A method for optimizing the configuration of energy storage capacity to improve the reliability of the distribution network

Abstract

A method for optimizing the configuration of energy storage capacity to improve the reliability of a distribution network includes the following steps: (1) Define the "benefit-investment ratio" of the energy storage system: #imgabs0# where C total is the construction cost of the energy storage system, f1 is the economic benefit of configuring the energy storage system, and f2 is the reliability benefit of configuring the energy storage system; (2) Select the power range of the energy storage system and select the capacity range according to the power range of the energy storage system; (3) Estimate the "benefit-investment ratio" of configuring energy storage systems with different capacities in the power supply network; (4) According to the "benefit-investment ratio" of the energy storage system under the current capacity, search for the optimal configuration capacity of the energy storage system, that is, the configuration capacity under the maximum "benefit-investment ratio" of the energy storage system is the optimal configuration capacity of the energy storage system. The present invention can intelligently and efficiently plan the configuration of energy storage power and capacity, realize the comprehensive optimization of the reliability benefit and economic benefit of energy storage configuration, and has the advantage of high intelligence level.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and in particular to a method for optimizing the configuration of energy storage capacity to improve the reliability of distribution networks. Background Art

[0002] With the gradual increase in the scale of household electricity loads, the power supply pressure of the distribution network of the power system has gradually increased. In recent years, in order to relieve the pressure of the power system, it is necessary to implement measures of "peak shaving and valley filling" to balance the power generation side and the load side of the power grid and improve the stability of the bus voltage frequency.

[0003] Traditional power grid "peak shaving and valley filling" technologies usually maintain the stability of the voltage and frequency of the large power grid by means of inputting and cutting off loads. However, this method has large energy losses, causes poor user experience, and low power supply reliability. With the development of power electronic technology, a large number of energy storage systems (battery energy storage systems, BESS) are connected to the power system through power electronic devices. The energy storage absorbs the electric energy of the power grid during the low electricity consumption period and feeds back the excess electricity to the power grid during the high electricity consumption period, effectively realizing the "peak shaving and valley filling" of the power grid. This method effectively suppresses the fluctuations of the power grid voltage and frequency and improves the power supply quality of users. However, with the increase in the power and capacity of the energy storage, the manufacturing cost and maintenance cost of the energy storage also increase. How to optimize the power and capacity of the energy storage in the distribution network and balance the benefits and costs of the energy storage has become a practical problem.

[0004] In existing research, the impact of the access of new energy and energy storage on the reliability of distribution networks has been considered. For example, CN104851053 A discloses a method for evaluating the reliability of a distribution network with a wind-storage complementary microgrid access, CN104851053 A discloses a method for evaluating the power supply reliability of a distribution network with wind, light and storage, and CN110188998 A discloses a method for evaluating the sequential construction reliability of wind turbines and energy storage in a distribution network. The above patent literatures study the improved method for evaluating the reliability of a distribution network with devices such as wind, light and storage access, which has improved the evaluation accuracy of the impact of energy storage on the power supply reliability of the distribution network to a certain extent. With the increase in the scale of energy storage access to the distribution network, it is necessary to optimize the energy storage capacity of the distribution network to reduce the degree of grid voltage fluctuation. CN108551175 B discloses a method for configuring the energy storage capacity of a distribution network, and CN112260300 A discloses a method and device for determining the energy storage configuration and the optimal delay years. The above patent literatures study the optimization of the energy storage capacity of the distribution network, which effectively suppresses the voltage fluctuation of the grid, but does not consider the economic cost and has certain limitations. CN108599206 B discloses a method for configuring hybrid energy storage in a distribution network under a high proportion of uncertain power supply scenarios. This method uses the method of nonlinear programming to optimize the configuration of new energy. However, the constraint conditions used in this method are the results of linearizing a nonlinear model, which makes it easy to obtain the maximum value point of the real model but difficult to obtain the global optimum. CN112232983 A discloses a method, an electronic device and a storage medium for optimizing the configuration of energy storage in an active distribution network. From the perspective of considering the voltage fluctuation suppression ability and the economy of energy storage, the method for configuring energy storage in a distribution network is studied, but the reliability benefit of energy storage is not considered and it is mainly considered from the perspective of the power market. CN110061492 A discloses a method for optimizing the configuration of the energy storage system capacity considering the power supply reliability of the distribution network. From the perspective of the lowest construction cost and providing the maximum emergency power support, the method for optimizing the energy storage capacity of the distribution network is studied, which can effectively solve the actual needs of the distribution network scenario with important loads. However, for ordinary electrical loads, it does not focus on the maximum emergency support ability, but pays more attention to the reliability power supply index to obtain better benefits on the basis of improving the power supply reliability.

[0005] Therefore, traditional methods for optimizing the configuration of energy storage capacity mostly consider aspects such as power flow constraints and voltage fluctuations, but do not comprehensively consider the reliability benefits and economic benefits of the distribution network with energy storage access. For the power grid, it is more concerned about how to use energy storage to improve the power supply reliability of a specific area while improving the economy. At the same time, traditional methods are mostly based on comparison and enumeration calculation methods, and it takes a long time to search for the optimal operating point. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the deficiencies of the above background technology and provide a method for optimizing the configuration of energy storage capacity to improve the reliability of the distribution network, which solves the problem that the comprehensive optimization of the reliability benefit and economic benefit of energy storage access to the distribution network is not considered in the existing methods, and realizes the optimal economic configuration of energy storage on the premise of improving the reliability of the distribution network.

[0007] The technical solution adopted by the present invention to solve its technical problems is a method for optimizing the configuration of energy storage capacity to improve the reliability of the distribution network, including the following steps:

[0008] (1) Define the "benefit-input ratio" of the energy storage system:

[0009]

[0010] where C total is the construction cost of the energy storage system, f1 is the economic benefit of configuring the energy storage system, and f2 is the reliability benefit of configuring the energy storage system;

[0011] (2) Select the power range of the energy storage system and select the capacity range according to the power range of the energy storage system;

[0012] (3) Estimate the "benefit-input ratio" of the energy storage system with different capacities configured in the power supply network;

[0013] (4) According to the "benefit-input ratio" of the energy storage system under the current capacity, search for the best configuration capacity of the energy storage system, that is, the configuration capacity under the maximum "benefit-input ratio" of the energy storage system is the best configuration capacity of the energy storage system.

[0014] Further, in step (1), the economic benefit f1 of configuring the energy storage system can be expressed as:

[0015] f1 = B1 + B2 (2)

[0016] where B1 represents the economic benefit of energy storage "peak shaving", and B2 represents the benefit of reducing the installed capacity of the generator set;

[0017] where the economic benefit B1 of energy storage "peak shaving" can be expressed as:

[0018]

[0019] where P id represents the discharge power of the energy storage system in the i-th hour, P ie represents the charging power of the energy storage system in the i-th hour, and R i is the real-time electricity price in the i-th hour;

[0020] where the benefit B2 of reducing the installed capacity of the generator set can be expressed as:

[0021] B2 = λk s P h (4)

[0022] where k s represents the price per unit installed capacity, λ is the asset depreciation rate, and P h is the power of the energy storage system when the load reaches its maximum value;

[0023] where the reliability benefit f2 of the configured energy storage system can be expressed as:

[0024]

[0025] where M represents the number of load points, and K j represents the number of power outages at load point j, P jk is the load value of load point j during the k-th power outage, T OFFjk is the power outage time of load point j during the k-th power outage, and C Ljk is the average power outage loss of load point j during the k-th power outage;

[0026] where the construction cost C of the energy storage system total can be expressed as:

[0027] C total = C INESS + C RESS (6)

[0028] where C INESS represents the one-time construction cost of the energy storage system, and C RESS represents the total maintenance cost of the energy storage system;

[0029] where the one-time construction cost C of the energy storage system INESS can be expressed as:

[0030] C INESS = k e E N + (k p + k f )P N (7)

[0031] where k e is the expenditure per unit capacity of the energy storage system; k p is the expenditure per unit power of the converter, and k f is the expenditure per unit power of the accessories, and E N represents the capacity of the energy storage system, and P N represents the power of the energy storage system;

[0032] where the total maintenance cost C of the energy storage system RESS can be expressed as:

[0033] C RESS = k r P N (8)

[0034] where k r is the maintenance expenditure per unit power.

[0035] Furthermore, the Monte Carlo method is used to simulate the average power supply availability index and the expected power shortage index of the system, and the power range of the energy storage system is selected according to the average power supply availability index and the expected power shortage index.

[0036] Furthermore, the power range of the energy storage system is selected according to the average power supply availability index and the expected power shortage index. The specific method is as follows: draw the curves of the average power supply availability index and the expected power shortage index changing with power; select the sum of the power value corresponding to the minimum value of the expected power shortage index and the power value corresponding to the maximum value of the expected power shortage index and divide it by 2 to define the reference power P N1 of the energy storage system; select the sum of the power value corresponding to the minimum value of the average power supply availability index and the power value corresponding to the maximum value of the average power supply availability index and divide it by 2 to define the reference power P N2 of the energy storage system; the expression of the power range of the energy storage system is (1 ± 10%)(P N1 + P N2 ).

[0037] Furthermore, in step (3), the Monte Carlo method is used to estimate the "benefit - investment ratio" of energy storage systems with different capacities configured in the power supply network.

[0038] Furthermore, in step (3), using the Monte Carlo method to estimate the "benefit - investment ratio" of energy storage systems with different capacities configured in the power supply network includes the following steps:

[0039] (3 - 1) Set the maximum Monte Carlo simulation duration T, and initialize the simulation time t = 0;

[0040] (3 - 2) Randomly generate faults for each load point in the system;

[0041] (3 - 3) Determine whether there is a fault at each load point at the current time t;

[0042] (3 - 4) If there is a fault, determine whether the power supply of the load point can be restored;

[0043] (3 - 5) If it can be restored, determine whether the energy storage system is normal;

[0044] (3 - 6) If it is normal, determine whether the power P BESS of the energy storage system is greater than the power P load of the current load;

[0045] (3 - 7) If it is greater, determine whether the remaining capacity of the energy storage system can maintain the normal operation of the system for one hour;

[0046] (3 - 8) If it can, restore power supply to the power outage area;

[0047] (3 - 9) Repeat steps (3 - 2) to (3 - 8) until the simulation time t reaches the maximum simulation duration T;

[0048] (3 - 10) Calculate the "benefit - input ratio" of the energy storage system at the current capacity.

[0049] Furthermore, in step (4), according to the "benefit - input ratio" of the energy storage system at the current capacity, use the particle swarm algorithm to search for the optimal configuration capacity of the energy storage system.

[0050] Furthermore, in step (4), using the particle swarm algorithm to search for the optimal configuration capacity of the energy storage system includes the following steps:

[0051] (4 - 1) Initialize the configuration parameters of the particle swarm algorithm;

[0052] (4 - 2) Define the fitness function as the "benefit - input ratio" of the energy storage system defined in step (1), and the maximum value of the individual fitness function of the particle is the "individual optimal solution" of each particle;

[0053] (4 - 3) Compare the maximum values of the individual fitness functions of all particles, select the maximum value among them, and define it as the "global optimal solution";

[0054] (4 - 4) Compare the current "global optimal solution" with the historical "global optimal solution", and update the particle velocity and position of the independent variable;

[0055] (4 - 5) Determine whether the condition for terminating the iterative operation is met. If the current number of iterations reaches the set maximum number of iterations, terminate the iteration, and select the configuration capacity corresponding to the maximum value among all "global optimal solutions" as the optimal configuration capacity of the energy storage system; otherwise, return to step (3) to continue execution.

[0056] Furthermore, in step (4 - 1), the specific initialization of the configuration parameters of the particle swarm algorithm is as follows: Set the maximum number of iterations, the number of independent variables, and the maximum particle velocity; the independent variable of the particle swarm algorithm is the capacity of the energy storage system; set the initial velocity and position of the particle swarm, and set the size of the particle swarm to M.

[0057] Furthermore, in step (4 - 4), the formulas for updating the velocity and position are expressed as:

[0058]

[0059] where ω (ω≥0) is the inertia weight; C1 is the individual learning factor, and C2 is the social learning factor; random(0,1) represents any value between 0 and 1; P id is the d-th dimension variable of the independent variable of the i-th particle; P gd is the d-th dimension variable of the global optimal solution; X id is the d-th dimension variable of the position of the i-th particle at the previous time; V id is the d-th dimension variable of the velocity set of the i-th particle. Compared with the prior art, the advantages of the present invention are as follows:

[0060] The present invention evaluates the comprehensive benefits of the reliability benefits and economic benefits of energy storage configuration, and proposes an energy storage capacity optimization configuration method for improving the reliability of a distribution network based on a particle swarm algorithm, which can intelligently and efficiently plan the configuration of energy storage power and capacity, achieve the comprehensive optimum of the reliability benefits and economic benefits of energy storage configuration, and has the advantage of high intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a flowchart of the method of the embodiment of the present invention.

[0062] Figure 2 is a flowchart of using the Monte Carlo method in the embodiment of the present invention to estimate the "benefit-investment ratio" of energy storage systems with different capacities configured in a power supply network.

[0063] Figure 3 is a schematic structural diagram of the IEEE-34 system in the embodiment of the present invention.

[0064] Figure 4 is a schematic diagram of the average power supply availability index of the IEEE-34 system in the embodiment of the present invention.

[0065] Figure 5 is a schematic diagram of the expected power shortage index of the IEEE-34 system in the embodiment of the present invention.

[0066] Figure 6 is a schematic diagram of the optimal "benefit-investment ratio" of the energy storage system in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0068] The present invention evaluates the comprehensive benefits of the reliability benefits and economic benefits of energy storage configuration, and proposes an energy storage capacity optimization configuration method for improving the reliability of a distribution network based on a particle swarm algorithm.

[0069] Referring to Figure 1 , the method of this embodiment includes the following steps:

[0070] (1) Define the "benefit - input ratio" of the energy storage system:

[0071]

[0072] Among them, C total is the construction cost of the energy storage system, f1 is the economic benefit of configuring the energy storage system, and f2 is the reliability benefit of configuring the energy storage system.

[0073] Among them, the economic benefit f1 of configuring the energy storage system can be expressed as:

[0074] f1 = B1 + B2 (2)

[0075] Among them, B1 represents the economic benefit of energy storage "peak shaving", and B2 represents the benefit of reduced generator installation capacity.

[0076] Among them, the economic benefit B1 of energy storage "peak shaving" can be expressed as:

[0077]

[0078] Among them, P id represents the discharge power of the energy storage system in the i - th hour, P ie represents the charging power of the energy storage system in the i - th hour, and R i is the real - time electricity price in the i - th hour.

[0079] Among them, the benefit B2 of reduced generator installation capacity can be expressed as:

[0080] B2 = λk s P h (4)

[0081] Among them, k s represents the price per unit of installed capacity, λ is the asset depreciation rate, and P h is the power of the energy storage system when the load reaches the maximum value.

[0082] Among them, the reliability benefit f2 of configuring the energy storage system can be expressed as:

[0083]

[0084] Among them, M represents the number of load points, K j represents the number of power outages at load point j, P jk is the load value of load point j during the k - th power outage, T OFFjk is the power outage time of load point j during the k - th power outage, and C Ljk is the average power outage loss of load point j during the k - th power outage.

[0085] Among them, the construction cost C of the energy storage system totalIt can be expressed as:

[0086] C total = C INESS + C RESS (6)

[0087] Among them, C INESS represents the one-time construction cost of the energy storage system, and C RESS represents the total maintenance cost of the energy storage system.

[0088] Among them, the one-time construction cost C of the energy storage system INESS can be expressed as:

[0089] C INESS = k e E N + (k p + k f )P N (7)

[0090] Among them, k e is the expenditure of the energy storage system per unit capacity. k p is the expenditure of the converter per unit power, and k f is the expenditure of the accessories per unit power. E N represents the capacity of the energy storage system, and P N represents the power of the energy storage system.

[0091] Among them, the total maintenance cost C of the energy storage system RESS can be expressed as:

[0092] C RESS = k r P N (8)

[0093] Among them, k r is the maintenance expenditure per unit power.

[0094] (2) Assume that the capacity of the energy storage system is infinite. Use the Monte Carlo method to simulate the average service availability index (ASAI) and the expected energy not supplied (EENS) of the system, and select the power range of the energy storage system according to ASAI and EENS. Specifically: draw the curves of ASAI and EENS changing with power respectively; select the sum of the power value corresponding to the minimum value of EENS and the power value corresponding to the maximum value of EENS and divide it by 2 to define the reference power P N1 of the energy storage system; select the sum of the power value corresponding to the minimum value of ASAI and the power value corresponding to the maximum value of ASAI and divide it by 2 to define the reference power PN2 ; The power range expression of the energy storage system is (1 ± 10%)(P N1 +P N2 ), ensuring that the power of the energy storage system is neither too large nor too small. Select the capacity range according to the power range of the energy storage system, E N =P N t, E N represents the capacity of the energy storage system, P N represents the power of the energy storage system, and t represents time.

[0095] (3) Use the Monte Carlo method to estimate the "benefit - input ratio" of energy storage systems with different capacities configured in the power supply network. The execution process is as Figure 2 shown.

[0096] Step S1: Set the maximum Monte Carlo simulation duration T, and initialize the simulation time t = 0 (hours);

[0097] Step S2: Randomly generate faults for each load point in the system;

[0098] Step S3: Determine whether there is a fault at each load point at the current time t; if so, go to Step S4, if not, go to Step S8;

[0099] Step S4: Determine whether the power supply of the load point can be restored; if so, go to Step S5; if not, the entire system is powered off;

[0100] Step S5: Determine whether the energy storage system is normal; if so, go to Step S6; if not, the entire system is powered off;

[0101] Step S6: Determine whether the power P BESS of the energy storage system is greater than the power P load of the current load; if so, go to Step S7; if not, the power - off area remains powered off;

[0102] Step S7: Determine whether the remaining capacity of the energy storage system can maintain the normal operation of the system for one hour, that is, determine whether E BESS -E min >P load , E BESS is the current state - of - charge capacity of the energy storage system, and E min is the minimum state - of - charge capacity of the energy storage system; if so, the power - off area is restored; if not, the power - off area remains powered off;

[0103] Step S8: t = t + 1;

[0104] Step S9: Determine whether T ≥ t; if so, return to Step S3; if not, go to Step S10;

[0105] Step S10: Calculate the "return - investment ratio" of the energy storage system at the current capacity.

[0106] (4) According to the "return - investment ratio" of the energy storage system at the current capacity, use the particle swarm algorithm to search for the optimal configuration capacity of the energy storage system, that is, the configuration capacity corresponding to the maximum "return - investment ratio" of the energy storage system is the optimal configuration capacity of the energy storage system. The specific process includes the following steps:

[0107] (4 - 1) Initialize the configuration parameters of the particle swarm algorithm: set the maximum number of iterations, the number of independent variables, and the maximum particle velocity; the independent variable of the particle swarm algorithm is the capacity of the energy storage system; set the initial velocity and position of the particle swarm, and set the size of the particle swarm to M;

[0108] (4 - 2) Define the fitness function as the "return - investment ratio" of the energy storage system defined in step (1), and the maximum value of the individual fitness function of the particle is the "individual optimal solution" of each particle;

[0109] (4 - 3) Compare the maximum values of the individual fitness functions of all particles, select the maximum value among them, and define it as the "global optimal solution". The global search goal of the particle swarm optimization (PSO) algorithm is to find the maximum "return - investment ratio" under the condition of changing the capacity of the energy storage system.

[0110] (4 - 4) Compare the current "global optimal solution" with the historical "global optimal solution", and update the particle velocity and position of the independent variable. The formulas for updating the velocity and position are expressed as:

[0111]

[0112] where ω (ω≥0) is the inertia weight. C1 is the individual learning factor, and C2 is the social learning factor. random(0,1) represents any value between 0 and 1. P id is the d - th variable of the independent variable of the i - th particle. P gd is the d - th variable of the global optimal solution. X id is the d - th variable of the previous position of the i - th particle. V id is the d - th variable of the velocity set of the i - th particle.

[0113] (4 - 5) Determine whether the condition for terminating the iterative operation is satisfied. If the current number of iterations reaches the set maximum number of iterations, terminate the iteration, and select the configuration capacity corresponding to the maximum value among all "global optimal solutions" as the optimal configuration capacity of the energy storage system. Otherwise, return to step (3) and continue to execute.

[0114] The method proposed in this embodiment is used for test verification on the nodes of the IEEE-34 standard system. The structure of IEEE-34 is as shown in Figure 3 . The failure rates of the load nodes of IEEE-34 are as shown in the matrix Lamda, and the mean time to repair (MTTR) of each point is as shown in the matrix MTTR34:

[0115] Lambda = [0.3979; 0.8209; 0.7666; 0.1206; 0.8577; 0.2586; 0.7049; 0.2429; 0.3521; 0.8118; 0.1850; 0.5135; 0.3920; 0.7194; 0.6776; 0.2065; 0.3197; 0.3226; 0.8566; 0.3348; 0.8950; 0.8833; 0.6400; 0.0412; 0.0518; 0.9458; 0.2257; 0.7303; 0.2191; 0.0101; 0.7205; 0.1289; 0.1327].

[0116] MTTR34 = [0.3546; 0.6970; 0.8490; 0.8724; 0.0411; 0.2098; 0.7382; 0.7379; 0.1978; 0.4534; 0.2299; 0.0704; 0.3979; 0.8555; 0.6809; 0.2954; 0.8536; 0.7195; 0.3405; 0.0495; 0.0174; 0.7846; 0.2554; 0.6597; 0.8496; 0.2965; 0.2238; 0.0066; 0.0684; 0.4306; 0.7953; 0.7759; 0.9673].

[0117] Assume that the connection location of the energy storage system has been determined, that is, it is connected to the load point 890. First, the power of the energy storage system needs to be determined. Without considering the capacity constraint of the energy storage system, the average service availability index (ASAI) and the expected energy not supplied (EENS) of the system are calculated according to Lamda and MTTR34, as shown in Figure 4 and Figure 5 respectively. From Figure 4 and Figure 5It can be seen that when the output power of the energy storage system is greater than 2 MW, the improvement rate of system reliability slows down. At the same time, after a 2-MW energy storage system is connected to the system, the ASAI is increased to 99.958%, and the EENS is reduced to 6000 kW·h. However, too large a power of the energy storage system may lead to waste of resources. Therefore, in order to coordinate the reliability benefits and the cost of the energy storage system, the rated power of the energy storage system is set to 1 MW. After determining the rated power of the energy storage system, the capacity of the energy storage system is determined, and the optimal "benefit-investment ratio" of the energy storage system is calculated as Figure 6 shown. The results show that the process is iteratively updated five times. Through the calculation of this method, when the capacity of the energy storage system is set to 2.8 kW·h, the best cost-benefit ratio can be obtained as 0.0128. That is, by configuring the power of the energy storage system to 1 MW, the benefits of the energy storage system running for 8 years can recover the cost.

[0118] The system under study selects the IEEE-34 standard node system. Without considering the load type, a general load model is selected for calculation. The core indicators of the key parameters for calculating the energy storage capacity configuration using the method of the present invention are shown in Table 1, which involves the cost calculation index parameters of the energy storage system, the single power outage loss, the asset depreciation rate, etc.

[0119] Table 1 Main parameters for calculating the energy storage capacity configuration

[0120]

[0121]

[0122] The present invention proposes the definition of the "benefit-investment ratio", and based on this, proposes a comprehensive configuration method for the energy storage capacity of the distribution network that takes into account both economy and reliability, which can achieve the optimal comprehensive benefit of the energy storage configuration of the distribution network. The proposed energy storage capacity optimization configuration method based on the particle swarm algorithm avoids the parameter trial and enumeration calculation of the traditional method, and realizes the intelligent planning of the energy storage configuration of the distribution network. It solves the problem that the existing method does not consider the comprehensive optimization of the reliability benefit and the economic benefit in the energy storage access to the distribution network, can intelligently and efficiently plan the configuration of the energy storage power and capacity, realizes the comprehensive optimization of the reliability benefit and the economic benefit of the energy storage configuration, and has the advantage of high intelligence.

[0123] Those skilled in the art can make various modifications and variations to the present invention. If these modifications and variations are within the scope of the claims of the present invention and their equivalent technologies, then these modifications and variations are also within the protection scope of the present invention.

[0124] The content not described in detail in the specification is the prior art well known to those skilled in the art.

Claims

1. An energy storage capacity optimization configuration method for improving the reliability of a distribution network, characterized in that It includes the following steps: (1) Define the "benefit - input ratio" of the energy storage system: (1) Among them, is the construction cost of the energy storage system, is the economic benefit of configuring the energy storage system, is the reliability benefit of configuring the energy storage system; (2) Select the power range of the energy storage system, and select the capacity range according to the power range of the energy storage system; use the Monte Carlo method to simulate and obtain the average power supply availability index and the expected power shortage index of the system, and select the power range of the energy storage system according to the average power supply availability index and the expected power shortage index; select the power range of the energy storage system according to the average power supply availability index and the expected power shortage index. The specific method is as follows: draw the curves of the average power supply availability index and the expected power shortage index changing with power; select the sum of the power value corresponding to the minimum value of the expected power shortage index and the power value corresponding to the maximum value of the expected power shortage index divided by 2 as the reference power of the energy storage system ; select the sum of the power value corresponding to the minimum value of the average power supply availability index and the power value corresponding to the maximum value of the average power supply availability index divided by 2 as the reference power of the energy storage system ; the expression of the power range of the energy storage system is ; , represents the capacity of the energy storage system, represents the power of the energy storage system, represents time; (3) Estimate the "benefit - input ratio" of energy storage systems with different capacities configured in the power supply network; use the Monte Carlo method to estimate the "benefit - input ratio" of energy storage systems with different capacities configured in the power supply network; (4) According to the "benefit - input ratio" of the energy storage system under the current capacity, search for the optimal configuration capacity of the energy storage system, that is, the configuration capacity corresponding to the maximum "benefit - input ratio" of the energy storage system is the optimal configuration capacity of the energy storage system.

2. The energy storage capacity optimization configuration method for improving the reliability of the distribution network according to claim 1, characterized in that: In step (1), the economic benefit of configuring the energy storage system is expressed as: (2) Among them, represents the economic benefit of energy storage for "peak shaving", represents the revenue of reduced generator installed capacity; Among them, the economic benefits of energy storage for "peak shaving" are expressed as: (3) Among them, represents the discharge power of the energy storage system at the th hour, represents the charging power of the energy storage system at the th hour, is the real-time electricity price at the th hour; Among them, the reduced installed capacity revenue of the generator set is expressed as: (4) Among them, represents the price per unit installed capacity, is the asset depreciation rate, is the power of the energy storage system when the load reaches the maximum value; Among them, the reliability benefit of configuring the energy storage system is expressed as: (5) Among them, represents the number of load points, represents the load point of the number of power outages, is the th power outage of the load point load value, is the th power outage of the load point power outage time, is the th power outage of the load point average power outage loss; Among them, the construction cost of the energy storage system is expressed as: (6) Among them, represents the one-time construction cost of the energy storage system, represents the total maintenance cost of the energy storage system; Among them, the one-time construction cost of the energy storage system is expressed as: (7) Among them, is the expenditure of the energy storage system per unit capacity; is the expenditure of the converter per unit power, is the expenditure of accessories per unit power, represents the capacity of the energy storage system, represents the power of the energy storage system; Among them, the total maintenance cost of the energy storage system is expressed as: (8) Among them, is the maintenance expenditure per unit power.

3. The energy storage capacity optimization configuration method for improving the reliability of the distribution network according to claim 1, characterized in that: In step (3), using the Monte Carlo method to estimate the "benefit - input ratio" of energy storage systems with different capacities configured in the power supply network includes the following steps: (3 - 1) Set the maximum simulation duration T of Monte Carlo, and initialize the simulation time t = 0; (3 - 2) Randomly generate faults for each load point in the system; (3 - 3) Judge whether there is a fault at each load point at the current time t; (3 - 4) If there is a fault, judge whether the power supply of the load point can be restored; (3 - 5) If it can be restored, judge whether the energy storage system is normal; If it is normal (3 - 6), judge the power of the energy storage system whether it is greater than the power of the current load ; (3 - 7) If it is greater, judge whether the remaining capacity of the energy storage system can maintain the normal operation of the system for one hour; (3 - 8) If it can, restore power supply to the power outage area; (3 - 9) Repeat steps (3 - 2) to (3 - 8) until the simulation time t reaches the maximum simulation duration T; (3 - 10) Calculate the "benefit - input ratio" of the energy storage system under the current capacity.

4. The energy storage capacity optimization configuration method for improving the reliability of the distribution network according to claim 1, characterized in that: In step (4), according to the "benefit - input ratio" of the energy storage system under the current capacity, use the particle swarm algorithm to search for the optimal configuration capacity of the energy storage system.

5. The energy storage capacity optimization configuration method for improving the reliability of the distribution network according to claim 4, characterized in that: In step (4), using the particle swarm algorithm to search for the optimal configuration capacity of the energy storage system includes the following steps: (4 - 1) Initialize the configuration parameters of the particle swarm algorithm; (4 - 2) Define the fitness function as the "benefit - input ratio" of the energy storage system defined in step (1), and the maximum value of the individual fitness function of the particle is the "individual optimal solution" of each particle; (4 - 3) Compare the maximum values of the individual fitness functions of all particles, select the maximum value among them, and define it as the "global optimal solution"; (4 - 4) Compare the current "global optimal solution" with the historical "global optimal solution", and update the particle velocity and position of the independent variable; (4 - 5) Judge whether the condition for terminating the iterative operation is met. If the current number of iterations reaches the set large number of iterations, terminate the iteration, and select the configuration capacity corresponding to the maximum value among all "global optimal solutions" as the optimal configuration capacity of the energy storage system; otherwise, return to step (3) to continue execution.

6. The energy storage capacity optimization configuration method for improving the reliability of the distribution network according to claim 5, characterized in that: In step (4 - 1), the specific initialization of the configuration parameters of the particle swarm algorithm is as follows: Set the maximum number of iterations, the number of independent variables, and the maximum particle velocity; the independent variable of the particle swarm algorithm is the capacity of the energy storage system; set the initial velocity and position of the particle swarm, and set the size of the particle swarm to M.

7. The energy storage capacity optimization configuration method for improving the reliability of the distribution network according to claim 5, characterized in that: In step (4 - 4), the formulas for updating the velocity and position are expressed as: (9) wherein is the inertial weight; C 1 is the individual learning factor, C 2 is the social learning factor; random (0, 1) represents any value between 0 and 1; is the th dimension variable of the th independent variable of the particle; is the th dimension variable of the global optimal solution; is the th dimension variable of the velocity set of the th updated particle, is the th dimension variable of the velocity set of the th particle before update, is the th dimension variable of the previous position of the th updated particle, is the th dimension variable of the previous position of the th particle before update.

Images