Distributed energy storage system locating and sizing method based on particle swarm optimization

Abstract

The invention discloses a distributed energy storage system locating and sizing method based on a particle swarm algorithm, and belongs to the technical field of power system operation analysis, and the method comprises the steps: S1, obtaining a first index and a second index based on the locating and sizing demands of a distributed energy storage system; s2, on the basis of constraint conditions, using the first index and the second index to construct a multi-target locating and sizing model; s3, acquiring a first weight coefficient of the first index and the second index based on an analytic hierarchy process, and converting the multi-target addressing and sizing model into a single-target addressing and positioning model based on the first weight coefficient; and S4, obtaining a second weight coefficient in the single-target site selection positioning model by using a particle swarm algorithm and historical data of the distributed energy storage system, and obtaining an optimal energy storage node of the distributed energy storage system and an optimal energy storage capacity of the optimal energy storage node based on the second weight coefficient. The problem that it is difficult to accurately obtain the weight of the locating and sizing demand index is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation analysis, and in particular to a distributed energy storage system site selection and capacity determination method based on a particle swarm algorithm. Background Art

[0002] With the large-scale development of power resources and their high penetration rate in the power grid, renewable energy generation continues to pose new challenges to the safe and efficient operation of distribution networks. Introducing distributed energy storage into distribution networks is an effective means to alleviate the pressure on distribution networks. By selecting nodes and reasonably deploying energy storage equipment at the selected nodes, it can respond quickly when failures occur in the distribution network or load fluctuations occur, provide necessary energy support, and improve the stability and reliability of the distribution network. For example, the patent number CN114614492A is a method for site selection and sizing of centralized-distributed energy storage for an active distribution network. First, under the premise of ensuring the safety and reliability of the power system, the distribution system containing certain nodes is evenly divided into corresponding areas. According to the improved active-voltage sensitivity analysis method, the node with the highest active-voltage sensitivity of each area is obtained. The economic and safety issues after the energy storage is connected to the power grid are considered. The voltage offset, the economy of energy storage, and the absorption rate are used as optimization targets. The hierarchical analysis method and the firefly algorithm are combined to determine the weights of the three indicators in the objective function, and the strategy model is solved to ensure the safe and stable operation of the distribution network. Although the above scheme can examine the problem from a global perspective through the hierarchical analysis method and avoid the site selection and sizing falling into the local optimal situation, the hierarchical analysis method relies on the subjective evaluation of experts when obtaining weights. Therefore, the weights obtained will inevitably have errors, and the existing errors will cause the final site selection and sizing to be not optimal, which makes it difficult to ensure the safe and efficient operation of the distribution network. Summary of the invention

[0003] In view of the problem that it is difficult to accurately obtain the weights of the site selection and sizing demand indicators in the prior art, the present invention provides a distributed energy storage system site selection and sizing method based on a particle swarm algorithm. The first weight coefficient is obtained through a hierarchical analysis method and the multi-objective site selection and sizing model is converted into a single-objective site selection and positioning model that can be used by the particle swarm algorithm according to the first weight coefficient, thereby avoiding the particle swarm algorithm from falling into a local optimum. The second weight coefficient in the single-objective site selection and positioning model is further accurately obtained through the particle swarm algorithm, which significantly improves the accuracy of the obtained site selection and sizing demand indicator weights, thereby ensuring the accuracy of the optimal site selection and optimal sizing finally determined.

[0004] In order to solve the above technical problems, the present invention provides a distributed energy storage system site selection and capacity determination method based on particle swarm algorithm, comprising the following steps: S1: Obtain the first index and the second index based on the site selection and capacity determination requirements of the distributed energy storage system; S2: Based on the constraints, a multi-objective site selection and capacity determination model is constructed using the first and second indicators; S3: obtaining first weight coefficients of the first indicator and the second indicator based on the hierarchical analysis method, and converting the multi-objective site selection and capacity determination model into a single-objective site selection and positioning model based on the first weight coefficient; S4: Use the particle swarm algorithm and the historical data of the distributed energy storage system to obtain the second weight coefficient in the single-target site selection and positioning model, and obtain the optimal energy storage node of the distributed energy storage system and the optimal energy storage capacity of the optimal energy storage node based on the second weight coefficient.

[0005] After adopting the above technical solution, the present invention has the following advantages: Since the particle swarm algorithm can only process a single objective function, the first weight coefficient is obtained through the hierarchical analysis method. After determining the weight coefficient of each objective in the multi-objective function, the multi-objective function can be converted into a single objective function that can be used by the particle swarm algorithm through weighted summation. At the same time, due to the characteristics of the hierarchical analysis method, decision makers can gradually go deep into each level of the problem for a comprehensive analysis. Therefore, the first weight coefficient is obtained through the hierarchical analysis method, and the multi-objective site selection and sizing model is converted into a single objective site selection and positioning model that can be used by the particle swarm algorithm according to the first weight coefficient, avoiding the particle swarm algorithm from falling into the local optimum. Further, the second weight coefficient in the single objective site selection and positioning model is obtained with high precision through the particle swarm algorithm, which significantly improves the accuracy of the weights of the site selection and sizing demand indicators obtained, thereby ensuring the accuracy of the optimal site selection and optimal sizing finally determined. The particle swarm algorithm is used to optimize the single-target site selection and positioning model to obtain the optimal energy storage nodes and energy storage capacity, optimize resource allocation, and improve the economic benefits and reliability of the obtained energy storage nodes and energy storage capacity; Constraints reflect the actual limitations and requirements in the site selection and capacity determination process. Therefore, a multi-objective site selection and capacity determination model is constructed through constraints to ensure the scientificity and rationality of the site selection and capacity determination plan. The problem of difficulty in accurately obtaining the weights of site selection and capacity determination demand indicators has been solved.

[0006] Preferably, in S2, the constraint conditions include at least energy storage node constraints and energy storage capacity constraints; The energy storage node constraint is Where D ESS represents the nodes in the distributed energy storage system, N nodes represents the nodes in the distribution area, D grid It represents the connection node between the distribution station area and the upper power grid; The energy storage capacity constraint is P ESS，min ≤P ESS ≤P ESS，max , where PESS represents the configuration capacity of the node in the distributed energy storage system, P ESS，min , P ESS，max They respectively represent the minimum configuration capacity and maximum configuration capacity of the nodes in the distributed energy storage system.

[0007] In this scheme, when constructing a multi-objective site selection and sizing model, constraints are imposed on energy storage nodes and node capacities to prevent node selection from important nodes, i.e., contact nodes, and unnecessary nodes, i.e., nodes outside the distribution network. This avoids waste of computing resources while ensuring the security of the distribution network. At the same time, the capacity configured by the nodes can respond quickly to load fluctuations. Therefore, while ensuring the security of the distribution network, it also provides energy support for the distribution network.

[0008] Preferably, in S2, the multi-objective site selection and capacity determination model is F=min[f1, f2], where F represents the multi-objective site selection and capacity determination model, f1 represents the first indicator, and f2 represents the second indicator.

[0009] Preferably, S3 includes: S31: Obtaining a judgment matrix of the first index and the second index relative to the site selection and positioning requirements based on expert evaluation in the analytic hierarchy process; S32: performing consistency check on the judgment matrix based on the order of the judgment matrix, if the check succeeds, obtaining first weight coefficients of the first indicator and the second indicator based on the judgment matrix, if the check fails, executing S31; S33: Convert the multi-objective site selection and capacity determination model into a single-objective site selection and positioning model based on the first weight coefficient.

[0010] In this scheme, consistency check of the judgment matrix can ensure the logical consistency of the judgment matrix, thereby enhancing the scientific nature of the decision. When the judgment matrix fails the consistency check, it indicates that there is a deviation in the expert evaluation. Therefore, the judgment matrix is ​​re-obtained for evaluation to ensure the rationality and scientific nature of the decision. The first weight coefficient is obtained through the hierarchical analysis method, and the first weight coefficient is used for weighted summation to convert the multi-objective function into a single objective function that can be used by the particle swarm algorithm. At the same time, the global characteristics of the hierarchical analysis method are used to avoid the particle swarm algorithm from falling into the local optimum, thereby improving the accuracy of the weights of the obtained site selection and sizing demand indicators.

[0011] Preferably, in S3, the single-target site selection and positioning model is H=λ1f1+λ2f2, wherein H represents the single-target site selection and positioning model, and λ1 and λ2 represent the first weight coefficient of the first indicator and the first weight coefficient of the second indicator respectively.

[0012] Preferably, S4 includes: S41: Based on the single-target site selection and positioning model, the particle swarm algorithm is used to obtain the optimal first index and the optimal second index; S42: Compare the first indicator corresponding to the historical energy storage node in the historical data and the second indicator corresponding to the historical energy storage capacity of the historical energy storage node with the optimal first indicator and the optimal second indicator to obtain comparison results, and obtain the second weight coefficient in the single-target site selection and positioning model based on the comparison results.

[0013] In this scheme, the optimal first indicator and the optimal second indicator obtained by the particle swarm algorithm correspond to the optimal energy storage node and the optimal energy storage capacity of the optimal energy storage node. The first indicator corresponding to the historical energy storage node and the second indicator corresponding to the historical energy storage capacity of the historical energy storage node are compared with the optimal first indicator and the optimal second indicator respectively. If the first indicator corresponding to the historical energy storage node is less than the optimal first indicator or the second indicator corresponding to the historical energy storage capacity of the historical energy storage node is less than the optimal second indicator, it indicates that the energy storage node or energy storage node capacity corresponding to the optimal first indicator and the optimal second indicator is not the optimal energy storage node and the optimal energy storage capacity, which further reflects that there is an error in the first weight coefficient obtained by the hierarchical analysis method. Therefore, the first weight coefficient is corrected through the comparison results to obtain the second weight coefficient in the single-target site selection and positioning model, which further improves the accuracy of the obtained site selection and capacity demand indicator weights, thereby ensuring the accuracy of the optimal site selection and optimal capacity finally determined.

[0014] Preferably, the S41 includes: S411: Use the particle swarm algorithm to randomly generate a number of particles and initialize the global optimal position and the optimal position of each particle; S412: Input each particle into the single target location selection model to obtain the fitness value of each particle; S413: Determine whether the fitness value of each particle is greater than the fitness corresponding to the optimal position of each particle. If greater than, update the optimal position of each particle based on the fitness value of each particle, and obtain the optimal first indicator and the optimal second indicator based on the updated optimal position of each particle. If less than or equal to, obtain the optimal first indicator and the optimal second indicator based on the fitness value of each particle.

[0015] Preferably, in S42, the obtaining of the second weight coefficient in the single-target site selection and positioning model based on the comparison result includes: obtaining a first difference between a first indicator corresponding to a historical energy storage node in the historical data and the optimal first indicator, obtaining a second difference between a second indicator corresponding to a historical energy storage capacity of the historical energy storage node and the optimal second indicator; if the first difference or the second difference is less than a preset difference, obtaining the first weight coefficient in the single-target site selection and positioning model based on the first difference and the second difference.

[0016] Beneficial effects of this program: Since the particle swarm algorithm can only process a single objective function, the first weight coefficient is obtained through the hierarchical analysis method. After determining the weight coefficient of each objective in the multi-objective function, the multi-objective function can be converted into a single objective function that can be used by the particle swarm algorithm through weighted summation. At the same time, due to the characteristics of the hierarchical analysis method, decision makers can gradually go deep into each level of the problem for a comprehensive analysis. Therefore, the first weight coefficient is obtained through the hierarchical analysis method, and the multi-objective site selection and sizing model is converted into a single objective site selection and positioning model that can be used by the particle swarm algorithm according to the first weight coefficient, which avoids the particle swarm algorithm from falling into the local optimum, thereby improving the accuracy of the weights of the site selection and sizing demand indicators obtained; By comparing the first indicator corresponding to the historical energy storage node and the second indicator corresponding to the historical energy storage capacity of the historical energy storage node with the optimal first indicator and the optimal second indicator obtained by using the particle swarm algorithm, and then correcting the first weight coefficient according to the comparison result to obtain the second weight coefficient in the single-target site selection and positioning model, the accuracy of the obtained site selection and capacity determination demand indicator weight is further improved, thereby ensuring the accuracy of the optimal site selection and optimal capacity determined in the end; The particle swarm algorithm is used to optimize the single-target site selection and positioning model to obtain the optimal energy storage nodes and energy storage capacity, optimize resource allocation, and improve the economic benefits and reliability of the obtained energy storage nodes and energy storage capacity; Constraints reflect the actual limitations and requirements in the site selection and capacity determination process. Therefore, a multi-objective site selection and capacity determination model is constructed through constraints to ensure the scientificity and rationality of the site selection and capacity determination plan. The problem of difficulty in accurately obtaining the weights of site selection and capacity determination demand indicators has been solved.

[0017] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the distributed energy storage system site selection and capacity determination method based on the particle swarm algorithm are implemented.

[0018] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the distributed energy storage system site selection and sizing method based on the particle swarm algorithm are implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0020] Figure 1 A flow chart of a method for site selection and capacity determination of a distributed energy storage system based on a particle swarm algorithm according to the present invention; Figure 2 A topological structure diagram of an improved IEEE-33 node distribution network in a distributed energy storage system site selection and capacity determination method based on a particle swarm algorithm according to the present invention; Figure 3 The first typical wind and solar power output curve in four seasons with a benchmark of 1MW in the distributed energy storage system site selection and capacity determination method based on the particle swarm algorithm of the present invention; Figure 4 The second typical wind and solar power output curve in four seasons with a benchmark of 1MW in the distributed energy storage system site selection and capacity determination method based on the particle swarm algorithm of the present invention; Figure 5 It is the typical four-season electricity consumption curve of residential load in the distributed energy storage system site selection and capacity determination method based on particle swarm algorithm of the present invention; Figure 6 This is the typical industrial load four-season electricity consumption curve in the distributed energy storage system site selection and capacity determination method based on particle swarm algorithm of the present invention Figure 7 It is a typical commercial load four-season electricity consumption curve in the distributed energy storage system site selection and capacity determination method based on particle swarm algorithm of the present invention; Figure 8 A comparison chart of the average annual power outage times of loads under the distributed energy storage system site selection and capacity determination method based on the particle swarm algorithm of the present invention and other schemes; Fig. 9 A comparison chart of the annual average power outage time of the distributed energy storage system site selection and capacity determination method based on the particle swarm algorithm of the present invention and other schemes; Fig.10 A comparison chart of the average power outage time under the distributed energy storage system site selection and capacity determination method based on the particle swarm algorithm of the present invention and other schemes; Fig.11This is a comparison chart of annual load loss under the distributed energy storage system site selection and capacity determination method based on the particle swarm algorithm of the present invention and other schemes. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0023] Embodiment 1: like Figure 1 As shown, the distributed energy storage system site selection and capacity determination method based on particle swarm algorithm includes the following steps: S1: Obtain the first index and the second index based on the site selection and capacity determination requirements of the distributed energy storage system.

[0024] In this embodiment, the site selection and capacity determination requirement is to improve the power supply reliability of the distributed energy storage system while reducing the operating cost of the distributed energy storage system. The first indicator is the comprehensive cost of the distributed energy storage system during its entire life cycle. The calculation formula of the first indicator is f1=C iic +C omc , where C iic With C omc are the initial investment cost and annual operation and maintenance cost of the ESS installed in the distributed energy storage system, C iic The calculation formula is: In the formula, α is the discount rate; Y is the useful life of ESS, C PE , C EE are ESS power cost and capacity cost, P ESS，n 、E ESS，n are the installed power and installed capacity of ESS at the nth energy storage node respectively; N ESS is the total number of ESS installations. The annual operation and maintenance cost of ESS is related to the charging and discharging power and the unit operation and maintenance cost, C omcThe calculation formula is: In the formula, C uoc is the unit operation and maintenance cost of ESS, P ESS (t) is the charge and discharge power of ESS at time t, and T is the total operation time of ESS in the whole life cycle. The second indicator is the average power outage time of distributed energy storage system. The average power outage time of the system is a key indicator to measure the reliability of the power system. It means the total duration of power outage for each user due to various reasons within a certain period of time, which directly reflects the stability of power supply and the reliability of system operation. The formula for calculating the power outage time is: Where N d is the number of system nodes; N i is the number of users at node i; y is the Y-1th operating year of ESS; is the power outage status of load node i at time t in year y. By obtaining the first index and the second index corresponding to the site selection and capacity demand, and then using the first index and the second index to quantify the site selection and capacity demand, the analysis process of the site selection and capacity demand is simplified, thereby improving the analysis efficiency.

[0025] S2: Based on the constraints, a multi-objective site selection and capacity determination model is constructed using the first indicator and the second indicator.

[0026] In S2, the constraint conditions include at least energy storage node constraints and energy storage capacity constraints; The energy storage node constraint is Where D ESS represents the nodes in the distributed energy storage system, N nodes represents the nodes in the distribution area, D grid It represents the connection node between the distribution station area and the upper power grid; The energy storage capacity constraint is P ESS，min ≤P ESS ≤P ESS，max , where P ESS represents the configuration capacity of the node in the distributed energy storage system, p ESS，min , P ESS，max They respectively represent the minimum configuration capacity and maximum configuration capacity of the nodes in the distributed energy storage system.

[0027] In S2, the multi-objective site selection and capacity determination model is F=min[f1, f2], where F represents the multi-objective site selection and capacity determination model, f1 represents the first indicator, and f2 represents the second indicator.

[0028] In this embodiment, the constraints also include output constraints, ESS configuration power constraints, ESS charging and discharging power constraints, ESS charge state constraints and distribution station area system power balance constraints. The output constraints are: Where P WT (t), PPV (t) are the output power of wind turbine and photovoltaic unit at time t, P WT，max , P PV，max are the maximum output power of wind turbines and photovoltaic units respectively. The ESS configuration power constraint is E ESS，min ≤E ESS ≤E ESS，max , where E ESS is the configuration power, E ESS，min 、E ESS，max The upper and lower limits of power are configured respectively. The ESS charging and discharging power constraints are Where P in (t), P out (t) are the charging and discharging power of ESS at time t, P in.max , P out.max are the maximum charging power and the maximum discharging power of ESS respectively; η in , η out are the charging and discharging efficiencies of ESS respectively. Overcharging and over-discharging of ESS will greatly reduce its service life. Therefore, constraints are set on the state of charge in the ESS optimization configuration model of the distribution station area. The ESS state of charge constraint is In the formula, E s (t) is the charge state of ESS at time t, E s，min 、E s，max are the maximum and minimum charge states of the ESS respectively. The power balance constraint of the distribution area system is P G +P PV +P WT +P out =P load +P in , where PG is the main network output power, P load The constraints reflect the actual limitations and requirements in the site selection and capacity determination process. Therefore, a multi-objective site selection and capacity determination model is constructed through the constraints to ensure the scientificity and rationality of the site selection and capacity determination plan.

[0029] S3: Obtain the first weight coefficient of the first indicator and the second indicator based on the hierarchical analysis method, and convert the multi-objective site selection and capacity determination model into a single-objective site selection and positioning model based on the first weight coefficient.

[0030] The S3 includes: S31: Obtaining a judgment matrix of the first index and the second index relative to the site selection and positioning requirements based on expert evaluation in the analytic hierarchy process; S32: performing consistency check on the judgment matrix based on the order of the judgment matrix, if the check succeeds, obtaining first weight coefficients of the first indicator and the second indicator based on the judgment matrix, if the check fails, executing S31; S33: Convert the multi-objective site selection and capacity determination model into a single-objective site selection and positioning model based on the first weight coefficient.

[0031] In S3, the single-target site selection and positioning model is H=λ1f1+λ2f2, where H represents the single-target site selection and positioning model, and λ1 and λ2 represent the first weight coefficient of the first indicator and the first weight coefficient of the second indicator respectively.

[0032] In this embodiment, a scale of 1-9 is used to represent the relative importance of the first indicator and the second indicator relative to the site selection and positioning requirements, where: 1 means that the two indicators are equally important; 3 means that one indicator is slightly more important than the other indicator; 5 means that one indicator is obviously more important than the other indicator; 7 means that one indicator is strongly more important than the other indicator; 9 means that one indicator is extremely more important than the other indicator; 2, 4, 6, and 8 represent the intermediate values ​​of the above adjacent judgments. If the judgment matrix obtained through expert evaluation is Using the formula λ is the maximum eigenvalue of the judgment matrix, n is the order of the judgment matrix, the larger the CI is, the worse the consistency is, and the closer the CI is to 0, the better the consistency is. If the judgment matrix satisfies the consistency, the judgment matrix is ​​first normalized, and the normalized result of the first column is The normalized result of the second column is Add the normalized results row by row to get the weight vector. The weight vector of the first indicator is The weight vector of the second indicator is The weight vector is normalized to obtain the final weight coefficient of the first indicator, which is 0.167, and the final weight coefficient of the second indicator is 0.833. The consistency test of the judgment matrix can ensure the logical consistency of the judgment matrix, thereby enhancing the scientific nature of the decision. When the judgment matrix fails to pass the consistency test, it indicates that there is a deviation in the expert evaluation. Therefore, the judgment matrix is ​​re-obtained for evaluation to ensure the rationality and scientific nature of the decision. The first weight coefficient is obtained through the hierarchical analysis method, and the first weight coefficient is used for weighted summation to convert the multi-objective function into a single objective function that can be used by the particle swarm algorithm. At the same time, the global characteristics of the hierarchical analysis method can avoid the particle swarm algorithm from falling into the local optimum, thereby improving the accuracy of the weights of the obtained site selection and capacity demand indicators.

[0033] S4: Use the particle swarm algorithm and the historical data of the distributed energy storage system to obtain the second weight coefficient in the single-target site selection and positioning model, and obtain the optimal energy storage node of the distributed energy storage system and the optimal energy storage capacity of the optimal energy storage node based on the second weight coefficient.

[0034] The S4 includes: S41: Based on the single-target site selection and positioning model, the particle swarm algorithm is used to obtain the optimal first index and the optimal second index; S42: Compare the first indicator corresponding to the historical energy storage node in the historical data and the second indicator corresponding to the historical energy storage capacity of the historical energy storage node with the optimal first indicator and the optimal second indicator to obtain comparison results, and obtain the second weight coefficient in the single-target site selection and positioning model based on the comparison results.

[0035] The S41 includes: S411: Use the particle swarm algorithm to randomly generate a number of particles and initialize the global optimal position and the optimal position of each particle; S412: Input each particle into the single target location selection model to obtain the fitness value of each particle; S413: Determine whether the fitness value of each particle is greater than the fitness corresponding to the optimal position of each particle. If greater than, update the optimal position of each particle based on the fitness value of each particle, and obtain the optimal first indicator and the optimal second indicator based on the updated optimal position of each particle. If less than or equal to, obtain the optimal first indicator and the optimal second indicator based on the fitness value of each particle.

[0036] In S42, the step of obtaining a second weight coefficient in the single target site selection and positioning model based on the comparison result includes: Obtain a first difference between a first indicator corresponding to a historical energy storage node in the historical data and the optimal first indicator, and obtain a second difference between a second indicator corresponding to a historical energy storage capacity of the historical energy storage node and the optimal second indicator; if the first difference or the second difference is less than a preset difference, obtain a second weight coefficient in the single-target site selection and positioning model based on the first difference and the second difference.

[0037] In this embodiment, the optimal first indicator and the optimal second indicator are obtained based on the optimal position of each particle after update. Specifically, the optimal fitness value is found from all particles, and the global extreme value is updated. The speed update formula and the position update formula are used to update the speed and position of each particle. The speed update formula is: v[i+1]=w*v[i]+c1*rand()*(pbest[i]-present[i])+c2*rand()*(gbest-present[i]), and the position update formula is present[i+1]=present[i]+v [i+1], where v[i] represents the velocity of the ith particle, w represents the inertia weight, c1 and c2 represent learning parameters, rand() represents a random number between 0 and 1, pbest[i] represents the optimal value searched by the ith particle, gbest represents the optimal value searched by the entire particle group, and present[i] represents the current position of the ith particle. Check whether the predetermined stop conditions are met, such as reaching the maximum number of iterations, the fitness value reaching a certain threshold or a range that meets certain conditions, etc. If the conditions are met, the algorithm ends and returns the global optimal solution; otherwise, return to S412 to continue iterating. The accuracy of the obtained optimal first index and the optimal second index is guaranteed through multiple iterations, thereby improving the accuracy of the comparison results. The first weight coefficient in the single-target site selection and positioning model is obtained based on the first difference and the second difference. Specifically, if the first difference or the second difference is less than the preset difference, it indicates that the optimal first indicator or the optimal second indicator obtained above does not match the actual optimal first difference or the actual optimal second difference, and the nodes and capacities corresponding to the first indicator and the second indicator of the historical energy storage nodes in the historical data are better than the nodes and capacities corresponding to the obtained optimal first indicator and the optimal second indicator, indicating that there is an error in the first weight coefficient obtained above. Therefore, when the first difference or the second difference is less than the preset difference, the first weight coefficient in the single-target site selection and positioning model is obtained based on the first difference and the second difference. The second weight coefficient in the single-target location selection and positioning model is obtained based on the first difference and the second difference. Specifically, if the first difference is less than the preset difference and the second difference is greater than the preset difference, in order to minimize the value of the single-target location selection and positioning model, the weight of the first indicator can be reduced or the weight of the second indicator can be increased according to actual needs. If the second difference is less than the preset difference and the first difference is greater than the preset difference, in order to minimize the value of the single-target location selection and positioning model, the weight of the first indicator can be increased or the weight of the second indicator can be reduced according to actual needs. If the first difference is less than the preset difference and the second difference is less than the preset difference, S3 is re-executed. The particle swarm algorithm is used to make adaptive adjustments to the functions in the single-target location selection and positioning model, which further improves the accuracy of the weights of the location selection and capacity demand indicators obtained, thereby ensuring the accuracy of the optimal location and optimal capacity finally determined.

[0038] In this embodiment, an improved IEEE-33 node distribution network is used for simulation analysis to verify the effectiveness of the single-target location selection and positioning model. Its topological structure is as follows: Figure 2 As shown. The main grid is connected to node 1, which can ensure the power balance of the distribution network by providing the missing power and absorbing the excess power. Considering the impact of distributed energy on the distribution network, WT with a rated power of 2.5MW and PV with a rated power of 6MW are connected to nodes 13 and 24 respectively. The benchmark used in the example is a typical wind and solar output curve of 1MW in four seasons. Figure 3 , 4 The load types in the distribution network are mostly divided into residential loads, industrial loads and commercial loads. The residential users access nodes are 1, 2, 5, 6, 8, 10, 11, 16, 17, 22, 23 nodes, the industrial load access nodes are 3, 4, 12, 14, 19, 20 nodes, and the commercial load access nodes are 7, 9, 13, 15, 18, 21. The typical four-season power consumption curves of the three loads are shown in the figure. Figure 5 , 6 , 7. The ESS is configured to include two energy storage types: supercapacitor and lithium battery. The technical parameters are shown in Table 1: Table 1. ESS technical parameter settings Energy Storage Technology Charge and discharge efficiency / % Capacity cost / (yuan / kWh) Power cost / (yuan / kW) Operation and maintenance cost / (yuan / kW) Lithium battery 88.5 1450 3000 0.08 Supercapacitor 92.5 1300 8500 0.05 In order to verify the superiority of this single-target site selection and positioning model, three simulation schemes are set up for comparison. The specific contents are as follows: Solution 1: Energy storage is configured in the distribution network, but the traditional particle swarm algorithm is used for solution; Solution 2: Energy storage is configured in the distribution network and a single-objective site selection and positioning model is used for solution; Option 3: No energy storage is configured in the distribution network.

[0039] Based on the above scheme settings, scheme 1 and scheme 2 are solved, and the ESS site selection and capacity determination results and the comprehensive benefits under different schemes are shown in Table 2 and Table 3: Table 2. ESS site selection and capacity determination results Table 3. Comprehensive benefits of ESS As shown in Table 2, the total capacity of lithium batteries and supercapacitors configured by the traditional particle swarm algorithm for site selection and capacity determination model is 3827.6601kWh and 5705.1805kWh, respectively, and the total construction cost is 36.54322 million yuan; the total capacity of lithium batteries and supercapacitors configured by the single-target site selection and positioning model is 1395.8879kWh and 3904.1506kWh, respectively, and the total construction cost is 20.67682 million yuan. The total cost of the single-target site selection and positioning model is 43.42% lower than that of the traditional algorithm, and the DG consumption is increased several times, which reflects the superiority of the single-target site selection and positioning model.

[0040] According to the above site selection and capacity determination results, the distribution network power supply reliability calculation of five indicators including the average number of system power outages, the average system power outage time, the average power outage duration of users, the system power outage load loss, and the average system power supply availability is continued for the three schemes. The calculation results are shown in Table 4: Table 4. System reliability calculation results From the comparison between Scheme 3 and Scheme 1 in Table 4, it can be seen that the connection of ESS in the distribution network has a significant effect on improving the power supply reliability. The average number of power outages decreased by 0.6837 (times / (year·household)), the average power outage time decreased by 7.1081h / (household·year), the average power outage time decreased by 4.4949h / time, and the annual load loss decreased by 4.8479MWh / (year·household). The comprehensive power supply reliability increased by 0.0811%, among which the average power outage time of the system improved most significantly, with a decrease of 49.84% compared with Scheme 3; Scheme 1 Compared with Scheme 2, it can be seen that the use of a single-objective site selection and positioning model to solve the ESS site selection and sizing model can further improve power supply reliability, with the average number of power outages reduced by 0.0964 (times / (year·household)), the average power outage time reduced by 3.5733h / (household·year), the average power outage time reduced by 1.0587h / time, and the annual load loss reduced by 0.3116MWh / (year·household). The comprehensive power supply reliability is improved by 0.017%, among which the improvement in the average power outage time of the system is the most obvious, with a reduction of 49.94% compared with Scheme 1.

[0041] Comparison of the average annual power outage times, average annual power outage duration, average power outage duration per time and annual load loss under different schemes Figure 8 to Figure 11 As shown, Figure 8-Figure 11Combined with the analysis in Table 4, Scheme 3 does not consider the improvement effect of ESS on the distribution network, so it performs the worst in terms of the four indicators of annual average power outage times, annual average power outage time, sub-average power outage time and annual load loss; Scheme 1 introduces the ESS system into the distribution network system and uses the traditional PSO algorithm to solve the model, and the results of the above four indicators are second; Scheme 2 further adopts the single-objective site selection and positioning model to solve on the basis of Scheme 1, and the improvement in the annual average power outage times, annual average power outage time, sub-average power outage time and annual load loss is the most obvious, verifying the feasibility and superiority of the single-objective site selection and positioning model.

[0042] The site selection and capacity determination proposed in the present invention adopts a single-objective site selection and positioning model for solution, which can significantly reduce the ESS installation cost, improve the DG absorption capacity and various power supply reliability indicators.

[0043] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the distributed energy storage system site selection and capacity determination method based on a particle swarm algorithm.

[0044] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for site selection and sizing of a distributed energy storage system based on a particle swarm algorithm are implemented.

[0045] The specific implementation described above is a preferred implementation of the distributed energy storage system site selection and sizing method based on the particle swarm algorithm of the present invention, and is not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A distributed energy storage system site selection and capacity determination method based on particle swarm algorithm, characterized in that: The following steps are involved: S1: Obtain the first index and the second index based on the site selection and capacity determination requirements of the distributed energy storage system; S2: Based on the constraints, a multi-objective site selection and capacity determination model is constructed using the first and second indicators; S3: obtaining first weight coefficients of the first indicator and the second indicator based on the hierarchical analysis method, and converting the multi-objective site selection and capacity determination model into a single-objective site selection and positioning model based on the first weight coefficient; S4: Use the particle swarm algorithm and the historical data of the distributed energy storage system to obtain the second weight coefficient in the single-target site selection and positioning model, and obtain the optimal energy storage node of the distributed energy storage system and the optimal energy storage capacity of the optimal energy storage node based on the second weight coefficient.

2. The method for site selection and capacity determination of a distributed energy storage system based on a particle swarm algorithm according to claim 1, characterized in that: In S2, the constraint conditions include at least energy storage node constraints and energy storage capacity constraints; The energy storage node constraint is Where D ESS represents the nodes in the distributed energy storage system, N nodes represents the nodes in the distribution area, D grid It represents the connection node between the distribution station area and the upper power grid; The energy storage capacity constraint is P ESS，min ≤P ESS ≤P ESS，max , where P ESS represents the configuration capacity of the node in the distributed energy storage system, P ESS，min , P ESS，max They respectively represent the minimum configuration capacity and maximum configuration capacity of the nodes in the distributed energy storage system.

3. The method for site selection and capacity determination of a distributed energy storage system based on a particle swarm algorithm according to claim 1, characterized in that: In S2, the multi-objective site selection and capacity determination model is F=min[f1, f2], where F represents the multi-objective site selection and capacity determination model, f1 represents the first indicator, and f2 represents the second indicator.

4. The method for site selection and capacity determination of a distributed energy storage system based on a particle swarm algorithm according to claim 1, characterized in that: The S3 includes: S31: Obtaining a judgment matrix of the first index and the second index relative to the site selection and positioning requirements based on expert evaluation in the analytic hierarchy process; S32: Perform consistency check on the judgment matrix based on the order of the judgment matrix. If the check succeeds, obtain the first weight coefficient of the first indicator and the second indicator based on the judgment matrix. If the check fails, execute S31. S33: Convert the multi-objective site selection and capacity determination model into a single-objective site selection and positioning model based on the first weight coefficient.

5. The method for site selection and capacity determination of a distributed energy storage system based on a particle swarm algorithm according to claim 3, characterized in that: In S3, the single-target site selection and positioning model is H=λ1f1+λ2f2, where H represents the single-target site selection and positioning model, and λ1 and λ2 represent the first weight coefficient of the first indicator and the first weight coefficient of the second indicator respectively.

6. The method for site selection and capacity determination of a distributed energy storage system based on a particle swarm algorithm according to claim 1, characterized in that: The S4 includes: S41: Based on the single-target site selection and positioning model, the particle swarm algorithm is used to obtain the optimal first index and the optimal second index; S42: Compare the first indicator corresponding to the historical energy storage node in the historical data and the second indicator corresponding to the historical energy storage capacity of the historical energy storage node with the optimal first indicator and the optimal second indicator to obtain comparison results, and obtain the second weight coefficient in the single-target site selection and positioning model based on the comparison results.

7. The method for site selection and capacity determination of a distributed energy storage system based on a particle swarm algorithm according to claim 6, characterized in that: The S41 includes: S411: Use the particle swarm algorithm to randomly generate a number of particles and initialize the global optimal position and the optimal position of each particle; S412: Input each particle into the single target location selection model to obtain the fitness value of each particle; S413: Determine whether the fitness value of each particle is greater than the fitness corresponding to the optimal position of each particle. If greater than, update the optimal position of each particle based on the fitness value of each particle, and obtain the optimal first indicator and the optimal second indicator based on the updated optimal position of each particle. If less than or equal to, obtain the optimal first indicator and the optimal second indicator based on the fitness value of each particle.

8. The method for site selection and capacity determination of a distributed energy storage system based on a particle swarm algorithm according to claim 6, characterized in that: In S42, the step of obtaining a second weight coefficient in the single target site selection and positioning model based on the comparison result includes: Obtain a first difference between a first indicator corresponding to a historical energy storage node in the historical data and the optimal first indicator, and obtain a second difference between a second indicator corresponding to a historical energy storage capacity of the historical energy storage node and the optimal second indicator; if the first difference or the second difference is less than a preset difference, obtain a second weight coefficient in the single-target site selection and positioning model based on the first difference and the second difference.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the distributed energy storage system site selection and capacity determination method based on a particle swarm algorithm as described in any one of claims 1 to 8 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the distributed energy storage system site selection and capacity determination method based on particle swarm algorithm as described in any one of claims 1 to 8 are implemented.

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