Distribution area energy storage optimal configuration method and system considering distributed photovoltaic bearing capacity

Abstract

The invention discloses a transformer area energy storage optimal configuration method and system considering distributed photovoltaic bearing capacity, and relates to the technical field of energy storage configuration, and the method comprises the following steps: obtaining key data of a transformer area containing distributed photovoltaic; based on photovoltaic and load historical data, generating a worst operation scene of the transformer area; and in the worst scene, establishing a double-layer optimization model of the zone area energy storage optimal configuration, and performing optimization by a planning layer by taking the minimum annual investment operation cost of the zone area energy storage and the power distribution network as an objective function, so as to obtain the distributed photovoltaic bearing capacity-considered zone area energy storage optimal configuration method and the distributed photovoltaic bearing capacity-considered zone area energy storage optimal configuration system. The optimal installation position and capacity of transformer area energy storage can be reasonably determined, an energy storage charging and discharging control strategy is refined, economical efficiency is considered while the problem that the bearing capacity of the transformer area is exceeded due to reverse transmission of distributed photovoltaic power is effectively solved, and the method is provided for solving the safety problems of transformer area reverse heavy overload, node voltage rising out-of-limit and the like. And a solution with practical application value is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage configuration, and particularly relates to a method and system for optimizing the configuration of substation area energy storage considering the bearing capacity of distributed photovoltaic power generation. Background Art

[0002] The disordered access of a high proportion of distributed photovoltaic power generation will lead to reverse power flow in the distribution network, bringing problems of insufficient bearing capacity, seriously affecting the safety of the power grid and power supply users. Especially in rural areas, the installed capacity of photovoltaic power generation far exceeds the load, which is extremely likely to cause reverse overload and voltage rise of distribution equipment beyond the bearing capacity of the substation area, and frequent burnout of distribution transformers. Therefore, it is urgent to adopt relevant technical means to solve the above problems.

[0003] Since configuring energy storage in the substation area is an effective way to solve the problem of exceeding the bearing capacity of the distributed photovoltaic substation area, energy storage can realize the dynamic capacity increase of the substation area transformer, effectively reduce the risk of reverse overload and node voltage over-limit of the distribution transformer. Many provinces and cities have introduced mandatory or recommended policies for configuring energy storage in renewable energy power stations, industrial and commercial, and household projects, encouraging distributed photovoltaic investment enterprises to lease or purchase energy storage capacity in substation areas with difficult power consumption, without increasing the pressure of new energy consumption on grid enterprises. However, the policies of each province and city only make rough and principled requirements for energy storage configuration, and do not give specific configuration scale basis in actual engineering applications. In the research of existing substation area energy storage configuration methods, the economy is not considered while effectively solving problems such as reverse overload and voltage over-limit of the substation area bearing capacity, and no refined energy storage operation control strategy is provided. Therefore, a method and system for optimizing the configuration of substation area energy storage considering the bearing capacity of distributed photovoltaic power generation are proposed to solve the above problems. Summary of the Invention

[0004] The purpose of the present invention is to solve at least one of the technical problems existing in the prior art and be able to solve the problems proposed in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A method for optimizing the configuration of substation area energy storage considering the bearing capacity of distributed photovoltaic power generation, comprising the following steps:

[0006] S1: Obtain key data of the substation area with distributed photovoltaic power generation;

[0007] S2: Generate the worst-case scenario of the substation area operation based on historical data of photovoltaic power generation and load;

[0008] S3: In the worst-case scenario, establish a two-layer optimization model for optimizing the configuration of substation area energy storage. The planning layer is optimized with the minimum annual investment and operation cost of substation area energy storage and the distribution network as the objective function, and the operation layer is optimized with the minimum sum of the output of the upper-layer power grid, system power loss, and overall node voltage deviation as the objective function;

[0009] S4: Solve the objective function of the planning layer using the improved simulated annealing - particle swarm optimization algorithm, and solve the objective function of the operation layer by converting the non - linear model into a second - order cone programming model to obtain the optimal distribution network energy storage configuration plan.

[0010] Further, the key data in S1 includes: historical data of photovoltaic power generation in the distribution network area, historical load data of the distribution network area, connection relationships of nodes and branches in the distribution network area, rated capacity of distribution transformers, line impedance parameters, etc.

[0011] Further, based on the historical data of photovoltaic power generation and load in S2, the K - means clustering algorithm is used for scenario reduction, and the upper boundary of photovoltaic power generation and the lower boundary of load are selected to generate the most severe scenario, where the most severe scenario is defined as the scenario with the maximum photovoltaic power output and the minimum load demand.

[0012] Further, the objective function of the planning layer in S3 comprehensively considers multiple key factors to achieve the optimization of the comprehensive cost. Specifically, this objective function aims to minimize the annual investment cost, annual operation and maintenance cost, power grid loss cost, cost of purchasing electricity from the superior power grid, and potential cost caused by voltage deviation of the energy storage system, and deduct the benefits obtained from storing energy at low electricity prices and releasing energy at high electricity prices. The constraint conditions of the planning layer model include energy storage installation location constraints, power and capacity constraints.

[0013] Further, the objective function of the operation layer in S3 includes the output of the upper - level power grid, system power loss, and overall node voltage deviation. The constraint conditions of the operation layer model include distribution network power flow constraints, reverse load rate constraints of distribution equipment, node voltage constraints, and energy storage operation constraints.

[0014] Further, the improvement method of the improved simulated annealing - particle swarm optimization algorithm in S4 is: introducing a random inertia weight, asymmetric learning factors on the basis of the basic particle swarm optimization algorithm, and introducing the simulated annealing algorithm; the process of converting the non - linear model into a second - order cone programming model is: introducing new variables to represent the quadratic terms of node voltage and branch current, and using the second - order cone relaxation theory to convert the model into a solvable second - order cone programming model.

[0015] Further, the optimal distribution network energy storage configuration plan in S4 includes the best energy storage installation location, the best energy storage access capacity, and the best daily charge - discharge power of the energy storage.

[0016] A distribution network energy storage optimization configuration system considering the bearing capacity of distributed photovoltaic power generation includes the following modules:

[0017] A data acquisition module, configured to: obtain historical data of photovoltaic power generation in the distribution network area, historical load data of the distribution network area, connection relationships of nodes and branches in the distribution network area, rated capacity of distribution transformers, line impedance parameters, etc.;

[0018] A scenario clustering module, configured to: generate the worst-case scenario of substation operation based on historical data of photovoltaic power and load.

[0019] A model construction module, configured to: establish a two-layer optimization model for optimizing the configuration of substation energy storage under the worst-case scenario. The planning layer is optimized with the minimum annual investment and operation cost of substation energy storage and distribution network as the objective function, and the operation layer is optimized with the minimum sum of the output of the upper-layer power grid, system power loss, and overall node voltage deviation as the objective function.

[0020] An algorithm execution module, configured to: solve the objective function of the planning layer by using an improved simulated annealing-particle swarm algorithm, and solve the objective function of the operation layer by converting it into a second-order cone programming model through a nonlinear model, so as to obtain the optimal substation energy storage configuration scheme.

[0021] Compared with the prior art, the beneficial effects of the present invention are:

[0022] (1) The method and system for optimizing the configuration of substation energy storage considering the bearing capacity of distributed photovoltaic power can reasonably determine the optimal installation location and capacity of substation energy storage, and refine the energy storage charge and discharge control strategy, taking into account economy while effectively solving the problem that the reverse power transmission of distributed photovoltaic power exceeds the bearing capacity of the substation area, and providing a practical solution for solving safety problems such as reverse overload and over-limit of node voltage rise in the substation area.

[0023] (2) The method and system for optimizing the configuration of substation energy storage considering the bearing capacity of distributed photovoltaic power cluster and reduce scenarios of historical photovoltaic power and load data by using the K-means clustering algorithm considering the uncertainty of photovoltaic power and load, so as to fully analyze the data while accelerating the operation speed, provide more real and objective typical scenarios for the uncertainty of photovoltaic power and load, thereby generating the worst-case scenario and obtaining the most robust energy storage configuration scheme.

[0024] (3) The method and system for optimizing the configuration of substation energy storage considering the bearing capacity of distributed photovoltaic power propose an algorithm combining improved simulated annealing-particle swarm and second-order cone programming for solving the energy storage configuration model, which has a higher calculation accuracy for the model and greatly shortens the calculation time while ensuring the accuracy. Description of the Drawings

[0025] The present invention will be further described below with reference to the drawings and embodiments:

[0026] Figure 1 It is the block diagram of the substation energy storage optimization configuration model described in Embodiment 1 of the present invention;

[0027] Figure 2 It is the flowchart for solving the substation energy storage optimization configuration model provided in Embodiment 1 of the present invention;

[0028] Figure 3 A schematic diagram of the topology of a case area provided in Example 1 of the present invention;

[0029] Figure 4 A schematic diagram of a control strategy for energy storage charging and discharging in a case area provided in Example 1 of the present invention;

[0030] Figure 5 A schematic diagram of the power situation of a case area provided in Example 1 of the present invention;

[0031] Figure 6 A schematic diagram of line power conditions in a case provided in Example 1 of the present invention;

[0032] Figure 7 A schematic diagram of the voltage status of nodes in the example provided in Embodiment 1 of the present invention;

[0033] Figure 8 A structural diagram of a substation energy storage optimization configuration system taking into account the distributed photovoltaic carrying capacity provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the present invention is further described below in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0035] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Embodiment 1

[0037] Reference Figure 1 This embodiment provides a method for optimizing energy storage configuration in a substation area taking into account the distributed photovoltaic carrying capacity, including:

[0038] Firstly, the worst scenario is generated based on the annual historical data of photovoltaic and load. Then, a two-layer optimization configuration model of energy storage in the substation is established through a comprehensive analysis of the economy of configuring energy storage in the substation and the safe and stable operation of the power grid, and an optimization configuration plan for energy storage in the substation is obtained. This method not only addresses the problem of distributed photovoltaic power reverse transmission exceeding the substation's carrying capacity, but also takes into account economy. It provides a solution with practical application value for solving safety problems such as reverse heavy overload in the substation and voltage increase beyond the limit at the node.

[0039] The described double - layer optimal configuration model for substation area energy storage is divided into a planning layer and an operation layer. The planning layer comprehensively considers the annual investment of energy storage and the annual operation cost of the distribution network as the objective function to select the installation location and capacity of energy storage. The operation layer considers factors related to the operation of the distribution network such as the output of the upper - layer power grid, active power loss, and voltage deviation. Under the constraint conditions such as the reverse load rate of distribution equipment and the node voltage deviation, the charge - discharge strategy of energy storage at different times is obtained.

[0040] In this embodiment, the objective function of the planning layer includes: energy storage investment cost, energy storage operation and maintenance cost, power loss cost, upper - layer power grid power purchase cost, overall node voltage deviation cost, and low - storage - high - generation income.

[0041] minF1=C DESS +C M +C BUY +C LOSS +C ΔU -D

[0042] In the formula, CDESS is the energy storage investment cost; CM is the energy storage operation and maintenance cost; CLOSS is the power loss cost; CBUY is the upper - layer power grid Power purchase cost cost; CΔU is the overall node voltage deviation cost, that is, the cost after converting voltage deviation and active power loss proportionally; D is the low - storage - high - generation income.

[0043]

[0044] In the formula, Cp is the unit power cost of energy storage; Ce is the unit capacity cost of energy storage; Cm is the unit power operation and maintenance cost of energy storage; Cgd is the upper - layer power grid power purchase cost; Clo is the power loss cost; Cprice is the time - of - use electricity price; Pess is the installed power of energy storage; Eess is the installed capacity of energy storage; Q is the annual power generation of energy storage; Pbuy,t is the output of the upper - layer power grid; Ploss,j,t is the line loss power; Pdch, Pch are the discharge and charge powers of energy storage; Uj,t is the voltage of node j; UN is the rated voltage; τ is the discount rate; T is the energy storage life; Nbra, Nbus are the number of branches and nodes; w is the dimension conversion coefficient of active power loss and node voltage deviation, with a value of 500kW / p.u.

[0045] In this embodiment, the constraint conditions of the planning layer include the energy storage installation location constraint, power and capacity constraint.

[0046] Energy storage installation location constraint:

[0047]

[0048] In the formula, xess is the energy storage installation node; Nnodes is all nodes of the low - voltage distribution substation area; Ngrid is the connection point between the substation area and the upper - layer power grid.

[0049] Energy storage power and capacity constraints:

[0050] E ess min ≤E ess ≤E ess max

[0051] In the formula, Pessmax and Pessmin are the upper and lower limits of the installed power of the energy storage; Eessmax and Eessmin are the upper and lower limits of the installed capacity of the energy storage.

[0052] In this embodiment, the objective function of the operation layer includes: the power output of the upper-level power grid, the system power loss, and the overall node voltage deviation.

[0053]

[0054] In this embodiment, the constraint conditions of the operation layer include the distribution network power flow constraint, the reverse load rate constraint of distribution equipment, the node voltage constraint, and the energy storage operation constraint.

[0055] Distribution network power flow constraint:

[0056]

[0057] In the formula, j→k is the set of child nodes of node j; Pij,t and Qij,t are the active and reactive powers flowing from node i to j at time t; Pjk,t and Qjk,t are the active and reactive powers flowing from node i to k at time t; rij and xij are the resistance and reactance of line ij; Iij,t is the current of branch ij at time t; PDj,t and QDj,t are the active and reactive loads of node j at time t; PGj,t and QGj,t are the active and reactive power outputs of photovoltaic and energy storage at node j at time t; Ui,t is the voltage of node i at time t.

[0058] Reverse load rate constraint of distribution transformer:

[0059]

[0060] In the formula, PPV is the active power output of distributed photovoltaic; PL is the equivalent power consumption load at the same time, λmax is the limit value of the reverse load rate, and it is stipulated in the "Guideline for Assessing the Grid Connection Capacity of Distributed Power Sources" that the reverse load rate does not exceed 80%.

[0061] Line capacity constraint:

[0062] -0.8·P ij·max ≤P ij,t ≤P ij·max

[0063] Wherein, Pij·max is the upper limit of active power flowing through line ij; the lower limit of active power is -80% of the rated condition.

[0064] Node voltage constraint:

[0065] U N (1 - ε1) ≤ U i,t ≤ U N (1 + ε2)

[0066] Wherein, UN is the system reference voltage; ε1 and ε2 are voltage deviation rates, and the voltage deviation of three-phase power supply lines of 10 kV and below is ±7% of the reference voltage.

[0067] Charging and discharging power constraint of energy storage system:

[0068]

[0069] Wherein, US is the charging and discharging state of the energy storage, and the values can be {0, 1}, representing the charging and discharging states respectively.

[0070] State of charge constraint of energy storage:

[0071]

[0072] Wherein, ηch and ηdch are the charging and discharging efficiencies of the energy storage; Esmax and Esmin are the maximum and minimum states of charge of the energy storage, and NT is the scheduling period.

[0073] In this embodiment, the solution of the distribution transformer area energy storage optimal configuration model involves location, capacity and charging and discharging strategies, thus constituting a complex mixed integer non-linear programming problem.

[0074] Referring to Figure 2 , to improve the solution speed and accuracy, an improved simulated annealing-particle swarm optimization algorithm is used to solve the objective function of the planning layer, improve the calculation convergence speed, enhance the global search ability, and solve the objective function of the operation layer by converting the non-linear model into a second-order cone programming model, reducing the calculation time while ensuring the accuracy, and effectively dealing with the complex planning problem in the most severe scenario.

[0075] In this embodiment, the expression and parameter update strategy of the improved simulated annealing-particle swarm optimization algorithm are as follows:

[0076]

[0077] Where k is the number of iterations; Vid,k, Pid,k, and Xid,k are the velocity, individual optimal position, and individual current position of the i-th particle during the k-th search in the D-dimensional space; ω is the inertia weight; Pgd,k is the global optimal position; r1 and r2 are random values within the range of [0, 1]; c1k and c2k are the individual and population learning factors during the k-th iteration process.

[0078]

[0079] Where c1s is the initial value of the individual learning factor; c1f is the termination value of the individual learning factor; c2s is the initial value of the population learning factor; c2f is the termination value of the population learning factor; Tmax is the maximum number of iterations.

[0080] ω = ω min + r(ω max - ω min ) + r N σ

[0081] Where ωmax and ωmin are the maximum and minimum inertia coefficients; r is a random value within the range of [0, 1]; rN is a random number with a normal distribution; σ is the standard deviation of the inertia coefficient from its mathematical expectation.

[0082] During the particle optimization process, assume that Pi,k is the position of the i-th particle in Pbest,k. If the objective function value of Pi,k is better than that of Pi,k+1, then when the Metropolis criterion is satisfied, the worse solution is accepted, thereby improving the convergence speed and accuracy.

[0083] In this embodiment, the operation layer non-linear model is converted into a second-order cone programming model as follows:

[0084]

[0085] Where αi,t is the square of the node voltage, βij,t is the square of the branch current, and || || is the second norm.

[0086] In this embodiment, to address the distributed PV carrying capacity issues such as reverse overload of distribution equipment in the substation area and over-limit of node voltage, an optimal energy storage configuration method for the substation area considering distributed PV carrying capacity is proposed. First, the K-means clustering algorithm is used to achieve scenario reduction, and the worst-case scenarios are generated by selecting the upper boundary of PV and the lower boundary of load. On this basis, the energy storage configuration model is divided into a planning layer and an operation layer. The planning layer comprehensively considers the annual investment of energy storage and the annual operating cost of the distribution network as the objective function, and the operation layer takes the output of the upper-level power grid, active power loss, and voltage deviation as the objective function, with constraints such as the reverse load rate of distribution equipment and node voltage deviation. For this mixed-integer linear programming model, an algorithm combining an improved simulated annealing-particle swarm algorithm and second-order cone programming is used to improve the solution speed and accuracy. The Cplex solver is called through the YALMIP toolbox in MATLAB for iterative solution to obtain the optimal installation location and capacity of energy storage, as well as to determine the optimal charge and discharge strategy of energy storage, taking into account economy and reducing the comprehensive cost of the distribution network and substation area energy storage on the premise of ensuring no reverse overload of substation area equipment and over-limit of node voltage.

[0087] In this embodiment, a certain actual PV overloaded substation area is used as an example to verify the proposed method. The grid structure of this substation area refers to Figure 3 as shown, the capacity of the distribution transformer is 100 kVA, the PV installed capacity refers to Table 1, the line impedance parameters refer to Table 2, and the peak-valley electricity price refers to Table 3.

[0088] Table 1

[0089]

[0090]

[0091] Table 2

[0092]

[0093] Table 3

[0094]

[0095] To comprehensively analyze the optimal energy storage configuration model for the substation area, the following three schemes are set:

[0096] Scheme 1: Do not configure energy storage.

[0097] Scheme 2: Configure energy storage and adopt a constant power charge and discharge strategy.

[0098] Scheme 3: Configure energy storage and adopt the charge and discharge control strategy of the present invention.

[0099] Substitute the above data into Step 2 to generate the most severe scenario. Apply the method in Step 3 to establish a two-layer optimization configuration model for the distribution network area energy storage. On this basis, apply the method in Step 4 to perform iterative solution to obtain the energy storage optimization configuration results of different schemes under the most severe scenario, as shown in Table 4.

[0100] Table 4

[0101]

[0102] In Scheme 1, no energy storage is configured. The annual cost of Scheme 2 is 183,700 yuan, and the annual cost of Scheme 3 is 129,175 yuan. The result of Scheme 3 is the best. Therefore, the model proposed in the present invention is proven to be cost-effective and feasible. The charge and discharge control strategy of Scheme 2 is charge and discharge at fixed time periods, and the charge and discharge control strategy of Scheme 3 refers to Figure 4 As shown, the treatment of the distribution network area bearing capacity problem by Scheme 2 and Scheme 3 mainly includes: referring to Figure 5 for the reverse overload problem of the distribution transformer, which is presented in the form of the power of the distribution network area; referring to Figure 6 for the reverse overload problem of the line, which is presented in the form of the line power; referring to Figure 7 for the problem of node voltage over-limit, which is presented in the form of the node voltage. Compared with Scheme 2, Scheme 3 has smaller fluctuations in the power and voltage of the distribution network area, better effects in treating the reverse overload of the distribution network area and the problem of node voltage over-limit, and lower comprehensive cost.

[0103] Embodiment 2

[0104] Referring to Figure 8 this embodiment provides a distribution network area energy storage optimization configuration system considering the bearing capacity of distributed photovoltaics, including:

[0105] A data acquisition module, configured to: obtain the historical data of the photovoltaic in the distribution network area, the historical data of the load in the distribution network area, the connection relationship between the nodes and branches in the distribution network area, the rated capacity of the distribution transformer, the line impedance parameters, etc.;

[0106] A scenario clustering module, configured to: generate the most severe scenario of the operation of the distribution network area based on the historical data of the photovoltaic and the load;

[0107] A model construction module, configured to: establish a two-layer optimization model for the distribution network area energy storage optimization configuration under the most severe scenario. The planning layer is optimized with the minimum annual investment and operation cost of the distribution network area energy storage and the distribution network as the objective function, and the operation layer is optimized with the minimum sum of the power output of the upper-layer power grid, the system network loss and the overall node voltage deviation as the objective function;

[0108] An algorithm execution module, configured to: solve the objective function of the planning layer by using an improved simulated annealing-particle swarm algorithm, and solve the objective function of the operation layer by converting the non-linear model into a second-order cone programming model to obtain the optimal distribution network area energy storage configuration scheme

[0109] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the scope of knowledge possessed by those of ordinary skill in the art.

Claims

1. A method for optimizing the configuration of distribution network energy storage considering the bearing capacity of distributed photovoltaic power generation, characterized in that: It includes the following steps. S1: Obtain the key data of the distribution area with distributed photovoltaic power. S2: Generate the most severe operation scenario of the distribution area based on the historical data of photovoltaic power and load. S3: In the most severe scenario, establish a two-layer optimization model for the optimal configuration of energy storage in the distribution area. The planning layer is optimized with the minimum annual investment and operation cost of the energy storage and distribution network as the objective function, and the operation layer is optimized with the minimum sum of the power output of the upper-layer power grid, the system power loss, and the overall node voltage deviation as the objective function. S4: Use the improved simulated annealing-particle swarm optimization algorithm to solve the objective function of the planning layer, and convert the nonlinear model into a second-order cone programming model to solve the objective function of the operation layer, so as to obtain the optimal energy storage configuration scheme for the distribution area.

2. The method for optimizing the configuration of substation area energy storage considering the bearing capacity of distributed photovoltaic according to claim 1, wherein: The key data based on S1 includes: the historical data of photovoltaic power in the distribution area, the historical data of load in the distribution area, the connection relationship between nodes and branches in the distribution area, the rated capacity of the distribution transformer, the line impedance parameters, etc.

3. The optimized configuration method of substation area energy storage considering the bearing capacity of distributed photovoltaics according to claim 1, characterized in that: Based on the historical data of photovoltaic power and load in S2, the K-means clustering algorithm is used for scenario reduction, and the upper boundary of photovoltaic power and the lower boundary of load are selected to generate the most severe scenario, where the most severe scenario is defined as the scenario with the maximum photovoltaic power output and the lowest load demand.

4. The method for optimizing the configuration of substation area energy storage considering the bearing capacity of distributed photovoltaic according to claim 1, wherein: The objective function of the planning layer in S3 comprehensively considers multiple key factors to achieve the optimization of the comprehensive cost. The constraint conditions of the planning layer model include the energy storage installation position constraint, the power and capacity constraint.

5. The method for optimizing the configuration of distribution transformer area energy storage considering the bearing capacity of distributed photovoltaics according to claim 1, wherein: The improvement method of the improved simulated annealing-particle swarm optimization algorithm in S4 is: introducing a random inertia weight, an asymmetric learning factor, and introducing the simulated annealing algorithm on the basis of the basic particle swarm optimization algorithm; the process of converting the nonlinear model into a second-order cone programming model is: introducing new variables to represent the quadratic terms of node voltage and branch current, and using the second-order cone relaxation theory to convert the model into a solvable second-order cone programming model.

6. The method for optimizing the configuration of substation area energy storage considering the bearing capacity of distributed photovoltaic according to claim 4, wherein: The objective function of the operation layer includes the power output of the upper-layer power grid, the system power loss, and the overall node voltage deviation. The constraint conditions of the operation layer model include the power flow constraint of the distribution network, the reverse load rate constraint of the distribution equipment, the node voltage constraint, and the energy storage operation constraint.

7. The method for optimizing the configuration of substation area energy storage considering the bearing capacity of distributed photovoltaic according to claim 5, characterized in that: The optimal energy storage configuration scheme for the distribution area includes the best energy storage installation position, the best energy storage access capacity, and the best daily charge and discharge power of the energy storage.

8. A regional energy storage optimization configuration system taking into account the distributed photovoltaic carrying capacity, characterized by: It includes the following modules: a data acquisition module, a scenario clustering module, a model construction module, and an algorithm execution module.

9. The optimized configuration system for distribution area energy storage considering the bearing capacity of distributed photovoltaics according to claim 8, wherein: The data acquisition module is signal-transmission-connected to the scenario clustering module, the scenario clustering module is signal-transmission-connected to the model construction module, and the model construction module is signal-transmission-connected to the algorithm execution module.

10. The optimized configuration system for substation area energy storage considering the bearing capacity of distributed photovoltaics according to claim 9, characterized in that: The algorithm execution module is configured to: use the improved simulated annealing-particle swarm optimization algorithm to solve the objective function of the planning layer, and convert the nonlinear model into a second-order cone programming model to solve the objective function of the operation layer, so as to obtain the optimal energy storage configuration scheme for the distribution area.

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