Flexible interconnection and energy storage collaborative planning method for low-voltage distribution area under high-proportion photovoltaic access

Abstract

The invention discloses a flexible interconnection and energy storage collaborative planning method for a low-voltage distribution transformer area under high-proportion photovoltaic access, and the method comprises the steps: building a structure-function two-dimensional cluster division model, dividing a distribution transformer area cluster through an electrical distance, a voltage balance degree and an active power balance degree, and reducing the dimensionality of coordinated planning; carrying out connectivity analysis on the transformer areas in the cluster, judging interconnection requirements by taking the maximum net load rate of the transformer and the minimum net load of the transformer areas as feasibility indexes, and generating a candidate scheme set; establishing a power distribution area interconnection device-energy storage double-layer planning model, minimizing equipment capacity configuration at an upper layer according to annual comprehensive investment cost, and formulating a collaborative operation strategy at a lower layer according to minimization of operation cost and maximization of new energy consumption; a hierarchical solution strategy and an improved genetic algorithm for cluster division are adopted to improve the convergence speed and precision, it is ensured that an optimal planning scheme meeting economical efficiency and operation adaptability is obtained, the problem that the influence of factors cannot be comprehensively considered in a single planning dimension is solved, and the actual operation requirement is met.

Description

Technical Field

[0001] This invention belongs to the field of low-carbon operation technology of low-voltage distribution network areas in power systems, and relates to a method for flexible interconnection and energy storage collaborative planning of low-voltage distribution network areas under high-proportion photovoltaic access. Background Technology

[0002] The energy sector is undergoing profound changes, with the scale of distributed photovoltaic (DPV) grid connection to low-voltage distribution substations expanding rapidly. However, this trend presents numerous challenges to traditional power systems. Traditional AC distribution substation operation modes and independent energy storage configurations have revealed a series of prominent problems when dealing with high-penetration photovoltaic scenarios, severely restricting the stable operation and efficient development of the power system. These problems are specifically manifested as follows: The drawbacks of traditional low-voltage distribution substation operation are becoming increasingly apparent. When facing heavy equipment loads, traditional low-voltage distribution substations mainly rely on increasing the capacity of primary equipment or upgrading the secondary system to alleviate the problem. However, this traditional model has significant drawbacks. On the one hand, increasing the capacity of primary equipment and upgrading the secondary system require substantial capital investment, resulting in high investment costs. On the other hand, after the capacity increase or upgrade is completed, the equipment may face low-load operation during long-term operation, causing resource waste. Furthermore, prolonged low-load operation can bring additional risks, seriously affecting the economy and stability of the power system.

[0003] Standalone energy storage configurations have limitations. While they can mitigate power fluctuations and contribute positively to the stable operation of the power system, their capacity planning fails to fully consider the synergistic benefits of flexible interconnection across distribution areas. This results in lower than ideal energy storage utilization rates in actual operation, leading to a large amount of idle energy storage resources and consequently limiting economic viability, making it difficult to maximize their advantages in scenarios with high proportions of distributed photovoltaic (PV) grid integration.

[0004] Research on low-voltage flexible distribution network interconnection technology is still incomplete. Existing technologies have proposed a low-voltage flexible distribution network interconnection technology architecture. This architecture constructs a dynamically adjustable DC interconnection channel through power electronic conversion devices, effectively realizing the cross-regional absorption of DPVG and the transfer of instantaneous heavy loads, providing a new solution for high-proportion distributed photovoltaic access. Currently, domestic and foreign scholars have conducted a series of studies on low-voltage flexible interconnected distribution networks, mainly focusing on interconnection scheme formulation, interconnection equipment capacity configuration, and maximum power supply capacity assessment. For example, by introducing flexible power electronic devices, the independent operation of AC and DC power within the distribution area has been achieved, and the efficient cross-regional utilization of distributed power sources has been promoted by leveraging the flexible power exchange mechanism between distribution areas. Focusing on two key issues—optimal selection of interconnection schemes and accurate planning of interconnection equipment capacity—important theoretical guidance and technical support have been provided for the practical deployment of flexible distribution network interconnection systems.

[0005] While the aforementioned studies have explored the reliability and economy of optimized configuration to some extent, most are limited to a single planning dimension. In the complex actual operating conditions of distribution substations in my country, a single planning dimension cannot fully consider the combined effects of various factors, making it difficult to meet actual operational needs. Furthermore, collaborative planning faces the bottleneck of solving high-dimensional decision spaces, which further restricts the promotion and implementation of existing technologies in practical applications.

[0006] In summary, existing technologies face numerous challenges in scenarios where high-proportion distributed photovoltaic (PV) grid integration is implemented in low-voltage distribution substations. Therefore, there is an urgent need to develop a collaborative planning method for flexible interconnection and energy storage in substations adapted to high-penetration PV scenarios. This will improve the economic efficiency, reliability, and PV absorption efficiency of the power system, thereby promoting sustainable development in the energy sector. Summary of the Invention

[0007] The purpose of this invention is to solve the problem that the single planning dimension in the existing technology cannot fully consider the comprehensive influence of various factors and is difficult to meet the actual operation requirements, and to provide a method for flexible interconnection and energy storage coordination planning of low-voltage distribution radio areas under high proportion of photovoltaic access.

[0008] To achieve the above objectives, the present invention employs the following technical solution: A method for planning flexible interconnection and energy storage coordination of low-voltage distribution network areas under high-proportion photovoltaic access includes the following steps: A structure-function dual-dimensional cluster partitioning model is constructed to divide the distribution area into clusters to reduce the coordination planning dimension: electrical distance is used as a modularity index to measure the degree of electrical coupling between nodes in the distribution area. At the same time, voltage balance and active power balance indices are introduced as functional indices to characterize the photovoltaic absorption capacity and power self-balance level within the cluster, respectively. A comprehensive evaluation model is constructed by integrating multiple indices to divide the distribution area into clusters with tight internal electrical coupling and strong power complementarity. Connectivity analysis is performed on the distribution transformer areas within the cluster to provide structured input for subsequent planning: the maximum net load rate of the transformer and the minimum net load of the distribution transformer area are used as feasibility indicators for flexible interconnection of distribution transformer areas to determine the interconnection needs of distribution transformer areas within the cluster and generate a set of interconnection candidate schemes that meet the needs and constraints. A two-layer planning model for distribution substation interconnection devices and energy storage is established: Based on the cluster interconnection structure of distribution substations, cross-substation consumption and load transfer of heavy-load transformers are realized, achieving optimized operation of distribution substations; The upper layer is the equipment capacity planning layer, which optimizes the capacity configuration of flexible interconnection devices and energy storage systems with the goal of minimizing the annual comprehensive investment cost; The lower layer is the operation strategy optimization layer, which formulates a collaborative operation strategy for photovoltaic surplus interconnection and peak load energy storage support based on the autonomous characteristics of the cluster, with the goal of minimizing operating costs and maximizing the consumption of new energy. A hierarchical solution strategy is adopted to solve the bi-level programming model: an improved genetic algorithm is used in the cluster partitioning stage, and the convergence speed and solution accuracy are improved by optimizing the chromosome encoding strategy; the upper-level planning is optimized by the NSGA-II algorithm with the crowding distance operator to obtain the Pareto optimal solution set; the lower-level operation optimization is solved by the interior point method, which determines the operating status and output of each device based on the device configuration scheme given in the upper level. Through iterative interaction between the upper and lower levels, the upper-level planning result is used as the lower-level operation constraint, and the lower-level operation result is fed back to correct the upper-level planning, so as to finally obtain the optimal planning scheme that satisfies both economy and operational adaptability.

[0009] The specific construction of the structure-function dual-dimensional cluster partitioning model is as follows: Electrical distance is used as a modularity index to measure the degree of electrical coupling between transformer substation nodes. The larger the modularity index, the better the aggregation effect of transformer substation nodes. The voltage balance index of the transformer cluster is defined to characterize the distributed photovoltaic absorption capacity within the cluster. When some clusters experience node voltage exceeding the limit due to insufficient photovoltaic absorption capacity, other clusters still have photovoltaic absorption capacity. Define cluster active power balance index, fully consider the power balance problem within the cluster, and reduce cluster power supply; A power grid cluster division model considering comprehensive indicators is established by taking into account factors such as the electrical distance between distribution substations, the voltage balance of the cluster, and the active power balance of the cluster.

[0010] The modularity index Q d Specifically defined as:

[0011]

[0012]

[0013]

[0014]

[0015] in, m This is the sum of the weights of the entire network area; Q ij For nodes i and nodes j Connected weights; k i , k j Represent all nodes respectively i ,node j The sum of the weights of connected edges; d ( i , j ) represents a node i and nodes j In the shared state of a partition, if they belong to the same partition, d ( i , j )=1, otherwise d ( i , j )=0; D ij and max( D ) are the maximum matrices composed of the electrical distance considering the overall node influence and the electrical distance between any two nodes, respectively; N This represents the total number of nodes in the distribution area. d in , d jn They are respectively the Taiwan area n Power changes affect the distribution area i The degree of voltage impact and the distribution area n Power changes affect the distribution area j The degree of influence of voltage; The voltage balance index of the transformer cluster Q v Specifically defined as:

[0016]

[0017]

[0018]

[0019] in, ΔV i max For clusters i Maximum voltage regulation capability;S ij PV , S ij QV These are the first and second active and reactive voltage sensitivity matrices, respectively. i Line 1 j Column elements; ΔP j,t max , ΔQ j,t ma They are respectively t The maximum adjustable active power and maximum adjustable reactive power within the cluster at any given time; ΔP j,t ESS,max , ΔP j,t load,max They are respectively t The maximum adjustable active power of energy storage and the maximum adjustable active power of load within the cluster at any given time; ΔQ j,t PV,max for t The maximum adjustable reactive power of the photovoltaic system within the cluster at any given time; ΔV i For clusters i The maximum voltage deviation; For clusters i Voltage balance; N c The total number of clusters to be divided; The cluster active power balance index Q p Specifically defined as:

[0020] in, P c,t For clusters c exist t The net merit of every moment; N c Total number of clusters; The following power grid cluster partitioning model considering comprehensive indicators is established:

[0021] In the formula: F Comprehensive evaluation indicators are used to classify the clusters of transformer substations; l 1. l 2. l 3 represents the weighting coefficients of each indicator, where l 1+ l 2+ l 3 = 1.

[0022] The specific steps for performing connectivity analysis on the stations within the cluster are as follows: Collect data on the source-load characteristics, transformer parameters, and spacing of each transformer area within the cluster. Based on a set threshold, the photovoltaic power stations are selected and categorized into an overloaded set and a photovoltaic surplus set, respectively. By pairing substations across groups to satisfy the interconnection distance constraint, a set of interconnectable combinations is generated, covering all candidate interconnection schemes that meet the requirements and constraints. The actual interconnection status of each combination is characterized by variables, thereby obtaining the interconnection scheme of the substations to be planned.

[0023] For each distribution substation within the cluster, the maximum net load factor of the transformer and the minimum net load of the substation are used as feasibility indicators for flexible interconnection of distribution substations to determine the interconnection requirements:

[0024]

[0025] in, T i load and P i pure These are the maximum net load rate of the transformer in the distribution area and the minimum net load of the distribution area, respectively. P i,t load and Q i,t load They are respectively t Timetable area i The total active and reactive power of the load; P i,t max for t Timetable area i The maximum output of distributed photovoltaic power; S i,T Taiwan District i The transformer capacity.

[0026] The upper-level planning model of the distribution area interconnection device-energy storage two-layer planning model: The objective function is to minimize the annual comprehensive cost of the entire distribution area, including the annualized comprehensive investment cost of flexible interconnection devices and the annualized comprehensive investment cost of cluster energy storage; The annualized comprehensive investment cost of flexible interconnection devices includes annualized investment costs, annual operation and maintenance costs, and costs of purchasing electricity from the upstream main grid; The annualized comprehensive investment cost of clustered energy storage includes the cost of the energy storage system, operation and maintenance costs, and energy storage revenue; The constraints include power flow constraints in distribution areas, power balance constraints in distribution areas, node voltage constraints, line power constraints, tie line power constraints, converter power constraints, and energy storage capacity constraints.

[0027] The upper-level planning model of the distribution area interconnection device-energy storage two-layer planning model is specifically as follows: The objective function is to minimize the overall annual cost of the entire distribution area, i.e.: min G = G 1+ G 2 in, G 1 and G 2 represents the combined investment cost of the flexible interconnection system and energy storage in the distribution area; Comprehensive investment cost of flexible interconnection system in the distribution area G 1: G 1=min( C con + C ope + C buy ) In the formula, C con This indicates the annualized investment cost of flexible interconnect devices; C ope This indicates the annual operation and maintenance cost of the flexible interconnect device; C buy This indicates the cost of electricity purchased from the main grid by the transformer cluster interconnection system. Annualized investment cost of flexible interconnect devices C con :

[0028]

[0029] In the formula: The annual value conversion factor for flexible interconnect devices; N This represents the total number of distribution transformer areas included in the distribution network. l con and S i,con They are respectively the Taiwan area i Installed converter capacity and investment cost per unit capacity; l h and C I line The first i The distance between the substations in each cluster and the investment and installation cost of DC interconnection lines per unit length; r andy con These are the discount rate and the lifespan of the interconnected device, respectively. Annual operation and maintenance costs of flexible interconnect devices C ope :

[0030] In the formula: The annual operating and maintenance cost per unit capacity of the converter; Comprehensive investment cost of flexible interconnected energy storage in the distribution area G 2:

[0031]

[0032] In the formula: C install Cost of energy storage system; C operation For operation and maintenance costs; C in For energy storage revenue; This is the annual value conversion factor for energy storage devices; y ess These refer to the service life of the energy storage device; Energy storage system cost C install It consists of two parts: capacity cost and power cost.

[0033] In the formula: N ess Configure candidate nodes for energy storage; l p and l e These are the unit power investment cost and unit capacity investment cost of energy storage power stations, respectively. P ess,i and E ess,i They are nodes i The rated power and rated capacity of the energy storage power station; Operation and maintenance costs C operation :

[0034] In the formula: l op ess The annual maintenance rate for energy storage equipment; Energy storage revenue C in :

[0035] In the formula: l coal For coal-fired power prices, P i,dis ess For nodes i Energy storage t Charge and discharge power during specific time periods; Distribution area power flow constraints:

[0036] In the formula: P i,t , Q i,t They are respectively t Time period nodes i Injected active and reactive power; U i,t , U j,t They are respectively t Time period nodes i , j The voltage amplitude; G ij , B ij Branch roads ij Conductivity and susceptance; i ij,t for t Voltage phase angle difference during the time period; Power balance constraints in transformer substations:

[0037] In the formula: P Gi , P pvi Inject nodes into the system and DPVG i The active power, kW; Q Gi , Q pvi Inject nodes into the system and DPVG i reactive power, kVar; P Li For nodes i The active power consumed, kW; Q Li For nodes i Reactive power consumed, kVar; P ess The active power absorbed or released by the ESS, in kW; Q essThe reactive power absorbed or released by the ESS, in kVar; Node voltage constraints: U i min ≤ U i ≤ U i max In the formula: U i max and U i min They are nodes i Upper and lower limits of voltage amplitude; Line power constraints: P ij min ≤ P ij ≤ P ij max In the formula: P ij max , P ij min These are the upper and lower limits of the active power that the line can transmit; Tie line power constraints: Connecting areas i and j The power exchange between them satisfies the following constraints:

[0038] In the formula: and These are the maximum and minimum allowable transmission power for DC tie lines, respectively. For DC tie lines in t Transmission power during a given time period; K line This is the transmission loss coefficient; Converter power constraints:

[0039] In the formula: For the first i The maximum installable power of the converter in each distribution area; Energy storage capacity constraints:

[0040] In the formula: , These are the maximum rated power and rated capacity that allow for the installation of energy storage, respectively. e max It is the maximum ratio of rated power to rated capacity.

[0041] The lower-level operation model of the distribution area interconnection device-energy storage two-layer planning model: The objective function is to minimize the annual operating cost of the system and maximize the renewable energy consumption capacity, including the electricity purchase cost of the transformer cluster interconnection system, the network loss cost, and the curtailment penalty. The constraints include system operation constraints, energy storage charging and discharging constraints, energy storage operating status constraints, and DPVG output constraints.

[0042] The lower-level operation model of the distribution substation interconnection device-energy storage two-layer planning model is as follows: The operating model aims to minimize the annual operating cost of the system under the given configuration at the planning level, that is, to minimize the electricity purchase cost of the distribution substation from conventional units and maximize the renewable energy consumption capacity. Objective function: min G 2= C buy + C loss +C p In the formula: C buy Electricity purchase cost for the district cluster interconnection system; C loss For network loss costs; C p As punishment for abandoning light; Electricity purchase cost for the transformer cluster interconnection system:

[0043] In the formula: T The planning period is 24 hours. l ep The unit cost of purchasing electricity from the main grid for the transformer cluster interconnection system; P i,t for t Injecting into the station area at all times i Active power; Δ t To minimize the time interval, we take 1 hour. Network loss cost of the trunking interconnection system:

[0044] Where: Ω ( i ) represents all i It is the set of end nodes of the first node; lloss For grid loss electricity price; The square of the branch current; r ij Branch resistance; Abandoned Light Penalty:

[0045] In the formula: l PV The penalty price for abandoning light is 2.5 yuan / kWh as used in the text; for t Timetable area i The actual output of photovoltaic power; for t Timetable area i The actual maximum photovoltaic output that can be sustained; System operating constraints:

[0046] In the formula: f ( i () is the set of nodes connected to the node; U i,t and U j,t They are nodes i and nodes j The voltage amplitude; and for t Time zone i Active and reactive power of conventional generating units; and For nodes i Place t The charging and discharging power of time-segmented energy storage; Energy storage charge and discharge constraints: When the active power generated by the photovoltaic system exceeds the active power consumed by the load, the energy storage battery charges / stores energy; when the photovoltaic output is less than the active power consumed by the load, the energy storage battery discharges / releases energy. Therefore, the regulation of the energy storage battery has bidirectional effectiveness, and the constraint formula is as follows: - P ess,i ≤ P ess,i ( t )≤ P ess,i In the formula: P ess,i ( t ) is the first i Cluster energy storage t Power at any moment; Energy storage operation status constraints: SOC min ≤ SOC i ( t )≤ SOC max In the formula: SOC i ( t ) is the first i Energy storage device t The energy storage charging and discharging state at any given moment. SOC max , SOC min These are the upper and lower limits of the energy storage state of charge, respectively. DPVG output constraints:

[0047] In the formula: For nodes i The maximum output of the installed DPVG.

[0048] The hierarchical solution strategy is used to solve the bi-level programming model. An improved genetic algorithm is used in the cluster partitioning phase, which improves convergence speed and solution accuracy by optimizing chromosome encoding strategy; The upper-level planning adopts the NSGA-II algorithm with the crowding distance operator to optimize the solution, so as to avoid the premature convergence problem of the traditional genetic algorithm and obtain the Pareto optimal solution set. The lower-level operation optimization is solved using the interior point method, which determines the operating status and output of each device based on the device configuration scheme given in the upper level. Through iterative interaction between upper and lower layers, the planning results of the upper layer are used as constraints for the operation of the lower layer, and the operation results of the lower layer are fed back to correct the planning of the upper layer, so as to finally obtain the optimal planning scheme that satisfies both economy and operational adaptability.

[0049] Compared with the prior art, the present invention has the following beneficial effects: The flexible interconnection and energy storage coordinated planning method for low-voltage distribution transformer substations under high-proportion photovoltaic access in this invention constructs a structure-function dual-dimensional cluster partitioning model. This model integrates multiple indicators such as electrical distance, voltage balance, and active power balance to partition distribution transformer substation clusters, effectively reducing the coordination planning dimensions and improving planning efficiency. Interconnectivity analysis is performed on substations within the cluster, using the maximum net load factor of the transformer and the minimum net load of the substation as feasibility indicators to determine interconnection needs and generate a set of candidate solutions, providing reliable structured input for subsequent planning. A distribution transformer substation interconnection device is established… This energy storage two-layer planning model, based on a clustered interconnection structure, enables cross-regional power consumption and load transfer of heavily loaded transformers. The upper layer optimizes equipment capacity configuration to minimize annual comprehensive investment costs, while the lower layer formulates a collaborative operation strategy to minimize operating costs and maximize renewable energy consumption. This ensures the economical and efficient operation of distribution substations and improves overall benefits. A hierarchical solution strategy is adopted. Cluster partitioning uses an improved genetic algorithm to enhance convergence speed and accuracy. The upper-layer planning uses the NSGA-II algorithm, which incorporates a congestion distance operator, to obtain the Pareto optimal solution set. The lower-layer operation optimization uses the interior-point method and iterative interaction between the upper and lower layers to ensure the optimal planning scheme that satisfies both economic efficiency and operational adaptability. This solves the problem that a single planning dimension cannot fully consider the impact of factors and better meets actual operational needs. Furthermore, this invention is specifically designed for high-proportion photovoltaic (PV) grid connection scenarios, effectively solving the power consumption problem caused by high-proportion PV grid connection, improving renewable energy utilization, and promoting energy structure optimization and sustainable development. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 Dynamic capacity expansion map of the power distribution area; Figure 2 This is a schematic diagram of a flexible interconnection mode for transformer substations based on VSC. Figure 3 This is a structural diagram of the dual-layer planning model for the distribution substation interconnection device-energy storage of the present invention; Figure 4 This is a flowchart of the model solution process for the present invention; Figure 5 A network structure diagram of the original planned area's power distribution stations; Figure 6 A schematic diagram illustrating the results of flexible interconnection of the planning back-end area cluster; Figure 7 The load rates of transformers in areas 6 and 26 during spring and autumn; Figure 8 The load rate of transformers in the summer distribution area is 6 and 26. Figure 9 The load rate of transformers in the 6th and 26th distribution areas during winter; Figure 10 The active power and SOC curves of ESS; Figure 11 Typical daily time-series characteristic curves for different load types in spring and autumn; Figure 12 This is a typical daily time-series characteristic curve for photovoltaic power output and DC load. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0053] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0054] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0055] The present invention discloses a method for flexible interconnection and energy storage coordinated planning of low-voltage distribution substations under high-proportion photovoltaic access, comprising the following steps: S1. Construct a structure-function dual-dimensional cluster partitioning model to divide the distribution area into clusters to reduce the coordination planning dimension: use electrical distance as a modularity index to measure the degree of electrical coupling between nodes in the distribution area, and introduce voltage balance and active power balance as functional indicators to characterize the photovoltaic absorption capacity and power self-balancing level within the cluster, respectively. By integrating multiple indicators, a comprehensive evaluation model is constructed to divide the distribution area into clusters with tight internal electrical coupling and strong power complementarity.

[0056] The specific steps for constructing a two-dimensional cluster partitioning model based on structure and function are as follows: Electrical distance is used as a modularity index to measure the degree of electrical coupling between transformer substation nodes. A higher modularity index indicates better node aggregation. Q dSpecifically defined as:

[0057]

[0058]

[0059]

[0060]

[0061] in, m This is the sum of the weights of the entire network area; Q ij For nodes i and nodes j Connected weights; k i , k j Represent all nodes respectively i ,node j The sum of the weights of connected edges; d ( i , j ) represents a node i and nodes j In the shared state of a partition, if they belong to the same partition, d ( i , j )=1, otherwise d ( i , j )=0; D ij and max( D ) are the maximum matrices composed of the electrical distance considering the overall node influence and the electrical distance between any two nodes, respectively; N This represents the total number of nodes in the distribution area. d in , d jn They are respectively the Taiwan area n Power changes affect the distribution area i The degree of voltage impact and the distribution area n Power changes affect the distribution area j The degree of influence of voltage.

[0062] A voltage balance index for a transformer substation cluster is defined to characterize the distributed photovoltaic (PV) absorption capacity within the cluster. When some clusters experience node voltage exceeding limits due to insufficient PV absorption capacity, other clusters still have PV absorption capacity. This transformer substation cluster voltage balance index... Q v Specifically defined as:

[0063]

[0064]

[0065]

[0066] in, ΔV i max For clusters i Maximum voltage regulation capability; S ij PV , S ij QV These are the first and second active and reactive voltage sensitivity matrices, respectively. i Line 1 j Column elements; ΔP j,t max , ΔQ j,t ma They are respectively t The maximum adjustable active power and maximum adjustable reactive power within the cluster at any given time; ΔP j,t ESS,max , ΔP j,t load,max They are respectively t The maximum adjustable active power of energy storage and the maximum adjustable active power of load within the cluster at any given time; ΔQ j,t PV,max for t The maximum adjustable reactive power of the photovoltaic system within the cluster at any given time; ΔV i For clusters i The maximum voltage deviation; For clusters i Voltage balance; N c The total number of clusters.

[0067] A cluster active power balance index is defined to fully consider the power balance problem within the cluster and reduce the power supplied by the cluster. The cluster active power balance index... Q p Specifically defined as:

[0068] in, P c,t For clusters c exist t The net merit of every moment; Nc Total number of clusters; The following power grid cluster partitioning model considering comprehensive indicators is established:

[0069] In the formula: F Comprehensive evaluation indicators are used to classify the clusters of transformer substations; l 1. l 2. l 3 represents the weighting coefficients of each indicator, where l 1+ l 2+ l 3 = 1.

[0070] A power grid cluster division model considering comprehensive indicators is established by taking into account factors such as the electrical distance between distribution substations, the voltage balance of the cluster, and the active power balance of the cluster.

[0071] S2 performs connectivity analysis on the distribution substations within the cluster to provide structured input for subsequent planning: using the maximum net load rate of the transformer and the minimum net load of the distribution substation as feasibility indicators for flexible interconnection of distribution substations, it judges the interconnection needs of the distribution substations within the cluster and generates a set of interconnection candidate schemes that meet the needs and constraints.

[0072] The specific steps for performing connectivity analysis on the stations within the cluster are as follows: The source-load characteristics, transformer parameters, and spacing data of each distribution substation within the cluster are statistically analyzed. For each distribution substation within the cluster, the maximum net load rate of the transformer and the minimum net load of the substation are used as feasibility indicators for flexible interconnection of distribution substations to determine the interconnection requirements.

[0073]

[0074] in, T i load and P i pure These are the maximum net load rate of the transformer in the distribution area and the minimum net load of the distribution area, respectively. P i,t load and Q i,t load They are respectively t Timetable area i The total active and reactive power of the load; P i,t max for t Timetable area i The maximum output of distributed photovoltaic power; Si,T Taiwan District i The transformer capacity.

[0075] Based on a set threshold, the photovoltaic power stations are selected and categorized into an overloaded set and a photovoltaic surplus set, respectively. By pairing substations across groups to satisfy the interconnection distance constraint, a set of interconnectable combinations is generated, covering all candidate interconnection schemes that meet the requirements and constraints. The actual interconnection status of each combination is characterized by variables, thereby obtaining the interconnection scheme of the substations to be planned.

[0076] S3. Establish a two-layer planning model for distribution substation interconnection devices and energy storage: Based on the cluster interconnection structure of distribution substations, realize cross-substation absorption and load transfer of heavy-load transformers, and achieve optimized operation of distribution substations; The upper layer is the equipment capacity planning layer, which optimizes the capacity configuration of flexible interconnection devices and energy storage systems with the goal of minimizing the annual comprehensive investment cost; The lower layer is the operation strategy optimization layer, which formulates a collaborative operation strategy for photovoltaic surplus interconnection and peak load energy storage support based on the cluster autonomy characteristics, with the goal of minimizing operating costs and maximizing new energy absorption.

[0077] The upper-level planning model of the distribution area interconnection device-energy storage two-layer planning model: The objective function is to minimize the annual comprehensive cost of the entire distribution area, including the annualized comprehensive investment cost of flexible interconnection devices and the annualized comprehensive investment cost of cluster energy storage; The annualized comprehensive investment cost of flexible interconnection devices includes annualized investment costs, annual operation and maintenance costs, and costs of purchasing electricity from the upstream main grid; The annualized comprehensive investment cost of clustered energy storage includes the cost of the energy storage system, operation and maintenance costs, and energy storage revenue; The constraints include power flow constraints in distribution areas, power balance constraints in distribution areas, node voltage constraints, line power constraints, tie line power constraints, converter power constraints, and energy storage capacity constraints.

[0078] The upper-level planning model of the distribution area interconnection device-energy storage two-layer planning model is specifically as follows: The objective function is to minimize the overall annual cost of the entire distribution area, i.e.: min G = G 1+ G 2 in, G 1 and G 2 represents the combined investment cost of the flexible interconnection system and energy storage in the distribution area; Comprehensive investment cost of flexible interconnection system in the distribution area G 1: G 1=min( C con +C ope + C buy ) In the formula, C con This indicates the annualized investment cost of flexible interconnect devices; C ope This indicates the annual operation and maintenance cost of the flexible interconnect device; C buy This indicates the cost of electricity purchased from the main grid by the transformer cluster interconnection system. Annualized investment cost of flexible interconnect devices C con :

[0079]

[0080] In the formula: The annual value conversion factor for flexible interconnect devices; N This represents the total number of distribution transformer areas included in the distribution network. l con and S i,con They are respectively the Taiwan area i Installed converter capacity and investment cost per unit capacity; l h and C I line The first i The distance between the substations in each cluster and the investment and installation cost of DC interconnection lines per unit length; r and y con These are the discount rate and the lifespan of the interconnected device, respectively. Annual operation and maintenance costs of flexible interconnect devices C ope :

[0081] In the formula: l ope con The annual operating and maintenance cost per unit capacity of the converter; Comprehensive investment cost of flexible interconnected energy storage in the distribution area G 2:

[0082]

[0083] In the formula: C install Cost of energy storage system;C operation For operation and maintenance costs; C in For energy storage revenue; This is the annual value conversion factor for energy storage devices; y ess These refer to the service life of the energy storage device; Energy storage system cost C install It consists of two parts: capacity cost and power cost.

[0084] In the formula: N ess Configure candidate nodes for energy storage; l p and l e These are the unit power investment cost and unit capacity investment cost of energy storage power stations, respectively. P ess,i and E ess,i They are nodes i The rated power and rated capacity of the energy storage power station; Operation and maintenance costs C operation :

[0085] In the formula: l op ess The annual maintenance rate for energy storage equipment; Energy storage revenue C in :

[0086] In the formula: l coal For coal-fired power prices, P i,dis ess For nodes i Energy storage t Charge and discharge power during specific time periods; Distribution area power flow constraints:

[0087] In the formula: P i,t , Q i,t They are respectively t Time period nodes i Injected active and reactive power; Ui,t , U j,t They are respectively t Time period nodes i , j The voltage amplitude; G ij , B ij Branch roads ij Conductivity and susceptance; i ij,t for t Voltage phase angle difference during the time period; Power balance constraints in transformer substations:

[0088] In the formula: P Gi , P pvi Inject nodes into the system and DPVG i The active power, kW; Q Gi , Q pvi Inject nodes into the system and DPVG i reactive power, kVar; P Li For nodes i The active power consumed, kW; Q Li For nodes i Reactive power consumed, kVar; P ess The active power absorbed or released by the ESS, in kW; Q ess The reactive power absorbed or released by the ESS, in kVar; Node voltage constraints: U i min ≤ U i ≤ U i max In the formula: U i max and U i min They are nodes i Upper and lower limits of voltage amplitude; Line power constraints: P ij min≤ P ij ≤ P ij max In the formula: P ij max , P ij min These are the upper and lower limits of the active power that the line can transmit; Tie line power constraints: Connecting areas i and j The power exchange between them satisfies the following constraints:

[0089] In the formula: and These are the maximum and minimum allowable transmission power for DC tie lines, respectively. For DC tie lines in t Transmission power during a given time period; K line This is the transmission loss coefficient; Converter power constraints:

[0090] In the formula: For the first i The maximum installable power of the converter in each distribution area; Energy storage capacity constraints:

[0091] In the formula: , These are the maximum rated power and rated capacity that allow for the installation of energy storage, respectively. e max It is the maximum ratio of rated power to rated capacity.

[0092] The lower-level operation model of the distribution area interconnection device-energy storage two-layer planning model: The objective function is to minimize the annual operating cost of the system and maximize the renewable energy consumption capacity, including the electricity purchase cost of the transformer cluster interconnection system, the network loss cost, and the curtailment penalty. The constraints include system operation constraints, energy storage charging and discharging constraints, energy storage operating status constraints, and DPVG output constraints.

[0093] The lower-level operation model of the distribution substation interconnection device-energy storage two-layer planning model is as follows: The operating model aims to minimize the annual operating cost of the system under the given configuration at the planning level, that is, to minimize the electricity purchase cost of the distribution substation from conventional units and maximize the renewable energy consumption capacity. Objective function: min G 2= C buy + C loss +C p In the formula: C buy Electricity purchase cost for the district cluster interconnection system; C loss For network loss costs; C p As punishment for abandoning light; Electricity purchase cost for the transformer cluster interconnection system:

[0094] In the formula: T The planning period is 24 hours. l ep The unit cost of purchasing electricity from the main grid for the transformer cluster interconnection system; P i,t for t Injecting into the station area at all times i Active power; Δ t To minimize the time interval, we take 1 hour. Network loss cost of the trunking interconnection system:

[0095] Where: Ω ( i ) represents all i It is the set of end nodes of the first node; l loss For grid loss electricity price; The square of the branch current; r ij Branch resistance; Abandoned Light Penalty:

[0096] In the formula: l PV The penalty price for abandoning light is 2.5 yuan / kWh as used in the text; for t Timetable area i The actual output of photovoltaic power; for t Timetable area i The actual maximum photovoltaic output that can be sustained; System operating constraints:

[0097] In the formula: f ( i () is the set of nodes connected to the node; U i,t and U j,t They are nodes i and nodes j The voltage amplitude; and for t Time zone i Active and reactive power of conventional generating units; and For nodes i Place t The charging and discharging power of time-segmented energy storage; Energy storage charge and discharge constraints: When the active power generated by the photovoltaic system exceeds the active power consumed by the load, the energy storage battery charges / stores energy; when the photovoltaic output is less than the active power consumed by the load, the energy storage battery discharges / releases energy. Therefore, the regulation of the energy storage battery has bidirectional effectiveness, and the constraint formula is as follows: - P ess,i ≤ P ess,i ( t )≤ P ess,i In the formula: P ess,i ( t ) is the first i Cluster energy storage t Power at any moment; Energy storage operation status constraints: SOC min ≤ SOC i ( t )≤ SOC max In the formula: SOC i ( t ) is the first i Energy storage device t The energy storage charging and discharging state at any given moment. SOC max , SOC min These are the upper and lower limits of the energy storage state of charge, respectively. DPVG output constraints:

[0098] In the formula: For nodes i The maximum output of the installed DPVG.

[0099] S4 employs a hierarchical solution strategy to solve the bi-level programming model: In the cluster partitioning stage, an improved genetic algorithm is used, optimizing the chromosome encoding strategy to improve convergence speed and solution accuracy; the upper-level planning uses the NSGA-II algorithm with a crowding distance operator for optimization, obtaining the Pareto optimal solution set; the lower-level operation optimization uses the interior point method, determining the operating status and output of each device based on the equipment configuration scheme given in the upper level. Through iterative interaction between the upper and lower levels, the upper-level planning results are used as constraints for the lower-level operation, and the lower-level operation results are fed back to correct the upper-level planning, ultimately obtaining the optimal planning scheme that satisfies both economic efficiency and operational adaptability.

[0100] The hierarchical solution strategy is used to solve the bi-level programming model. An improved genetic algorithm is used in the cluster partitioning phase, which improves convergence speed and solution accuracy by optimizing chromosome encoding strategy; The upper-level planning adopts the NSGA-II algorithm with the crowding distance operator to optimize the solution, so as to avoid the premature convergence problem of the traditional genetic algorithm and obtain the Pareto optimal solution set. The lower-level operation optimization is solved using the interior point method, which determines the operating status and output of each device based on the device configuration scheme given in the upper level. Through iterative interaction between upper and lower layers, the planning results of the upper layer are used as constraints for the operation of the lower layer, and the operation results of the lower layer are fed back to correct the planning of the upper layer, so as to finally obtain the optimal planning scheme that satisfies both economy and operational adaptability.

[0101] In solving regional planning problems, intelligent optimization algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Simulated Annealing (SA), and Multi-Strategy Gravity Search (GSA) have been widely applied. Comprehensive comparisons show that the Genetic Algorithm (GA) exhibits superior global search capabilities in multi-objective optimization: its selection, crossover, and mutation operators effectively avoid local optima traps and possess fast convergence speed and strong robustness. However, traditional genetic algorithms are prone to premature convergence in the later stages of iteration. Therefore, this invention introduces a fast non-dominated sorting strategy based on the crowding distance operator to obtain the Pareto optimal solution set by enhancing population diversity. For the linear programming subproblem in the model, this paper uses the interior-point method instead of the traditional linear programming method. This algorithm has advantages such as fast convergence speed, wide applicability, and the ability to handle non-smooth problems, significantly improving the flexibility of optimization scheduling.

[0102] The solution process of the bi-level programming model constructed in this invention is as follows: Figure 4 As shown. The upper layer aims to minimize the annual comprehensive investment cost, with the equipment planning scheme as the optimization variable. Under the condition of satisfying the equipment capacity constraints, the NSGA-II algorithm is used for optimization. The lower layer, given the upper layer planning scheme, aims to minimize the operating cost and maximize the consumption of new energy based on the cluster autonomy characteristics. The optimization variables are the operating status and output of each piece of equipment. Under the condition of satisfying the operating constraints of each piece of equipment, the interior point method is used to plan the operating status of the equipment. The relationship between the upper and lower layer optimizations is that the result of the upper layer planning optimization is used as the upper layer constraint of the lower layer operation optimization variables, and the result of the lower layer operation optimization provides feedback correction to the result of the upper layer planning optimization, finally obtaining the Pareto solution set that satisfies the operation matching.

[0103] Example Example Description: A modified IEEE 33-node system is used for verification. Node 1 is the interconnection node with the upstream power grid, and the remaining nodes are the nodes where the high-voltage side of the transformer in the distribution area connects to the 10kV power grid. Therefore, each node except node 1 is equivalent to a distribution substation. The topology is as follows: Figure 5 Considering the differences in load levels and load characteristic curves among various distribution areas, distribution area nodes that are not interconnected with the upper-level power grid are divided into 5 types; the time-series characteristic curves of each type of load in spring and autumn and the photovoltaic output curves are shown in [reference needed]. Figure 11 , Figure 12 The typical daily curves for spring and autumn are used as the baseline, while the summer and winter curves are scaled proportionally by 1.15 times (simulating high-temperature load growth) and 0.9 times (simulating low-temperature load decay), respectively. The real-time actual load of the distribution area is calculated by multiplying the maximum AC and DC loads by the load factor and then summing them. The relevant economic parameters for energy storage planning are shown in Table 1.

[0104]

[0105] Meanwhile, to compare the proposed solution, different planning schemes are set up: Option 1: Flexible interconnection planning without distribution area clusters, without energy storage configuration; Option 2: Considering the flexible interconnection plan for the transformer substation cluster, no energy storage configuration is required; Option 3: The option proposed in this invention.

[0106] Optimization Result Analysis In the baseline scheme (Scheme 1) without optimization planning, transformer substation 26 experiences significant overload or heavy load during peak evening hours in different seasons, with the maximum load rate of the transformer exceeding 90% of its rated capacity. To alleviate this problem, Scheme 2 utilizes a flexible interconnection between transformer substation 6 and transformer substation 26, employing a tie line to achieve cross-substation load power support, and leveraging the reactive power compensation capability of the VSC to effectively control the maximum load rate of the transformer in transformer substation 26 below 0.8. However, under extreme summer load scenarios, due to the combined effect of low photovoltaic output and high-temperature load, the transformer load rate in transformer substation 26 still approaches the safe threshold (0.8), requiring further optimization.

[0107] Option 3, building upon Option 2, introduces clustered energy storage devices and achieves spatiotemporal energy transfer through a multi-timescale coordinated control strategy. Specifically: 1. Peak photovoltaic power generation period: When the output of renewable energy in the 26 distribution areas exceeds the local load demand, the surplus power is preferentially transferred to adjacent heavily loaded distribution areas through the interconnection line; if there is still a power surplus, the energy storage is charged to improve the local consumption rate of renewable energy. 2. Peak load periods: Energy storage discharge reduces the power demand of the upstream distribution network in the distribution area, thereby lowering the transformer load rate. Simulation results show that in Scheme 3, the maximum load rate of transformers in distribution areas 26 and 6 is stable below 0.7, which is a further optimization of 20% compared to Scheme 2.

[0108] Table 2 shows the annual comprehensive cost and its composition for distribution network substations under the three planning schemes. As can be seen from Table 2, Scheme 3 presented in this paper yields the best results in terms of network loss cost, curtailment cost, and comprehensive operating cost of the distribution network substation. Compared with Schemes 1 and 2, although the annual investment and maintenance costs increase by RMB 46,400 and RMB 250,800 respectively in the short term, the comprehensive operating cost decreases by RMB 449,500 and RMB 107,500 respectively, and the grid connection rate increases by 18.08% and 11.23% respectively. This indicates that the substation cluster interconnection plan can effectively improve the economic efficiency of the entire regional substation operation. In addition, this planning scheme realizes cross-substation grid connection of distributed photovoltaic power and load transfer of instantaneously overloaded transformers, which to some extent delays the capacity upgrade and transformation of substation transformers.

[0109]

[0110] Figure 10 This is a graph showing the active power and SOC curves of the ESS configured in transformer area 6 in Scheme 3. From 00:00 to 06:00, there is no photovoltaic output and the electricity price is at its lowest point, so the energy storage in transformer area 6 charges. From 07:00 to 10:00, the energy storage is fully charged to its maximum state of charge, and there is a small load peak, so the energy storage discharges to reduce the peak. From 11:00 to 15:00, the electricity price is still within the normal range, but the solar intensity is high, and the photovoltaic output power is large, even exceeding the cluster load power, making it impossible to absorb the excess photovoltaic power. Therefore, the ESS is used to charge, reducing the amount of curtailed photovoltaic power. From 16:00 to 21:00, the load demand is high, the photovoltaic output is low, making it difficult to meet the load demand, and the electricity price is at its peak. The energy storage discharges or does not operate to generate the maximum economic benefit. Finally, from 22:00 to 24:00, it is again a period of low electricity prices, so the energy storage does not operate or charges to store low-priced electricity to prepare for the load peak the next morning. Option 3 improves the economic efficiency of the distribution system in the transformer substation while increasing the renewable energy absorption rate.

[0111] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for flexible interconnection and energy storage coordination planning in low-voltage distribution areas with high penetration of photovoltaic access, characterized in that, The method comprises the following steps: A structure-function two-dimensional cluster division model is constructed to divide the distribution areas into clusters to reduce the coordination planning dimension: the electrical distance is taken as a modularity index to measure the electrical coupling degree between the nodes of the distribution areas, and the voltage balance degree and the active power balance degree indexes are introduced as function indexes to respectively represent the photovoltaic consumption capacity and the power self-balancing level in the cluster, a comprehensive evaluation model is constructed through multi-index fusion to divide the distribution areas into clusters with internal electrical tight coupling and strong power complementation; The connectability of the distribution areas in the cluster is analyzed to provide a structured input for subsequent planning: the maximum net load rate of the transformer and the minimum net load of the distribution area are taken as the flexible interconnection feasibility indexes of the distribution area to judge the interconnection demand of the distribution areas in the cluster, and a set of interconnection candidate schemes meeting the demand and the constraints is generated; A distribution area interconnection device-energy storage two-layer planning model is established: based on the interconnection structure of the distribution area clusters, the cross-distribution area consumption and the load transfer of the heavy load transformer are realized to realize the optimal operation of the distribution area; the upper layer is a device capacity planning layer, the minimum annual comprehensive investment cost is taken as the target to optimize the capacity configuration of the flexible interconnection device and the energy storage system; The lower layer is an operation strategy optimization layer, based on the cluster autonomy characteristic, the minimum operation cost and the maximum new energy consumption are taken as the targets to develop a photovoltaic surplus mutual-load peak energy storage support collaborative operation strategy; A hierarchical solving strategy is adopted to solve the two-layer planning model: the improved genetic algorithm is adopted in the cluster division stage to improve the convergence speed and the solving accuracy through the optimization of the chromosome coding strategy; the NSGA-II algorithm with the introduction of the crowding distance operator is adopted in the upper layer planning to obtain the Pareto optimal solution set; the interior point method is adopted in the lower layer operation optimization to determine the operation state and the output of each device based on the device configuration scheme given by the upper layer, through the iteration interaction between the upper layer and the lower layer, the upper layer planning result is taken as the lower layer operation constraint, the lower layer operation result is fed back to correct the upper layer planning, and finally the optimal planning scheme meeting the economy and the operation adaptability is obtained.

2. The flexible interconnection and energy storage coordinated planning method for low-voltage distribution areas with high proportion of photovoltaic access according to claim 1, characterized in that, The structure-function two-dimensional cluster division model is specifically constructed as follows: The electrical distance is taken as a modularity index to measure the electrical coupling degree between the nodes of the distribution areas, and the greater the modularity index, the better the aggregation effect of the distribution area nodes; A distribution area cluster voltage balance degree index is defined to represent the distributed photovoltaic consumption capacity in the cluster, when part of the clusters have insufficient photovoltaic consumption capacity and the node voltage exceeds the limit, other clusters still have photovoltaic consumption capacity; A cluster active power balance degree index is defined to fully consider the power balance problem in the cluster and reduce the cluster power transmission; A power grid cluster division model considering comprehensive indexes is established by comprehensively considering the electrical distance of the distribution area, the cluster voltage balance degree, and the cluster active power balance degree.

3. The flexible interconnection and energy storage coordinated planning method for low-voltage distribution areas with high proportion of photovoltaic access according to claim 2, characterized in that, The modularity indicator Q d is defined as: ； ； ； ； ； wherein, m is the sum of weights of the entire network of the transformer area; Q ij is the node i and the weight connected with the node j ; k i , k j respectively represent the sum of edge weights connected with the node i , the node j ; δ ( i , j ) represents the same status of the node i and the node j in a partition, if they are in the same partition, δ ( i , j )=1, otherwise δ ( i , j )=0; D ij and max( D ) are respectively the maximum matrix composed of the electrical distance considering the overall node influence and the electrical distance between any two nodes; N is the total number of nodes of the transformer area; d in , d jn respectively represent the influence degree of the power change of the transformer area n on the voltage of the transformer area i and the influence degree of the power change of the transformer area n on the voltage of the transformer area j ; The index of voltage balance degree of the transformer cluster Q v Specifically defined as: ； ； ； ； wherein, ΔV i max is the maximum voltage regulation capability of the cluster i ; S ij PV , S ij QV are the first i row and the first j column elements of the active and reactive voltage sensitivity matrix, respectively; ΔP j,t max , ΔQ j,t ma are the maximum adjustable active power and the maximum adjustable reactive power of the cluster at t ; ΔP j,t ESS,max , ΔP j,t load,max are the maximum adjustable active power of the energy storage and the maximum adjustable active power of the load in the cluster at t ; ΔQ j,t PV,max is the maximum adjustable reactive power of the photovoltaic in the cluster at t ; ΔV i is the maximum voltage deviation of the cluster i ; is the voltage balance degree of the cluster i ; N c is the total number of the divided clusters; The cluster active balance index Q p is defined as: ； wherein, P c,t is the cluster c at t the net active at the instant; N c is the total number of clusters; The power grid cluster division model considering comprehensive indexes is established as follows: ； In the formula: F is the comprehensive evaluation index of the division of the transformer area cluster; λ 1, λ 2, λ 3 is the weight coefficient of each index, wherein λ 1+ λ 2+ λ 3=1.

4. The flexible interconnection and energy storage coordinated planning method for low-voltage distribution areas with high proportion of photovoltaic access according to claim 1, characterized in that, The connectability analysis of the distribution areas in the cluster is specifically as follows: The source and load characteristics, transformer parameters and distance data of the distribution areas in the cluster are counted; The distribution areas are screened based on the set threshold value and are respectively classified into the heavy load set and the photovoltaic excess set; The cross-group pairing meets the interconnection distance constraint of the transformer area, generates a combinable set, covers all interconnected candidate schemes meeting the demand and constraint, and obtains a transformer area interconnection scheme to be planned by representing the actual interconnection state of each combination by a variable.

5. The flexible interconnection and energy storage coordinated planning method for low-voltage distribution areas with high proportion of photovoltaic access according to claim 4, characterized in that, For each transformer area in the cluster, the transformer maximum net load rate and the transformer area minimum net load are used as the flexible interconnection feasibility index of the power distribution transformer area to determine the transformer area interconnection demand. ； ； in, T i load and P i pure These are the maximum net load rate of the transformer in the distribution area and the minimum net load of the distribution area, respectively. P i,t load and Q i,t load They are respectively t Timetable area i The total active and reactive power of the load; P i,t max for t Timetable area i The maximum output of distributed photovoltaic power; S i,T Taiwan District i The transformer capacity.

6. The method of claim 1, wherein, The upper planning model of the power distribution transformer area interconnection device-energy storage double-layer planning model: The objective function is to minimize the annual comprehensive cost of the entire power distribution transformer area, including the annual investment comprehensive cost of the flexible interconnection device and the annual investment comprehensive cost of the cluster energy storage; The annual investment comprehensive cost of the flexible interconnection device includes the annual investment cost, the annual operation and maintenance cost, and the cost of purchasing electricity from the upper main network; The annual investment comprehensive cost of the cluster energy storage includes the energy storage system cost, the operation and maintenance cost, and the energy storage income; The constraint conditions include the power distribution transformer area power flow constraint, the transformer area power balance constraint, the node voltage constraint, the line power constraint, the tie line power constraint, the converter power constraint, and the energy storage capacity constraint.

7. The flexible interconnection and energy storage coordinated planning method for low-voltage distribution areas with high proportion of photovoltaic access according to claim 6, characterized in that, The upper planning model of the power distribution transformer area interconnection device-energy storage double-layer planning model is specifically: The objective function is to minimize the annual comprehensive cost of the entire power distribution transformer area, i.e.: min G = G 1+ G 2; Wherein, G 1 and G 2 are the comprehensive investment costs of the flexible interconnection system and energy storage of the transformer area, respectively; Investment cost of flexible interconnection system of transformer area G 1: G 1 = min( C con + C ope + C buy ); In the formula, C con represents the annual investment cost of the flexible interconnection device; C ope represents the annual operation and maintenance cost of the flexible interconnection device; C buy represents the cost of purchasing electricity from the superior main network by the transformer area cluster interconnection system; Annualized capital cost of flexible interconnection C con : ； ； In the formula: is the equivalent annual conversion factor of the flexible interconnection device; N is the total number of distribution areas included in the distribution network; λ con and S i,con are the installed converter capacity and the unit capacity investment cost of the converter, respectively; i l h and C I line are the distance between the distribution areas in the first i cluster and the unit length DC tie line installation cost, respectively; r and y con are the discount rate and the service life of the interconnection device, respectively.​ Annual operating and maintenance costs for flexible interconnection devices C ope :; ； In the formula: is the annual operating and maintenance cost per unit capacity of the converter; Investment cost of flexible interconnection energy storage in transformer area G 2: ； ； In the formula: C install is the cost of the energy storage system; C operation is the operation and maintenance cost; C in is the energy storage benefit; is the equal annual conversion factor of the energy storage device; y ess is the service life of the energy storage device, respectively; Energy storage system cost C install The cost is composed of both the capacity cost and the power cost: ； In the formula: N ess The candidate node is configured for energy storage; λ p And λ e The unit power investment cost and the unit capacity investment cost of the energy storage power station are respectively P ess,i And E ess,i The rated power and the rated capacity of the energy storage power station at the node i are respectively Operating and maintenance costs C operation : ； In the formula: λ op ess Annual maintenance rate for energy storage device energy storage revenue C in : ； In the formula: λ coal is the coal-fired electricity price, P i,dis ess is the node i is the energy storage t is the period charging and discharging power; The power distribution transformer area power flow constraint is: ； wherein: P i,t , Q i,t are respectively t period node i injected active and reactive power; U i,t , U j,t are respectively t period node i , j voltage magnitude of G ij , B ij are respectively conductance and susceptance of branch ij θ ij,t is t period voltage phase angle difference;​ The transformer area power balance constraint is: ； wherein: P Gi , P pvi Psys, kVar is the reactive power of the system and the DPVG injection node i , Q Gi , Q pvi Qsys, kVar is the reactive power of the system and the DPVG injection node i , P Li Pcons, kVar is the consumed reactive power of the node i , Q Li Qcons, kVar is the consumed reactive power of the node i , P ess Pess, kVar is the absorbed or emitted reactive power of the ESS Q ess Qess, kVar is the absorbed or emitted reactive power of the ESS The node voltage constraint is: U i min ≤ U i ≤ U i max ； wherein: U i max and U i min are nodes i voltage amplitude upper and lower limits; The line power constraint is: P ij min ≤ P ij ≤ P ij max ； In the formula: P ij max , P ij min are the upper and lower limits of the active power that the line can transmit, respectively. The tie line power constraint is: The power exchange between the connected zones i and j satisfies the following constraints: ； In the formula: and are the maximum and minimum transmission power allowed by the DC tie line, respectively; is the transmission power of the DC tie line in the t time period; K line is the transmission loss coefficient; The converter power constraint is: ； In the formula: is the maximum installable power of the converter of the n-th i zone. The energy storage capacity constraint is: ； In the formulae: , are the maximum values of the rated power and the rated capacity, respectively, which are allowed to be installed; The lower operation model of the power distribution transformer area interconnection device-energy storage double-layer planning model is: max is the maximum ratio of the rated power to the rated capacity. 8.The method of claim 1, wherein, The objective function is to minimize the annual operation cost of the system and maximize the new energy consumption capacity, including the power purchase cost of the transformer cluster interconnection system, the network loss cost, and the light abandonment penalty; The constraint conditions include the system operation constraint, the energy storage charging and discharging constraint, the energy storage operation state constraint, and the DPVG output constraint. The lower operation model of the power distribution transformer area interconnection device-energy storage double-layer planning model is specifically:

9. The flexible interconnection and energy storage coordinated planning method for low-voltage distribution areas with high proportion of photovoltaic access according to claim 8, characterized in that, The operation model minimizes the annual operation cost of the system under the given configuration in the planning layer, i.e. the power purchase cost of the power distribution transformer area to the conventional unit is minimized and the new energy consumption capacity is maximized; The objective function is: The power purchase cost of the transformer cluster interconnection system is: min G 2= C buy + C loss +C p ; In the formula: C buy is the electricity purchase cost of the cluster interconnection system; C loss is the network loss cost; C p is the light abandonment penalty; λ ； In the formula: T is the planning period, taken 24h; The network loss cost of the transformer cluster interconnection system is: ep is the unit cost of purchasing electricity from the upper master network for the substation cluster interconnection system; P i,t for t Injecting into the station area at all times i Active power; Δ t To minimize the time interval, we take 1 hour. λ ； wherein: Ω i represents a set of all end nodes with i as a head node; The light abandonment penalty is: loss is the net loss price; is the square of the branch current; r ij is the branch resistance; λ ； In the formula: The system operation constraint is: PV The penalty price for abandoning light is 2.5 yuan / kWh as used in the text; for t Timetable area i The actual output of photovoltaic power; for t Timetable area i The actual maximum photovoltaic output that can be sustained; The energy storage charging and discharging constraint is: ； In the formula: f ( i () is the set of nodes connected to the node; U i,t and U j,t They are nodes i and nodes j The voltage amplitude; and for t Time zone i Active and reactive power of conventional generating units; and For nodes i Place t The charging and discharging power of time-segmented energy storage; When the active power output of the photovoltaic is greater than the active power consumption of the load, the energy storage battery is charged / energy storage; when the active power output of the photovoltaic is less than the active power consumption of the load, the energy storage battery is discharged / energy release, therefore, the energy storage battery adjustment has bidirectional effectiveness, and the constraint formula is: The energy storage operation state constraint is: - P ess,i ≤ P ess,i ( t )≤ P ess,i ； In the formula: P ess,i t is the first i cluster energy storage t moment power;​ SOC SOC min ≤ SOC i ( t )≤ SOC max ； In the formula: SOC i t is the first energy storage device i at the moment of energy storage charging and discharging state, t SOC max , The DPVG output constraint is: min respectively, are the upper and lower limits of the energy storage state of charge coefficients;​​ The double-layer planning model is solved by using a hierarchical solving strategy: ； In the formulae: is the node i installed DPVG output upper limit.

10. The method of claim 1, wherein the method is characterized by, In the cluster division stage, an improved genetic algorithm is used to improve the convergence speed and solving accuracy by optimizing the chromosome coding strategy; In the upper planning, the NSGA-II algorithm with the introduction of the crowding distance operator is used for optimization to avoid the premature convergence problem of the traditional genetic algorithm and obtain the Pareto optimal solution set; In the lower operation optimization, the interior point method is used for solving to determine the operation state and output of each device based on the given device configuration scheme in the upper layer. ​ Through the iteration interaction between the upper and lower layers, the upper layer planning result is taken as the lower layer operation constraint, and the lower layer operation result is fed back to correct the upper layer planning, so that the optimal planning scheme meeting the economy and operation adaptability is finally obtained.

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