High-Reliability Distributed Photovoltaic Energy Storage Optimization Configuration Method and Device for Power Distribution Networks

Abstract

This invention relates to a method and apparatus for optimizing the configuration of distributed photovoltaic (PV) energy storage in a power distribution network with high reliability. Based on objective functions of minimizing investment costs, minimizing expected power shortages, and minimizing line losses in the power distribution network, a multi-objective optimization method is used to obtain optimized solutions for multiple sets of PV energy storage capacities. When a natural disaster occurs in the power system, the optimal power supply path for the PV energy storage device to supply power to high-level and primary loads is determined with the objective of minimizing the marginal cost of the PV energy storage device. From the optimized solutions of the multiple sets of PV energy storage capacities, the optimal solution matching the optimal power supply path is selected. This invention can comprehensively consider the reliability and economy of the power distribution network to optimize the configuration of distributed PV energy storage devices.

Description

Technical Field

[0001] This invention relates to a method and apparatus for optimizing the configuration of distributed photovoltaic energy storage with high reliability in power distribution networks. Background Technology

[0002] Technological advancements and increasing market competitiveness of renewable energy systems such as solar and wind power plants have created favorable conditions for the shift in power generation from large-scale centralized facilities to small-scale distributed energy systems. Distributed generation is a suitable option for sustainable development, benefiting from reduced environmental impact, load management advantages, and the opportunity to supply power to remote areas. Photovoltaic (PV) systems can still provide profitability for grid-connected users even when the produced energy is consumed. Due to the intermittent and random nature of solar energy, PV power plants require energy storage systems to compensate for fluctuations and meet nighttime energy demand. As power systems become increasingly complex, the reliability requirements for distribution networks are becoming higher. To prevent large-scale blackouts and grid collapse caused by disasters, it is necessary to strengthen the reliability of distribution networks and rationally plan existing distribution network networks by appropriately allocating renewable energy generation.

[0003] Differentiated planning methods can rationally utilize resources and optimize the matching of loads and power sources by prioritizing key areas. This is a crucial method for improving the reliability of distribution networks. With the goal of establishing a smart grid, it employs scientific and effective differentiated planning and design to ensure the safe operation of the core grid backbone and important lines at all voltage levels. Power systems require constant instantaneous balance; when user loads change proactively, the grid will react passively. Typically, user loads are classified into primary, secondary, and tertiary loads. In certain situations, special-level loads can also be designated as needed. Therefore, in the event of severe disasters, photovoltaic energy storage should be prioritized for special-level and primary loads to ensure their normal operation and voltage stability.

[0004] Considering the actual situation of the power system, in order to meet the structural requirements of power grids at all levels and improve system reliability, differentiated power grid planning analyzes the components of the load in the power system, ensures the power supply of super-level and first-level loads, and increases the capacity requirements of distributed photovoltaic energy storage. Domestic and foreign scholars have conducted research on distribution network planning, mainly in two categories: measures and concepts. Some research methods attempt to start with optimal power flow planning methods to enable the power system to operate in an optimal state; however, this research only focuses on congestion indicators in the power system and fails to consider the reliability and economy of power grid planning. Some literature addresses the risks and disasters that may be encountered in power grid planning under market conditions, using genetic algorithms to solve for the optimal power grid planning scheme; however, this method does not clearly explain the investment and returns for investors, making it difficult for investors to make choices. Other literature considers the cost of line investment and establishes models with the goal of minimizing line investment, but ignores the reliability of the power grid. Summary of the Invention

[0005] The purpose of this invention is to provide a method and device for optimizing the configuration of distributed photovoltaic energy storage in a power distribution network with high reliability, which can comprehensively consider the reliability and economy of the power distribution network and optimize the configuration of distributed photovoltaic energy storage devices.

[0006] Based on the same inventive concept, this invention has two independent technical solutions:

[0007] 1. A method for optimizing the configuration of distributed photovoltaic energy storage based on the high reliability of distribution networks. Based on the objective functions of minimizing investment costs, minimizing the expected power shortage, and minimizing line network losses of the distribution network, the method uses a multi-objective optimization approach to find the optimal solutions for multiple sets of photovoltaic energy storage capacities.

[0008] Furthermore, when a natural disaster occurs in the power system, with the goal of minimizing the marginal cost of the photovoltaic energy storage device, the optimal power supply path between the photovoltaic energy storage device and the power supply paths of the special-grade and first-grade loads is determined; from the optimized solutions of the multiple sets of photovoltaic energy storage capacity, the optimal solution of photovoltaic energy storage capacity that matches the optimal power supply path is selected.

[0009] Furthermore, the objective function for minimizing investment costs is:

[0010]

[0011] In the formula, N PV、 N ES These are the group numbers for photovoltaic generators and energy storage units, C. ES P is the sum of the comprehensive equipment cost and installation cost of the energy storage unit. Storage.p For the active power output of the energy storage unit, C PV C is the sum of the equipment cost and installation cost of the photovoltaic generator. MGThe construction cost of the power distribution network can be expressed as C. MG =c×p MG +d, where c is the ratio coefficient of distribution network configuration capacity and construction cost, including the comprehensive cost of switch control, reactive power compensation and harmonic processing units, P MG P is the active power of the distribution network area, d is the constant cost of distribution network construction, and P is the active power of the distribution network area. pv.i It is the active power output of the i-th photovoltaic generator, and the connection point between the distribution network and public facilities is defined as the common coupling point.

[0012]

[0013] C DG For the cost of reactive power, P DG (i) represents the reactive power provided by the distributed power source at the time of sampling, and T represents the number of days the photovoltaic energy storage system operates within a year.

[0014] Furthermore, the objective function for minimizing insufficient battery power is:

[0015] P in =P Line，in +P ES ×P island

[0016] In the formula, P in To minimize the probability of insufficient battery power, P Line,in P represents the probability of faults in the internal lines of the distribution network. ES P is the failure probability of the energy storage unit. island The probability that the distribution network is powered only by distributed generation.

[0017] Furthermore, the objective function for minimizing network loss is:

[0018]

[0019] In the formula, minF3(x) is the minimum line loss; B is the set of branches of the network, and (i,j)∈B means that (i,j) are two nodes of a branch; g ij V represents the electrical conductance between nodes i and j; i and V j θ represents the voltage magnitudes at nodes i and j; ij It is the node phase angle θ i and θ j The phase difference between them.

[0020] Furthermore, the multi-objective optimization method includes constraints, which are power flow equation constraints, load design capacity constraints, node voltage constraints, distributed power output constraints, energy storage charge continuity constraints, energy storage power constraints, and energy storage charging and discharging power constraints.

[0021] Furthermore, the multi-objective optimization method employs the particle swarm optimization algorithm.

[0022] Furthermore, the objective function for minimizing the marginal cost of the photovoltaic energy storage device is:

[0023]

[0024] f represents marginal cost, which is the cost of path matching based on differentiated construction costs and economic losses during disasters; C ES P is the sum of the comprehensive equipment cost and installation cost of the energy storage unit. Storage.p For the active power output of the energy storage unit; T min The cost loss is the minimum economic loss when a disaster occurs, and N is the number of photovoltaic energy storage units.

[0025] Furthermore, when a natural disaster occurs in the power system, the critical and primary loads that require power supply are first ranked according to their importance, and then the optimal power supply path for photovoltaic energy storage devices to supply power to the critical and primary loads is found one by one according to the ranking.

[0026] 2. A distributed photovoltaic energy storage optimization configuration device based on high reliability of distribution network, used to execute the above method.

[0027] The beneficial effects of this invention are as follows:

[0028] This invention, based on the objective functions of minimizing investment costs, minimizing expected power shortages, and minimizing line losses in the distribution network, uses a multi-objective optimization method to find the optimal solution for photovoltaic (PV) energy storage capacity. When a natural disaster occurs in the power system, based on the objective function of minimizing the marginal cost of PV energy storage devices, it finds the most suitable PV energy storage devices for super-high and first-tier loads. Guided by improving the reliability of the distribution network, this invention proposes a distributed PV energy storage optimization configuration method based on the principles of PV energy storage capacity optimization and grid differentiation planning. This method combines PV energy storage optimization configuration with grid differentiation planning principles, effectively improving the reliability and economy of the distribution network while reducing energy losses and enhancing the reliability and security of PV energy storage power generation. This invention can achieve certain PV energy storage capacity configuration functions. Compared with ordinary distributed power generation configuration methods, this invention's configuration method is more economical and flexible, ensuring that distributed PV and energy storage can alleviate the pressure on the distribution network to a certain extent. In the event of a natural disaster or unexpected power outage in the distribution network, this invention provides timely active and reactive power, improving the reliability of the distribution network.

[0029] The objective function for minimizing investment costs in this invention is:

[0030]

[0031] In the formula, N PV N ES These are the group numbers for photovoltaic generators and energy storage units, C. ES P is the sum of the comprehensive equipment cost and installation cost of the energy storage unit. Storage.p For the active power output of the energy storage unit, C PV C is the sum of the equipment cost and installation cost of the photovoltaic generator. MG The construction cost of the power distribution network can be expressed as C. MG =c×p MG +d, where c is the ratio coefficient of distribution network configuration capacity and construction cost, including the comprehensive cost of switch control, reactive power compensation and harmonic processing units, P MG P is the active power of the distribution network area, d is the constant cost of distribution network construction, and P is the active power of the distribution network area. pv.i It is the active power output of the i-th photovoltaic generator, and the connection point between the distribution network and public facilities is defined as the common coupling point.

[0032]

[0033] C DG For the cost of reactive power, P DG (i) represents the reactive power provided by the distributed power source at the time of sampling, and T represents the number of days the photovoltaic energy storage system operates within a year.

[0034] The objective function for minimizing insufficient battery power is:

[0035] P in =P Line，in +P ES ×P island

[0036] In the formula, P in To minimize the probability of insufficient battery power, P Line,in P represents the probability of faults in the internal lines of the distribution network. ES P is the failure probability of the energy storage unit. island The probability that the distribution network is powered only by distributed generation.

[0037] The objective function for minimizing network loss is:

[0038]

[0039] In the formula, minF3(x) is the minimum line loss; B is the set of branches of the network, and (i,j)∈B means that (i,j) are two nodes of a branch; g ij V represents the electrical conductance between nodes i and j; i and V j θ represents the voltage magnitudes at nodes i and j; ij It is the node phase angle θ i and θ j The phase difference between them.

[0040] The multi-objective optimization method includes constraints, which are power flow equation constraints, load design capability constraints, node voltage constraints, distributed power output constraints, energy storage charge continuity constraints, energy storage power constraints, and energy storage charging and discharging power constraints.

[0041] This invention, through the above-mentioned objective functions of minimizing investment cost, minimizing power shortage, and minimizing line network loss, as well as the specific setting of constraints, further ensures that when optimizing the capacity configuration of photovoltaic energy storage devices, the reliability and economy of the distribution network are effectively improved, while reducing energy loss and improving the reliability and safety of photovoltaic energy storage power generation.

[0042] The objective function for minimizing the marginal cost of the photovoltaic energy storage device described in this invention is:

[0043]

[0044] f represents marginal cost, which is the cost of path matching based on differentiated construction costs and economic losses during disasters; C ES P is the sum of the comprehensive equipment cost and installation cost of the energy storage unit. Storage.p For the active power output of the energy storage unit; Tmin The cost loss is the minimum economic loss when a disaster occurs, and N is the number of photovoltaic energy storage units.

[0045] When a natural disaster occurs in the power system, the critical and primary loads requiring power supply are first prioritized according to their importance, and then the most suitable photovoltaic energy storage device is found for each load in order of priority.

[0046] This invention, through the aforementioned differentiated cost planning and differentiated step-by-step planning methods, further ensures that when optimizing the configuration of photovoltaic energy storage devices, the economy and reliability of the distribution network are improved, and the optimal path matching the load is obtained. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method for optimizing the configuration of distributed photovoltaic energy storage for high reliability in power distribution networks according to the present invention. Detailed Implementation

[0048] The present invention will now be described in detail with reference to the embodiments shown in the accompanying drawings. However, it should be noted that these embodiments are not intended to limit the present invention. Equivalent changes or substitutions in function, method, or structure made by those skilled in the art based on these embodiments are all within the scope of protection of the present invention.

[0049] Example 1:

[0050] Optimization Configuration Method of Distributed Photovoltaic Energy Storage Based on High Reliability of Distribution Network

[0051] (I) Optimization of Distributed Photovoltaic Energy Storage Capacity

[0052] The increasing demand for grid-connected distributed photovoltaic (PV) power generation from end-users is putting greater pressure on the distribution network. The rational combined planning of distributed power generation and energy storage batteries can improve the reliability and stability of the grid while ensuring its economic efficiency and environmental friendliness. As end-user loads increase, the power generation capacity of the distribution network is prone to shortages, necessitating the grid connection of renewable energy generation. However, with the addition of distributed PV energy storage, the investment and construction costs of the grid will significantly increase, along with system maintenance costs and transmission losses.

[0053] Based on the above description, given the known location of photovoltaic energy storage construction, and taking cost, expected energy not supplied (EENS), and line network loss as objectives, the optimal solution is obtained through a multi-objective optimization method, resulting in a reasonable photovoltaic energy storage capacity. This provides good economics for differentiated planning and meets load requirements with higher reliability, flexibility, and adaptability.

[0054] like Figure 1As shown, a distributed photovoltaic energy storage optimization configuration method based on high reliability of distribution network is proposed. Based on the objective functions of minimizing investment cost, minimizing power shortage expectation, and minimizing line network loss of distribution network, the optimal solution of multiple photovoltaic energy storage capacity is obtained through multi-objective optimization method.

[0055] 1. Minimize investment cost objective function

[0056] The objective function for minimizing investment costs is:

[0057]

[0058] In the formula, N PV、 N ES These are the group numbers for photovoltaic generators and energy storage units, C. ES P is the sum of the comprehensive equipment cost and installation cost of the energy storage unit. Storage.p For the active power output of the energy storage unit, C PV C is the sum of the equipment cost and installation cost of the photovoltaic generator. MG The construction cost of the power distribution network can be expressed as C. MG =c×p MG +d, where c is the ratio coefficient of distribution network configuration capacity and construction cost, including the comprehensive cost of switch control, reactive power compensation and harmonic processing units, P MG P is the active power of the distribution network area, d is the constant cost of distribution network construction, and P is the active power of the distribution network area. pv.i It is the active power output of the i-th photovoltaic generator, and the connection point between the distribution network and public facilities is defined as the common coupling point.

[0059]

[0060] C DG For the cost of reactive power, P DG (i) represents the reactive power provided by the distributed power source at the time of sampling, and T represents the number of days the photovoltaic energy storage system operates within a year.

[0061] 2. Minimize the expected objective function of insufficient battery power.

[0062] Minimize the Expected Energy Shortage (EENS). EENS is a measure of whether the power supply is sufficient. The objective function for minimizing the expected energy shortage is:

[0063] P in =P Line，in +P ES ×P island

[0064] In the formula, P in To minimize the probability of insufficient battery power, P Line,inP represents the probability of faults in the internal lines of the distribution network. ES P is the failure probability of the energy storage unit. island The probability that the distribution network is powered only by distributed generation.

[0065] 3. Minimize the network loss objective function

[0066] The objective function for minimizing network loss is:

[0067]

[0068] In the formula, minF3(x) is the minimum line loss; B is the set of branches of the network, and (i,j)∈B means that (i,j) are two nodes of a branch; g ij V represents the electrical conductance between nodes i and j; i and V j θ represents the voltage magnitudes at nodes i and j; ij It is the node phase angle θ i and θ j The phase difference between them.

[0069] 4. Constraints

[0070] The multi-objective optimization method includes constraints, which are power flow equation constraints, load design capability constraints, node voltage constraints, distributed power output constraints, energy storage charge continuity constraints, energy storage power constraints, and energy storage charging and discharging power constraints.

[0071] (1) Power flow equation constraints

[0072]

[0073]

[0074] Among them, P DG，i and Q DG,i It refers to active power generation output and reactive power generation output, while P di and Q di It is a node i Active and reactive loads, G ij and B ij is the real and imaginary part of the nodal admittance matrix, and N is the number of busbars.

[0075] (2) Load design capability constraints

[0076]

[0077] Among them, P L，i P is the load-bearing capacity of the i-th load. TL It is the total load design bearing capacity.

[0078] (3) Output constraints of the generator

[0079] P r，min ≤P G，r，t ≤P r，max

[0080] Among them, P r，min P G,r，t and P r，max These are the minimum output of the r-th distributed power source, the actual output of the r-th power source at time t, and the maximum output of the r-th distributed power source, respectively.

[0081] (4) Voltage constraints of nodes

[0082] U i，min ≤U i，t ≤U i，max

[0083] Among them, U i,min U i,t and U i,max These are the minimum allowable voltage of node voltage i, the actual voltage of node i at time t, and the maximum allowable voltage of node voltage i, respectively.

[0084] (5) Energy storage charge continuity constraint

[0085]

[0086] Among them, A oc,x,0 and A oc,x,t These are the initial and state of charge (SOC) values ​​of the photovoltaic energy storage system x at time t, respectively; P ch，x and P dis，x These represent the charging and discharging power of the photovoltaic energy storage system x, respectively. and These are the charging and discharging efficiencies of the photovoltaic energy storage system x, respectively; E bess,x ΔT represents the rated capacity of the energy storage system x; ΔT represents the number of days the photovoltaic energy storage system operates.

[0087] (6) Power constraints of energy storage systems

[0088] P DESS，i，t =b i (P dis，i，t -P ch，i，t )

[0089] Wherein, PDESS,i,t is the actual charging and discharging power of the photovoltaic energy storage system at node i at time t; bi is a 0-1 state variable, where 1 indicates that the photovoltaic energy storage is connected at the i-th node in the grid, and 0 indicates that it is not connected; Pch,i,t and Pdis,i,t are the charging and discharging power of the photovoltaic energy storage at node i at time t, respectively.

[0090] (7) Energy storage system charging and discharging power constraints

[0091]

[0092] P ch，i， t and P dis，i， t They are nodes i The charging and discharging power of photovoltaic energy storage at time t; P bess The rated power of the photovoltaic energy storage connected to the i-th node.

[0093] 5. Solution process

[0094] Distributed energy storage capacity optimization configuration is a multi-objective nonlinear integer programming problem, which includes determining the location, power, and capacity of energy storage access.

[0095] This nonlinear problem can be solved using the Particle Swarm Optimization (PSO) algorithm. PSO is an evolutionary algorithm suitable for solving continuous nonlinear problems. It starts with random solutions and iteratively seeks the optimal solution. Each potential solution to the optimization problem can be viewed as a particle, and each particle has a fitness determined by its own parameters and its mapping relationship with the objective function. Employing a linearly decreasing weight control strategy can effectively improve the algorithm's optimization speed; the specific formula is as follows:

[0096] By iteratively solving the multi-objective nonlinear function, several solutions for distributed photovoltaic and energy storage capacity under the objective function are obtained, providing capacity configuration options for the next step of differentiated planning.

[0097] v ij，k+1 =ωv ij，k +c1r1(P bij，k -x ij，k )+c2r2(g bj，k -x ij，k )

[0098]

[0099] x ij，k+1 =x ij，k +v ij，k+1

[0100] in, i For the first i Particle; j is the particle dimension; k is the iteration number; ω is the weight coefficient; p b For individual extreme values; g b The global extremum is represented by c1 and c2, which are learning factors; r1 and r2 are random numbers between 0 and 1; ω ini ω represents the initial weights;end The weights when iterating to the maximum number of generations; x ij The position of the particle.

[0101] (II) Optimization of Distributed Photovoltaics and Energy Storage for High-Reliability Distribution Networks

[0102] When a natural disaster occurs in the power system, it may trigger a cascading failure, resulting in significant losses. Therefore, it is necessary to plan for construction costs after a disaster to further improve the economics of distributed photovoltaic (PV) and energy storage. This invention uses a hierarchical planning approach to prioritize the importance of top-tier and first-tier loads. It then uses an optimal path method to find the lowest-cost path between PV energy storage and top-tier / first-tier loads, optimizing the matching of these loads with distributed PV energy storage. Finally, it calculates the required capacity for these loads and selects the optimal distributed PV energy storage capacity from the perspectives of distribution network reliability and economy. This ensures that when different top-tier / first-tier loads in the distribution network experience unexpected power outages, the optimized distributed PV energy storage can provide timely power through the lowest-cost path, improving the reliability and disaster resilience of the distribution network and reducing losses caused by disasters.

[0103] 1. Differentiated cost planning

[0104] Although the duration of a disaster in the distribution network is relatively short, the network still incurs losses during such events, necessitating consideration of economic indicators. Therefore, the objective of differentiated cost planning can be defined as minimizing both differentiated construction costs and the economic losses (marginal costs) during disasters. The objective function for minimizing the marginal cost of photovoltaic energy storage devices is as follows:

[0105]

[0106] Where f is the marginal cost, which is the cost of path matching based on differentiated construction costs and economic losses during disasters; C ES P is the sum of the comprehensive equipment cost and installation cost of the energy storage unit. Storage.p For the active power output of the energy storage unit; T min The cost loss is the minimum economic loss in the event of a disaster, and N is the number of photovoltaic energy storage units. Of course, the costs of distributed photovoltaic energy storage systems vary significantly depending on their specifications. Therefore, each distributed photovoltaic energy storage unit in the distribution network needs to be optimized in a targeted manner to maximize the optimization effect.

[0107] 2. Differentiated hierarchical planning

[0108] When a natural disaster occurs in the power system, the critical and primary loads that require power supply are first sorted according to their importance. Then, based on the objective of minimizing the marginal cost of the photovoltaic energy storage device, the optimal power supply path for the photovoltaic energy storage device to supply power to the critical and primary loads is determined. From the optimized solutions of the multiple sets of photovoltaic energy storage capacity, the optimal solution of photovoltaic energy storage capacity that matches the optimal power supply path is selected.

[0109] In practice, after determining the special-grade, primary-grade loads and distributed photovoltaic energy storage, the distribution network can serve as the connection link between user loads and photovoltaic energy storage.

[0110] (1) User load is sorted according to importance;

[0111] (2) After the sorting is completed, according to the above sorting results, find the connection path with the lowest cost or the optimal path. For the load r, the optimization problem is as follows:

[0112]

[0113] Among them, f r For the rth indivual The marginal cost of guaranteed power supply to the load; C ES P is the sum of the overall equipment cost and installation cost of the ES unit. Storage.p This represents the active power output of the energy storage system. N is the number of photovoltaic energy storage units, and T is the active power output. rmin This represents the cost loss at which the economic loss corresponding to the r-th load is minimized during a disaster.

[0114] 3. Constraints of Differentiated Hierarchical Programming

[0115] When matching super-grade and first-grade loads (r) with distributed photovoltaic (PV) energy storage, the optimal path method is used for corresponding matching to obtain the optimal path and marginal cost for matching super-grade and first-grade loads with each distributed PV energy storage power source. The following constraints need to be considered:

[0116] (1) The substation capacity shall not exceed the limit:

[0117] (2) The capacity of the transmission lines shall not exceed the limit.

[0118] (3) Marginal cost equals the cost of newly added distributed photovoltaic energy storage that needs to be optimized in the line, the losses after the disaster, and the construction cost:

[0119]

[0120] from The lowest cost option is selected to complete the power supply path between the special-grade and first-grade loads and the distributed photovoltaic energy storage, and a step-by-step planning and matching approach is adopted for each different load.

[0121] Once the optimal power supply path is determined, the capacity configuration most suitable for the matching path is selected from the above multiple sets of optimized solutions for photovoltaic energy storage capacity, thereby completing the optimized configuration of distributed photovoltaic and energy storage.

[0122] After differentiated planning of the power grid, distributed photovoltaic (PV) energy storage power generation equipment located near high-voltage and primary loads should be prioritized for capacity optimization. This is because optimized distributed PV energy storage capacity can be well matched with load capacity, and transmission paths can be significantly shortened. In the event of unexpected power outages, distributed PV energy storage can promptly deliver power. The distribution network can then adjust voltage and dispatch energy storage in a timely manner, ensuring that the voltage of the distribution network remains within the normal range, reducing the amount and duration of critical load reduction during faults, and improving the reliability and usability of the distribution network.

[0123] Example 2:

[0124] A distributed photovoltaic energy storage optimization configuration device based on high reliability of distribution network. The device is used to execute the method described in Embodiment 1.

[0125] The detailed descriptions listed above are merely specific descriptions of feasible embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. All equivalent embodiments or modifications made without departing from the spirit of the present invention should be included within the scope of protection of the present invention.

[0126] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

Claims

1. A method for optimizing the configuration of distributed photovoltaic energy storage based on high reliability of distribution networks, characterized in that: Based on the objective functions of minimizing investment costs, minimizing expected power shortages, and minimizing line losses in the distribution network, multiple optimal solutions for photovoltaic energy storage capacity are obtained through a multi-objective optimization method. When a natural disaster occurs in the power system, the optimal power supply path between the photovoltaic energy storage device and the power supply path of the special-grade and first-grade loads is determined with the goal of minimizing the marginal cost of the photovoltaic energy storage device. From the optimized solutions of the multiple sets of photovoltaic energy storage capacity, the optimal solution of photovoltaic energy storage capacity that matches the optimal power supply path is selected; When a natural disaster occurs in the power system, the critical and primary loads that need power supply are first sorted according to their importance, and then the optimal power supply path for photovoltaic energy storage devices to supply power to the critical and primary loads is found one by one according to the sorting.

2. The method for optimizing the configuration of distributed photovoltaic energy storage based on high reliability of distribution networks according to claim 1, characterized in that: The objective function for minimizing investment costs is: In the formula, N PV、 N ES These are the group numbers for photovoltaic generators and energy storage units, respectively. C ES This is the sum of the overall equipment cost and installation cost of the energy storage unit. P Storage.p For the active power output of the energy storage unit, C PV This is the sum of the equipment cost and installation fee of the photovoltaic generator. C MG The cost of power distribution network construction is expressed as... C MG = c×p MG +d ,in c The ratio of distribution network capacity to construction cost, including the comprehensive cost of switch control, reactive power compensation, and harmonic processing units. P MG d represents the active power of the distribution network area, and d represents the constant cost of distribution network construction. P pv.i It is the active power output of the i-th photovoltaic generator, and the connection point between the distribution network and public facilities is defined as the common coupling point. C DG The cost of reactive power, P DG (i) represents the reactive power provided by the distributed power source at the time of sampling, and T represents the number of days the photovoltaic energy storage system operates within a year.

3. The method for optimizing the configuration of distributed photovoltaic energy storage based on high reliability of distribution networks according to claim 1, characterized in that: The objective function for minimizing insufficient battery power is: In the formula, P in To minimize the probability of insufficient battery power, P Line,in This represents the probability of faults in the internal lines of the distribution network. P ES It is the failure probability of the energy storage unit. P island The probability that the distribution network is powered only by distributed generation.

4. The method for optimizing the configuration of distributed photovoltaic energy storage based on high reliability of distribution networks according to claim 1, characterized in that: The objective function for minimizing network loss is: In the formula, minF3(x) is the minimum line loss; B is the set of branches of the network, and (i,j)∈B means that (i,j) are two nodes of a branch; g ij V represents the electrical conductance between nodes i and j; i and V j θ represents the voltage magnitudes at nodes i and j; ij It is the node phase angle θ i and θ j The phase difference between them.

5. The method for optimizing the configuration of distributed photovoltaic energy storage based on high reliability of distribution networks according to claim 1, characterized in that: The multi-objective optimization method includes constraints, which are power flow equation constraints, load design capability constraints, node voltage constraints, distributed power output constraints, energy storage charge continuity constraints, energy storage power constraints, and energy storage charging and discharging power constraints.

6. The method for optimizing the configuration of distributed photovoltaic energy storage based on high reliability of distribution networks according to claim 1, characterized in that: The multi-objective optimization method employs the particle swarm optimization algorithm.

7. The method for optimizing the configuration of distributed photovoltaic energy storage based on high reliability of distribution networks according to claim 1, characterized in that: The objective function for minimizing the marginal cost of the photovoltaic energy storage device is: f represents the marginal cost, which is the cost of path matching based on the differentiated construction cost and the economic losses during disasters; C ES This is the sum of the overall equipment cost and installation cost of the energy storage unit. P Storage.p For the active power output of the energy storage unit; T min This refers to the cost loss that minimizes economic losses during a disaster. N This refers to the number of photovoltaic energy storage units.

8. A distributed photovoltaic energy storage optimization configuration device based on high reliability of distribution network, characterized in that: Used to perform the method according to any one of claims 1 to 7.

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