Energy storage configuration method considering operation risk and economic benefit of power distribution network

Abstract

The invention relates to an energy storage configuration method considering operation risks and economic benefits of a power distribution network. The method comprises the following steps: clustering historical data of renewable energy power generation and loads in the power distribution network to obtain typical scenes and corresponding probabilities of operation of the power distribution network; an energy storage configuration and scheduling strategy double-layer optimization model is established, an upper layer planning model takes energy storage device site selection and constant volume as decision variables and takes minimum energy storage configuration cost and power distribution network comprehensive operation cost as targets, and an energy storage optimal configuration scheme is obtained; the lower-layer scheduling model obtains an optimal scheduling strategy of the energy storage system in the typical scene by taking the minimum energy storage charging and discharging behavior decision variable in the typical scene, the minimum power distribution network operation cost and the minimum power distribution network comprehensive operation risk as targets, and corrects a configuration scheme of the upper-layer energy storage system; and finally, solving the model by using a particle swarm algorithm. Compared with the prior art, the method can effectively configure the energy storage system and formulate the energy storage scheduling strategy, and achieves the purpose of optimizing the operation cost while guaranteeing the operation safety of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the field of distribution network risk optimization and energy storage optimization configuration, and in particular to an energy storage configuration method that takes distribution network operation risks and economic benefits into consideration. Background Art

[0002] As the penetration rate of distributed renewable energy continues to increase, the distribution network will inevitably face greater risks due to the intermittent operation of renewable energy, and will also generate more economic expenditures. During the peak period of renewable energy generation, too much power access will cause the voltage of the node to rise sharply, and the line will find it difficult to transmit excess power. When the load in the distribution network reaches its peak at night, renewable energy generation, mainly photovoltaic power generation, is at a low point, causing the voltage of the node to drop sharply. In order to improve the operational reliability and economy of the distribution network, energy storage systems, as an important flexibility resource, have received widespread attention and application. However, how to reasonably configure energy storage systems and formulate scheduling strategies while considering the risks of the distribution network is still an urgent problem to be solved. Summary of the invention

[0003] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide an energy storage configuration method that takes into account the operating risks and economic benefits of the distribution network.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] A method for configuring energy storage considering the operation risk and economic benefit of a distribution network, comprising:

[0006] Collect historical data on distributed renewable energy generation and load in the distribution network, and cluster the historical data to obtain typical scenarios and corresponding probabilities of distribution network operation;

[0007] According to the distribution network system parameters and typical scenarios, a two-layer optimization model of energy storage configuration and dispatch strategy is established;

[0008] The upper-level planning model uses the location and capacity of energy storage devices as decision variables, and takes the minimum energy storage configuration cost and the comprehensive operation cost of the distribution network as the goal to obtain the optimal configuration plan of the energy storage system.

[0009] The lower-level dispatch model is a decision variable for energy storage charging and discharging behavior based on typical scenarios of energy storage systems. It takes the minimization of distribution network operation costs and comprehensive operation risks as the goal, obtains the optimal dispatch strategy of the energy storage system under typical scenarios, and corrects the dispatch strategy of the upper-level energy storage system configuration plan.

[0010] The particle swarm algorithm is used to solve the two-layer optimization model to obtain the optimal configuration scheme of energy storage and the optimal scheduling strategy under typical scenarios.

[0011] As an optimal technical solution, the specific process of clustering historical data to obtain typical scenarios of distribution network operation and corresponding probabilities is: clustering historical data using a clustering algorithm, taking the center of the cluster as the typical operation scenario, and taking the proportion of each category as the probability of the typical scenario.

[0012] As a preferred technical solution, the upper-level planning model objective function is:

[0013]

[0014] In the formula, is the configuration cost of the energy storage device; is the operating cost of the energy storage device; C grid The cost of purchasing and selling electricity to the superior power grid; C loss It is the network operation loss cost.

[0015] As a preferred technical solution, the configuration cost and operation cost of the energy storage device are as follows:

[0016]

[0017] Where D is the number of days; c in,i and S i are the unit capacity configuration cost and capacity of the i-th energy storage, respectively; r is the discount rate; N is the service life of the energy storage; c om is the unit power operation and maintenance cost; P ESS,i is the charging and discharging power of the i-th energy storage.

[0018] As a preferred technical solution, the constraints for optimizing the upper-level planning model include:

[0019] Power balance constraints:

[0020]

[0021] Where P DG,j (t) is the access power of the jth distributed generation at time t; P load (t) is the total load at time t;

[0022] Power flow balance constraints:

[0023]

[0024] Where P i and Q i are the active and reactive powers injected into node i respectively; V i and V j are the voltage amplitudes of nodes i and j respectively; j∈i represents all nodes directly connected to node i; G ij and Bij are the real and imaginary parts of the node admittance matrix respectively; θ ij is the phase angle difference between node i and node j;

[0025] Energy storage equipment constraints:

[0026]

[0027] In the formula, S SOC,max and S SOC,min are the maximum and minimum values ​​of the state of charge of the energy storage device, S SOC (t) is the state of charge at time t; P ESS,max and P ESS,min are the maximum and minimum values ​​of the charging and discharging power of the energy storage device, P ESS (t) is the charge and discharge power at time t.

[0028] As a preferred technical solution, the objective function of the lower-level dispatching model to minimize the distribution network operation cost and the comprehensive operation risk of the distribution network is:

[0029]

[0030] In the formula, is the operating cost of the energy storage device; C grid The cost of purchasing and selling electricity to the superior power grid; C los is the network operation loss cost; Risk is the operation risk of the distribution network.

[0031] As a preferred technical solution, the comprehensive operation risk of the distribution network includes the risk of node voltage exceeding the limit and the risk of line power overload:

[0032]

[0033] Where P i , Sev i They represent the voltage over-limit risk probability and the severity of voltage over-limit at node i respectively; P l , Sev l They represent the power overload probability and severity of power overload of line l respectively; k is the risk factor; θ(V i ) is the voltage offset indicator; V Base is the reference voltage, V max and V min PR is the upper and lower limits of the voltage allowable value; l is the load factor of line l, PR max It is the maximum value allowed for the line load rate.

[0034] As a preferred technical solution, the constraints for optimizing the lower-layer scheduling model include:

[0035] Node voltage constraints:

[0036] V lowe ≤V i (t)≤V upper

[0037] Where V upper and V lower are the maximum and minimum boundary values ​​of the node voltage amplitude respectively;

[0038] Line power constraints:

[0039] P ij ≤P ij,max

[0040] Where P ij,max is the maximum transmission power of the line from node i to node j;

[0041] Energy storage operation constraints:

[0042]

[0043] In the formula, δ is the self-discharge rate of energy storage; η is the charge and discharge efficiency of energy storage; S SOC,i (t start ) and S SOC,i (t en ) represent the charge state at the start and end of a cycle respectively.

[0044] As a preferred technical solution, the process of using the particle swarm algorithm to solve the double-layer optimization model is as follows:

[0045] Input distribution network system parameters, typical operation data and constraints;

[0046] The upper-level planning model randomly generates energy storage locations and capacities within the constraints as the initial population of the upper-level particle swarm, and then transmits the energy storage location and capacity data of each particle to the lower-level scheduling model. The lower-level scheduling model solves the scheduling strategy of each energy storage and returns the scheduling strategy of the energy storage to the upper-level planning model. The upper-level planning model calculates the fitness of the upper-level particles according to the scheduling strategy and the objective function of the upper-level planning model, and updates the speed and position of the upper-level particles, and then updates the individual optimal position and global optimal position of the upper-level particles. The upper-level planning model repeats the above process until the termination condition is met;

[0047] The lower-level scheduling model determines the distribution network parameters according to the location and capacity of the energy storage input by the upper-level planning model, and then randomly generates the charging and discharging behavior of the energy storage within the constraint range to generate the initial population, and then solves the multi-objective function of the lower-level scheduling model to obtain the fitness of the lower-level particles, and updates the speed and position of the lower-level particles, and then updates the individual optimal position and global optimal position of the lower-level particles. The lower-level scheduling model repeats the above process. When the lower-level scheduling model meets the termination conditions, the lower-level Pareto solution set is generated, and the optimal energy storage scheduling strategy is determined by combining the entropy weight method and the hierarchical analysis method, and returned to the upper-level planning model;

[0048] After repeated iterations of the upper and lower layer models, the optimal energy storage configuration of the current distribution network and the optimal energy storage scheduling plan under typical scenarios are obtained.

[0049] As a preferred technical solution, the method for updating the particle speed and position is as follows:

[0050]

[0051] In the formula, is the velocity of particle i at the kth iteration, is the velocity of particle i at the k-1th iteration, ω is the inertia weight; is the individual optimal position of particle i, is the global optimal position; c1, c2 are learning factors; r1, r2 are random numbers in [0, 1]; is the position of particle i at the kth iteration, is the position of particle i at the k-1th iteration.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The present invention proposes a method for energy storage configuration that considers the operating risks and economic benefits of the distribution network and a two-layer optimization configuration model. In the upper-layer planning model, the energy storage system configuration cost and the distribution network operating cost are considered to explore the optimal configuration plan of the energy storage system; the lower-layer scheduling model is responsible for formulating the energy storage scheduling strategy, with the goal of optimizing the operating risks and operating costs of the distribution network, considering the node voltage over-limit risks and line power overload risks, exploring the optimal scheduling strategy of the energy storage system in typical scenarios, and returning to correct the upper-layer energy storage configuration plan. It can effectively configure the energy storage system and formulate the energy storage scheduling strategy, while ensuring the safe operation of the distribution network, achieving the goal of optimizing the operating cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flow chart of the steps of the energy storage configuration method considering the distribution network operation risk and economic benefits of the present invention;

[0055] Figure 2This is a schematic diagram of the dual-layer optimization model architecture of energy storage configuration and scheduling strategy of the present invention;

[0056] Figure 3 A diagram of an IEEE 33-node power distribution system in one embodiment of the present invention;

[0057] Figure 4 This is a comprehensive operational risk diagram of three schemes in one embodiment of the present invention. DETAILED DESCRIPTION

[0058] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0059] Example 1

[0060] like Figure 1 As shown in the figure: A method for energy storage configuration considering the operation risk and economic benefits of the distribution network, the steps are as follows:

[0061] S1. Collect historical data on distributed renewable energy generation and load in the distribution network, and cluster the historical data to obtain typical scenarios and corresponding probabilities of distribution network operation;

[0062] S2. According to the distribution network system parameters and typical scenarios, a two-layer optimization model of energy storage configuration and dispatch strategy is established. Figure 2 As shown, the upper-level planning model takes the location and capacity of the energy storage device as decision variables, and takes the minimum energy storage configuration cost and the comprehensive operation cost of the distribution network as the goal to obtain the optimal configuration plan of the energy storage system;

[0063] The lower-level dispatch model is a decision variable for energy storage charging and discharging behavior based on typical scenarios of energy storage systems. It takes the minimization of distribution network operation costs and comprehensive operation risks as the goal, obtains the optimal dispatch strategy of the energy storage system under typical scenarios, and corrects the dispatch strategy of the upper-level energy storage system configuration plan.

[0064] S3, optimizing the upper-level planning model with the goal of minimizing the energy storage configuration cost and the comprehensive operation cost of the distribution network;

[0065] S4, optimizing the lower-level dispatching model with the goal of minimizing the distribution network operation cost and the comprehensive operation risk of the distribution network;

[0066] S5. Use the particle swarm algorithm to solve the double-layer model and obtain the optimal configuration plan and optimal scheduling strategy for energy storage.

[0067] Step S1 clusters historical data to obtain typical scenarios of distribution network operation and the corresponding probability calculation process is as follows: first, use k-means or other clustering algorithms to cluster historical data, take the center of the cluster as the typical operation scenario, and take the proportion of each category as the probability of the typical scenario. The selection standard for the number of clusters uses the Calinski-Harabasz index, which is also called the variance ratio criterion. It calculates the ratio of the compactness within the cluster to the separation between clusters. The larger the value, the better the clustering effect. The calculation formula is as follows:

[0068]

[0069] Where tr(B) is the trace of the sum of squared deviations between clusters; tr(W) is the trace of the sum of squared deviations within clusters; k is the number of clusters; and n is the total number of data.

[0070] Step S3: Energy storage configuration cost and distribution network comprehensive operation cost include energy storage configuration cost, energy storage operation cost, power purchase cost from the superior power grid and network loss cost. Its objective function is:

[0071]

[0072] In the formula, is the configuration cost of the energy storage device; is the operating cost of the energy storage device; C grid The cost of purchasing and selling electricity to the superior power grid; C loss is the network operation loss cost; D is the number of days; c in,i and S i are the unit capacity configuration cost and capacity of the i-th energy storage, respectively; r is the discount rate; N is the service life of the energy storage; c om is the unit power operation and maintenance cost; P ESS,i is the charging and discharging power of the i-th energy storage. A positive value indicates discharging, and a negative value indicates charging.

[0073] Step S4: The comprehensive operation risk of the distribution network is the node voltage over-limit risk and the line power overload risk. The specific calculation method is as follows:

[0074]

[0075] Where Risk is the operation risk of the distribution network; P i , Sev i They represent the voltage over-limit risk probability and the severity of voltage over-limit at node i respectively; P l , Sev l They represent the power overload probability and severity of power overload of line l respectively; k is the risk factor, the larger the value, the more sensitive it is to risk, θ(V i ) is the voltage offset indicator; VBase is the reference voltage, V max and V min PR is the upper and lower limits of the voltage allowable value; l is the load factor of line l, PR max It is the maximum value allowed for the line load rate.

[0076] Step S4 The objective function of the distribution network operation cost and the comprehensive operation risk of the distribution network is:

[0077]

[0078] Step S3 optimizes the upper-level planning model with the goal of minimizing the energy storage configuration cost and the comprehensive operation cost of the distribution network. The constraints include:

[0079] Power balance constraints:

[0080]

[0081] Where P DG,j (t) is the access power of the jth distributed generation at time t; P load (t) is the total load at time t.

[0082] Power flow balance constraints:

[0083]

[0084] Where P i and Q i are the active and reactive powers injected into node i respectively; V i and V j are the voltage amplitudes of nodes i and j respectively; j∈i represents all nodes directly connected to node i; G ij and B ij are the real and imaginary parts of the node admittance matrix respectively; θ ij is the phase angle difference between node i and node j.

[0085] Energy storage equipment constraints:

[0086]

[0087] In the formula, S SOC,ma and S SOC,min are the maximum and minimum values ​​of the state of charge of the energy storage device, S SOC (t) is the state of charge at time t; P ESS,ma and P ESS,min are the maximum and minimum values ​​of the charging and discharging power of the energy storage device, P ESS (t) is the charge and discharge power at time t.

[0088] Step S4 optimizes the lower-level dispatching model with the goal of minimizing the distribution network operation cost and the comprehensive operation risk of the distribution network. The constraints include:

[0089] Node voltage constraints:

[0090] V lowe ≤V i (t)≤V upper

[0091] Where V upper and V lowe are the maximum and minimum boundary values ​​of the node voltage amplitude respectively.

[0092] Line power constraints:

[0093] P ij ≤P ij,max

[0094] Where P ij,max is the maximum transmission power of the line from node i to node j.

[0095] Energy storage operation constraints:

[0096]

[0097] In the formula, δ is the self-discharge rate of energy storage; η is the charge and discharge efficiency of energy storage; S SOC,i (t start ) and S SOC,i (t end ) represent the charge state at the start and end of a cycle respectively.

[0098] Step S5: The particle swarm algorithm solves the double-layer model as follows: first input the distribution network system parameters, typical operation data, and constraints; the upper-level planning model randomly generates energy storage locations and capacities within the constraints as the initial population of the upper-level particle swarm, and then passes the energy storage location and capacity data of each particle into the lower-level scheduling model. The lower-level scheduling model solves the scheduling strategy of each energy storage and returns the scheduling strategy of the energy storage to the upper-level planning model. The upper-level planning model calculates the fitness of the upper-level particles according to the scheduling strategy, and updates the speed and position of the upper-level particles, and then updates the individual optimal position and global optimal position of the upper-level particles. The upper-level planning model repeats the above process until the termination condition is met. The lower-level scheduling model is responsible for formulating the scheduling strategy for energy storage. The lower-level scheduling model determines the distribution network parameters according to the location and capacity of the energy storage input by the upper-level planning model. Then, it randomly generates the charging and discharging behavior of the energy storage within the constraints to generate the initial population. Then, it solves the multi-objective function of the lower-level scheduling model to obtain the fitness of the lower-level particles, and updates the speed and position of the lower-level particles. Then, it updates the individual optimal position and the global optimal position of the lower-level particles. The lower-level scheduling model repeats the above process. When the lower-level scheduling model meets the termination conditions, it generates the lower-level Pareto solution set, combines the entropy weight method and the hierarchical analysis method to determine the optimal energy storage scheduling strategy, and returns it to the upper-level planning model. Finally, after repeated iterations of the upper and lower-level models, the optimal energy storage configuration of the current distribution network and the optimal energy storage scheduling scheme under typical scenarios are obtained.

[0099] The particle speed and position are updated as follows:

[0100]

[0101] In the formula, is the velocity of particle i at the kth iteration, is the velocity of particle i at the k-1th iteration, is the position of particle i at the kth iteration, is the position of particle i at the k-1th iteration, ω is the inertia weight, is the individual optimal position of particle i, is the global optimal position, c1, c2 are learning factors, and r1, r2 are random numbers in [0, 1].

[0102] Example 2

[0103] This embodiment provides a specific case to further illustrate the solution of the present invention. The specific simulation example and calculation parameters are as follows. Figure 3As shown, this implementation example adopts the IEEE 33-node distribution system. The base voltage of the distribution network is 12.66 kV, and the maximum and minimum values ​​of the node voltage are set to 1.07 pu and 0.93 pu; the maximum value of the line power is set to the maximum rated power. 500 kW wind turbines are connected at nodes 18 and 32, 1.2 MW photovoltaics are connected at nodes 18 and 33, and 600 kW photovoltaics are connected at node 16. The initial total load of the distribution network is 3715 kW + j2300 kvar, and 1000 electric vehicles are connected at node 28.

[0104] The parameters of the energy storage device and the electricity price are as follows:

[0105] Table 1 Energy storage device parameters

[0106]

[0107]

[0108] Table 2 Time-of-use electricity prices

[0109]

[0110] This example considers three simulation scenarios:

[0111] Option 1: No access to energy storage;

[0112] Solution 2: Use a single-layer model for optimization;

[0113] Solution 3: Use the double-layer model proposed in the present invention for optimization.

[0114] Table 3 Results of three schemes

[0115]

[0116] By analyzing the results of the three schemes in Table 3, compared with Scheme 1, which does not introduce energy storage for regulation, its cost and grid operation risk are higher than the case of introducing energy storage. It can be concluded that energy storage can make profits through low-price energy storage and high-price power generation, which can effectively reduce the cost of purchasing electricity from the upper power grid. At the same time, energy storage is charged during overvoltage and discharged during low voltage to alleviate the operation risk of the distribution network. Compared with Scheme 2 and Scheme 3, both schemes install energy storage in the node at the end of the line. The reason is that most new energy power generation equipment is installed at the end of the line, and when the load in the system is large, the voltage at the end of the line will be significantly reduced. From the specific numerical analysis, Scheme 3 is better than Scheme 2 in terms of cost and risk indicators. After adopting the double-layer optimization model, it can further optimize the scheduling of energy storage and formulate a more reasonable energy storage scheduling strategy, which reduces the overall cost by 665 yuan and the risk index by 0.1047.

[0117] The comprehensive risk indicators of different schemes are as follows: Figure 4 As shown in the figure, during the peak period of photovoltaic output (10:00-16:00), the distribution network is difficult to absorb photovoltaic power, resulting in an increase in the operating risk of the system. During the evening peak period (18:00-24:00), the increase in load leads to a risk of low voltage at the end of the system. After adopting the method proposed by the present invention, the total comprehensive risk is reduced by 44% compared with that before access, effectively improving the safety of the distribution network system operation.

[0118] In summary, the present invention provides an energy storage configuration method that takes into account the operating risks and economic benefits of the distribution network. First, based on the historical data of wind power, photovoltaics, and loads in the distribution network, a clustering method is used to select typical operating scenarios and corresponding probabilities. Then, a two-layer optimization model for energy storage configuration and scheduling strategy is constructed, including an upper optimization model and a lower optimization model: the upper planning model considers the configuration cost of the energy storage system and the operating cost of the distribution network, and explores the optimal configuration plan of the energy storage system; the lower scheduling model aims to optimize the operating risks and operating costs of the distribution network, explores the optimal scheduling strategy of the energy storage system under typical scenarios, and corrects the upper energy storage configuration plan. Finally, the particle swarm algorithm is used to solve the model. The method proposed in the present invention can effectively configure the energy storage system and formulate an energy storage scheduling strategy, while ensuring the safe operation of the distribution network, achieving the goal of optimizing the operating cost.

[0119] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for energy storage configuration considering the operation risk and economic benefit of a distribution network, characterized in that the steps include: Collect historical data on distributed renewable energy generation and load in the distribution network, and cluster the historical data to obtain typical scenarios and corresponding probabilities of distribution network operation; According to the distribution network system parameters and typical scenarios, a two-layer optimization model of energy storage configuration and dispatch strategy is established; The upper-level planning model uses the location and capacity of energy storage devices as decision variables, and takes the minimum energy storage configuration cost and the comprehensive operation cost of the distribution network as the goal to obtain the optimal configuration plan of the energy storage system. The lower-level dispatch model is a decision variable for energy storage charging and discharging behavior based on typical scenarios of energy storage systems. It takes the minimization of distribution network operation costs and comprehensive operation risks as the goal, obtains the optimal dispatch strategy of the energy storage system under typical scenarios, and corrects the dispatch strategy of the upper-level energy storage system configuration plan. The particle swarm algorithm is used to solve the two-layer optimization model to obtain the optimal configuration scheme of energy storage and the optimal scheduling strategy under typical scenarios.

2. The energy storage configuration method considering the operation risk and economic benefit of the distribution network according to claim 1 is characterized in that: The specific process of clustering historical data to obtain typical scenarios of distribution network operation and corresponding probabilities is: clustering historical data using a clustering algorithm, taking the center of the cluster as the typical operation scenario, and taking the proportion of each category as the probability of the typical scenario.

3. The energy storage configuration method considering the operation risk and economic benefit of the distribution network according to claim 1 is characterized in that: The objective function of the upper-level planning model is: In the formula, is the configuration cost of the energy storage device; is the operating cost of the energy storage device; C grid The cost of purchasing and selling electricity to the superior power grid; C loss It is the network operation loss cost.

4. The energy storage configuration method considering the operation risk and economic benefit of the distribution network according to claim 3 is characterized in that: The configuration cost and operation cost of the energy storage device are as follows: Where D is the number of days; c in,i and S i are the unit capacity configuration cost and capacity of the i-th energy storage respectively; r is the discount rate; N is the service life of energy storage; c om is the unit power operation and maintenance cost; P ESS,i is the charging and discharging power of the i-th energy storage.

5. The energy storage configuration method considering the operation risk and economic benefit of the distribution network according to claim 1, characterized in that: The constraints for optimization of the upper-level planning model include: Power balance constraints: Where P DG,j (t) is the access power of the jth distributed generation at time t; P load (t) is the total load at time t; Power flow balance constraints: Where P i and Q i are the active and reactive powers injected into node i respectively; V i and V j are the voltage amplitudes of nodes i and j respectively; j∈i represents all nodes directly connected to node i; G ij and B ij are the real and imaginary parts of the node admittance matrix respectively; θ ij is the phase angle difference between node i and node j; Energy storage equipment constraints: In the formula, S SOC,max and S SOC,min are the maximum and minimum values ​​of the state of charge of the energy storage device, S SOC (t) is the state of charge at time t; P ESS,max and P ESS,min are the maximum and minimum values ​​of the charging and discharging power of the energy storage device, P ESS (t) is the charge and discharge power at time t.

6. The energy storage configuration method considering the operation risk and economic benefit of the distribution network according to claim 1 is characterized in that: The objective function of the lower-level dispatching model to minimize the distribution network operation cost and the comprehensive operation risk of the distribution network is: In the formula, is the operating cost of the energy storage device; C grid The cost of purchasing and selling electricity to the superior power grid; C loss is the network operation loss cost; Risk is the operation risk of the distribution network.

7. The energy storage configuration method considering the operation risk and economic benefit of the distribution network according to claim 6 is characterized in that: The comprehensive operation risks of the distribution network include node voltage over-limit risks and line power overload risks: Where P i , Sev i They represent the voltage over-limit risk probability and the severity of voltage over-limit at node i respectively; P l , Sev l They represent the power overload probability and severity of power overload of line l respectively; k is the risk factor; θ(V i ) is the voltage offset indicator; V Base is the reference voltage, V max and V min PR is the upper and lower limits of the voltage allowable value; l is the load factor of line l, PR max It is the maximum value allowed for the line load rate.

8. The energy storage configuration method considering the operation risk and economic benefit of the distribution network according to claim 1 is characterized in that: The constraints for optimizing the lower-layer scheduling model include: Node voltage constraints: V lower ≤V i (t)≤V upper Where V upper and V lower are the maximum and minimum boundary values ​​of the node voltage amplitude respectively; Line power constraints: P ij ≤P ij,max Where P ij,max is the maximum transmission power of the line from node i to node j; Energy storage operation constraints: In the formula, δ is the self-discharge rate of energy storage; η is the charge and discharge efficiency of energy storage; S SOC,i (t start ) and S SOC,i (t end ) represent the charge state at the start and end of a cycle respectively.

9. The energy storage configuration method considering the operation risk and economic benefit of the distribution network according to claim 1, characterized in that: The specific process of solving the two-layer optimization model using the particle swarm algorithm is as follows: Input distribution network system parameters, typical operation data and constraints; The upper-level planning model randomly generates energy storage locations and capacities within the constraints as the initial population of the upper-level particle swarm, and then transmits the energy storage location and capacity data of each particle to the lower-level scheduling model. The lower-level scheduling model solves the scheduling strategy of each energy storage and returns the scheduling strategy of the energy storage to the upper-level planning model. The upper-level planning model calculates the fitness of the upper-level particles according to the scheduling strategy and the objective function of the upper-level planning model, and updates the speed and position of the upper-level particles, and then updates the individual optimal position and global optimal position of the upper-level particles. The upper-level planning model repeats the above process until the termination condition is met; The lower-level scheduling model determines the distribution network parameters according to the location and capacity of the energy storage input by the upper-level planning model, and then randomly generates the charging and discharging behavior of the energy storage within the constraint range to generate the initial population, and then solves the multi-objective function of the lower-level scheduling model to obtain the fitness of the lower-level particles, and updates the speed and position of the lower-level particles, and then updates the individual optimal position and global optimal position of the lower-level particles. The lower-level scheduling model repeats the above process. When the lower-level scheduling model meets the termination conditions, the lower-level Pareto solution set is generated, and the optimal energy storage scheduling strategy is determined by combining the entropy weight method and the hierarchical analysis method, and returned to the upper-level planning model; After repeated iterations of the upper and lower layer models, the optimal energy storage configuration of the current distribution network and the optimal energy storage scheduling plan under typical scenarios are obtained.

10. The energy storage configuration method considering the distribution network operation risk and economic benefits according to claim 9, characterized in that: The particle speed and position are updated as follows: In the formula, is the velocity of particle i at the kth iteration, is the velocity of particle i at the k-1th iteration, ω is the inertia weight; is the individual optimal position of particle i, is the global optimal position; c1, c2 are learning factors; r1, r2 are random numbers in [0, 1]; is the position of particle i at the kth iteration, is the position of particle i at the k-1th iteration.

Images