Source-grid-load-storage collaborative planning method and system for high proportion of new energy access

Abstract

This application provides a source-grid-load-storage collaborative planning method and system for high-proportion renewable energy access, relating to the field of power system capacity expansion planning technology. The method clusters the original source-load scenarios to obtain representative scenarios and their weights; relaxes the network transmission constraints of the source-grid-load-storage collaborative planning model and solves it to obtain the initial expansion capacity of wind power, photovoltaics, and energy storage; determines inactive network transmission constraints based on the worst-case power flow of the network transmission constraints, and divides the fixed expansion capacity and the expansion capacity to be reallocated according to their proportion; determines the network transmission tension based on historical line operation data, the weights of representative scenarios, and the over-limit portion of the worst-case power flow, and allocates the expansion capacity to be reallocated on a per-capacity unit basis to obtain the final expansion capacity of wind power and photovoltaics; fixes the final expansion capacity of wind power and photovoltaics, restores the network transmission constraints, and adjusts the energy storage expansion capacity on a per-scenario basis. This application can reduce the solution complexity of the planning problem and improve the solution efficiency.

Description

Technical Field

[0001] This application relates to the field of power system capacity expansion planning technology, and in particular to a new energy and energy storage collaborative configuration technology that considers source-load uncertainty and transmission network security constraints. Background Technology

[0002] With the ongoing low-carbon energy transition and the construction of new power systems, the proportion of new energy sources such as wind power and photovoltaics integrated into the power system is constantly increasing. New energy output is characterized by randomness, fluctuation, and intermittency, and load levels also change over time. The high proportion of new energy integration places higher demands on system power balance, regulation capabilities, and the safe operation of the transmission network. Energy storage can achieve energy transfer over time through charge and discharge regulation. Therefore, coordinating the determination of expansion locations and capacities for wind power, photovoltaics, and energy storage under different source and load operating conditions is a crucial issue in power system planning.

[0003] Existing source-grid-load-storage coordinated planning technologies typically construct multiple source-load operation scenarios based on historical or forecast data of wind power, photovoltaics, and loads, and establish corresponding multi-scenario coordinated planning models. These models generally use the expansion locations and capacities of wind power, photovoltaics, and energy storage as planning decision variables, and optimize based on equipment investment costs and system operating costs. They also consider constraints such as system power balance, thermal power unit operation, energy storage charging and discharging and energy state, and transmission line capacity. Some planning models also consider carbon emission costs, carbon emission limits, the proportion of renewable energy generation, and minimum construction capacity requirements for renewable energy and energy storage. They obtain coordinated expansion schemes for renewable energy and energy storage by directly solving mixed-integer programming models or using mathematical decomposition methods.

[0004] However, in large-scale power systems with a high proportion of renewable energy integration, multiple source-load scenarios increase the number of planning variables and operational constraints. Furthermore, each scenario and operating period requires consideration of numerous network transmission constraints. Simultaneously, the energy state of energy storage exhibits cross-period coupling, and expansion decisions between renewable energy and energy storage also influence each other, resulting in a large collaborative planning model and long solution time. Existing scenario reduction methods primarily aim to decrease the number of scenarios involved in the calculation, but they struggle to simultaneously reduce the computational burden arising from detailed transmission network constraints and the coupling of renewable energy and energy storage planning variables. Network equivalence or node aggregation may weaken the restrictions imposed by specific transmission lines on renewable energy access locations. Mathematical decomposition methods typically require iterative iterations between different planning problems, and their solution efficiency remains affected by model size and convergence process. Therefore, how to reduce the solution complexity of the source-grid-load-storage collaborative planning problem while preserving the operational characteristics of multiple scenarios and detailed transmission network constraints is a key technical challenge that needs to be addressed. Summary of the Invention

[0005] This application provides a source-grid-load-storage coordinated planning method and system for high-proportion renewable energy integration. This method helps solve the technical problems of high complexity and low efficiency in solving the source-grid-load-storage coordinated planning problem while preserving the operational characteristics of multiple scenarios and network transmission constraints.

[0006] This application provides a source-grid-load-storage coordinated planning method for high-proportion renewable energy integration, including: Clustering of the original source load scenarios yields multiple representative scenarios and their weights; Relax the network transmission constraints of the source-grid-load-storage collaborative planning model, substitute the representative scenario and its weight into the model for solution, and obtain the initial expansion capacity of wind power, photovoltaic and energy storage. Determine the worst-case power flow for each network transmission constraint under each representative scenario and each operating period, and judge each network transmission constraint one by one; for any network transmission constraint, if the worst-case power flow of the network transmission constraint under each representative scenario and each operating period does not exceed the corresponding line transmission capacity limit, the network transmission constraint is determined as an inactive network transmission constraint; the proportion of the inactive network transmission constraints to all network transmission constraints is taken as a fixed proportion, and the initial expansion capacity of wind power and photovoltaic power is divided into fixed expansion capacity and expansion capacity to be redistributed according to the fixed proportion; Based on historical line operation data, the congestion weight of each network transmission constraint under each operating period is determined. Based on the weight of each representative scenario and the congestion weight, the portion of the worst-case power flow exceeding the corresponding line transmission capacity limit of each network transmission constraint is weighted and summed to obtain the network transmission tension corresponding to the new energy capacity space allocation scheme. The wind power and photovoltaic capacity to be redistributed and expanded are divided into multiple capacity units; each capacity unit is sequentially allocated to a feasible candidate node of the corresponding new energy type, wherein the new energy expansion capacity of the feasible candidate node after the allocation of the corresponding capacity unit does not exceed the maximum allowable expansion capacity, and the network transmission tension corresponding to the new energy capacity space allocation scheme formed after allocation is minimized; based on the fixed expansion capacity and the allocation results of each capacity unit, the final expansion capacity of wind power and photovoltaic is obtained. The final expansion capacity of wind power and photovoltaic power is fixed and the network transmission constraints are restored. The initial expansion capacity of energy storage is adjusted according to the system operation feasibility under each representative scenario to obtain the final energy storage expansion capacity.

[0007] Another aspect of this application provides a source-grid-load-storage coordinated planning system for high-proportion renewable energy access, including: The scene clustering module is used to cluster the original source load scenes to obtain multiple representative scenes and their weights; The initial expansion capacity determination module is used to relax the network transmission constraints of the source-grid-load-storage collaborative planning model, and to solve the model by substituting the representative scenario and its weights to obtain the initial expansion capacity of wind power, photovoltaic and energy storage. The new energy expansion capacity allocation module is used to determine the worst-case power flow of each network transmission constraint under each representative scenario and each operating period, and to judge each network transmission constraint one by one. For any network transmission constraint, when the worst-case power flow of the network transmission constraint under each representative scenario and each operating period does not exceed the corresponding line transmission capacity limit, the network transmission constraint is determined as an inactive network transmission constraint, and the proportion of the inactive network transmission constraint to all network transmission constraints is used as a fixed proportion. According to the fixed proportion, the initial expansion capacity of wind power and photovoltaic power is divided into fixed expansion capacity and expansion capacity to be redistributed, respectively. The network transmission stress determination module is used to determine the congestion weight of each network transmission constraint under each operating period based on historical line operation data, and to perform a weighted summation of the portion of the worst-case power flow exceeding the corresponding line transmission capacity limit for each network transmission constraint based on the weight of each representative scenario and the congestion weight, so as to obtain the network transmission stress corresponding to the new energy capacity space allocation scheme. The new energy expansion capacity allocation module is used to divide the wind power and photovoltaic expansion capacity to be reallocated into multiple capacity units; to allocate each capacity unit to the corresponding feasible candidate node of the new energy type in sequence, wherein the new energy expansion capacity of the feasible candidate node after allocating the corresponding capacity unit does not exceed the maximum allowable expansion capacity, and to minimize the network transmission tension corresponding to the new energy capacity space allocation scheme formed after allocation; and to obtain the final expansion capacity of wind power and photovoltaic based on the fixed expansion capacity and the allocation results of each capacity unit. The energy storage expansion capacity determination module is used to fix the final expansion capacity of the wind power and photovoltaic power and restore the network transmission constraints. It adjusts the initial expansion capacity of the energy storage according to the system operation feasibility under each representative scenario to obtain the final energy storage expansion capacity.

[0008] Compared with existing technologies, this application clusters the original source-load scenarios, uses a small number of representative scenarios to characterize the source-load variation features of each scenario cluster, and uses the weights of the representative scenarios to retain the relative proportion of each scenario cluster in the original source-load scenarios. Based on this, the network transmission constraints are temporarily relaxed to solve for the initial expansion capacity of wind power, photovoltaics, and energy storage. Thus, while providing an initial capacity basis for subsequent capacity adjustments, it reduces the number of scenarios and network transmission constraints that need to be processed in the initial planning solution, thereby reducing the scale of the initial planning problem.

[0009] This application identifies inactive network transmission constraints based on worst-case power flow under different representative scenarios and operating periods. Based on the proportion of inactive network transmission constraints, the initial expansion capacity of wind and solar power is divided into fixed expansion capacity and expansion capacity to be reallocated. Furthermore, by combining representative scenario weights, congestion weights reflected in historical line operation data, and the over-limit portion of the worst-case power flow, the network transmission stress corresponding to different renewable energy capacity spatial allocation schemes is determined. By allocating each capacity unit to feasible candidate nodes that do not exceed the maximum allowable expansion capacity and minimize network transmission stress, the spatial distribution of wind and solar power expansion capacity can be adjusted according to the potential pressure on the transmission network caused by different capacity node distributions while retaining some initial expansion capacity. This avoids repeatedly solving a complete collaborative planning model containing all planning variables and network transmission constraints to determine the location of each renewable energy expansion.

[0010] Furthermore, after determining the final expansion capacity of wind power and photovoltaic power, this application fixes and restores the network transmission constraints. Then, based on the system operational feasibility under each representative scenario, it adjusts the energy storage expansion capacity scenario by scenario. This allows the spatial allocation of new energy expansion capacity and the determination of energy storage expansion capacity to be processed sequentially, avoiding the simultaneous solution of energy storage operation variables and network transmission constraints for all representative scenarios. Therefore, this application can coordinate the determination of the final expansion capacity of wind power, photovoltaic power, and energy storage while considering the differences in source and load scenarios and transmission network limitations. This reduces the solution complexity of large-scale source-grid-load-storage coordinated planning problems, shortens the solution time, and improves the solution efficiency. Attached Figure Description

[0011] Figure 1 This is a flowchart of a source-grid-load-storage coordinated planning method for high-proportion renewable energy access provided in an embodiment of this application; Figure 2 This is a flowchart illustrating the determination of inactive network transmission constraints and network transmission stress provided in the embodiments of this application; Figure 3 This is a flowchart illustrating the sorting of representative scenarios and the adjustment of energy storage expansion capacity on a scenario-by-scenario basis, as provided in the embodiments of this application. Figure 4 This is a comparison chart of the wind power, photovoltaic and energy storage capacities before and after planning in different test systems provided in the embodiments of this application; Figure 5 This is a schematic diagram of the structure of a source-grid-load-storage coordinated planning system for high-proportion renewable energy access provided in an embodiment of this application. Detailed Implementation

[0012] The present application will now be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0013] The first embodiment of this application provides a source-grid-load-storage coordinated planning method for high-proportion renewable energy access, used to determine the expansion capacity of wind power, photovoltaic power, and energy storage based on different source-load operating states and transmission network operating limitations. Please refer to... Figure 1 , Figure 1 This is a flowchart of the source-grid-load-storage coordinated planning method for high-proportion renewable energy access provided in the first embodiment of this application.

[0014] like Figure 1 As shown, this embodiment first clusters the original source-load scenarios to obtain multiple representative scenarios and their weights; then, it relaxes the network transmission constraints of the source-grid-load-storage collaborative planning model to solve for the initial expansion capacity of wind power, photovoltaics, and energy storage; based on this, it determines the inactive network transmission constraints according to the worst-case power flow of each network transmission constraint, and divides the initial expansion capacity of wind power and photovoltaics into fixed expansion capacity and expansion capacity to be redistributed, respectively, and determines the network transmission tension corresponding to the new energy capacity spatial allocation scheme based on historical line operation data and the worst-case power flow; subsequently, it allocates the expansion capacity to be redistributed to each capacity unit based on the network transmission tension to obtain the final expansion capacity of wind power and photovoltaics; finally, it fixes the final expansion capacity of wind power and photovoltaics and restores the network transmission constraints, and adjusts the initial expansion capacity of energy storage to each scenario to obtain the final energy storage expansion capacity.

[0015] The following combination Figure 1 The method provided in this embodiment will be described in detail.

[0016] Step S101: Cluster the original source load scenes to obtain multiple representative scenes and their weights.

[0017] The original source-load scenarios are used to characterize the changes in load and renewable energy output in the power system under different operating conditions. In this embodiment, original source-load scenarios are constructed based on historical or predicted data. Each original source-load scenario includes load data, wind power output data, and photovoltaic output data for each operating period. Thus, multiple original source-load scenarios can characterize the changes in load demand and renewable energy output under different source-load operating conditions.

[0018] Considering the large number of original source-load scenarios, directly substituting all original source-load scenarios into the source-grid-load-storage collaborative planning model would increase the number of scenarios participating in the planning solution and the corresponding number of operational constraints. Therefore, this embodiment clusters the original source-load scenarios, grouping those with similar source-load variation characteristics into the same scenario cluster, and determining the corresponding representative scenario and its weight based on each scenario cluster.

[0019] Specifically, Euclidean distance is used to measure the similarity between different original source payload scenes, hierarchical clustering is performed according to the Ward connectivity criterion, and the number of representative scenes is determined by the Gap statistic. After hierarchical clustering is completed, the centroid of each scene cluster is determined as the corresponding representative scene, and the weight of the corresponding representative scene is determined according to the number of original source payload scenes contained in each scene cluster.

[0020] No. The representative scenarios and their weights are as follows:

[0021] The weights of each representative scenario satisfy the following:

[0022] in, This indicates the total number of original source load scenes. This indicates the total number of scenes. Indicates the first A cluster of scenes, Indicates the first The number of original source payload scenes contained in each scene cluster. Indicates the first A primary source load scenario, Indicates the first A representative scenario, Indicates the first The weights of each representative scenario.

[0023] Through the above processing, a large number of original source load scenarios are transformed into a smaller number of weighted representative scenarios. This reduces the number of scenarios that need to be processed in subsequent planning and solving, while the source load variation characteristics and relative proportions of the corresponding scenario clusters are represented by the centroids of the scenario clusters and their weights.

[0024] The multiple representative scenarios and their weights obtained in step S101 are used for subsequent source-grid-load-storage coordinated planning: the representative scenarios and their weights are used as inputs to the source-grid-load-storage coordinated planning model in step S102; the weights of the representative scenarios are also used in step S104 to determine the network transmission tension corresponding to the new energy capacity spatial allocation scheme, and in step S106 to determine the processing order of each representative scenario.

[0025] Step S102: Relax the network transmission constraints of the source-grid-load-storage collaborative planning model, substitute the representative scenario and its weight into the model for solution, and obtain the initial expansion capacity of wind power, photovoltaic and energy storage.

[0026] This step is used to determine the initial expansion capacity of wind power, photovoltaic and energy storage based on the representative scenarios and their weights obtained in step S101, without considering the spatial distribution limitations of the transmission network on the expansion capacity. This provides an initial capacity basis for the subsequent spatial redistribution of new energy expansion capacity and the scenario-by-scenario adjustment of energy storage expansion capacity.

[0027] To illustrate the relaxation of network transmission constraints and the model solution process, the source-grid-load-storage collaborative planning model used in this embodiment will be explained first.

[0028] The source-grid-load-storage coordinated planning model uses the expansion capacity of wind power, photovoltaics, and energy storage as planning decision variables, and aims to minimize the sum of the annualized construction investment cost of new energy and energy storage equipment, the annualized system operating cost, and the carbon emission cost. The constraints of the source-grid-load-storage coordinated planning model include constraints on the expansion capacity and planning investment of wind power, photovoltaics, and energy storage; carbon emission and policy constraints; system power balance constraints; grid transmission constraints; output, ramp-up, and start-up / shutdown constraints of thermal power units; and charging / discharging power, energy state, and continuous-time energy conversion constraints of energy storage equipment.

[0029] Specifically, the objective function of the source-grid-load-storage collaborative planning model is:

[0030] in, Indicates the overall system cost. This indicates the annualized construction investment cost of new energy and energy storage equipment. This represents the system's annualized operating cost. This indicates the cost of carbon emissions.

[0031] The annualized construction investment cost of new energy and energy storage equipment is:

[0032] in, , and They represent wind farms Photovoltaic power stations and energy storage equipment The expansion capacity; , and These represent the unit investment costs for wind power, solar power, and energy storage, respectively. , and These represent the service life of wind power, photovoltaic, and energy storage equipment, respectively. By annualizing the construction investment cost according to the equipment's service life, the construction investment costs of different types of equipment can be compared on a unified time scale.

[0033] The annualized operating cost of the system is:

[0034] in, This represents the total number of representative scenarios obtained in step S101. Indicates the first The weights of each representative scenario Indicates the first The system operating costs corresponding to each representative scenario are then calculated. Therefore, the system operating costs corresponding to different representative scenarios are included in the overall system cost according to the weight of each representative scenario.

[0035] Carbon emission costs are determined based on the carbon emissions generated by the operation of thermal power units. In one implementation, when the system's carbon emissions do not exceed the carbon emission quota, the carbon emission cost is zero; when the system's carbon emissions exceed the carbon emission quota, the carbon emission cost is determined based on the portion exceeding the carbon emission quota and the carbon emission tax rate.

[0036] Based on the above objective function, the expansion capacity and planned investment of wind power, photovoltaic power, and energy storage should meet the following requirements:

[0037] in, , and They represent wind farms Photovoltaic power stations and energy storage equipment Maximum allowable expansion capacity INV This indicates the upper limit of the planned investment cost.

[0038] The carbon emission and policy constraints include upper limits on total system carbon emissions, upper limits on electricity carbon intensity, minimum expansion capacity constraints for wind power, minimum expansion capacity constraints for photovoltaic power, minimum expansion capacity constraints for energy storage, and constraints on the proportion of renewable energy generation. This ensures that the planning results meet the corresponding carbon emission control requirements and the requirements for renewable energy and energy storage construction.

[0039] For each representative scenario and each runtime segment, the system should meet power balance constraints and network security operation constraints:

[0040] in, Indicates thermal power unit In the Representative scenarios and runtime segments The power generation capacity below; and They represent wind farms Wind power output and wind curtailment power; and Representing photovoltaic power stations Photovoltaic output and curtailed power; and These represent energy storage devices. The charging power and discharging power; Indicates load The power consumption.

[0041] Indicates the line In the Representative scenarios and runtime segments The transmission power at the following levels; , , , and These represent the nodes of thermal power units, loads, wind farms, photovoltaic power stations, and energy storage devices relative to the line, respectively. The power transmission distribution factor; and They represent the lines respectively. The lower and upper limits of power flow are defined. The power flow calculation relationships of the lines and the upper and lower limits of power flow together constitute the network transmission constraints.

[0042] Thermal power units should meet the upper and lower limits of output, the limits of upward ramping, the limits of downward ramping, and the constraints of start-up and shutdown states, so that the power generation and operating status of thermal power units in different representative scenarios and different operating periods meet the corresponding equipment operation requirements.

[0043] Energy storage devices should meet the constraints of charge / discharge power, energy state, and continuous time-period energy conversion:

[0044] in, and These represent energy storage devices. The charging efficiency and discharging efficiency; Indicates energy storage devices In the Representative scenarios and runtime segments The charging and discharging states under these conditions; and These represent the maximum charging power and the maximum discharging power, respectively. Indicates energy state; and These represent the lower limit and upper limit of energy storage, respectively. Indicates the total number of runtime segments; This indicates the set energy state of the energy storage device during the first and last operating periods; Indicates the duration of a single runtime segment; Indicates energy storage devices The original energy storage capacity; Indicates energy storage devices The expansion capacity; Indicates energy storage devices Maximum depth of discharge.

[0045] After constructing the source-grid-load-storage collaborative planning model based on the above objective function and constraints, the multiple representative scenarios and their weights obtained in step S101 are substituted into the source-grid-load-storage collaborative planning model.

[0046] In this step, relaxing the network transmission constraints of the source-grid-load-storage collaborative planning model means that when solving for the initial expansion capacity, the calculation relationship for determining the line power flow based on the injected power and power transmission distribution factor of each node is temporarily not applied, nor are the upper and lower limits of the line power flow applied. This allows the solution for the initial expansion capacity to be temporarily unrestricted by the spatial distribution of the expansion capacity by the transmission network. Apart from the network transmission constraints, the objective function of the source-grid-load-storage collaborative planning model, as well as the constraints on the expansion capacity and planning investment of wind power, photovoltaic power, and energy storage, carbon emissions and policy constraints, system power balance constraints, output, ramp-up, and start-up / shutdown status constraints of thermal power units, and charging / discharging power, energy state, and continuous-time energy conversion constraints of energy storage devices remain unchanged.

[0047] Solving the source-grid-load-storage collaborative planning model after relaxing network transmission constraints yields the initial expansion capacity of wind power, photovoltaic power, and energy storage:

[0048] in, , and They represent wind farms Photovoltaic power stations and energy storage equipment The initial expansion capacity, This represents the initial planning results, which consist of the initial expansion capacity of each wind farm, each photovoltaic power station, and each energy storage device.

[0049] The initial total expansion capacity for wind power, solar power, and energy storage are as follows:

[0050] Since this step does not consider the limitations of the transmission network on the spatial distribution of the expansion capacity when solving for the initial expansion capacity, the obtained initial expansion capacity is used to reflect the capacity demand of wind power, photovoltaics, and energy storage under the constraints of investment, carbon emissions, policies, and system operation. Subsequent steps S103-S105 divide and spatially redistribute the initial expansion capacity of wind power and photovoltaics according to the operational constraints of the transmission network; step S106 uses the initial total expansion capacity of energy storage as the initial lower bound for adjusting the energy storage expansion capacity on a scenario-by-scenario basis, and determines the final energy storage expansion capacity after restoring the network transmission constraints.

[0051] Step S103: Determine the worst-case power flow of each network transmission constraint under each representative scenario and each operating period, and judge each network transmission constraint one by one; for any network transmission constraint, when the worst-case power flow of the network transmission constraint under each representative scenario and each operating period does not exceed the corresponding line transmission capacity limit, the network transmission constraint is determined as an inactive network transmission constraint; the proportion of the inactive network transmission constraints to all network transmission constraints is taken as a fixed proportion, and the initial expansion capacity of wind power and photovoltaic power is divided into fixed expansion capacity and expansion capacity to be redistributed according to the fixed proportion.

[0052] Step S102 obtains the initial expansion capacity of wind power, photovoltaic power, and energy storage under relaxed network transmission constraints. However, this initial expansion capacity does not fully reflect the impact of the spatial distribution of different expansion capacities on the transmission network. Therefore, this step reconsiders the line transmission capacity limitations of the transmission network. By determining the worst-case power flow corresponding to each network transmission constraint, it identifies inactive network transmission constraints that have weak spatial distribution constraints on the initial expansion capacity obtained in step S102. Based on the number of inactive network transmission constraints, it determines the portion of the initial expansion capacity of wind power and photovoltaic power that can be fixed and the portion that needs to be redistributed.

[0053] Specifically, for the same transmission line, its forward transmission capacity limit and reverse transmission capacity limit are each recorded as a network transmission constraint, and all network transmission constraints are numbered. Thus, each network transmission constraint corresponds to a transmission line and a power flow direction of that transmission line.

[0054] Let the total number of network transmission constraints be... Network transmission constraints In representative scenarios and runtime segment The worst-case scenario is as follows:

[0055] And satisfy:

[0056] in, Indicates the number of power injection nodes. Represents a node Constraints on network power transmission The power transmission distribution factor corresponding to the line power flow. Represents a node In representative scenarios and runtime segment The net injection power below, and These represent the lower and upper limits of the net injected power, respectively.

[0057] The net injected power of each node is determined based on the power output of the thermal power units, loads, wind farms, photovoltaic power stations, and energy storage devices connected to the corresponding node under the corresponding representative scenario and operating period. The lower and upper limits of the net injected power are determined based on the power generation capacity, load demand, and energy storage charging and discharging capacity of the corresponding node.

[0058] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the determination of inactive network transmission constraints and network transmission stress provided in the first embodiment of this application. Figure 2 As shown, the worst-case power flow of each network transmission constraint under each representative scenario and each operating period is compared with the corresponding line transmission capacity limit.

[0059] Specifically, the transmission constraints of each network are assessed one by one. Regarding network transmission constraints... If the following conditions are met:

[0060] Then the network transmission constraint It was identified as an inactive network transmission constraint.

[0061] in, Indicates network transmission constraints The transmission capacity limit of the corresponding line in the corresponding power flow direction. Indicating the above relationship requires consideration of current network transmission constraints. The fact that a network transmission constraint holds true in each representative scenario and each operating segment does not mean that all network transmission constraints will not exceed their limits simultaneously. If a network transmission constraint satisfies the above relationship only in some representative scenarios or some operating segments, but the worst-case power flow exceeds the corresponding line transmission capacity limit in other representative scenarios or operating segments, then that network transmission constraint will not be identified as an inactive network transmission constraint.

[0062] Let the number of transmission constraints in the inactive network be... The fixed proportion of inactive network transmission constraints to all network transmission constraints is:

[0063] in, Indicates a fixed proportion. and Let represent the number of inactive network transmission constraints and the total number of network transmission constraints, respectively. .

[0064] According to the fixed ratio, the initial expansion capacity of each wind farm and each photovoltaic power station obtained in step S102 is divided into fixed expansion capacity and expansion capacity to be redistributed:

[0065]

[0066] in, and Wind farm and photovoltaic power station The initial expansion capacity; and Wind farm and photovoltaic power station Fixed expansion capacity; and Wind farm and photovoltaic power station The capacity to be redistributed and expanded.

[0067] The fixed expansion capacity is retained at the original planned node where the corresponding initial expansion capacity was determined in step S102, while the expansion capacity to be reallocated is redistributed to feasible candidate nodes of the corresponding new energy type in subsequent step S105. For each wind farm or photovoltaic power station, the sum of its fixed expansion capacity and the expansion capacity to be reallocated is equal to the corresponding initial expansion capacity.

[0068] The higher the proportion of inactive network transmission constraints, the lower the degree of restriction on the spatial distribution of the initial expansion capacity obtained in step S102 under the worst-case power flow conditions, and thus the more fixed expansion capacity can be retained on the original planned nodes; the lower the proportion of inactive network transmission constraints, the more new energy expansion capacity needs to be reallocated in subsequent steps.

[0069] When the number of inactive network transmission constraints equals the total number of network transmission constraints, the fixed ratio is 1, and the wind and solar power capacity to be redistributed is 0. The initial wind and solar power capacity obtained in step S102 does not need to be redistributed and can be directly used as the final wind and solar power capacity. In this case, there is no need to allocate capacity units based on network transmission tension. When the number of inactive network transmission constraints is less than the total number of network transmission constraints, the capacity to be redistributed is allocated according to steps S104 and S105.

[0070] For network transmission constraints where the worst-case power flow exceeds the corresponding line transmission capacity limit, the portion exceeding the line transmission capacity limit is combined with the representative scenario weight and congestion weight in step S104 to determine the network transmission tension corresponding to the new energy capacity space allocation scheme.

[0071] Step S104: Determine the congestion weight of each network transmission constraint under each operating period based on historical line operation data, and perform a weighted summation of the portion of the worst-case power flow exceeding the corresponding line transmission capacity limit for each network transmission constraint based on the weight of each representative scenario and the congestion weight, to obtain the network transmission tension corresponding to the new energy capacity space allocation scheme.

[0072] This step converts the potential transmission pressure caused by the new energy capacity allocation scheme on different transmission lines and operating periods into a comparable network transmission stress. The network transmission stress considers both the relative proportion of each representative scenario in the original source-load scenario and the historical congestion differences of different transmission lines under different operating periods, and only counts the portion of the power flow that exceeds the transmission capacity limit of the corresponding line in the worst case.

[0073] Specifically, historical power flow data for each transmission line during each operating period is obtained over multiple historical operating days. Since power flow may be transmitted in either the forward or reverse direction, the absolute transmission capacity of the transmission line is determined based on its forward and reverse transmission capacity limits.

[0074] line The absolute transmission capacity is:

[0075] in, Indicates the line absolute transmission capacity, and They represent the lines respectively. The lower limit and upper limit of trends.

[0076] line In the runtime segment and the Historical load rate on a historical operating day ,line In the runtime segment Historical near-congestion probability and congestion weight They are respectively:

[0077] in, Indicates the line In the Running time segment of each historical running day Historical load rate Indicates the line In the Running time segment of each historical running day The historical trend that followed.

[0078] Indicates the line In the runtime segment The historical near-congestion probability; This indicates the total number of historical operating days; This represents the load rate threshold used to determine whether a line is in a near-congestion state. This indicates an indicator function that takes the value 1 when the condition within the parentheses is true and 0 when the condition within the parentheses is false.

[0079] Historical near-congestion probability is used to represent the probability of line congestion over the stated number of historical operating days. In the runtime segment The frequency that is in a near-congestion state. The higher the historical near-congestion probability, the more likely the corresponding line is to approach its transmission capacity limit during that operating period.

[0080] Indicates the line In the runtime segment Congestion weights; Indicates the total number of transmission lines; Indicates the total number of runtime segments; and These represent the summation indices for transmission lines and operating segments, respectively. This represents a smoothing parameter used to prevent lines that have not experienced historical near-congestion from having a congestion weight of zero during the corresponding operating period.

[0081] Since step S103 records the forward and reverse transmission capacity limits of the same transmission line as network transmission constraints, it is necessary to map the congestion weights corresponding to the lines to the corresponding network transmission constraints. When the network transmission constraints... By line When forward or reverse transmission capacity limits are formed, network transmission constraints occur. In the runtime segment The congestion weights are as follows:

[0082] in, Indicates network transmission constraints The corresponding transmission line. The forward and reverse network transmission constraints of the same transmission line are based on the congestion weight of that line during the corresponding operating period.

[0083] In this application, the renewable energy capacity spatial allocation scheme refers to the allocation result of wind power and photovoltaic expansion capacity at each planned node, including the wind power expansion capacity and photovoltaic expansion capacity corresponding to each planned node. For any renewable energy capacity spatial allocation scheme, based on the wind power and photovoltaic expansion capacity of each planned node determined by the scheme, the upper and lower limits of the net injected power of the nodes under each representative scenario and each operating period are determined, and the worst-case power flow of each network transmission constraint is determined according to the method described in step S103. Thus, different renewable energy expansion capacity node distributions form different renewable energy capacity spatial allocation schemes, and each renewable energy capacity spatial allocation scheme corresponds to the network transmission stress calculated based on its capacity node distribution.

[0084] Please continue reading. Figure 2 When network transmission constraints j In the k Representative scenarios and runtime segments t When the worst-case power flow does not exceed the corresponding line transmission capacity limit, the excess portion of the network transmission constraint is set to zero; when the worst-case power flow exceeds the corresponding line transmission capacity limit, the difference between the two is taken as the excess portion of the network transmission constraint.

[0085] The network transmission strain corresponding to the aforementioned new energy capacity allocation scheme is:

[0086] in, To represent the number of scenes, Number of runtime segments This represents the total number of network transmission constraints. For the first The weights of each representative scenario For network power transmission constraints In the runtime segment The congestion weight is below, For network power transmission constraints In the Representative scenarios and runtime segments The worst-case scenario trend, For network power transmission constraints The transmission capacity limit for the corresponding line; This indicates the portion of the power flow that exceeds the transmission capacity limit of the corresponding line in the worst-case scenario.

[0087] The network transmission stress is greater than or equal to zero. The higher the value of the network transmission stress, the greater the transmission stress that the corresponding new energy capacity allocation scheme may cause in each representative scenario and operating period; when the network transmission stress is zero, it means that all network transmission constraints have not exceeded the corresponding line transmission capacity limit under the corresponding worst case.

[0088] The network transmission tension obtained in step S104 is used in step S105 to compare the new energy capacity space allocation scheme formed by the trial allocation of capacity units to different feasible candidate nodes, and to determine the allocation node corresponding to each capacity unit.

[0089] Step S105: Divide the wind power and photovoltaic capacity to be redistributed into multiple capacity units; allocate each capacity unit to the corresponding feasible candidate node of the new energy type in sequence, wherein the new energy expansion capacity of the feasible candidate node after the allocation of the corresponding capacity unit does not exceed the maximum allowable expansion capacity, and minimizes the network transmission tension corresponding to the new energy capacity space allocation scheme formed after allocation; obtain the final expansion capacity of wind power and photovoltaic based on the fixed expansion capacity and the allocation results of each capacity unit.

[0090] Step S103 divides the initial expansion capacity of wind power and photovoltaic power into fixed expansion capacity and expansion capacity to be redistributed, respectively. Step S104 provides the network transmission tension corresponding to the spatial allocation scheme of new energy capacity. This step uses the network transmission tension as the basis for comparison between different candidate nodes, and allocates the expansion capacity to be redistributed for wind power and photovoltaic power to feasible candidate nodes of the corresponding new energy type on a unit-by-unit basis, thereby adjusting the spatial distribution of wind power and photovoltaic expansion capacity among different planning nodes.

[0091] Specifically, capacity units are allocated separately for wind power and photovoltaic power. (Assume new energy types...) For wind power or solar power, that is:

[0092] in, Indicates wind power, It refers to photovoltaics.

[0093] New energy types The corresponding total capacity to be redistributed and expanded is The capacity to be redistributed and expanded is divided into multiple capacity units in an equal-capacity manner, with each capacity unit having a capacity of [size missing]. Then the new energy type The total number of corresponding capacity units is:

[0094] in, Indicates new energy type The corresponding total number of capacity units; Indicates new energy type The capacity size of each capacity unit.

[0095] For wind power, the total capacity to be redistributed and expanded is the sum of the capacity to be redistributed and expanded for each wind farm obtained in step S103; for photovoltaic power, the total capacity to be redistributed and expanded is the sum of the capacity to be redistributed and expanded for each photovoltaic power station obtained in step S103.

[0096] Feasible candidate node sets were determined separately for wind power and solar power. These feasible candidate nodes are planned nodes that allow for the construction of the corresponding new energy type and have remaining expansion capacity. New Energy Type The corresponding set of feasible candidate nodes is denoted as .

[0097] To determine the type of new energy The The actual allocation node for each capacity unit attempts to allocate that capacity unit to the set of feasible candidate nodes. The trial allocation refers to temporarily increasing the current capacity unit to the current expansion capacity of each candidate node without immediately determining the actual allocation result, thus forming a temporary new energy capacity space allocation scheme corresponding to each candidate node.

[0098] For each temporary renewable energy capacity allocation scheme, the corresponding network transmission stress is determined according to the method described in step S104. Each capacity unit is assigned to a candidate node. The network transmission strain corresponding to the resulting new energy capacity spatial allocation scheme is denoted as: .

[0099] In the In the secondary capacity unit allocation, the selected candidate nodes are:

[0100] And satisfy:

[0101] in, Indicates the allocation order of capacity units. ; Indicates a feasible candidate node; Indicates new energy type The Candidate nodes selected by each capacity unit; Indicates the first Before the allocation of secondary capacity units, candidate nodes New energy type The current expansion capacity; Indicates candidate nodes For new energy types The maximum allowable expansion capacity.

[0102] In determining the first When selecting candidate nodes for a capacity unit, first exclude candidate nodes that would exceed the maximum allowable expansion capacity after adding the current capacity unit. Then, select the candidate node that minimizes network transmission strain from the remaining feasible candidate nodes. Thus, the actual node allocated to a capacity unit simultaneously satisfies both the maximum allowable expansion capacity limit and the condition of minimizing network transmission strain.

[0103] Determine candidate nodes After that, the first Each capacity unit is allocated to the candidate node, and the new energy type at the candidate node is determined. The current expansion capacity increases Based on this, the current new energy capacity allocation plan will be updated.

[0104] In one implementation, after each capacity unit allocation is completed, the set of feasible candidate nodes is updated based on the current expansion capacity and the maximum allowed expansion capacity of each candidate node. When the remaining allowed expansion capacity of a candidate node is insufficient to allocate another capacity unit, the candidate node is removed from the set of feasible candidate nodes for the corresponding new energy type.

[0105] New energy types are allocated sequentially in the manner described above. The corresponding capacity units are allocated until all the new energy type's capacity to be redistributed and expanded is allocated. After the capacity units for wind power and photovoltaic are allocated, the fixed expansion capacity retained in the original planning node in step S103 is combined with the redistributed expansion capacity obtained by each node to obtain the final expansion capacity of wind power and the final expansion capacity of photovoltaic at each planning node.

[0106] The capacity allocation process keeps the total expansion capacity of wind power and photovoltaic power unchanged, only adjusting the spatial distribution of the corresponding expansion capacity among different planning nodes. That is, the final total expansion capacity of wind power is the same as the initial total expansion capacity of wind power obtained in step S102, and the final total expansion capacity of photovoltaic power is the same as the initial total expansion capacity of photovoltaic power obtained in step S102.

[0107] This step compares the trial allocation results of capacity units on different candidate nodes, and uses the network transmission tension as the basis for the spatial allocation of capacity units. This allows the node distribution of wind power and photovoltaic expansion capacity to reflect the transmission constraints corresponding to different representative scenarios, different time periods, and different lines. It eliminates the need to repeatedly solve the complete source-grid-load-storage collaborative planning model that includes all expansion capacity decision variables and all network transmission constraints for each candidate node.

[0108] The final expansion capacity of wind power and photovoltaic power obtained in step S105 remains fixed in step S106 and serves as the planning condition for adjusting the energy storage expansion capacity on a scenario-by-scenario basis after restoring network transmission constraints.

[0109] Step S106: Fix the final expansion capacity of the wind power and photovoltaic power and restore the network transmission constraints. Adjust the initial expansion capacity of the energy storage according to the system operation feasibility under each representative scenario to obtain the final energy storage expansion capacity.

[0110] Step S105 has determined the final expansion capacity of wind power and photovoltaic power based on the grid transmission tension. This step treats the final expansion capacity of wind power and photovoltaic power as a fixed planning condition and will not adjust it further; at the same time, it restores the temporarily relaxed grid transmission constraints in step S102, and determines the energy storage expansion capacity that meets the system operation requirements for each scenario by solving the single-scenario sub-problems corresponding to each representative scenario in turn.

[0111] The restoration of network transmission constraints refers to reapplying the calculation relationships and upper and lower limits of line power flow determined based on the injected power and power transmission distribution factor of each node in the single-scenario sub-problems corresponding to each representative scenario, as described in step S102. Except for fixing the final expansion capacity of wind power and photovoltaic power, the single-scenario sub-problems retain the system power balance constraints, network transmission constraints, thermal power unit operation constraints, and energy storage device operation constraints corresponding to the current representative scenario in the source-grid-load-storage collaborative planning model, and treat the energy storage expansion capacity of each node as a planning variable to be determined.

[0112] Specifically, the result obtained in step S101 Each representative scene is renumbered to obtain a set of representative scenes:

[0113] in, This represents a set of scenes; Indicating the first step in the sequential scenario planning process One processed Representative scenarios, ; This indicates the total number of scenes; representing scenes The corresponding weights are denoted as .

[0114] Arrange the representative scenarios in descending order of weight, such that the weight of each representative scenario satisfies the following:

[0115] Therefore, the representative scenarios with higher weights are processed first, and then the remaining representative scenarios are processed in descending order of weight.

[0116] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating the sequence of representative scenarios and the adjustment of energy storage expansion capacity scenario-by-scenario, as provided in the first embodiment of this application. Figure 3 As shown, the energy storage planning problem encompassing all representative scenarios is transformed into multiple single-scenario subproblems that are solved sequentially according to the weight of the representative scenarios. The energy storage expansion capacity obtained from the previous single-scenario subproblem is passed between each single-scenario subproblem through the lower bound of the energy storage expansion capacity.

[0117] When solving the single-scenario subproblem corresponding to the first representative scenario, the initial total expansion capacity of energy storage obtained in step S102 is used as the lower bound of the total expansion capacity of energy storage; when solving the single-scenario subproblems corresponding to subsequent representative scenarios, the energy storage expansion capacity of each node obtained from the previous single-scenario subproblem is used as the lower bound of the energy storage expansion capacity of the corresponding node in the current single-scenario subproblem.

[0118] The lower bound of energy storage expansion capacity in each individual scenario sub-problem satisfies:

[0119] in, This indicates the number of the energy storage device or energy storage planning node; This indicates the energy storage device obtained in step S102. The initial expansion capacity; This represents the initial total expansion capacity of energy storage obtained in step S102; Indicates solving the first... The energy storage device obtained after addressing the sub-problems of each representative scenario is... The capacity for expansion.

[0120] For the first representative scenario, the final expanded wind power and solar power capacity obtained in fixed step S105 is used to restore grid transmission constraints, and the initial total expanded energy storage capacity is also used. As a lower bound for the total energy storage expansion capacity, we solve the single-scenario subproblems corresponding to the representative scenario. Through these single-scenario subproblems, we determine the energy storage expansion capacity at each node that satisfies the system power balance, network transmission constraints, thermal power unit operation requirements, and energy storage device operation requirements under the representative scenario. .

[0121] For the One representative scenario was processed, among which The final expansion capacity of wind power and photovoltaic power is fixed, and the energy storage expansion capacity of each node obtained from the previous single-scenario sub-problem is used. Using these as lower bounds for the energy storage expansion capacity of the corresponding nodes in the current single-scenario subproblem, solve the single-scenario subproblem representing the scenario to obtain the energy storage expansion capacity of each node. .

[0122] By setting a lower bound for the node energy storage expansion capacity, the energy storage expansion capacity of any node obtained from subsequent single-scenario subproblems is not lower than the energy storage expansion capacity of the corresponding node in the previous single-scenario subproblem, thus avoiding the reduction of the previously determined node energy storage expansion capacity requirements when dealing with subsequent representative scenarios.

[0123] After solving all single-scenario sub-problems in descending order of representative scenario weight, the energy storage expansion capacity of each node obtained from the single-scenario sub-problem corresponding to the last representative scenario is taken as the final energy storage expansion capacity:

[0124] in, Indicates solving the first... The energy storage device obtained after addressing the sub-problems of each representative scenario is... The expansion capacity, Indicates energy storage devices The final expansion capacity.

[0125] This step, based on the fixed final expansion capacity of wind and solar power, transforms the process of determining energy storage expansion capacity, which includes multiple representative scenarios, into multiple single-scenario sub-problems executed in order of scenario weights. Capacity requirements are then passed between adjacent single-scenario sub-problems through a lower bound on the energy storage expansion capacity. This avoids simultaneously solving for all energy storage operation variables and network transmission constraints corresponding to all representative scenarios in a single planning problem, thus reducing the solution scale of the energy storage expansion capacity determination process.

[0126] Through steps S101-S106, the final expansion capacity of wind power, photovoltaic power, and energy storage at each planning node are obtained, thus forming a source-grid-load-storage coordinated planning result oriented towards high proportion of new energy access.

[0127] The technical effects of this application will be further explained below with reference to application examples.

[0128] In this application example, the PEGASE 89-node system, the IEEE 300-node system, and the PEGASE 1354-node system were selected as test objects, and the wind power, photovoltaic, and energy storage expansion capacity of each test system were determined using the methods described in steps S101-S106.

[0129] Please refer to Figure 4 , Figure 4 This is a comparison chart of the wind power, photovoltaic, and energy storage capacities before and after planning in different test systems provided in the first embodiment of this application. Figure 4 The three sub-graphs correspond to the PEGASE 89-node system, the IEEE 300-node system, and the PEGASE 1354-node system, respectively. The units for wind power and photovoltaic capacity are megawatts, and the units for energy storage capacity are megawatt-hours. "Pre-planning capacity" refers to the original capacity of wind power, photovoltaic, and energy storage equipment in each test system, and "post-planning capacity" refers to the sum of the original capacity and the corresponding expansion capacity determined using the method of this application.

[0130] Depend on Figure 4 It is understood that the method described in this application can determine the corresponding wind power, photovoltaic and energy storage expansion capacity for power systems of different scales, and form a source-grid-load-storage coordinated planning result that is adapted to the source-load operation status and transmission network constraints of the corresponding test system.

[0131] To verify the solution efficiency of the method described in this application, it was compared with direct solution methods and regularized Benders decomposition methods. The solution times of each method on different test systems are shown below.

[0132]

[0133] As shown in the table above, the solution times of the method described in this application are 164.6s, 46.6s, and 6841.0s in the PEGASE 89-node system, IEEE 300-node system, and PEGASE 1354-node system, respectively.

[0134] Compared with the direct solution method, the solution time of the method described in this application is reduced by 86.12%, 89.75% and 94.46% in the three test systems mentioned above, respectively; compared with the regularized Benders decomposition method, the solution time of the method described in this application is reduced by 70.46%, 81.68% and 67.79% in the three test systems mentioned above, respectively.

[0135] The above results show that this application reduces the number of scenarios participating in the planning solution by clustering the original source-load scenarios; obtains the initial expansion capacity by relaxing network transmission constraints, and allocates the wind power and photovoltaic expansion capacity on a per-capacity unit basis based on the network transmission tension, avoiding the repeated solution of the complete collaborative planning model containing all planning variables and network transmission constraints for different new energy expansion locations; after determining the final expansion capacity of wind power and photovoltaic, the energy storage expansion capacity is determined by solving the single-scenario sub-problems corresponding to each representative scenario in sequence, reducing the number of scenario operation variables and constraints participating in the energy storage planning solution simultaneously.

[0136] Therefore, this application can determine the expansion capacity of wind power, photovoltaic and energy storage under different source and load operating conditions and transmission network constraints, and reduce the solution complexity of source-grid-load-storage coordinated planning problem, shorten the solution time, and improve the solution efficiency of source-grid-load-storage coordinated planning for large-scale power systems.

[0137] The second embodiment of this application provides a source-grid-load-storage coordinated planning system for high-proportion renewable energy access. Please refer to... Figure 5 , Figure 5 This is a schematic diagram of the structure of the source-grid-load-storage coordinated planning system for high-proportion renewable energy access provided in the second embodiment of this application.

[0138] like Figure 5 As shown, the source-grid-load-storage coordinated planning system includes a scenario clustering module 510, an initial expansion capacity determination module 520, a new energy expansion capacity allocation module 530, a network transmission tension determination module 540, a new energy expansion capacity allocation module 550, and an energy storage expansion capacity determination module 560.

[0139] The scene clustering module is used to cluster the original source load scenes to obtain multiple representative scenes and their weights.

[0140] Specifically, the scene clustering module acquires original source-load scenes constructed based on historical or predicted data, uses Euclidean distance to measure the similarity between different original source-load scenes, performs hierarchical clustering according to the Ward connection criterion, and determines the number of representative scenes using the Gap statistic. The centroid of each scene cluster is determined as the corresponding representative scene, and the weight of the corresponding representative scene is determined based on the number of original source-load scenes contained in each scene cluster. The specific processing procedure of the scene clustering module can refer to step S101 in the first embodiment, and will not be repeated here.

[0141] The scenario clustering module sends the representative scenarios and their weights to the initial expansion capacity determination module. The representative scenarios and their weights are also used by the network transmission tension determination module to determine network transmission tension, and by the energy storage expansion capacity determination module to determine the processing order of each representative scenario.

[0142] The initial expansion capacity determination module is used to relax the network transmission constraints of the source-grid-load-storage collaborative planning model, and substitute the representative scenario and its weight into the source-grid-load-storage collaborative planning model for solution to obtain the initial expansion capacity of wind power, photovoltaic and energy storage.

[0143] Specifically, when solving for the initial expansion capacity, the initial expansion capacity determination module does not apply the calculation relationship for determining the line power flow based on the injected power and power transmission distribution factor of each node, nor does it apply upper and lower limits for line power flow; the objective function and other constraints in the source-grid-load-storage collaborative planning model remain unchanged. The specific processing procedure of the initial expansion capacity determination module can refer to step S102 in the first embodiment.

[0144] The initial expansion capacity determination module sends the initial expansion capacity of wind power and photovoltaic power to the new energy expansion capacity allocation module, and sends the initial expansion capacity of energy storage to the energy storage expansion capacity determination module.

[0145] The new energy expansion capacity allocation module is used to determine the worst-case power flow of each network transmission constraint under each representative scenario and each operating period, and to judge each network transmission constraint one by one. For any network transmission constraint, when the worst-case power flow of the network transmission constraint under each representative scenario and each operating period does not exceed the corresponding line transmission capacity limit, the network transmission constraint is determined as an inactive network transmission constraint, and the proportion of the inactive network transmission constraint to all network transmission constraints is used as a fixed proportion. According to the fixed proportion, the initial expansion capacity of wind power and photovoltaic power is divided into fixed expansion capacity and expansion capacity to be redistributed, respectively.

[0146] Specifically, the new energy expansion capacity allocation module determines the worst-case power flow corresponding to the transmission constraints of each network, provided that the net injected power at each node satisfies the system power balance and the upper and lower limits of the net injected power, and compares the worst-case power flow with the corresponding line transmission capacity limit. The specific processing procedure of the new energy expansion capacity allocation module can refer to step S103 in the first embodiment.

[0147] The new energy expansion capacity allocation module sends the fixed expansion capacity and the expansion capacity to be reallocated to the new energy expansion capacity allocation module, and sends the worst-case power flow corresponding to each network transmission constraint to the network transmission tension determination module.

[0148] The network transmission stress determination module is used to determine the congestion weight of each network transmission constraint under each operating period based on historical line operation data, and to perform a weighted summation of the portion of the worst-case power flow exceeding the corresponding line transmission capacity limit for each network transmission constraint based on the weight of each representative scenario and the congestion weight, so as to obtain the network transmission stress corresponding to the new energy capacity space allocation scheme.

[0149] Specifically, the network transmission stress determination module determines the historical load rate, historical near-congestion probability, and congestion weight sequentially based on the historical power flow of each line under each operating segment over multiple historical operating days, and maps the congestion weight corresponding to the line to the network transmission constraint formed by the forward transmission capacity limit or reverse transmission capacity limit of that line. The specific processing procedure of the network transmission stress determination module can refer to step S104 in the first embodiment.

[0150] During the trial allocation of capacity units by the new energy expansion capacity allocation module, the network transmission tension determination module receives the new energy capacity space allocation schemes corresponding to each trial allocation result, determines the network transmission tension corresponding to the corresponding new energy capacity space allocation scheme, and sends the network transmission tension to the new energy expansion capacity allocation module.

[0151] The new energy expansion capacity allocation module is used to divide the wind power and photovoltaic expansion capacity to be reallocated into multiple capacity units respectively; to allocate each capacity unit to the corresponding feasible candidate node of the new energy type in sequence, wherein the new energy expansion capacity of the feasible candidate node after allocating the corresponding capacity unit does not exceed the maximum allowable expansion capacity, and to minimize the network transmission tension corresponding to the new energy capacity space allocation scheme formed after allocation; and to obtain the final expansion capacity of wind power and photovoltaic based on the fixed expansion capacity and the allocation results of each capacity unit.

[0152] Specifically, the new energy capacity expansion allocation module compares the network transmission tension corresponding to each trial allocation result, eliminates candidate nodes that exceed the maximum allowable expansion capacity after adding the current capacity unit, and selects the candidate node that minimizes the network transmission tension from the remaining feasible candidate nodes. The specific processing procedure of the new energy capacity expansion allocation module can refer to step S105 in the first embodiment.

[0153] The new energy expansion capacity allocation module sends the final expansion capacity of wind power and photovoltaic power to the energy storage expansion capacity determination module.

[0154] The energy storage expansion capacity determination module is used to fix the final expansion capacity of wind power and photovoltaic power and restore the network transmission constraints. It adjusts the initial expansion capacity of energy storage according to the system operation feasibility under each representative scenario to obtain the final energy storage expansion capacity.

[0155] Specifically, the energy storage expansion capacity determination module arranges the representative scenarios in descending order of weight and solves the single-scenario sub-problems corresponding to each representative scenario in turn. When solving the first single-scenario sub-problem, the sum of the initial energy storage expansion capacities of each node is used as the lower bound of the total energy storage expansion capacity. When solving subsequent single-scenario sub-problems, the energy storage expansion capacities of each node obtained from the previous single-scenario sub-problem are used as the lower bounds of the energy storage expansion capacities of the corresponding nodes in the current single-scenario sub-problem. The energy storage expansion capacities of each node obtained from the last single-scenario sub-problem are used as the final energy storage expansion capacity. The specific processing procedure of the energy storage expansion capacity determination module can refer to step S106 in the first embodiment.

[0156] The source-grid-load-storage collaborative planning system ultimately outputs the final expansion capacity of wind power, photovoltaic power, and energy storage for each planning node.

[0157] Each module in this embodiment can be implemented by a processor executing a computer program stored in memory, or by a hardware circuit with corresponding data processing functions, or by a combination of software and hardware. The system described in this embodiment is based on the same technical concept as the method described in the first embodiment. Content not described in detail in this embodiment can be referred to the corresponding content in the first embodiment.

[0158] The embodiments provided in this application have been described in detail above. The specific embodiments described in this application are only for helping to understand the technical solutions of this application and should not be construed as limiting the scope of protection of this application. Equivalent substitutions or modifications made by those skilled in the art without departing from the technical concept of this application should all fall within the scope of protection of this application.

Claims

1. A source-grid-load-storage coordinated planning method for high-proportion renewable energy access, characterized in that, include: Clustering of the original source load scenarios yields multiple representative scenarios and their weights; Relax the network transmission constraints of the source-grid-load-storage collaborative planning model, substitute the representative scenario and its weight into the model for solution, and obtain the initial expansion capacity of wind power, photovoltaic and energy storage. Determine the worst-case power flow for each network transmission constraint under each representative scenario and each operating period, and judge each network transmission constraint one by one; for any network transmission constraint, if the worst-case power flow of the network transmission constraint under each representative scenario and each operating period does not exceed the corresponding line transmission capacity limit, the network transmission constraint is determined as an inactive network transmission constraint; the proportion of the inactive network transmission constraints to all network transmission constraints is taken as a fixed proportion, and the initial expansion capacity of wind power and photovoltaic power is divided into fixed expansion capacity and expansion capacity to be redistributed according to the fixed proportion; Based on historical line operation data, the congestion weight of each network transmission constraint under each operating period is determined. Based on the weight of each representative scenario and the congestion weight, the portion of the worst-case power flow exceeding the corresponding line transmission capacity limit of each network transmission constraint is weighted and summed to obtain the network transmission tension corresponding to the new energy capacity space allocation scheme. The wind power and photovoltaic capacity to be redistributed and expanded are respectively divided into multiple capacity units; Each capacity unit is sequentially allocated to a feasible candidate node of the corresponding new energy type. The new energy expansion capacity of the feasible candidate node after the allocation of the corresponding capacity unit does not exceed the maximum allowable expansion capacity, and the network transmission tension corresponding to the new energy capacity space allocation scheme formed after the allocation is minimized. Based on the fixed expansion capacity and the allocation results of each capacity unit, the final expansion capacity of wind power and photovoltaic power is obtained. The final expansion capacity of wind power and photovoltaic power is fixed and the network transmission constraints are restored. The initial expansion capacity of energy storage is adjusted according to the system operation feasibility under each representative scenario to obtain the final energy storage expansion capacity.

2. The source-grid-load-storage coordinated planning method for high-proportion renewable energy access according to claim 1, characterized in that, The fixed expansion capacity and the expansion capacity to be reallocated are determined according to the following formula: in, This indicates the fixed ratio; and Wind farm and photovoltaic power station The initial expansion capacity; and Wind farm and photovoltaic power station Fixed expansion capacity; and Wind farm and photovoltaic power station The capacity to be redistributed and expanded.

3. The source-grid-load-storage coordinated planning method for high-proportion renewable energy access as described in claim 1, characterized in that, The network transmission tension is determined according to the following formula: in, To represent the number of scenes, Number of runtime segments This represents the total number of network transmission constraints. For the first The weights of each representative scenario For network power transmission constraints In the runtime segment The congestion weight is below, For network power transmission constraints In the Representative scenarios and runtime segments The worst-case scenario trend, For network power transmission constraints The transmission capacity limit for the corresponding line.

4. The source-grid-load-storage coordinated planning method for high-proportion renewable energy access according to claim 1, characterized in that, The capacity to be redistributed and expanded is divided into multiple capacity units according to an equal capacity method, including new energy types. The corresponding total number of capacity units for: No. Candidate nodes selected in secondary capacity unit allocation for: in, Indicates wind power or solar power. Indicates new energy type The capacity to be redistributed and expanded Indicates new energy type The capacity size of each capacity unit, Indicates new energy type The corresponding set of feasible candidate nodes Indicates the first Before secondary capacity unit allocation, the node New energy types The current expansion capacity, Represents a node For new energy types Maximum allowable expansion capacity Indicates the first One capacity unit is attempted to be allocated to the node. The corresponding network transmission tension, and .

5. The source-grid-load-storage coordinated planning method for high-proportion renewable energy access according to claim 3, characterized in that, line In the runtime segment and the Historical load rate on a historical operating day ,line In the runtime segment Historical near-congestion probability and congestion weight They are respectively: When network power transmission constraints By line When a forward or reverse transmission capacity limit is formed, the network transmission constraint In the runtime segment The congestion weight is ; in, Indicates the line In the Running time segment of each historical running day Historical load rate This indicates the corresponding historical trend. Indicates the line absolute transmission capacity, Indicates the line In the runtime segment The historical near-congestion probability; , , These represent the total number of historical operating days, the total number of transmission lines, and the total number of operating periods, respectively. and These represent the summation indices for the transmission line and the operating segment, respectively. Indicates the line is in operation segment The historical near-congestion probability; The load rate threshold representing a near-congestion state. Indicates an indicator function, Indicates the smoothing parameter. Indicates the line In the runtime segment The congestion weight is below, Indicates network transmission constraints The corresponding power transmission lines.

6. The source-grid-load-storage coordinated planning method for high-proportion renewable energy access according to claim 1, characterized in that, The method of adjusting the initial expansion capacity of the energy storage on a scenario-by-scenario basis includes: Arrange the representative scenarios in descending order of weight, and solve the sub-problems of each representative scenario in turn. When solving the first single-scenario subproblem, the sum of the initial energy storage expansion capacity of each node is used as the lower bound of the total energy storage expansion capacity; when solving subsequent single-scenario subproblems, the energy storage expansion capacity of each node obtained from the previous single-scenario subproblem is used as the lower bound of the energy storage expansion capacity of the corresponding node in the current single-scenario subproblem. The energy storage expansion capacity of each node obtained from the last single-scenario sub-problem is taken as the final energy storage expansion capacity.

7. The source-grid-load-storage coordinated planning method for high-proportion renewable energy access according to claim 1, characterized in that, Network transmission constraints In the Representative scenarios and runtime segments The worst-case scenario trend for: in: in, Indicates the number of power injection nodes. Represents a node Constraints on network power transmission The power transmission distribution factor corresponding to the line power flow. Represents a node In representative scenarios and runtime segment The net injection power below, and These represent the lower and upper limits of the net injected power, respectively.

8. The source-grid-load-storage coordinated planning method for high-proportion renewable energy access according to claim 1, characterized in that, The clustering of the original source-load scene includes: Euclidean distance is used to measure the similarity between original source and load scenes, hierarchical clustering is performed according to the Ward connection criterion, and the number of representative scenes is determined by the Gap statistic. The centroid of each scene cluster is determined as the corresponding representative scene, and the weight of the corresponding representative scene is determined according to the number of original source payload scenes contained in each scene cluster. The representative scenarios and their weights are represented as follows: in, This indicates the total number of original source load scenes. This indicates the total number of scenes. Indicates the first A scenario cluster, Indicates the first The number of original source payload scenes contained in each scene cluster. Indicates the first A primary source load scenario, Indicates the first A representative scenario, Indicates the first The weights of each representative scenario.

9. The source-grid-load-storage coordinated planning method for high-proportion renewable energy access according to claim 1, characterized in that, The fixed expansion capacity is retained at the planning node corresponding to the initial expansion capacity, and the total expansion capacity of wind power and photovoltaic power remains unchanged during the allocation of each capacity unit.

10. A source-grid-load-storage coordinated planning system for high-proportion renewable energy access, characterized in that, include: The scene clustering module is used to cluster the original source load scenes to obtain multiple representative scenes and their weights; The initial expansion capacity determination module is used to relax the network transmission constraints of the source-grid-load-storage collaborative planning model, and to solve the model by substituting the representative scenario and its weights to obtain the initial expansion capacity of wind power, photovoltaic and energy storage. The new energy expansion capacity allocation module is used to determine the worst-case power flow of each network transmission constraint under each representative scenario and each operating period, and to judge each network transmission constraint one by one. For any network transmission constraint, when the worst-case power flow of the network transmission constraint under each representative scenario and each operating period does not exceed the corresponding line transmission capacity limit, the network transmission constraint is determined as an inactive network transmission constraint, and the proportion of the inactive network transmission constraint to all network transmission constraints is used as a fixed proportion. According to the fixed proportion, the initial expansion capacity of wind power and photovoltaic power is divided into fixed expansion capacity and expansion capacity to be redistributed, respectively. The network transmission stress determination module is used to determine the congestion weight of each network transmission constraint under each operating period based on historical line operation data, and to perform a weighted summation of the portion of the worst-case power flow exceeding the corresponding line transmission capacity limit for each network transmission constraint based on the weight of each representative scenario and the congestion weight, so as to obtain the network transmission stress corresponding to the new energy capacity space allocation scheme. The new energy expansion capacity allocation module is used to divide the wind power and photovoltaic expansion capacity to be reallocated into multiple capacity units respectively; and to allocate each capacity unit to the corresponding feasible candidate node of the new energy type in sequence. The new energy expansion capacity of the feasible candidate node after allocating the corresponding capacity unit does not exceed the maximum allowable expansion capacity, and the network transmission tension corresponding to the new energy capacity space allocation scheme formed after allocation is minimized. Based on the fixed expansion capacity and the allocation results of each capacity unit, the final expansion capacity of wind power and photovoltaic power is obtained. The energy storage expansion capacity determination module is used to fix the final expansion capacity of the wind power and photovoltaic power and restore the network transmission constraints. It adjusts the initial expansion capacity of the energy storage according to the system operation feasibility under each representative scenario to obtain the final energy storage expansion capacity.

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