Distributed optimization method of microgrid access power distribution network

Abstract

The invention relates to the technical field of energy management, in particular to a distributed optimization method for accessing a micro-grid to a power distribution network, which comprises the following steps of: extracting load data of the micro-grid and the power distribution network in real time, analyzing load requirements of nodes by combining space-time parameters of power transmission, and accurately calculating power distribution and flow paths of each node; load prediction is carried out by using an autoregression model, a node load change trend is analyzed, when the load of a certain node exceeds the transmission capability of the power distribution network, overload is transferred to other nodes in time, a power flow path is optimized through a fuzzy control algorithm, a bottleneck problem is solved, and reasonable allocation of node loads is realized; and an optimized power scheduling scheme is generated, power distribution and load adjustment of each time period are refined along with continuous optimization of power scheduling, coordination between the micro-grid and the power distribution network is more compact through flexible adjustment of real-time load adjustment and node load distribution, an unstable risk caused by load imbalance is avoided, and the power distribution efficiency is improved. And overall efficient and stable operation is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a distributed optimization method for accessing a microgrid to a distribution network. Background Art

[0002] The purpose of the field of energy management technology is to achieve a balance between energy supply and demand by improving energy efficiency, reducing energy costs, promoting sustainable development, and enhancing system stability and reliability, ensuring the smooth operation of the power grid and the optimal use of resources. Through intelligent scheduling and precise control, it coordinates the flow of various types of energy, reduces waste, lowers costs, and promotes the development of green and low-carbon energy to support the stable, reliable and efficient operation of the power system.

[0003] The purpose of the distributed optimization method for microgrid access to distribution network is to enable the microgrid to achieve efficient power dispatching, load management, energy storage and demand response when accessing the distribution network through distributed optimization methods, and optimize the access of the microgrid and the coordinated operation of the distribution network to ensure a stable supply of electricity, while improving the overall efficiency of the system and energy utilization. By coordinating energy production, storage and consumption within the microgrid and combining it with the operation needs of the distribution network, intelligent and efficient energy management can be achieved.

[0004] Existing technologies lack dynamic adjustment capabilities and real-time response mechanisms, resulting in the inability to promptly resolve the contradiction between load demand and transmission capacity in the coordinated operation of microgrids and distribution networks. Load distribution and adjustment ignore the real-time nature of grid load fluctuations and time period changes, causing power flow lags and system instability risks. When load demand suddenly increases or decreases, traditional methods cannot detect bottlenecks in a timely manner, and may not be able to select the optimal path when transferring loads between nodes, affecting the overall efficiency and power transmission capacity, making the allocation efficiency of power resources low and unable to achieve optimal resource allocation and conservation over a large range. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a distributed optimization method for connecting a microgrid to a distribution network.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a distributed optimization method for microgrid access to a distribution network, comprising the following steps:

[0007] Step 1: Extract the real-time load data of the microgrid and distribution network based on the sensor, analyze the load demand of each microgrid node and distribution network, and extract the power distribution and load demand data of the nodes in different time periods. Combined with the spatial distribution characteristics, calculate the transmission power of each node and generate the spatiotemporal power transmission parameters;

[0008] Step 2: Use the autoregressive model to model the load data of the microgrid nodes, and analyze the load change trend of each node in combination with the spatiotemporal power transmission parameters. When the load exceeds the preset value of the transmission capacity of the current distribution network, the excess load is transferred, the power distribution is recalculated, and the power flow direction of the node is adjusted to generate a load adjustment and transmission path plan;

[0009] Step 3: Based on the load adjustment and transmission path scheme, determine whether there is a bottleneck in each transmission path. If a bottleneck occurs, use a fuzzy control algorithm to adjust the power flow path according to the load demand of the node, change the distribution ratio of the node load, and generate a transmission bottleneck optimization plan;

[0010] Step 4: Based on the transmission bottleneck optimization scheme, the constraints in the power scheduling process are evaluated. When the power allocation requirements of the nodes are not met, the load is recalculated and allocated between the microgrid and the distribution network to generate a power scheduling optimization scheme;

[0011] Step 5: Combined with the power scheduling optimization scheme, dynamically adjust the power allocation in each time period, gradually optimize the power transmission path, and generate an optimized energy utilization scheme;

[0012] Step 6: Based on the optimized energy utilization plan, real-time load adjustment is performed between the microgrid and the distribution network to determine whether the load is unbalanced. If an imbalance occurs, the load distribution of the microgrid nodes is adjusted and the power flow is recalculated to generate a load balancing adjustment plan;

[0013] Step 7: According to the load balancing adjustment plan, select and adjust the scheduling strategy and node load distribution, optimize the node load distribution and power flow path, and generate a stable coordinated scheduling plan.

[0014] As a further solution of the present invention, the specific steps of generating the space-time power transmission parameters are:

[0015] Extract real-time load data of microgrid and distribution network from sensors, calculate power demand and load demand of each node, and generate node power demand data;

[0016] According to the node power demand data, combined with the spatial distribution characteristics of the area where the node is located, the power demand and load data of the node are analyzed, the power allocation of each node in different time periods is calculated, and the time period power allocation data is generated;

[0017] Based on the power allocation data for the time period and in combination with the spatial distribution of load demand, power flow calculations are performed between nodes to generate spatiotemporal power transmission parameters.

[0018] As a further solution of the present invention, the specific steps of generating the load adjustment and transmission path solution are:

[0019] Based on the spatiotemporal power transmission parameters, an autoregressive model is used to model the load data of the microgrid nodes, the load change trend of each node is analyzed, and the node load overload status information is obtained;

[0020] Based on the node load overload status information, determine whether the load exceeds the transmission capacity of the distribution network, select the corresponding node for load transfer, calculate and execute load transfer and power flow adjustment, and generate a load transfer plan;

[0021] Based on the load transfer scheme, the power distribution and load flow direction of the nodes are adjusted, and combined with the optimization of the transmission path, power adjustment and path optimization are performed to generate a load adjustment and transmission path scheme.

[0022] As a further solution of the present invention, the autoregressive model is according to the formula:

[0023]

[0024] in: Time The load data, is a constant term, are the coefficients of the autoregressive model, is the order of the autoregressive model, is the error term, For external input data The coefficient of is the order of external input data, For external input data at time point The numerical value of is the weight coefficient, is the seasonal or time period adjustment factor, is the correction factor, is the standard deviation of the load data.

[0025] As a further solution of the present invention, the specific steps of generating the transmission bottleneck optimization solution are:

[0026] Based on the load adjustment and transmission path scheme, detect whether a power bottleneck occurs in the power flow of the transmission path, identify bottleneck nodes and paths, calculate the power load of each path, and generate a transmission bottleneck detection result;

[0027] According to the transmission bottleneck detection result, a bottleneck path is selected, a fuzzy control algorithm is used to adjust the node power flow direction, and the power distribution ratio is changed, the path adjustment and load distribution are changed, and a power flow path adjustment plan is generated;

[0028] Based on the power flow path adjustment scheme, power distribution is optimized, the load flow direction of the node is adjusted, and a transmission bottleneck optimization scheme is generated.

[0029] As a further solution of the present invention, the fuzzy control algorithm is according to the formula:

[0030]

[0031] in: is the adjusted power flow direction, is the current power flow error, is the rate of change of the power flow error, is the weight coefficient of the fuzzy control rule, is the weight coefficient of the fuzzy control rule, is the error membership function, is the membership function of the rate of change, is the number of error rules, is the number of rate-of-change rules, is the time period adjustment coefficient, is the adjustment function of external influencing factors, is the external environmental factor of the time period, is the error change rate adjustment coefficient, is the correction function for the influence of the rate of change.

[0032] As a further solution of the present invention, the specific steps of generating the power scheduling optimization solution are:

[0033] Based on the transmission bottleneck optimization scheme, determine whether the power allocation of each node is insufficient or overloaded, evaluate the power allocation check and node status, and generate a power allocation evaluation result;

[0034] Based on the power distribution evaluation result, recalculate the load between the microgrid and the distribution network, perform load redistribution and optimization, and generate a load redistribution plan;

[0035] Based on the load redistribution scheme, the power flow between the microgrid and the distribution network is adjusted, new power allocation and transmission path adjustment are performed, and a power scheduling optimization scheme is generated.

[0036] As a further solution of the present invention, the specific steps of generating the optimized energy utilization plan are:

[0037] Based on the power scheduling optimization scheme, power demand is calculated in each time period, node power allocation is dynamically adjusted, power flow paths are gradually optimized, power adjustment and paths of each node are analyzed, and power allocation adjustment data is generated;

[0038] Based on the power allocation adjustment data, power load evaluation is performed on each transmission path, power flow in the path is adjusted, load optimization of the transmission path is performed, and an optimized transmission path solution is generated;

[0039] Based on the optimized transmission path scheme, the power flow direction between nodes and the power demand of the nodes are adjusted to generate an optimized energy utilization scheme.

[0040] As a further solution of the present invention, the specific steps of generating the load balancing adjustment solution are:

[0041] Based on the optimized energy utilization scheme, the real-time load between the microgrid and the distribution network is detected, and the load of the nodes is determined one by one to determine whether the load is balanced, and a load imbalance state is generated;

[0042] Based on the load imbalance state, select microgrid nodes and perform load adjustment operations, and recalculate node power requirements to generate a load redistribution plan;

[0043] Based on the load redistribution scheme, the power flow path between the microgrid and the distribution network is adjusted, the node power distribution is updated and the power flow is recalculated to generate a load balancing adjustment scheme.

[0044] As a further solution of the present invention, the specific steps of generating the stable coordinated scheduling solution are:

[0045] Based on the load balancing adjustment scheme, load data of each node is obtained, nodes with uneven load distribution are identified, node load distribution ratios are adjusted, and node load distribution adjustment data is generated;

[0046] Based on the node load distribution adjustment data, evaluate the load condition of the existing power flow path, adjust the power flow path, and generate a power flow path optimization plan;

[0047] Based on the power flow path optimization scheme, the optimized load distribution and power flow path are integrated, and a scheduling operation is performed to generate a stable coordinated scheduling scheme.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are:

[0049] 1. In the present invention, by extracting the load data of the microgrid and the distribution network in real time, combining the spatiotemporal parameters of power transmission to analyze the load demand of the node, accurately calculating the power distribution and flow path of each node, using the autoregressive model to predict the load, analyzing the node load change trend, when the load of a node exceeds the transmission capacity of the distribution network, the overload is transferred to other nodes in time to ensure that the load balance is not broken;

[0050] 2. In the present invention, the power flow path is optimized through fuzzy control algorithm, the bottleneck problem is solved, the reasonable allocation of node load is realized, and an optimized power scheduling scheme is generated. With the continuous optimization of power scheduling, the power allocation and load regulation in each period are refined, further improving the energy utilization rate and the stability of power supply;

[0051] 3. In the present invention, through real-time load adjustment and flexible regulation of node load distribution, the coordination between the microgrid and the distribution network is closer, avoiding the risk of instability caused by load imbalance, ensuring overall efficient and stable operation, improving the overall energy utilization rate, and reducing the waste and cost that may occur in power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0053] Figure 2 It is a schematic diagram of the refinement of S1 of the present invention;

[0054] Figure 3 It is a schematic diagram of the refinement of S2 of the present invention;

[0055] Figure 4 It is a schematic diagram of the refinement of S3 of the present invention;

[0056] Figure 5 It is a schematic diagram of the refinement of S4 of the present invention;

[0057] Figure 6 It is a detailed schematic diagram of S5 of the present invention;

[0058] Figure 7 It is a detailed schematic diagram of S6 of the present invention;

[0059] Figure 8 It is a detailed schematic diagram of S7 of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] See also Figure 1 The present invention provides a technical solution: a distributed optimization method for accessing a microgrid to a distribution network, comprising the following steps:

[0062] S1: Extract the real-time load data of the microgrid and distribution network based on the sensors, analyze the load demand of each microgrid node and the distribution network, and extract the power distribution and load demand data of the nodes in different time periods. Combined with the spatial distribution characteristics, calculate the transmission power of each node and generate the spatiotemporal power transmission parameters.

[0063] S2: Use the autoregressive model to model the load data of the microgrid nodes, and analyze the load change trend of each node in combination with the spatiotemporal power transmission parameters. When the load exceeds the preset value of the current distribution network's transmission capacity, the excess load is transferred, the power distribution is recalculated, and the power flow direction of the node is adjusted to generate a load adjustment and transmission path plan;

[0064] S3: Based on the load adjustment and transmission path plan, determine whether there is a bottleneck in each transmission path. If a bottleneck occurs, use the fuzzy control algorithm to adjust the power flow path according to the load demand of the node, change the distribution ratio of the node load, and generate a transmission bottleneck optimization plan;

[0065] S4: Based on the transmission bottleneck optimization scheme, the constraints in the power scheduling process are evaluated. When the power allocation requirements of the nodes are not met, the load weight is recalculated and allocated between the microgrid and the distribution network to generate a power scheduling optimization scheme;

[0066] S5: Combined with the power scheduling optimization plan, dynamically adjust the power allocation in each time period, gradually optimize the power transmission path, and generate an optimized energy utilization plan;

[0067] S6: Based on the optimized energy utilization plan, real-time load adjustment is performed between the microgrid and the distribution network to determine whether the load is unbalanced. If an imbalance occurs, the load distribution of the microgrid nodes is adjusted and the power flow is recalculated to generate a load balance adjustment plan;

[0068] S7: According to the load balancing adjustment plan, select and adjust the scheduling strategy and node load distribution, optimize the node load distribution and power flow path, and generate a stable coordinated scheduling plan.

[0069] See also Figure 2 , the specific steps to generate space-time power transmission parameters are:

[0070] S101: extracting real-time load data of the microgrid and the distribution network according to sensors, calculating the power demand and load demand of each node, and generating node power demand data;

[0071] S102: Analyze the power demand and load data of the nodes according to the node power demand data and the spatial distribution characteristics of the area where the nodes are located, calculate the power allocation of each node in different time periods, and generate time period power allocation data;

[0072] S103: Based on the time period power allocation data and the spatial distribution of load demand, calculate the power flow between nodes and generate time-space power transmission parameters;

[0073] Based on the sensor, the real-time load data of the microgrid and the distribution network are extracted, and the Kalman filter algorithm is adopted, specifically the first-order recursive Kalman filter, in which the state transfer matrix is ​​the unit matrix, and the observation matrix and the state matrix are updated based on the real-time data output by the sensor. The load data obtained by the sensor is filtered and smoothed to remove noise data to obtain more accurate load data. Combined with the time series characteristics of the load data, the autoregressive model is used for fitting, and the node power demand data is generated based on the time series data;

[0074] According to the node power demand data, combined with the spatial distribution characteristics of the node area, the K-means clustering algorithm is used, where K is set to 4, the Euclidean distance between nodes is calculated, the cluster center is initialized by the standard deviation method, the nodes are spatially clustered, the power demand and load data of the nodes in each cluster are analyzed, and the timestamp data is combined with the polynomial regression algorithm. The quadratic polynomial is used for fitting, and the coefficients are solved by the least squares method. The power distribution of each node in different time periods is calculated to generate time period power distribution data;

[0075] Based on the power allocation data of the time period, combined with the spatial distribution of load demand, and using the power flow calculation method, the voltage between the nodes of the power grid is set to 1.0pu, the admittance matrix is ​​obtained through the power system model, the power flow calculation between the nodes is performed, and the spatiotemporal power transmission parameters are generated.

[0076] See also Figure 3 ,The specific steps of generating load adjustment and transmission path scheme are:

[0077] S201: Based on the spatiotemporal power transmission parameters, the load data of the microgrid nodes are modeled using an autoregressive model, the load change trend of each node is analyzed, and the node load overload status information is obtained;

[0078] S202: Based on the node load overload status information, determine whether the load exceeds the transmission capacity of the distribution network, select the corresponding node for load transfer, calculate and execute load transfer and power flow adjustment, and generate a load transfer plan;

[0079] S203: Based on the load transfer plan, adjust the power distribution and load flow direction of the node, and combine the optimization of the transmission path to perform power adjustment and path optimization to generate a load adjustment and transmission path plan;

[0080] For the spatiotemporal power transmission parameters, an autoregressive model is used, specifically a first-order autoregressive model, in which the order of the model is set to 1. The parameters of the model are estimated by the least squares method, and the load data of the node is used to build the model. The time series data of the load of each node is calculated, and the change trend of the node load is analyzed. The predicted load data is compared with the threshold of the current node load to obtain the status information of whether the node is overloaded, and the node load overload status information is generated;

[0081] Based on the node load overload status information, the threshold judgment method is adopted, and the overload judgment threshold is set to 120% of the node load. When the node load exceeds the set threshold, it is judged whether there is a situation where the load exceeds the transmission capacity of the distribution network. The overloaded node is selected, and the load transfer path is calculated by the maximum flow minimum cut algorithm in combination with the load transfer algorithm. The power flow direction of each node is adjusted to generate a load transfer plan.

[0082] Based on the load transfer scheme, a linear programming algorithm is used. The objective function is set as the optimization of node power allocation. The constraints are load balance and transmission capacity limitations. The power allocation of nodes is optimized by the simplex method. Combined with the optimization of the transmission path, the Dijkstra shortest path algorithm is used to calculate the power transmission path and generate load adjustment and transmission path schemes.

[0083] Autoregressive model, according to the formula:

[0084]

[0085] in: Time The load data, is a constant term, are the coefficients of the autoregressive model, is the order of the autoregressive model, is the error term, For external input data The coefficient of is the order of external input data, For external input data at time point The numerical value of is the weight coefficient, is the seasonal or time period adjustment factor, is the correction factor, is the standard deviation of the load data;

[0086] Execution process: First, through historical load data and the corresponding coefficients , using past load data to predict current load demand and calculate the load demand of each microgrid node at a certain time point Load value, introducing external influencing factors , utilization coefficient Perform weighted calculations to supplement the external factors affecting load changes. By weighting external data, the multi-dimensional characteristics of load changes can be more accurately reflected. Then, seasonal factors and specific time period adjustments are considered. , through the weight coefficient Adjust the model to adapt to the impact of different time points on load, standard deviation term It further helps to adjust the fluctuation amplitude of load data so that the model can adapt to different load fluctuations. Finally, by integrating all parameters, the load prediction results of the microgrid nodes are obtained. Through the optimized power flow path and adjusted node load distribution, the energy transmission and load balance of the microgrid connected to the distribution network are optimized to ensure the stable operation of the distribution network under different load demands.

[0087] See also Figure 4 ,The specific steps to generate the transmission bottleneck optimization solution are:

[0088] S301: Based on the load adjustment and transmission path scheme, detect whether a power bottleneck occurs in the power flow of the transmission path, identify bottleneck nodes and paths, calculate the power load of each path, and generate a transmission bottleneck detection result;

[0089] S302: According to the transmission bottleneck detection result, a bottleneck path is selected, a fuzzy control algorithm is used to adjust the node power flow direction, and the power distribution ratio is changed, the path adjustment and load distribution are changed, and a power flow path adjustment plan is generated;

[0090] S303: Based on the power flow path adjustment plan, optimize power distribution, adjust the load flow direction of the node, and generate a transmission bottleneck optimization plan;

[0091] Based on the load adjustment and transmission path scheme, a power flow analysis algorithm, specifically the Newton-Raphson method, is used to calculate the power flow on the transmission path, detect whether there is a power bottleneck, identify bottleneck nodes and paths, and calculate the power load of each path based on the admittance matrix and node voltage settings of the transmission path to generate transmission bottleneck detection results;

[0092] According to the transmission bottleneck detection results, the bottleneck path is selected and the fuzzy control algorithm is adopted. Specifically, the membership function is set as a triangular function, the input parameters are set as the node load and the power flow direction, and the output parameter is the power flow direction adjustment amount. The fuzzy reasoning mechanism is used to adjust the node power flow direction and change the power allocation ratio. The path adjustment algorithm is combined with the maximum flow minimum cut algorithm to adjust the path and generate a power flow path adjustment plan.

[0093] Based on the power flow path adjustment scheme, a linear programming algorithm is used. The objective function is set as the balance of power flow at each node. The constraints include node load balance and the maximum load capacity of the transmission path. The simplex method is used to optimize node power distribution, adjust the load flow direction of the node, and generate a transmission bottleneck optimization scheme.

[0094] Fuzzy control algorithm, according to the formula:

[0095]

[0096] in: is the adjusted power flow direction, is the current power flow error, is the rate of change of the power flow error, is the weight coefficient of the fuzzy control rule, is the weight coefficient of the fuzzy control rule, is the error membership function, is the membership function of the rate of change, is the number of error rules, is the number of rate-of-change rules, is the time period adjustment coefficient, is the adjustment function of external influencing factors, is the external environmental factor of the time period, is the error change rate adjustment coefficient, is the correction function for the influence of the rate of change;

[0097] Implementation process: By calculating the current power flow error and the rate of change of power flow error , determine the adjustment direction of power flow, and then use fuzzy control rules to calculate the membership of the error and error change rate, that is, and , and weight each fuzzy rule according to the membership value to obtain the influence of each rule on power flow adjustment, the weight coefficient and Controls the contribution of rules in adjustments and introduces the influence coefficient of time periods and external environmental factors , by adjusting the time period coefficient and error change rate correction factor , dynamically optimize the power flow path, and adjust the coefficients and correction functions according to external environment changes, including , to cope with the impact of different time periods and environmental factors on power flow, ensure the balance and efficient transmission of power flow between microgrids and distribution networks, and finally generate an optimized power flow path.

[0098] See also Figure 5 ,The specific steps of generating the power scheduling optimization scheme are:

[0099] S401: Based on the transmission bottleneck optimization solution, determine whether the power allocation of each node is insufficient or overloaded, evaluate the power allocation check and node status, and generate a power allocation evaluation result;

[0100] S402: Based on the power distribution evaluation result, recalculate the load between the microgrid and the distribution network, perform load redistribution and optimization, and generate a load redistribution plan;

[0101] S403: Based on the load redistribution plan, adjust the power flow between the microgrid and the distribution network, perform new power allocation and transmission path adjustment, and generate a power dispatch optimization plan;

[0102] Based on the transmission bottleneck optimization scheme, a power load assessment algorithm is adopted, specifically the load flow calculation method. The power allocation of each node is calculated by the Newton-Raphson method to determine whether the power of each node is insufficient or overloaded. Combined with the power load and transmission capacity of the node, the power allocation check and status of each node are evaluated to generate the power allocation assessment result;

[0103] Based on the power distribution evaluation results, a load redistribution algorithm is adopted. Through the linear programming method, the constraints are set as the power demand and load capacity of each node. The objective function is to minimize the total loss after load redistribution. The simplex method is used to recalculate the load distribution between the microgrid and the distribution network, perform load redistribution and optimization, and generate a load redistribution plan.

[0104] Based on the load redistribution scheme, the shortest path method is adopted. The power flow between nodes is set as the input parameter. The power flow path is calculated by the Dijkstra algorithm. The transmission path is adjusted in combination with the new power allocation. The new power allocation and transmission path adjustment are executed to generate a power scheduling optimization scheme.

[0105] See also Figure 6 ,The specific steps to generate an optimized energy utilization plan are:

[0106] S501: Based on the power scheduling optimization solution, power demand is calculated in each time period, node power allocation is dynamically adjusted, power flow paths are gradually optimized, power adjustment and paths of each node are analyzed, and power allocation adjustment data is generated;

[0107] S502: Based on the power allocation adjustment data, perform power load evaluation on each transmission path, adjust the power flow in the path, optimize the load of the transmission path, and generate an optimized transmission path solution;

[0108] S503: Based on the optimized transmission path solution, adjust the power flow direction between nodes and the power demand of the nodes to generate an optimized energy utilization solution;

[0109] Based on the power scheduling optimization scheme, a greedy algorithm is used to set the power demand of each time period as input. The power allocation of each time period is iteratively calculated. The power allocation of each node is gradually adjusted by combining the node power demand and transmission capacity. The power flow path is optimized using the gradient descent method, the node power adjustment and path are calculated, and the power allocation adjustment data is generated.

[0110] Based on the power allocation adjustment data, the load flow analysis algorithm is used to calculate the power load of each transmission path through the power flow calculation method, and the power flow in the path is adjusted. The path load optimization goal is set to minimize the power transmission loss. The path load is adjusted in combination with the maximum flow algorithm to generate an optimized transmission path solution.

[0111] Based on the optimized transmission path scheme, a linear optimization algorithm is used. The objective function is set as the optimal path selection for power flow. The constraints are the power demand of each node and the path transmission capacity. The simplex method is used to adjust the power flow direction between nodes, and the power demand of the nodes is combined for optimization to generate an optimized energy utilization plan.

[0112] See also Figure 7 ,The specific steps of generating a load balancing adjustment plan are:

[0113] S601: Based on the optimized energy utilization plan, the real-time load between the microgrid and the distribution network is detected, and the load of the nodes is determined one by one to determine whether the load is balanced, and a load imbalance state is generated;

[0114] S602: Based on the load imbalance state, select microgrid nodes and perform load adjustment operations, recalculate node power requirements, and generate a load redistribution plan;

[0115] S603: Based on the load redistribution plan, adjust the power flow path between the microgrid and the distribution network, update the node power distribution and recalculate the power flow, and generate a load balancing adjustment plan;

[0116] Based on the optimization of energy utilization scheme, the power load monitoring algorithm is adopted. Through the dynamic load detection method, the load detection interval is set to 1 minute, and the load data between the microgrid and the distribution network is collected in real time. It is determined whether the load of each node is balanced one by one, and the load balance threshold is set to ±5%. If it exceeds the range, it is judged as load imbalance and a load imbalance state is generated;

[0117] Based on the load imbalance status, the microgrid nodes are selected, and the load adjustment algorithm is adopted. Through the optimization-based greedy algorithm, the goal is to minimize the load imbalance, the power demand of each node is calculated, and the node load distribution is adjusted by the least square method during the recalculation process to generate a load redistribution plan;

[0118] Based on the load redistribution scheme, the power flow adjustment algorithm is adopted to calculate the power flow path between the microgrid and the distribution network through the power flow calculation method, and the power flow is recalculated according to the adjusted node power distribution, the node power distribution is updated and the path is adjusted to generate a load balancing adjustment plan.

[0119] See also Figure 8 ,The specific steps to generate a stable coordinated scheduling solution are:

[0120] S701: Based on the load balancing adjustment scheme, obtain the load data of each node, identify the nodes with uneven load distribution, adjust the node load distribution ratio, and generate node load distribution adjustment data;

[0121] S702: Based on the node load distribution adjustment data, evaluate the load condition of the existing power flow path, adjust the power flow path, and generate a power flow path optimization plan;

[0122] S703: Based on the power flow path optimization plan, the optimized load distribution and power flow path are integrated, and a scheduling operation is performed to generate a stable coordinated scheduling plan;

[0123] Based on the load balancing adjustment scheme, a load imbalance detection algorithm is adopted, specifically a threshold judgment method. The load balancing threshold is set to ±10%. The load data of each node is obtained, and the difference between the node load and the surrounding nodes is analyzed one by one. Nodes with uneven load distribution are identified, and the load distribution ratio is adjusted using the proportional adjustment method to ensure that the load of each node is close to balanced, and the node load distribution adjustment data is generated;

[0124] Based on the node load distribution adjustment data, the power flow calculation method is used to combine the node power and the transmission capacity of the path to evaluate the load of the existing power flow path, set the path load optimization goal to minimize the loss, adjust the power flow path, and generate the power flow path optimization plan;

[0125] Based on the power flow path optimization scheme, a scheduling integration algorithm is adopted. Through the linear programming method, the objective function is set as the optimal node power flow. The constraints are the node load distribution and the capacity of the transmission path. The simplex method is used to integrate the optimized load distribution and power flow path, and the scheduling operation is performed to generate a stable and coordinated scheduling scheme.

[0126] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A distributed optimization method for microgrid access to a distribution network, characterized in that: The following steps are involved: Step 1: Extract the real-time load data of the microgrid and distribution network based on the sensor, analyze and extract the power distribution and load demand data of each microgrid node and distribution network in different time periods, calculate the transmission power of each node, and generate the spatiotemporal power transmission parameters; Step 2: Use the autoregressive model to model the load data of the microgrid nodes, analyze the load change trend of each node in combination with the spatiotemporal power transmission parameters, calculate each power flow direction, and generate a load adjustment and transmission path plan; Step 3: Based on the load adjustment and transmission path scheme, determine whether there is a bottleneck in each transmission path. If a bottleneck occurs, use a fuzzy control algorithm to adjust the power flow path according to the load demand of the node, change the distribution ratio of the node load, and generate a transmission bottleneck optimization plan; Step 4: Based on the transmission bottleneck optimization scheme, the constraints in the power scheduling process are evaluated. If the power allocation requirements of the nodes are not met, the load weight is recalculated and allocated to generate a power scheduling optimization scheme; Step 5: Combined with the power scheduling optimization scheme, dynamically adjust the power allocation in each time period, gradually optimize the power transmission path, and generate an optimized energy utilization scheme; Step 6: Based on the optimized energy utilization plan, real-time load adjustment is performed between the microgrid and the distribution network. If an imbalance occurs, the load distribution of the microgrid nodes is recalculated and adjusted to generate a load balancing adjustment plan; Step 7: According to the load balancing adjustment plan, select and adjust the scheduling strategy and node load distribution, optimize the node load distribution and power flow path, and generate a stable coordinated scheduling plan.

2. The distributed optimization method for microgrid access to a distribution network according to claim 1, characterized in that: The specific steps of generating the space-time power transmission parameters are: Extract real-time load data of microgrid and distribution network from sensors, calculate power demand and load demand of each node, and generate node power demand data; According to the node power demand data, combined with the spatial distribution characteristics of the area where the node is located, the power demand and load data of the node are analyzed, the power allocation of each node in different time periods is calculated, and the time period power allocation data is generated; Based on the power allocation data for the time period and in combination with the spatial distribution of load demand, power flow calculations are performed between nodes to generate spatiotemporal power transmission parameters.

3. The distributed optimization method for accessing a microgrid to a distribution network according to claim 1, characterized in that: The specific steps of generating the load adjustment and transmission path scheme are: Based on the spatiotemporal power transmission parameters, an autoregressive model is used to model the load data of the microgrid nodes, the load change trend of each node is analyzed, and the node load overload status information is obtained; Based on the node load overload status information, determine whether the load exceeds the transmission capacity of the distribution network, select the corresponding node for load transfer, calculate and execute load transfer and power flow adjustment, and generate a load transfer plan; Based on the load transfer scheme, the power distribution and load flow direction of the nodes are adjusted, and combined with the optimization of the transmission path, power adjustment and path optimization are performed to generate a load adjustment and transmission path scheme.

4. The distributed optimization method for accessing a microgrid to a distribution network according to claim 1, characterized in that: The autoregressive model is based on the formula: ； in: Time The load data, is a constant term, are the coefficients of the autoregressive model, is the order of the autoregressive model, is the error term, For external input data The coefficient of is the order of external input data, For external input data at time point The numerical value of is the weight coefficient, is the seasonal or time period adjustment factor, is the correction factor, is the standard deviation of the load data.

5. The distributed optimization method for microgrid access to a distribution network according to claim 1, characterized in that: The specific steps of generating the transmission bottleneck optimization solution are: Based on the load adjustment and transmission path scheme, detect whether a power bottleneck occurs in the power flow of the transmission path, identify bottleneck nodes and paths, calculate the power load of each path, and generate a transmission bottleneck detection result; According to the transmission bottleneck detection result, a bottleneck path is selected, a fuzzy control algorithm is used to adjust the node power flow direction, and the power distribution ratio is changed, the path adjustment and load distribution are changed, and a power flow path adjustment plan is generated; Based on the power flow path adjustment scheme, power distribution is optimized, the load flow direction of the node is adjusted, and a transmission bottleneck optimization scheme is generated.

6. The distributed optimization method for accessing a microgrid to a distribution network according to claim 1, characterized in that: The fuzzy control algorithm is based on the formula: ； in: is the adjusted power flow direction, is the current power flow error, is the rate of change of the power flow error, is the weight coefficient of the fuzzy control rule, is the weight coefficient of the fuzzy control rule, is the error membership function, is the membership function of the rate of change, is the number of error rules, is the number of rate-of-change rules, is the time period adjustment coefficient, is the adjustment function of external influencing factors, is the external environmental factor of the time period, is the error change rate adjustment coefficient, is the correction function for the change rate.

7. The distributed optimization method for accessing a microgrid to a distribution network according to claim 1, characterized in that: The specific steps of generating the power scheduling optimization scheme are: Based on the transmission bottleneck optimization scheme, determine whether the power allocation of each node is insufficient or overloaded, evaluate the power allocation check and node status, and generate a power allocation evaluation result; Based on the power distribution evaluation result, recalculate the load between the microgrid and the distribution network, perform load redistribution and optimization, and generate a load redistribution plan; Based on the load redistribution scheme, the power flow between the microgrid and the distribution network is adjusted, new power allocation and transmission path adjustment are performed, and a power scheduling optimization scheme is generated.

8. The distributed optimization method for microgrid access to a distribution network according to claim 1, characterized in that: The specific steps of generating the optimized energy utilization scheme are: Based on the power scheduling optimization scheme, power demand is calculated in each time period, node power allocation is dynamically adjusted, power flow paths are gradually optimized, power adjustment and paths of each node are analyzed, and power allocation adjustment data is generated; Based on the power allocation adjustment data, power load evaluation is performed on each transmission path, power flow in the path is adjusted, load optimization of the transmission path is performed, and an optimized transmission path solution is generated; Based on the optimized transmission path scheme, the power flow direction between nodes and the power demand of the nodes are adjusted to generate an optimized energy utilization scheme.

9. The distributed optimization method for accessing a microgrid to a distribution network according to claim 1, characterized in that: The specific steps of generating the load balancing adjustment scheme are: Based on the optimized energy utilization scheme, the real-time load between the microgrid and the distribution network is detected, and the load of the nodes is determined one by one to determine whether the load is balanced, and a load imbalance state is generated; Based on the load imbalance state, select microgrid nodes and perform load adjustment operations, and recalculate node power requirements to generate a load redistribution plan; Based on the load redistribution scheme, the power flow path between the microgrid and the distribution network is adjusted, the node power distribution is updated and the power flow is recalculated to generate a load balancing adjustment scheme.

10. The distributed optimization method for microgrid access to a distribution network according to claim 1, characterized in that: The specific steps of generating the stable coordinated scheduling scheme are: Based on the load balancing adjustment scheme, load data of each node is obtained, nodes with uneven load distribution are identified, node load distribution ratios are adjusted, and node load distribution adjustment data is generated; Based on the node load distribution adjustment data, evaluate the load condition of the existing power flow path, adjust the power flow path, and generate a power flow path optimization plan; Based on the power flow path optimization scheme, the optimized load distribution and power flow path are integrated, and a scheduling operation is performed to generate a stable coordinated scheduling scheme.

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