Active power distribution network energy storage optimization configuration method, device, equipment and medium

Abstract

The invention discloses an active power distribution network energy storage optimization configuration method and device, equipment and a medium, and relates to the field of power distribution network energy storage optimization, and the method comprises the steps: obtaining the power flow distribution of an active power distribution network at a current moment; constructing an energy storage optimization configuration model of the active power distribution network; based on the power flow distribution at the current moment, an improved butterfly optimization algorithm is adopted to carry out iterative solution on the energy storage optimal configuration model, and an optimal configuration scheme of the energy storage system in the active power distribution network at the current moment is determined; the improved butterfly optimization algorithm is a butterfly algorithm adopting a dynamic switching probability and a dynamic variance Gaussian mutation strategy; the optimal configuration scheme is a configuration scheme corresponding to the maximum number of iterations; the configuration scheme comprises the access position, the capacity and the charging and discharging power of the energy storage system, optimal configuration of the energy storage system in the active power distribution network can be achieved, and the optimal configuration scheme of the energy storage system in the power distribution network is determined.

Description

Technical Field

[0001] The present application relates to the field of distribution network energy storage optimization, and in particular to an active distribution network energy storage optimization configuration method, device, equipment and medium. Background Art

[0002] At present, the traditional power system dominated by fossil energy is gradually transforming to an energy structure dominated by renewable energy such as wind, light, and water. However, distributed generation (DG) devices dominated by photovoltaic and wind power have the characteristics of unstable output power, which will not only cause drastic fluctuations in the voltage of the system nodes, but also affect the power quality of the system. In addition, wind and solar power sources have obvious anti-peak characteristics. Their output does not match the load demand of the system in time and space dimensions. The imbalance between supply and demand between the source and load of the distribution network will also increase, making the peak-to-valley difference of the system load larger, increasing the peak-to-peak pressure of the system, and the DG power that cannot be absorbed will flow back layer by layer, causing the local voltage of the distribution network to increase or even exceed the limit, resulting in increased network losses in the distribution network. These problems undoubtedly bring more severe challenges to the peak-to-peak and frequency-regulation and safe dispatching of the power grid.

[0003] With its flexible charging and discharging regulation and time-shifting energy capabilities, the energy storage system (ESS) has become an important regulatory resource for optimizing the operation of the power grid. As a key technology for building a new power system, the large-scale application of energy storage is inevitable. The access location, capacity selection and corresponding operation strategy of the energy storage system are inseparable from the stable operation of the distribution network. If it is not reasonably optimized and configured, it is equivalent to adding a lot of disturbing power sources to the distribution network, which is not conducive to the safe and stable operation of the power grid.

[0004] In the above context, in order to more reasonably configure energy storage in the active distribution network to face the upcoming energy transformation challenges, it is necessary to fully consider the operating characteristics of the active distribution network and energy storage in the planning stage, determine its site selection range through reasonable methods, and optimize its capacity configuration and operation strategy. Therefore, how to achieve the optimal configuration of energy storage in the active distribution network has become a problem that needs to be solved urgently. Summary of the invention

[0005] The purpose of this application is to provide a method, device, equipment and medium for optimizing the configuration of energy storage in an active distribution network, so as to achieve the optimal configuration of the energy storage system in the active distribution network and determine the optimal configuration scheme of the energy storage system in the distribution network.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] In a first aspect, the present application provides a method for optimizing energy storage configuration of an active distribution network, comprising:

[0008] Obtain the power flow distribution of the active distribution network at the current moment;

[0009] Constructing an energy storage optimization configuration model for an active distribution network; the energy storage optimization configuration model includes an objective function and constraints; the objective function is determined based on a grid vulnerability measurement index, active network loss, and energy storage system capacity; the constraints include: operation constraints, electrical quantity constraints, node voltage and branch power flow electrical constraints, and energy storage system constraints; the energy storage system constraints include: energy storage system capacity constraints, energy storage system charge and discharge power constraints, and energy storage system access location constraints;

[0010] Based on the power flow distribution at the current moment, an improved butterfly optimization algorithm is used to iteratively solve the energy storage optimization configuration model to determine the optimal configuration scheme of the energy storage system in the active distribution network at the current moment; the improved butterfly optimization algorithm is a butterfly algorithm that adopts a dynamic switching probability and a dynamic variance Gaussian mutation strategy; the optimal configuration scheme is the configuration scheme corresponding to the maximum number of iterations; the configuration scheme includes: the access location, capacity and charging and discharging power of the energy storage system.

[0011] In a second aspect, the present application provides an active distribution network energy storage optimization configuration device, comprising:

[0012] The power flow distribution acquisition module is used to obtain the power flow distribution of the active distribution network at the current moment;

[0013] A configuration model building module is used to build an energy storage optimization configuration model for an active distribution network; the energy storage optimization configuration model includes an objective function and constraints; the objective function is determined based on a grid vulnerability measurement index, active network loss, and energy storage system capacity; the constraints include: operation constraints, electrical quantity constraints, node voltage and branch power flow electrical constraints, and energy storage system constraints; the energy storage system constraints include: energy storage system capacity constraints, energy storage system charge and discharge power constraints, and energy storage system access location constraints;

[0014] The configuration optimization module is used to iteratively solve the energy storage optimization configuration model based on the power flow distribution at the current moment by using an improved butterfly optimization algorithm to determine the optimal configuration scheme of the energy storage system in the active distribution network at the current moment; the improved butterfly optimization algorithm is a butterfly algorithm that adopts a dynamic switching probability and a dynamic variance Gaussian mutation strategy; the optimal configuration scheme is the configuration scheme corresponding to the maximum number of iterations; the configuration scheme includes: the access location, capacity and charging and discharging power of the energy storage system.

[0015] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described active distribution network energy storage optimization configuration methods.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for optimizing configuration of energy storage in an active distribution network.

[0017] According to the specific embodiments provided in this application, this application has the following technical effects:

[0018] The present application provides an active distribution network energy storage optimization configuration method, device, equipment and medium. By constructing an energy storage optimization configuration model of the active distribution network, an improved butterfly optimization algorithm is used to iteratively solve the energy storage optimization configuration model to determine the optimal configuration scheme of the energy storage system in the active distribution network at the current moment. The improved butterfly optimization algorithm is a butterfly algorithm that adopts a dynamic switching probability and a dynamic variance Gaussian mutation strategy. The present application realizes the optimal configuration of the energy storage system in the active distribution network, and can determine the optimal configuration scheme of the energy storage system in the distribution network (including the access location, capacity and charging and discharging power of the energy storage system), thereby ensuring the safe and stable operation of the active distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 This is an application environment diagram of an active distribution network energy storage optimization configuration method in one embodiment of the present application;

[0021] Figure 2 A schematic diagram of a flow chart of an active distribution network energy storage optimization configuration method provided in one embodiment of the present application;

[0022] Figure 3 A flowchart of a butterfly optimization algorithm provided in one embodiment of the present application;

[0023] Figure 4 A flowchart of an improved butterfly optimization algorithm provided in one embodiment of the present application;

[0024] Figure 5 A flowchart for solving an active distribution network energy storage optimization configuration model using an improved butterfly optimization algorithm provided in an embodiment of the present application;

[0025] Figure 6 A schematic diagram of functional modules of an active distribution network energy storage optimization configuration method device provided by another embodiment of the present application;

[0026] Figure 7 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0029] The active distribution network energy storage optimization configuration method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the power flow distribution of the active distribution network at the current moment to the server 104. After the server 104 receives the power flow distribution of the active distribution network at the current moment, for the power flow distribution of the active distribution network at the current moment, the server 104 constructs an energy storage optimization configuration model for the active distribution network; based on the power flow distribution at the current moment, the improved butterfly optimization algorithm is used to iteratively solve the energy storage optimization configuration model to determine the optimal configuration scheme of the energy storage system in the active distribution network at the current moment; the improved butterfly optimization algorithm is a butterfly algorithm that adopts a dynamic switching probability and a dynamic variance Gaussian mutation strategy; the optimal configuration scheme is the configuration scheme corresponding to the maximum number of iterations; the configuration scheme includes: the access location, capacity and charging and discharging power of the energy storage system.

[0030] The server 104 can feed back the obtained optimal configuration solution to the terminal 102. In addition, in some embodiments, the active distribution network energy storage optimization configuration method can also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 can directly process the power flow distribution of the active distribution network at the current moment, or the server 104 can obtain the power flow distribution of the active distribution network at the current moment from the data storage system and process the power flow distribution of the active distribution network at the current moment.

[0031] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0032] In an exemplary embodiment, Figure 2 As shown, a method for optimizing the configuration of energy storage in an active distribution network is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 203. Among them:

[0033] Step 201, obtaining the power flow distribution of the active power distribution network at the current moment.

[0034] Step 202: construct an energy storage optimization configuration model for the active distribution network; the energy storage optimization configuration model includes an objective function and constraint conditions.

[0035] Among them, the objective function is determined based on the grid vulnerability measurement index, active network loss and energy storage system capacity; the constraints include: operation constraints, electrical quantity constraints, node voltage and branch flow electrical constraints and energy storage system constraints; the energy storage system constraints include: energy storage system capacity constraints, energy storage system charging and discharging power constraints and energy storage system access location constraints.

[0036] Step 203, based on the power flow distribution at the current moment, an improved butterfly optimization algorithm is used to iteratively solve the energy storage optimization configuration model to determine the optimal configuration scheme of the energy storage system in the active distribution network at the current moment; the improved butterfly optimization algorithm is a butterfly algorithm that uses a dynamic switching probability and a dynamic variance Gaussian mutation strategy.

[0037] Among them, the optimal configuration scheme is the configuration scheme corresponding to the maximum number of iterations; the configuration scheme includes: the access location, capacity and charging and discharging power of the energy storage system.

[0038] By implementing the above steps 201 to 203, the optimal configuration scheme of the energy storage system in the distribution network can be determined, and the optimal access location, capacity and charge and discharge power of the energy storage system can be obtained, thereby ensuring the safe and stable operation of the active distribution network.

[0039] In another exemplary embodiment of the present application, this embodiment first introduces the butterfly optimization algorithm.

[0040] The Butterfly Optimization Algorithm (BOA) has been widely used in the processing of multidimensional complex function problems such as mechanical design, feature extraction, and clustering. It has been generally proven to have the advantages of simple operation, few adjustment parameters, and good robustness. However, there are currently few studies that apply BOA to solving energy storage planning problems.

[0041] BOA was developed to simulate the behavior of butterflies when foraging and courting. According to scientific observations, butterflies can accurately identify the source of odors by smell. They can distinguish different odors and perceive the intensity of odors. When butterflies change their positions, their adaptability will change accordingly. Information can be transmitted between butterfly populations through odors. Each butterfly has a certain probability of detecting the odor emitted by the best individual. When the butterfly successfully detects the odor emitted by the best individual, it will move to the position with the strongest odor. This step is called global search in the BOA algorithm. In the second case, if the butterfly does not detect the odor of the best individual, the individual will move randomly. This step is called local development.

[0042] In the BOA algorithm, each butterfly has a unique fragrance and individual olfactory sense, which is the main feature of the algorithm. The generation of individual butterfly fragrance involves three important concepts: perception mode, stimulus intensity and power index. The calculation formula for fragrance concentration is: f = cI a .

[0043] Where f represents the intensity of the fragrance emitted by the butterfly itself that can be perceived by other butterflies (i.e., fragrance concentration); c is the perception mode of the butterfly, which takes values ​​between [0, 1]; I is the stimulus intensity, which is affected by the objective function; a is a power index that depends on the perception mode and is used to explain the degree of fragrance absorption, which takes values ​​between [0, 1].

[0044] The calculation process of the butterfly optimization algorithm is as follows:

[0045] (1) Initialize the population and various parameters.

[0046] Execute BOA, first enter the algorithm initialization phase, generate butterfly population x m (m=1,2,...N), x m represents the spatial position of the mth butterfly in the butterfly group, initializes the values ​​of the perception mode c, power index a and switching probability p, and calculates the initial objective function value f(x m )Determine the stimulus intensity I for each butterfly.

[0047] (2) Calculate the individual fitness F m (x).

[0048] Calculate the fitness value of each individual in the population and sort them in ascending order, find the butterfly in the best position, and record its position bestposition and its fitness value best fitness.

[0049] (3) Calculate the concentration of fragrance produced by each butterfly.

[0050] (4)Iterative process.

[0051] The iteration phase is the main solution phase of BOA. In the iteration process, local search and global search are two key steps. In each iteration, the butterflies will move to a new position and re-evaluate their fitness. The butterflies in the new position will emit a new scent, and f = cI a Calculate the fragrance concentration. Set the random number r (rand) to determine whether the butterfly's search method is to perform a local search or a global search. When the value of p is greater than the set random number, BOA performs a global search, and each butterfly moves toward the optimal butterfly position g. * Move, this process is described by the following formula: When the value of p is less than the random number, a local search is performed, and the random movement process of the butterfly can be described by the following formula:

[0052]

[0053] Where: is the solution vector of the mth butterfly in the lth iteration; g * Represents the best solution among all solutions in the current iteration; and represents the solution vector of the nth and kth butterflies of the same population in the solution space; the concentration of the fragrance emitted by the mth butterfly is represented by f m Indicates that r is a random number in [0, 1].

[0054] (5) End. If the program termination condition is met, it stops and outputs the optimal value and corresponding parameters, otherwise it continues the iterative process. The basic BOA process is as follows Figure 3 shown.

[0055] The embodiments of the present application improve the problems existing in the search mechanism and mutation strategy of the butterfly optimization algorithm. In order to make BOA more suitable for solving high-dimensional nonlinear model problems, avoid falling into local optimality too early, and achieve better convergence accuracy, the following improvements are made to the mutation method and optimization strategy of the algorithm.

[0056] (1) Improve dynamic switching probability.

[0057] In the algorithm, local optimization corresponds to the development phase, and global optimization corresponds to the exploration phase. p is used to balance the two phases. In traditional BOA, p = 0.6 is often used as a fixed value. The dynamic conversion probability formula is as follows:

[0058]

[0059] Among them, l is the current iteration number and Maxiter is the maximum iteration number.

[0060] The dynamic switching probability increases linearly with the increase in the number of iterations. The setting of this dynamic formula prevents the algorithm from obtaining better exploration capabilities in the early optimization stage. This application proposes a new dynamic switching probability applied to the improved BOA, where p takes values ​​in the range of [0.7, 0.8]. In the early stage of the algorithm, a relatively small p value should be set to improve the exploration ability of the algorithm. With the increase in the number of iterations, it should be adaptively adjusted to a larger p value to enhance the development ability of the algorithm. The proposed dynamic switching probability formula is as follows:

[0061]

[0062] Among them, p represents the dynamic switching probability of the current iteration number l; Maxiter represents the maximum number of iterations; α represents the initial setting value of the switching probability; β represents the control factor.

[0063] (2) Dynamic variance Gaussian mutation strategy.

[0064] In order to increase population diversity, expand the search space, and improve the possibility of the algorithm jumping out of the local optimum, an improved dynamic variance Gaussian mutation strategy is adopted. The Gaussian probability density function can be expressed as:

[0065]

[0066] Where μ represents the mean; σ represents the standard deviation; when μ=0, σ can be 0.5, 1 or 2.

[0067] Traditional BOA is susceptible to being trapped in local optimality, so in the early iterations, it is necessary to explore a larger range to quickly find the global optimal solution. It is necessary to balance the optimization accuracy and avoid prematurely falling into the local optimal solution. In order to solve this problem, we can use the idea of ​​variance change in Gaussian random numbers. In the initial stage, the variance is set to be relatively large, so that the population searches in an interval with a large range of variation, which makes it easier to jump out of the local optimal value; in the later stage of the iteration, as the variance gradually decreases, the population will have a higher probability of exploring in an interval with a relatively small range of variation, thereby improving the accuracy of the optimization and making it easier for the algorithm to reach the global optimal solution. Set the maximum value of the variance σ max and the minimum value σ min, the dynamic calculation formula of variance is:

[0068] σ(l)=σ max -(σ max -σ min )×l / Maxiter;

[0069] In summary, the improved BOA local search and global search formulas are improved as follows:

[0070]

[0071]

[0072] The process of the improved butterfly optimization algorithm is as follows Figure 4 shown.

[0073] Based on the above introduction, when the improved butterfly optimization algorithm is used for the optimal configuration of the energy storage system in the active distribution network, step 202 specifically includes:

[0074] (1) Determine the butterfly population of the current iteration number and the basic parameters of the current iteration number; each butterfly individual in the butterfly population corresponds to a configuration scheme of the energy storage system; the basic parameters include: perception mode, power index and dynamic switching probability; the dynamic switching probability of the current iteration number is determined according to the current iteration number, the maximum iteration number, the initial setting value of the switching probability and the control factor.

[0075] The calculation formula for the dynamic switching probability of the current iteration number is:

[0076]

[0077] Among them, p represents the dynamic switching probability of the current iteration number l; Maxiter represents the maximum number of iterations; α represents the initial setting value of the switching probability; β represents the control factor.

[0078] (2) Determine the fragrance concentration of each individual butterfly at the current iteration number based on the basic parameters of the current iteration number, substitute the current flow distribution into the energy storage optimization configuration model, and calculate the fitness value of each individual butterfly at the current iteration number; the fitness value is equal to the value of the objective function.

[0079] (3) Sort the fitness values ​​of all butterfly individuals in the butterfly population under the current number of iterations, and determine the butterfly individual with the largest fitness value as the optimal configuration solution under the current number of iterations.

[0080] (4) Determine whether the current number of iterations has reached the maximum number of iterations. If so, the optimal configuration scheme under the current number of iterations is determined as the optimal configuration scheme of the energy storage system in the active distribution network at the current moment. If not, based on the dynamic switching probability of the current number of iterations and the fragrance concentration of each individual butterfly under the current number of iterations, an improved search formula is used to update the butterfly population of the current number of iterations, and the updated butterfly population is used as the butterfly population of the next number of iterations, and the above step (1) is returned. The improved search formula includes an improved local search formula and an improved global search formula; the improved search formula is determined according to the dynamic variance Gaussian mutation strategy.

[0081] The above-mentioned method based on the dynamic switching probability of the current iteration number and the fragrance concentration of each individual butterfly at the current iteration number uses an improved search formula to update the butterfly population of the current iteration number, specifically including: judging whether the dynamic switching probability of the previous iteration number is greater than the set random number. If so, based on the fragrance concentration of each individual butterfly at the current iteration number, the butterfly population of the current iteration number is updated using an improved global search formula. If not, based on the fragrance concentration of each individual butterfly at the current iteration number, the butterfly population of the current iteration number is updated using an improved local search formula.

[0082] Among them, the improved local search formula is:

[0083]

[0084] in, represents the configuration scheme corresponding to the mth butterfly individual in the l+1th iteration; represents the configuration scheme corresponding to the mth butterfly individual in the lth iteration; r represents the set random number, r∈[0,1]; represents the configuration scheme corresponding to the nth butterfly individual in the lth iteration; represents the configuration scheme corresponding to the k-th butterfly individual in the l-th iteration; the n-th butterfly individual and the k-th butterfly individual belong to the same butterfly population; f m represents the fragrance concentration of the mth butterfly; normrnd(0,σ(l)) represents a random number with mean 0 and variance σ(l).

[0085] The improved global search formula is:

[0086]

[0087] Among them, g * Indicates the optimal configuration solution under the current number of iterations.

[0088] In practical applications, a more specific implementation process of using the improved butterfly optimization algorithm to solve the active distribution network energy storage optimization configuration model is as follows:

[0089] When using the improved BOA to solve the distributed energy storage system (DESS) planning model, it is first necessary to encode the butterfly and describe the complete individual image of the butterfly. Each butterfly individual is a configuration scheme of a DESS. Each butterfly individual consists of the location of each DESS and the active power at each moment, and the encoding form is as follows:

[0090] X i =[P dessi.1 ,P dessi.2 ,...P dessi.T-1 ,P dessi.T ,loc i ];

[0091] Where P dessi.T is the charging and discharging power of the i-th energy storage system at time T in the planning period, loc i is the access location of the i-th energy storage system. The process of butterfly individual coding is: the charging and discharging power of energy storage system No. 1 at time T is [P dess1.1 ,P dess1.2 ,...P dess1.T-1 ,P dess1.T ], the charging and discharging power of the energy storage system No. 2 at time T is [P dess2.1 ,P dess2.2 ,...P dess2.T-1 ,P dess2.T ], ..., the charge and discharge power of the N energy storage system at time T is [P dessN.1 ,P dessN.2 ,...P dessN.T-1 ,P dessN.T ], the access locations of N energy storage systems are represented as [loc1,loc2,...,loc N ].

[0092] The configuration capacity should be calculated based on the optimized charging and discharging capacity of the DESS and the charging and discharging time. During the charging and discharging period of the DESS, the cumulative charging or discharging capacity of the DESS at each time point should be calculated, and the cumulative charging or discharging capacity of the DESS in each subsequent charging or discharging period should be calculated. The optimal configuration capacity of the DESS should take the maximum of these two absolute values. The specific calculation process is shown in the following formula:

[0093]

[0094] In the formula, E i is the optimal configuration capacity of the i-th DESS; 1~θ1,θ1~θ2,...,θ j ~θd is the period of time that requires continuous charging or continuous discharging in the power optimization result; Δt is the sampling time interval of sample data; η is the charging and discharging efficiency of DESS.

[0095] In summary, the overall steps for solving the DESS optimization configuration model for active distribution network are as follows:

[0096] (1) Read the example system parameters, daily load, and typical scenario data of wind power and photovoltaic output, perform power flow calculation, and obtain the initial power flow distribution of the system.

[0097] (2) Determine the number of DESSs N to be configured DESS , set the rated power and installation range of DESS. Initialize the basic parameters of the algorithm (such as the number of iterations, sensing mode c and power intensity index a), randomly generate the DESS installation location, and the charge and discharge power of DESS in each period, and form N butterfly individuals.

[0098] (3) Using the MATPOWER toolbox, the power flow calculation is performed based on the solutions of all current individuals. The fitness value (i.e., the objective function value) of each butterfly individual is calculated, and the position of the optimal butterfly individual is obtained by comparison. The switching probability is determined based on the number of iterations, and the fragrance emitted by the optimal butterfly individual is calculated to update the positions of the remaining butterfly individuals.

[0099] (4) Compare the updated butterfly fitness. If the fitness is better, replace the fitness value and position of the optimal butterfly individual in step (3). Otherwise, perform Gaussian mutation on the optimal butterfly individual in step (3) according to the number of iterations.

[0100] (5) Update the butterfly odor perception weight coefficient and repeat step (4) until the butterfly population movement is completed.

[0101] (6) Check whether the maximum number of iterations of the algorithm has been reached. If not, return to step (4) to continue execution; otherwise, proceed to the next step.

[0102] (7) After the solution is completed, the charging and discharging plan and optimal position of the DESS at each moment are output. The capacity of each DESS is calculated based on the obtained charging and discharging strategy. The optimal objective function values ​​under different configuration numbers are compared, and the configuration results are sorted and output.

[0103] The process of solving the energy storage optimization configuration model of active distribution network using the improved butterfly optimization algorithm is as follows: Figure 5 shown.

[0104] In another exemplary embodiment of the present application, this embodiment mainly introduces the objective function and constraint conditions in step 202.

[0105] 1. Objective function.

[0106] (1) Grid vulnerability measurement indicators.

[0107] The planning of energy storage capacity and distribution is a multi-objective optimization problem. Reasonable planning of energy storage will reduce the threat of a large number of DG access to the safety of medium and low voltage distribution networks, improve power quality and improve power flow distribution. The following three indicators are used to measure whether the planning of energy storage is reasonable.

[0108] In order to quantitatively indicate the degree of improvement in power quality caused by adding energy storage to the grid, the proposed grid vulnerability index is a state vulnerability index based on the safety of grid operation. This index measures the grid's risk resistance and improves power quality by analyzing the degree to which the voltage of each node deviates from the standard value. The higher the vulnerability, the more unstable the node voltage, the lower the power quality, and the worse the risk resistance. The vulnerability v(t,i) of any node i in the network at time t is expressed as:

[0109]

[0110] Where: U t,i is the voltage of node i at time t; U i,o is the rated voltage of node i; 0.07 is the maximum voltage offset.

[0111] The cross section at time t is normalized as follows:

[0112]

[0113] In the formula, v(t,i max )、v(t,i min ) are the maximum and minimum vulnerability values ​​of all nodes at time t before normalization. The average vulnerability of the active distribution network at time t, BV(t), is:

[0114]

[0115] Where N is the number of system nodes; V(t,i) is the normalized value of the vulnerability of each node at time section t.

[0116] Since the voltage collapse of any node will cause a chain reaction of collapse of other nodes, the grid vulnerability index must also consider the balance of vulnerability distribution of each node. For this reason, the balance index method is used to represent the balance degree J of grid vulnerability, ranging from 0 to 1, 0 represents absolute balance, and 1 represents absolute imbalance. The formula for the balance degree J(t) of the vulnerability of the active distribution network at time t is:

[0117]

[0118] In the formula, p t,i is the ratio of the vulnerability of node i at time t to the total vulnerability of the power grid at the current time, and the expression is:

[0119]

[0120] Finally, the average vulnerability index and balance index of the power grid under time section t are weighted and added (the weights are 0.5 for each), and then the average value of the 24-hour day is calculated to obtain the final power grid vulnerability measurement index GV:

[0121]

[0122] f1=GV;

[0123] GV is usually between 0 and 1. The closer the GV index is to 1, the higher the overall vulnerability of the power grid and the worse the power supply security; the closer the GV index is to 0, the higher the power supply security.

[0124] (2) Active network loss.

[0125] The energy storage system can be equivalent to a power source or load in the distribution network. Its regulation function can reduce the flow of current in the feeder, thereby reducing the active network loss of the distribution network. The daily active network loss is:

[0126]

[0127] (3) Rated capacity of energy storage.

[0128] Considering the energy storage configuration, the total energy storage capacity is adopted as one of the objective functions, and the rated capacity of the energy storage is the maximum charge and discharge capacity within a test cycle.

[0129]

[0130]

[0131] Taking into account the grid vulnerability index, active network loss and energy storage capacity, in step 202, the objective function of the energy storage optimization configuration model is expressed as:

[0132] minF = min(f1,f2,f3);

[0133] f1=GV;

[0134]

[0135]

[0136]

[0137]

[0138] Where F represents the objective function; f1 represents the first function; f2 represents the second function; f3 represents the third function; GV represents the grid vulnerability measurement index; BV(t) represents the average vulnerability of the active distribution network at time t; J(t) represents the balance degree of the vulnerability of the active distribution network at time t; M L Represents the node set of all branches in the active distribution network; G ij represents the conductance set of nodes i and j; U i,t represents the voltage of node i at time t; U j,t represents the voltage of node j at time t; δ ij,t represents the phase difference between the starting and ending nodes i and j of the branch at time t; t0 represents the initial time; t0 to t0+zΔt are the charging and discharging time periods; Δt represents the charging and discharging duration; z represents a positive integer; pstore,b(t) represents the power of the b-th energy storage system at time t; N store Represents the number of energy storage systems; W b Represents the rated capacity of the bth energy storage system.

[0139] 2. Constraints.

[0140] The operation constraints are used to constrain the active distribution network to meet the active power balance and reactive power balance at all times; the electrical quantity constraints include: net load power constraints and wind and solar power abandonment penalty constraints; the node voltage and branch flow electrical constraints are used to constrain the node voltage to be within a set proportion range and the branch transmission capacity does not exceed the upper limit; the energy storage system constraints are used to constrain the energy storage system capacity, charging and discharging power and access location to not exceed the corresponding upper limits.

[0141] (1) Operational constraints.

[0142] To ensure the normal operation of the system, the active distribution network needs to always meet the active and reactive power balance. This application does not consider the reactive power of DG and DESS, and the expression of the operation constraint is as follows:

[0143]

[0144] Q grid (t) = Q ld (t)+Q loss (t);

[0145] Where P grid (t), Q grid (t) is the active power and reactive power supplied to the distribution network by the upper level at time t; N DG 、N store are the number of DG and DESS respectively; P DG.d(t) is the active power generated by DG d at time t; P store.e(t) is the charge and discharge power of the e-th DESS at time t, where discharge is a negative value and charge is a positive value; P ld (t), Q ld (t), P loss (t), Q loss (t) are the total active load, reactive load, total active loss and reactive loss of the system at time t respectively.

[0146] (2) Electrical quantity constraints.

[0147] Net load power constraint is: 0≤P ld ≤P ldmax .P ld Indicates net load power; P ldmax Indicates the upper limit of net load power.

[0148] When the output of wind and solar power is greater than the sum of the load and the minimum output of the unit, wind and solar power will be abandoned. In order to ensure that the demand for renewable energy is met first when there is a surplus of renewable energy output, Pun is set as the penalty constraint for abandoning wind and solar power:

[0149]

[0150]

[0151] In the formula, C w P is the penalty for wind curtailment, set at 500 yuan / kWh; w.t , P pv.t , P ld.t are the power values ​​of wind turbine, photovoltaic and load at time t respectively; p dg is the benchmark electricity price for DG grid access; p buy(t) represents the real-time electricity price of electricity purchased from the superior power grid at time t; P min Indicates the minimum output of the unit.

[0152] (3) Electrical constraints on node voltage and branch power flow.

[0153] To ensure the safe and stable operation of the active distribution network, the voltage of each node and the power flow of the branch must meet certain electrical constraints to ensure that the voltage of each node is within 5% and the transmission capacity of the branch does not exceed the upper limit:

[0154] V min ≤V N ≤V max ;

[0155] P ij =V i V j (G ij cosθ ij +B ij sinθ ij)-V i 2 G ij ;

[0156] P ij ≤P ijmax ;

[0157] Where P ij is the line power flow between node i and node j, G ij is the real part of the branch admittance, P ijmax is the maximum line transmission capacity between node i and node j, V N Represents the node voltage, V min Represents the lower limit of node voltage, V max Represents the upper limit of node voltage, V i represents the voltage at node i, V j represents the voltage at node j, θ ij Indicates the voltage phase angle, B ij Represents the imaginary part of the branch admittance.

[0158] (4) Energy storage system constraints.

[0159] Taking into account the service life of energy storage and ensuring that the energy storage has sufficient energy capacity for the next day, the state of charge constraints must be followed, and the charging and discharging energy within a cycle must meet the energy balance.

[0160]

[0161] Soc min ≤Soc(t)≤Soc max ;

[0162] Soc(1) = Soc(T) = 0.5;

[0163] Where, Soc(t+Δt) represents the charge of the energy storage system at time t+Δt; Soc(t) represents the charge of the energy storage system at time t; P store.i (t+Δt) represents the battery charge and discharge power at time t+Δt; Δt represents unit time; η represents the battery charge and discharge efficiency; E store.i Represents the power generation of the energy storage system; Soc min Indicates the lower limit of the state of charge; Soc max Indicates the upper limit of the state of charge; Soc(1) indicates the initial state of charge of the energy storage system; Soc(T) indicates the input state of charge starting from T=1.

[0164] Due to demand and budget constraints, DESS capacity has an upper limit. max , while the DESS charging and discharging power is limited by the conversion equipment, the energy storage system capacity constraint and the energy storage system charging and discharging power constraint are:

[0165]

[0166] Among them, E max Indicates the maximum value of DESS capacity; P max Indicates the maximum charging and discharging power of the energy storage system.

[0167] The location constraints of the energy storage system must ensure that the location particle variables of the energy storage are integer real numbers when the algorithm is updated and iterated. The energy storage system access location constraints are:

[0168] loc∈N grid ∈N * ;

[0169] Where N grid is the node of the distribution network except the balancing node, N * It indicates that the energy storage variable must be a positive integer within the range of the distribution network excluding the balancing node, and loc indicates the access location.

[0170] The above-mentioned active distribution network energy storage optimization configuration method is implemented based on the improved butterfly optimization algorithm. Its overall idea can be described as follows: S1: Build a butterfly optimization algorithm model. S2: Construct an active distribution network energy storage optimization configuration model. S3: Improve the problems existing in the search mechanism and mutation strategy of the butterfly algorithm. S4: Solve the active distribution network energy storage optimization configuration model by improving the butterfly optimization algorithm.

[0171] The embodiments of the present application can determine the optimal configuration scheme of the energy storage system in the distribution network, obtain the optimal access location, capacity and charging and discharging power of the energy storage system, thereby ensuring the safe and stable operation of the active distribution network.

[0172] Based on the same inventive concept, the embodiment of the present application also provides an active distribution network energy storage optimization configuration device for implementing the active distribution network energy storage optimization configuration method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the active distribution network energy storage optimization configuration device provided below can refer to the limitations of the active distribution network energy storage optimization configuration method above, and will not be repeated here.

[0173] In an exemplary embodiment, Figure 6 As shown, an active distribution network energy storage optimization configuration device is provided, comprising:

[0174] The power flow distribution acquisition module 701 is used to acquire the power flow distribution of the active power distribution network at the current moment.

[0175] The configuration model building module 702 is used to build an energy storage optimization configuration model for the active distribution network; the energy storage optimization configuration model includes an objective function and constraints; the objective function is determined based on the grid vulnerability measurement index, active network loss and energy storage system capacity; the constraints include: operation constraints, electrical quantity constraints, node voltage and branch flow electrical constraints and energy storage system constraints; the energy storage system constraints include: energy storage system capacity constraints, energy storage system charging and discharging power constraints and energy storage system access location constraints.

[0176] Configuration optimization module 703 is used to iteratively solve the energy storage optimization configuration model based on the power flow distribution at the current moment by using an improved butterfly optimization algorithm to determine the optimal configuration scheme of the energy storage system in the active distribution network at the current moment; the improved butterfly optimization algorithm is a butterfly algorithm that adopts a dynamic switching probability and a dynamic variance Gaussian mutation strategy; the optimal configuration scheme is the configuration scheme corresponding to the maximum number of iterations; the configuration scheme includes: the access location, capacity and charging and discharging power of the energy storage system.

[0177] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the current flow distribution of the active distribution network. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing the configuration of energy storage in an active distribution network is implemented.

[0178] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0179] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0180] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0181] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0182] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0183] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, etc., but is not limited thereto.

[0184] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0185] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for optimizing energy storage configuration in an active distribution network, characterized in that: The active distribution network energy storage optimization configuration method comprises: Obtain the power flow distribution of the active distribution network at the current moment; Constructing an energy storage optimization configuration model for an active distribution network; the energy storage optimization configuration model includes an objective function and constraints; the objective function is determined based on a grid vulnerability measurement index, active network loss, and energy storage system capacity; the constraints include: operation constraints, electrical quantity constraints, node voltage and branch power flow electrical constraints, and energy storage system constraints; the energy storage system constraints include: energy storage system capacity constraints, energy storage system charge and discharge power constraints, and energy storage system access location constraints; Based on the power flow distribution at the current moment, an improved butterfly optimization algorithm is used to iteratively solve the energy storage optimization configuration model to determine the optimal configuration scheme of the energy storage system in the active distribution network at the current moment; the improved butterfly optimization algorithm is a butterfly algorithm that adopts a dynamic switching probability and a dynamic variance Gaussian mutation strategy; the optimal configuration scheme is the configuration scheme corresponding to the maximum number of iterations; the configuration scheme includes: the access location, capacity and charging and discharging power of the energy storage system.

2. The active distribution network energy storage optimization configuration method according to claim 1, characterized in that: Based on the current power flow distribution, the improved butterfly optimization algorithm is used to iteratively solve the energy storage optimization configuration model to determine the optimal configuration scheme of the energy storage system in the active distribution network at the current moment, specifically including: Determine the butterfly population of the current iteration number and the basic parameters of the current iteration number; each butterfly individual in the butterfly population corresponds to a configuration scheme of the energy storage system; the basic parameters include: perception mode, power index and dynamic switching probability; the dynamic switching probability of the current iteration number is determined according to the current iteration number, the maximum iteration number, the initial setting value of the switching probability and the control factor; Determine the fragrance concentration of each individual butterfly at the current iteration number based on the basic parameters of the current iteration number, substitute the current flow distribution into the energy storage optimization configuration model, and calculate the fitness value of each individual butterfly at the current iteration number; the fitness value is equal to the value of the objective function; Sort the fitness values ​​of all butterfly individuals in the butterfly population under the current number of iterations, and determine the butterfly individual with the largest fitness value as the optimal configuration solution under the current number of iterations; Determine whether the current number of iterations has reached the maximum number of iterations; If so, the optimal configuration scheme under the current number of iterations is determined as the optimal configuration scheme of the energy storage system in the active distribution network at the current moment; If not, based on the dynamic switching probability of the current iteration number and the fragrance concentration of each individual butterfly at the current iteration number, an improved search formula is used to update the butterfly population of the current iteration number, and the updated butterfly population is used as the butterfly population of the next iteration number, and the step of determining the butterfly population of the current iteration number and the basic parameters of the current iteration number is returned; the improved search formula includes an improved local search formula and an improved global search formula; the improved search formula is determined according to the dynamic variance Gaussian mutation strategy.

3. The active distribution network energy storage optimization configuration method according to claim 2 is characterized in that: Based on the dynamic switching probability of the current iteration number and the fragrance concentration of each butterfly individual at the current iteration number, the butterfly population of the current iteration number is updated using an improved search formula, which includes: Determine whether the dynamic switching probability of the previous iteration number is greater than the set random number; If so, based on the fragrance concentration of each individual butterfly at the current iteration number, the butterfly population at the current iteration number is updated using an improved global search formula; If not, based on the fragrance concentration of each individual butterfly at the current iteration number, the butterfly population at the current iteration number is updated using an improved local search formula.

4. The active distribution network energy storage optimization configuration method according to claim 2, characterized in that: The improved local search formula is: in, represents the configuration scheme corresponding to the mth butterfly individual in the l+1th iteration; represents the configuration scheme corresponding to the mth butterfly individual in the lth iteration; r represents the set random number, r∈[0,1]; represents the configuration scheme corresponding to the nth butterfly individual in the lth iteration; represents the configuration scheme corresponding to the k-th butterfly individual in the l-th iteration; the n-th butterfly individual and the k-th butterfly individual belong to the same butterfly population; f m represents the fragrance concentration of the mth butterfly; normrnd(0,σ(l)) represents a random number with mean 0 and variance σ(l); The improved global search formula is: Among them, g * Indicates the optimal configuration solution under the current number of iterations.

5. The active distribution network energy storage optimization configuration method according to claim 1, characterized in that: The expression of the objective function is: minF = min(f1,f2,f3); f1=GV; Where F represents the objective function; f1 represents the first function; f2 represents the second function; f3 represents the third function; GV represents the grid vulnerability measurement index; BV(t) represents the average vulnerability of the active distribution network at time t; J(t) represents the balance degree of the vulnerability of the active distribution network at time t; M L Represents the node set of all branches in the active distribution network; G ij represents the conductance set of nodes i and j; U i,t represents the voltage of node i at time t; U j,t represents the voltage of node j at time t; δ ij,t represents the phase difference between the starting and ending nodes i and j of the branch at time t; t0 represents the initial time; t0 to t0+zΔt are the charging and discharging time periods; Δt represents the charging and discharging duration; z represents a positive integer; pstore,b(t) represents the power of the b-th energy storage system at time t; N store Represents the number of energy storage systems; W b Represents the rated capacity of the bth energy storage system.

6. The active distribution network energy storage optimization configuration method according to claim 1, characterized in that: The operation constraints are used to constrain the active distribution network to meet the active power balance and reactive power balance at all times; the electrical quantity constraints include: net load power constraints and wind and solar power abandonment penalty constraints; the node voltage and branch flow electrical constraints are used to constrain the node voltage to be within a set proportion range and the branch transmission capacity does not exceed the upper limit; the energy storage system constraints are used to constrain the energy storage system capacity, charging and discharging power and access location to not exceed the corresponding upper limits.

7. The method for optimizing the configuration of energy storage in an active distribution network according to claim 2, characterized in that: The calculation formula for the dynamic switching probability of the current iteration number is: Among them, p represents the dynamic switching probability of the current iteration number l; Maxiter represents the maximum number of iterations; α represents the initial setting value of the switching probability; β represents the control factor.

8. An active distribution network energy storage optimization configuration device, characterized in that: The active distribution network energy storage optimization configuration device comprises: The power flow distribution acquisition module is used to obtain the power flow distribution of the active distribution network at the current moment; A configuration model building module is used to build an energy storage optimization configuration model for an active distribution network; the energy storage optimization configuration model includes an objective function and constraints; the objective function is determined based on a grid vulnerability measurement index, active network loss, and energy storage system capacity; the constraints include: operation constraints, electrical quantity constraints, node voltage and branch power flow electrical constraints, and energy storage system constraints; the energy storage system constraints include: energy storage system capacity constraints, energy storage system charge and discharge power constraints, and energy storage system access location constraints; The configuration optimization module is used to iteratively solve the energy storage optimization configuration model based on the power flow distribution at the current moment by using an improved butterfly optimization algorithm to determine the optimal configuration scheme of the energy storage system in the active distribution network at the current moment; the improved butterfly optimization algorithm is a butterfly algorithm that adopts a dynamic switching probability and a dynamic variance Gaussian mutation strategy; the optimal configuration scheme is the configuration scheme corresponding to the maximum number of iterations; the configuration scheme includes: the access location, capacity and charging and discharging power of the energy storage system.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the active distribution network energy storage optimization configuration method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the active distribution network energy storage optimization configuration method described in any one of claims 1 to 7 is implemented.

Images