a ;

[0050] Where r1 is the variance σ of the voltage evaluation value a The coefficient of

[0051] Get the frequency data set F of the current sampling time node, F={F1,F2…F k …F q};

[0052] Among them, F k represents the frequency dataset of the kth node, and q represents the number of nodes in the current first-level network;

[0053] Calculate the frequency deviation value e based on the frequency data set F f ,

[0054] Among them, f k represents the measured frequency of the kth node, fs is the rated frequency, and r2 is the coefficient of the frequency deviation value;

[0055] Based on the voltage stability value e vAnd the frequency deviation value e f Generate power quality evaluation value Q2,

[0056]

[0057] Among them, d3 is the voltage stability value e v The weight of d4 is the frequency deviation value e f The weight of .

[0058] In some embodiments of the present invention, the generating of the secondary network based on the selected primary network reconstruction solution includes:

[0059] Sort the evaluation values ​​of the first-level network reconstruction strategies in the evaluation value set, and select the first-level network reconstruction strategy with the largest evaluation value;

[0060] Based on the selected primary network reconstruction strategy, the primary network lines are controlled by line switches to generate a secondary network.

[0061] In some embodiments of the present invention, the process of formulating a control strategy based on establishing an energy storage model in the secondary network to restore power supply to the microgrid includes:

[0062] Obtain historical data of distributed power sources in the secondary network, including: charging and discharging efficiency and capacity attenuation of distributed power sources;

[0063] Establish energy storage models based on historical data of distributed power sources;

[0064] Obtain real-time operating data of distributed power sources in the current secondary network;

[0065] Combine the real-time operation data of distributed power sources and the energy storage model to generate energy storage status prediction data for each distributed power source in the current secondary network;

[0066] Generate control instructions for each distributed power source in the current secondary network based on the energy storage status prediction data.

[0067] In some embodiments of the present invention, the generating of control instructions for each distributed power source of the current secondary network includes:

[0068] Generate the current power balance situation in the secondary network based on the energy storage status prediction data;

[0069] Based on the current power balance in the secondary network, a voltage or power regulation instruction is generated, and the charging and discharging power of the energy storage system is controlled through the voltage or frequency regulation instruction. Based on the charging and discharging power of the energy storage system, the microgrid is controlled to resume power supply.

[0070] Compared with the prior art, the microgrid power supply restoration method based on island partitioning and network reconstruction provided by the embodiment of the present invention has the following advantages:

[0071] By dividing the microgrid based on fault information and distributed power information to generate multiple first-level networks, the fault area can be quickly isolated when a fault occurs, reducing the impact of the fault on the entire microgrid.

[0072] By generating and evaluating multiple first-level network reconstruction schemes, the optimal network structure is selected. Based on the historical reliability statistics of nodes, the reconstructed network is guaranteed to have higher reliability, which helps reduce the risk of power outages caused by unreasonable network structure.

[0073] By evaluating voltage data and analyzing frequency data sets, a comprehensive assessment of the power quality in the network can be made.

[0074] The network is constructed according to the priority of the power load and the remaining output power of the distributed power generation, and the power resources of the distributed power generation are allocated first, thus avoiding the unreasonable allocation of power resources.

[0075] By establishing an energy storage model and generating control instructions based on predicted energy storage status data, efficient utilization of distributed power sources can be achieved. Adjusting the charge and discharge power of the energy storage system based on power balance optimizes energy management across the microgrid and improves resource utilization efficiency.

[0076] By sorting the evaluation values ​​of the first-level network reconstruction strategies in the evaluation value set and selecting the optimal solution, and then using line switches to control the on and off of the first-level network lines to generate the second-level network, an effective second-level network structure can be quickly established. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 This is a flow chart of a microgrid power supply restoration method based on island partitioning and network reconstruction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following embodiments are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0079] In the description of the present invention, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0080] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0081] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0082] Example 1:

[0083] The embodiment of the present invention provides a microgrid power supply restoration method based on island division and network reconstruction, such as Figure 1 Shown, including:

[0084] Based on the acquired fault information and distributed power information, the microgrid is divided into multiple first-level networks;

[0085] Construct a connectivity graph based on the nodes in the first-level network, calculate the circuit loss of the connectivity graph, and generate multiple first-level network reconstruction schemes;

[0086] Evaluating the first-level network reconstruction scheme to generate an evaluation value set of the first-level network reconstruction scheme;

[0087] Selecting a first-level network reconstruction scheme based on the evaluation value set, and generating a second-level network based on the selected first-level network reconstruction scheme;

[0088] Formulate control strategies based on the energy storage model established in the secondary network to restore power to the microgrid;

[0089] Among them, the nodes in the first-level network include: distributed power sources and power loads.

[0090] In this embodiment, nodes in the primary network are obtained, and based on the lines between the nodes, the nodes in the primary network are connected so that any node has a path to another node to generate a connectivity graph.

[0091] Example 2:

[0092] The generating of multiple first-level networks includes:

[0093] Generate the area where the current microgrid needs to be shut down based on the acquired fault information;

[0094] The fault information includes: fault type and fault location;

[0095] Obtain the line connection relationship, distributed power supply and power load of the area that needs power outage, and build a topological model of the current area;

[0096] Set the priority A for the power load within the topology model based on historical data, where A = {A1, A2…Ai…An};

[0097] Among them, Ai represents the priority of the i-th power load, and n represents the number of power loads in the current topology model;

[0098] A first-level network partitioning strategy is formulated based on the priority of power load, and multiple first-level networks are generated by combining the first-level network partitioning strategy and the topology model.

[0099] In this embodiment, when generating the areas requiring power outage in the current microgrid based on the acquired fault information, the fault types are classified in detail. For example, the fault types may be classified into short circuit faults, open circuit faults, ground faults, etc. Different fault types may affect different areas requiring power outages.

[0100] The fault location must be pinpointed to the specific line segment or device. If the fault occurs on a critical transmission line, it may cause a large power outage; however, if the fault occurs on a branch line or terminal device, the power outage area may be relatively small.

[0101] Consider the potential for fault spread. For example, if a short circuit is not addressed promptly, it can trigger a chain reaction of overloads, overheating, and other issues, expanding the power outage area. By simulating the spread of a fault, you can more accurately determine the areas requiring power outages.

[0102] When obtaining the line connections, distributed power sources, and power loads for the area requiring power outages, ensure data integrity. For line connections, draw a detailed line diagram, including information such as the line's starting point, end point, and connection nodes.

[0103] For distributed power sources, record the type (e.g., solar, wind, etc.), capacity, output characteristics, etc. of each distributed power source. For power loads, in addition to recording the load size, also consider the load type (e.g., industrial load, residential load, etc.), as different types of loads have different power supply requirements and priorities.

[0104] Accuracy of topological model building

[0105] The topology model of the current area is constructed based on the obtained line connection relationship, distributed power generation, and power load. During the construction process, an appropriate topology structure representation method, such as an adjacency matrix or adjacency table, is used.

[0106] Verify the topology model. Ensure that the line connections, power supply, and load distribution in the model match the actual situation. Verification can be carried out by comparing with historical operation data or on-site survey results.

[0107] When setting the priority A for the electricity consumption load inside the topology model based on historical data, consider factors such as the importance of the load and the impact of power outages on users. For example, for emergency support loads such as hospitals and fire departments, a higher priority should be set; for ordinary residential life loads, the priority is relatively low.

[0108] Analyze historical power outage data to understand the losses of different loads during power outages. For example, industrial production loads may cause significant economic losses during power outages, and priorities should be set reasonably according to the size of the losses.

[0109] When formulating a primary network division strategy based on the priority of electricity consumption load, clarify the division principle. For example, prioritize ensuring the power supply to high-priority loads and divide high-priority loads into relatively independent and reliable primary networks.

[0110] Generate multiple primary networks according to the primary network division strategy and the topology model. During the generation process, consider the reliability, economy, and flexibility of the network. For example, minimize the weak links in the network, reduce line losses, and ensure that the network can adapt to different operating conditions.

[0111] Example 3:

[0112] When formulating a primary network division strategy based on the priority of electricity consumption load, it includes:

[0113] Set the priority preset value a1 based on historical data;

[0114] Formulate a primary network division strategy by comparing the priority of the electricity consumption load with the priority preset value. The primary network division strategy includes:

[0115] If a1 < Ai, the current electricity consumption load is the first type of electricity consumption load;

[0116] If Ai ≤ a1, the current electricity consumption load is the second type of electricity consumption load;

[0117] Sequentially obtain the first type of electricity consumption load in the current topology model and the distributed power sources connected to the first type of electricity consumption load;

[0118] And calculate the remaining output power P of the distributed power source B1 , P B1 =P B -P1;

[0119] Among them, PB P is the output power of the current distributed power source, and P1 is the predicted value of the demand power of the first type of electrical load connected to the current distributed power source;

[0120] If the remaining output power P of the distributed power source B1 < 0, an optimization strategy for the electricity consumption behavior of the current first type of electrical load is generated;

[0121] If P B1 ≥0, then select the second type of electrical load connected to the distributed power source according to the value of P B1 to generate a primary network.

[0122] In this embodiment, the electricity consumption types in the electricity consumption area are obtained based on historical data, the electricity consumption types are classified, the urgency levels of the electricity consumption types are classified, and a priority preset value a1 is set. a1 < Ai indicates that the current user type belongs to a user type with a greater urgency level, such as a hospital, etc., which is the first type of electrical load, and Ai ≤ a is the second type of electrical load, such as residents, etc.

[0123] When calculating the remaining output power PB1 = PB - P1 of the distributed power source, it is necessary to accurately predict the predicted value P1 of the demand power of the first type of electrical load connected to the current distributed power source. A combination of multiple prediction methods can be used, such as time series analysis and neural network prediction.

[0124] For time series analysis, an autoregressive moving average model (ARIMA) is established using historical load data to perform short-term prediction of future load demands. At the same time, a neural network (such as a long short-term memory network, LSTM) is used to predict the long-term trend of the load, and the results of both are combined to obtain a more accurate P1.

[0125] When the remaining output power PB1 of the distributed power source < 0, an optimization strategy for the electricity consumption behavior of the current first type of electrical load is generated. This may include measures such as adjusting the operating time of non-critical devices and reducing the power demand of some devices. For example, for some non-critical production processes in industrial enterprises, they can be adjusted to run during low electricity price periods to reduce the overall electricity demand.

[0126] When PB1 ≥ 0, select the second type of electrical load connected to the distributed power source according to the value of PB1 to generate a primary network. During the selection process, factors such as the capacity demand of the second type of electrical load and the distance from the distributed power source need to be considered. Priority is given to selecting the second type of electrical load with a relatively short distance and a capacity demand matching PB1 to reduce line losses and improve power supply efficiency.

[0127] Embodiment 4:

[0128] When generating multiple primary network reconstruction schemes, it includes:

[0129] Obtain the characteristic information of the current primary network, including: the magnitude of the current and the magnitude of the resistance on each line;

[0130] Calculate the power loss P of the j-th line in the current primary network j =I j 2 R j ;

[0131] where, I j is the current on line j, and R j is the resistance of line j;

[0132] Calculate the total line loss P M ,

[0133] M is the total number of lines in the primary network under the current line combination;

[0134] And set the preset value c1 of the line loss value based on historical data, and select the line combination with P M < c1 to generate multiple primary network reconstruction schemes.

[0135] In this embodiment, for a large-scale primary network, due to the huge number of line combinations, an approximate algorithm is used to quickly estimate the total line loss. For example, a clustering-based algorithm is used to cluster similar lines, first calculate the line combination loss after clustering, and then gradually refine the calculation to obtain a relatively accurate total line loss result within an acceptable time.

[0136] When setting the preset value c1 of the line loss value based on historical data, the statistical characteristics of the historical data should be fully analyzed. Calculate the average value E(P) and standard deviation σ of the historical line loss data. c1 can be set to E(P)+kσ (where k is a coefficient determined according to the actual situation, such as k = 1 or k = 2).

[0137] Consider the impact of network development and load change trends on c1. If the network is expected to increase the load or new lines are connected, appropriately increase the value of c1 to ensure that the generated primary network reconstruction scheme can adapt to future changes.

[0138] When selecting the line combination with P M < c1 to generate multiple primary network reconstruction schemes, the line combinations that meet the conditions need to be further screened and optimized. For example, exclude those line combinations that, although meeting the power loss requirements, will cause the network topology to be too complex or the reliability to decrease.

[0139] Example 5:

[0140] When evaluating the set of values for generating the primary network reconstruction scheme, it includes:

[0141] Generate a reliability evaluation value Q1 and a power quality evaluation value Q2 based on the operating data of each node in the current first-level network reconstruction solution;

[0142] The evaluation value Q of the current first-level network reconstruction solution is generated based on the reliability evaluation value Q1 and the power quality evaluation value Q2. Z ;

[0143] Q Z =w1*Q1+w2*Q2;

[0144] Among them, w1 is the weight of the reliability evaluation value Q1, and w2 is the weight of the power quality evaluation value Q2.

[0145] Example 6:

[0146] The generation of the reliability evaluation value Q1 includes:

[0147] Obtain historical reliability statistical parameters of each node in the current first-level network reconstruction plan, including historical equipment failure rate and historical fault repair time;

[0148] Based on the historical equipment failure rate and historical fault repair time, generate the equipment failure rate evaluation value and fault repair time evaluation value of each node respectively;

[0149] Calculate the reliability evaluation value Q1 of the current first-level network reconstruction plan;

[0150]

[0151] Among them, q represents the number of nodes in the current first-level network, Q ak represents the equipment failure evaluation value of the kth node, d1 represents the weight of the equipment failure evaluation value of the kth node, Q bk represents the fault repair time evaluation value of the kth node, d2 represents the weight of the fault repair time evaluation value of the kth node, and d1+d2=1, Q ak and Q bk The value range is the same.

[0152] In this embodiment, when determining the weight d1 of the equipment failure evaluation value and the weight d2 of the fault repair time evaluation value, a balance is made based on the network's criticality and load type. For networks primarily requiring continuous production (such as chemical production enterprises), the weight d2 of the fault repair time may be relatively high, such as d2 = 0.6, because the longer the production interruption, the greater the loss. For networks with extremely high requirements for equipment stability (such as data centers), the weight d1 of the equipment failure evaluation value may be relatively high, such as d1 = 0.7.

[0153] Dynamically adjust the weight based on actual network failure statistics. If a large number of power outages are caused by equipment failures over a period of time, increase d1 appropriately. If long fault repair times become a major issue, increase d2 appropriately.

[0154] Example 7:

[0155] The generating of the power quality evaluation value Q2 includes:

[0156] Obtain voltage and frequency data of each node in the current first-level network reconstruction plan;

[0157] Evaluate the voltage data, generate the voltage evaluation value of each node in the current first-level network, and calculate the variance σ of the voltage evaluation value in the current first-level network a , generating the voltage stability value e v , e v =r1*σ a ;

[0158] Where r1 is the variance σ of the voltage evaluation value a The coefficient of

[0159] Get the frequency data set F of the current sampling time node, F={F1,F2…F k …F q};

[0160] Among them, F k represents the frequency dataset of the kth node, and q represents the number of nodes in the current first-level network;

[0161] Calculate the frequency deviation value e based on the frequency data set F f ,

[0162] Among them, f k represents the measured frequency of the kth node, fs is the rated frequency, and r2 is the coefficient of the frequency deviation value;

[0163] Based on the voltage stability value e v And the frequency deviation value e f Generate power quality evaluation value Q2,

[0164]

[0165] Among them, d3 is the voltage stability value e v The weight of d4 is the frequency deviation value e f The weight of .

[0166] This embodiment considers the temporal correlation of voltage evaluation values. If the voltage evaluation value fluctuates significantly over a short period of time (e.g., within several adjacent sampling points), these fluctuations are weighted to emphasize voltage stability over longer timescales. For example, an exponentially weighted moving average method is used to calculate the variance, so that recent voltage evaluation values ​​have a greater influence on the variance calculation.

[0167] The value of coefficient r1 should be determined based on the sensitivity of the actual network to voltage stability. For precision electronic equipment load networks that have high requirements for voltage stability, the value of r1 should be larger.

[0168] In determining the voltage stability value e v When calculating the weight of the frequency deviation value ef, a reasonable distribution is made based on the load type and the network's requirements for power quality. For loads that are sensitive to voltage stability (such as electronic equipment manufacturing plants), the voltage stability value e v The weight value of d3 is larger, such as d3 = 0.7; for loads that are sensitive to frequency stability (such as motor loads), the value of d4 is larger, such as d4 = 0.7.

[0169] Example 8:

[0170] The generating of the secondary network based on the selected primary network reconstruction solution includes:

[0171] Sort the evaluation values ​​of the first-level network reconstruction strategies in the evaluation value set, and select the first-level network reconstruction strategy with the largest evaluation value;

[0172] Based on the selected primary network reconstruction strategy, the primary network lines are controlled by line switches to generate a secondary network.

[0173] In this embodiment, before using circuit switches to control the opening and closing of the primary network lines to generate the secondary network, detailed planning of the circuit switch operations is required. The order of operations is determined, with branch switches that have less impact on the entire network being disconnected first, such as those away from critical loads and distributed generation sources, to minimize the impact of the operation on the network.

[0174] For multiple parallel branches, calculate the transient current and voltage fluctuations for different switching sequences. Select the sequence that minimizes transient fluctuations. For example, use electromagnetic transient simulation software (such as PSCAD) to perform simulations and compare the network responses for different switching sequences.

[0175] During the operation of the circuit breaker, the network voltage, current, power and other parameters are monitored in real time. If the parameters are found to be out of the normal range (such as the voltage deviation exceeds ±5% of the rated voltage), the operation is stopped immediately and adjustments are made.

[0176] The operating speed of the circuit breakers is dynamically adjusted based on monitoring results. For example, when large voltage fluctuations are detected, the switching speed is slowed down to give the network enough time to adapt to the topology changes. When the parameters stabilize, the switching speed can be appropriately increased to improve the efficiency of generating the secondary network.

[0177] After the secondary network is generated, perform a comprehensive inspection of the network. Verify that network connectivity, power balance, voltage quality, and other indicators meet requirements. If not, fine-tune the circuit breaker status or re-evaluate and adjust the primary network reconstruction strategy.

[0178] Example 9:

[0179] The process of formulating a control strategy based on establishing an energy storage model in the secondary network to restore power to the microgrid includes:

[0180] Obtain historical data of distributed power sources in the secondary network, including: charging and discharging efficiency and capacity attenuation of distributed power sources;

[0181] Establish energy storage models based on historical data of distributed power sources;

[0182] Obtain real-time operating data of distributed power sources in the current secondary network;

[0183] Combine the real-time operation data of distributed power sources and the energy storage model to generate energy storage status prediction data for each distributed power source in the current secondary network;

[0184] Generate control instructions for each distributed power source in the current secondary network based on the energy storage status prediction data.

[0185] In this embodiment, in addition to charge and discharge efficiency and capacity decay, other historical operating data of the distributed power supply should also be collected, such as power generation curves under different environmental conditions (temperature, humidity, etc.), start and stop times, fault frequency, etc. This data helps to more comprehensively understand the performance characteristics of the distributed power supply.

[0186] The historical data collection period should be long enough to cover operating conditions in different seasons and under different load demands. For example, for solar distributed power generation, data should be collected for at least the past year, including the impact of seasonal changes in sunlight intensity on power generation efficiency.

[0187] After acquiring historical data, the accuracy of the data is verified. Anomalous data points are removed by comparing and analyzing the data with other relevant data (such as meteorological data and load records). For example, if the charge and discharge efficiency at a certain moment deviates significantly from the normal range and does not match the environmental conditions and load conditions at the time, the data is marked as suspicious and further verified.

[0188] Establish data quality assessment indicators, such as data completeness rate, data deviation rate, etc. For data sources with low data completeness rate or high deviation rate, optimize or replace data collection equipment to ensure the reliability of historical data.

[0189] Combine real-time operating data with the energy storage model. Using the Kalman filter algorithm, the predicted results of the energy storage model are used as a priori estimates, and the real-time operating data is used as the observation value to optimally estimate the energy storage status of the distributed power supply.

[0190] For different types of distributed power sources, the data fusion algorithm is adjusted according to their characteristics. For example, for large distributed power sources with high inertia (such as large hydroelectric generators), a weighted average data fusion algorithm can be used, giving higher weight to the energy storage model's prediction results; for distributed power sources with fast response speeds (such as small lithium battery energy storage systems), real-time operating data is given higher weight.

[0191] When generating energy storage status prediction data, not only the current storage capacity should be considered, but also multi-dimensional information such as the storage's health status and remaining service life should be analyzed. For example, by analyzing factors such as the battery's internal resistance trend and the shape of the charge and discharge curve, the battery's health status and remaining service life can be predicted.

[0192] Energy storage status prediction data is generated at different time scales based on different application scenarios and requirements. For example, for short-term microgrid control (e.g., minute to hour), high-precision real-time energy storage status prediction data is generated; for long-term planning (e.g., daily to weekly), energy storage status trend prediction data is generated that takes into account capacity decay and environmental impacts.

[0193] Example 10:

[0194] The generation of control instructions for each distributed power source of the current secondary network includes:

[0195] Generate the current power balance situation in the secondary network based on the energy storage status prediction data;

[0196] Based on the current power balance in the secondary network, a voltage or power regulation instruction is generated, and the charging and discharging power of the energy storage system is controlled through the voltage or frequency regulation instruction. Based on the charging and discharging power of the energy storage system, the microgrid is controlled to resume power supply.

[0197] In this embodiment, the regulation instructions when there is excess power supply include:

[0198] For the energy storage system, when there is excess power supply, the voltage can be appropriately increased to encourage the load to consume more power, and the voltage adjustment amount ΔV is determined according to the excess power ΔP.

[0199] Send voltage regulation instructions to distributed power sources. For distributed power sources with voltage regulation capabilities (such as some inverter-type distributed power sources), monitor the power response of the load and adjust the voltage regulation amount in a timely manner according to the increase in load power.

[0200] And calculate the charging power distribution of each distributed power source. The charging power of each distributed power source is determined according to factors such as the remaining capacity ratio and charging efficiency of the distributed power source.

[0201] Send charging power adjustment instructions to the distributed power supply to control it to charge at the specified power. At the same time, monitor the state of charge (SOC) of the energy storage system. When the SOC approaches the upper limit, gradually reduce the charging power to prevent overcharging.

[0202] The adjustment instructions when power supply is insufficient include:

[0203] When the power supply is insufficient, the voltage is appropriately reduced to reduce unnecessary load consumption. Determine the voltage adjustment amount.

[0204] Send voltage regulation instructions to distributed power sources to adjust their output voltage. At the same time, monitor the power response of the load, such as the power reduction of certain voltage-sensitive loads, and adjust the voltage regulation amount in a timely manner according to the reduction in load power.

[0205] Calculate the discharge power allocation for each distributed power source (DG). Consider factors such as the DG's remaining capacity and discharge efficiency. For example, send a discharge power adjustment command to the DG to control it to discharge at a specified power. Simultaneously, monitor the DG's state of charge (SOC). When the SOC approaches the lower limit, prioritize reducing the DG's discharge power or stopping discharge to avoid over-discharge.

[0206] Evaluate the power restoration status of the microgrid and monitor the power restoration time and quality of critical loads (such as hospitals and communication base stations). If the power restoration time of critical loads is too long or the power quality does not meet the requirements (such as voltage fluctuations exceeding the allowable range or excessive frequency deviation), adjust the control instructions of the distributed generation.

[0207] Control instructions are dynamically adjusted based on the overall operating status of the microgrid, such as the stability of power balance and the SOC balance of the energy storage system. For example, if frequent fluctuations in power balance are detected in a certain area during power restoration, the control strategy for the distributed power supply in that area can be adjusted, such as increasing the input of backup power or adjusting the charge and discharge power distribution of the energy storage system.

[0208] Finally, it should be noted that it is apparent that those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, to the extent such modifications and variations fall within the scope of the present invention and its equivalents, the present invention is intended to include such modifications and variations.

[0209] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. A microgrid power supply restoration method based on island partitioning and network reconstruction, characterized in that: including: Dividing the microgrid based on the obtained fault information and distributed power information to generate multiple first-level networks; Constructing a connected graph based on the nodes in the first-level network, calculating the circuit losses of the connected graph, generating multiple first-level network reconstruction schemes; evaluating the first-level network reconstruction schemes to generate a set of evaluation values for the first-level network reconstruction schemes; Selecting a first-level network reconstruction scheme based on the set of evaluation values, and generating a second-level network based on the selected first-level network reconstruction scheme; Formulating a control strategy based on the energy storage model established in the second-level network to restore the power supply of the microgrid; Among them, the nodes in the first-level network include: distributed power sources and electrical loads.

2. The microgrid power supply restoration method based on island partitioning and network reconstruction according to claim 1, characterized in that: When generating multiple first-level networks, it includes: Generating the areas where power outages are required in the current microgrid based on the obtained fault information; Among them, the fault information includes: the type of fault and the location where the fault occurs; Obtaining the line connection relationships, distributed power sources and electrical loads in the areas where power outages are required, and constructing a topological model of the current area; Setting a priority level A for the electrical loads inside the topological model based on historical data, A = {A1, A2... Ai... An}; Among them, Ai represents the priority level of the i-th electrical load, and n represents the number of electrical loads in the current topological model; Formulating a first-level network division strategy based on the priority levels of the electrical loads, and generating multiple first-level networks by combining the first-level network division strategy and the topological model.

3. The microgrid power supply restoration method based on island partitioning and network reconstruction according to claim 2, characterized in that: When formulating a first-level network division strategy based on the priority levels of the electrical loads, it includes: Setting a preset priority value a1 based on historical data; Formulating a first-level network division strategy by comparing the priority levels of the electrical loads with the preset priority value. The first-level network division strategy includes: If a1 < Ai, the current electrical load is a first-type electrical load; If Ai ≤ a1, the current electrical load is a second-type electrical load; Sequentially obtaining the first-type electrical loads in the current topological model and the distributed power sources connected to the first-type electrical loads; And calculate the remaining output power P of the distributed power supply B1 , P B1 =P B -P1; Among them, P B is the output power of the current distributed power source, and P1 is the demand power forecast value of the first type of power load connected to the current distributed power source; If the remaining output power P of the distributed generation B1 <0, then generate the current power consumption behavior optimization strategy for the first type of power load; If P B1 ≥0, then according to P B1 The value of selects the second type of electrical load connected to the distributed generation to generate a primary network.

4. The microgrid power supply restoration method based on island partitioning and network reconstruction according to claim 3 is characterized in that: When generating multiple first-level network reconstruction schemes, it includes: Obtaining the characteristic information of the current first-level network, including: the magnitude of the current and the magnitude of the resistance on each line; Calculate the power loss P of the jth line in the current first-level network j =I j 2 R j ; Among them, I j is the current on line j, R j is the resistance of circuit j; Calculate the total line loss P under different line combinations M , M is the total number of lines in the first-level network under the current line combination; And set the preset value c1 of the line loss value based on historical data, and select P M Generate multiple first-level network reconstruction schemes by combining the lines with <c1>.

5. The microgrid power supply restoration method based on island partitioning and network reconstruction according to claim 4 is characterized in that: When generating a set of evaluation values for the first-level network reconstruction schemes, it includes: Generating a reliability evaluation value Q1 and a power quality evaluation value Q2 based on the operating data of each node in the current first-level network reconstruction scheme; The evaluation value Q of the current first-level network reconstruction solution is generated based on the reliability evaluation value Q1 and the power quality evaluation value Q2. Z ; Q Z =w1*Q1+w2*Q2; Among them, w1 is the weight of the reliability evaluation value Q1, and w2 is the weight of the power quality evaluation value Q2.

6. The microgrid power supply restoration method based on island division and network reconstruction according to claim 5, characterized in that: When generating the reliability evaluation value Q1, it includes: Obtaining the historical reliability statistical parameters of each node in the current first-level network reconstruction scheme, including historical equipment failure rates and historical fault repair times; Generating equipment failure rate evaluation values and fault repair time evaluation values for each node respectively based on the historical equipment failure rates and historical fault repair times; Calculating the reliability evaluation value Q1 of the current first-level network reconstruction scheme; Among them, q represents the number of nodes in the current first-level network, Q ak represents the equipment failure evaluation value of the kth node, d1 represents the weight of the equipment failure evaluation value of the kth node, Q bk represents the fault repair time evaluation value of the kth node, d2 represents the weight of the fault repair time evaluation value of the kth node, and d1+d2=1, Q ak and Q bk The value range is the same.

7. The microgrid power supply restoration method based on island division and network reconstruction according to claim 6, characterized in that: When generating the power quality evaluation value Q2, it includes: Obtaining the voltage data and frequency data of each node in the current first-level network reconstruction scheme; Evaluate the voltage data, generate the voltage evaluation value of each node in the current first-level network, and calculate the variance σ of the voltage evaluation value in the current first-level network a , generating the voltage stability value e v , e v =r1*σ a ; Where r1 is the variance σ of the voltage evaluation value a The coefficient of Get the frequency data set F of the current sampling time node, F={F1,F2…F k …F q }; Among them, F k represents the frequency dataset of the kth node, and q represents the number of nodes in the current first-level network; Calculate the frequency deviation value e based on the frequency data set F f , Among them, f k represents the measured frequency of the kth node, fs is the rated frequency, and r2 is the coefficient of the frequency deviation value; Based on the voltage stability value e v And the frequency deviation value e f Generate power quality evaluation value Q2, Among them, d3 is the voltage stability value e v The weight of d4 is the frequency deviation value e f The weight of .

8. The microgrid power supply restoration method based on island division and network reconstruction according to claim 7, characterized in that: When generating a second-level network based on the selected first-level network reconstruction scheme, it includes: Sort the evaluation values ​​of the first-level network reconstruction strategies in the evaluation value set, and select the first-level network reconstruction strategy with the largest evaluation value; Based on the selected primary network reconstruction strategy, the primary network lines are controlled by line switches to generate a secondary network.

9. The microgrid power supply restoration method based on island division and network reconstruction according to claim 8, characterized in that: The process of formulating a control strategy based on establishing an energy storage model in the secondary network to restore power supply to the microgrid includes: Obtain historical data of distributed power sources in the secondary network, including: charging and discharging efficiency and capacity attenuation of distributed power sources; Establish energy storage models based on historical data of distributed power sources; Obtain real-time operating data of distributed power sources in the current secondary network; Combine the real-time operation data of distributed power sources and the energy storage model to generate energy storage status prediction data for each distributed power source in the current secondary network; Generate control instructions for each distributed power source in the current secondary network based on the energy storage status prediction data.

10. The microgrid power supply restoration method based on island division and network reconstruction according to claim 9, characterized in that: The generation of control instructions for each distributed power source of the current secondary network includes: Generate the current power balance situation in the secondary network based on the energy storage status prediction data; Based on the current power balance in the secondary network, a voltage or power regulation instruction is generated, and the charging and discharging power of the energy storage system is controlled through the voltage or frequency regulation instruction. Based on the charging and discharging power of the energy storage system, the microgrid is controlled to resume power supply.

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