Power distribution network multi-target joint planning method and device and readable storage medium

Abstract

The invention discloses a multi-objective combined planning method and device for a power distribution network and a readable storage medium, and aims to establish a combined planning model of a distributed photovoltaic and distributed battery energy storage system by taking minimization of annual average comprehensive cost, voltage fluctuation and network loss of the power distribution network as a multi-objective function, so that the method is more efficient and comprehensive, and the planning efficiency is improved. The improved third-generation non-dominated genetic algorithm is used for solving the joint planning model of the distributed photovoltaic and distributed battery energy storage system, parallel processing and balanced optimization of multiple optimization targets can be achieved through the improved third-generation non-dominated genetic algorithm, the population diversity is improved, the search capability of the algorithm is enhanced, and the optimization efficiency of the distributed photovoltaic and distributed battery energy storage system is improved. And the optimal installation position and capacity of the distributed photovoltaic and distributed battery energy storage system are solved, so that the overall operation efficiency of the system is improved, the fluctuation of a power grid is effectively inhibited, and the resource utilization rate is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to a distribution network multi-objective joint planning method, device and readable storage medium. Background Art

[0002] As the global fossil energy crisis becomes increasingly severe, the installed capacity of distributed generation (DG) has achieved rapid growth in recent years, especially the proportion of renewable energy in DG has increased significantly. However, DG is constrained by natural factors such as weather and environment, and its output is significantly intermittent and volatile, which poses a severe challenge to the voltage stability of the distribution network. In addition, DG has also profoundly changed the power flow distribution of the system, which may cause reverse overloading of the line. Its reverse peak-shaving characteristics have exacerbated the frequency of "wind and solar abandonment" in distributed power sources.

[0003] To meet the above challenges, distributed battery energy storage system (DBESS) has become one of the important solutions to optimize the problem of high-penetration distributed power sources with its characteristics of bidirectional power regulation, energy time transfer and rapid response. Specifically, the access of distributed photovoltaic (DPV) can not only provide the necessary power and voltage support for the remote nodes of the distribution network, but the synergy of DBESS can effectively alleviate the problems of increased network loss and voltage limit caused by DPV access. Therefore, the joint planning strategy of DPV and DBESS is regarded as an effective way to solve the complex problems of DPV access to the distribution network.

[0004] Looking back at the existing research, rich results have been accumulated on the site selection and capacity determination of DG in the distribution network. For example, focusing on the security of the distribution network, the type, access location and capacity of DG are optimized by comprehensively considering the correlation between uncertain factors, but its limitation is that the optimization goal is relatively single; or, a planning model with two layers is constructed. The upper layer aims to minimize the annual operating cost by optimizing the location and capacity of DPV, while the lower layer focuses on the location and capacity adjustment of DBESS to minimize load fluctuations, but this model faces the problems of long calculation time and difficult convergence caused by double-layer optimization.

[0005] In view of the common characteristics of distributed generation in distribution networks, such as small capacity, wide distribution, large output fluctuations, and the difficulties faced by centralized control of the system, such as numerous variables and complex processes, cluster control strategies have gradually become the mainstream trend in the field of distribution network control. However, existing cluster control methods mostly focus on system-level integration and optimization, and the consideration of DBESS specific control strategies is still insufficient.

[0006] In summary, future research should further explore how to deeply integrate cluster control strategies and refined DBESS control logic under the joint planning framework of DPV and DBESS to achieve efficient, stable and economical grid-connected operation of distributed power sources. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a distribution network multi-objective joint planning method, device and readable storage medium, which can improve the overall operating efficiency of the system, effectively suppress power grid volatility and optimize resource utilization.

[0008] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0009] A multi-objective joint planning method for a distribution network comprises the following steps:

[0010] A joint planning model for distributed photovoltaic and distributed battery energy storage systems is established with the multi-objective function of minimizing the annual average comprehensive cost of the distribution network, voltage fluctuations, and network losses.

[0011] Using an improved third-generation non-dominated genetic algorithm to solve the joint planning model of the distributed photovoltaic and distributed battery energy storage systems, the installation location and capacity of the distributed photovoltaic and distributed battery energy storage systems are obtained;

[0012] The improved third generation non-dominated genetic algorithm uses a logical chaos map combined with a hybrid initialization method to initialize the population, and uses an adaptive local search strategy based on a confidence interval upper bound algorithm to perform local search.

[0013] Furthermore, the joint planning model of distributed photovoltaic and distributed battery energy storage systems is established by taking minimization of the annual average comprehensive cost of the distribution network, voltage fluctuation and network loss as a multi-objective function, including:

[0014] A multi-objective function is established to minimize the annual average comprehensive cost of the distribution network, voltage fluctuations, and network losses;

[0015] Establish voltage deviation range constraints, power balance constraints, power flow constraints, charging and discharging constraints of distributed battery energy storage systems, and cluster distribution constraints of distributed photovoltaic and distributed battery energy storage systems;

[0016] A joint planning model of a distributed photovoltaic and distributed battery energy storage system is generated according to the multi-objective function, the voltage deviation range constraint, the power balance constraint, the power flow constraint, the charging and discharging constraint of the distributed battery energy storage system, and the cluster distribution constraint of the distributed photovoltaic and distributed battery energy storage system.

[0017] Furthermore, the multi-objective function established by minimizing the annual average comprehensive cost of the distribution network, voltage fluctuation and network loss includes:

[0018] minF=min{F C ,F U ,F P};

[0019]

[0020] In the formula, F represents the multi-objective function, F C represents the average annual comprehensive cost of the distribution network, F U Indicates voltage fluctuation, F P Indicates network loss, p e,t represents the time-of-use electricity price within time period t, P L,t represents the amount of electricity purchased from the main grid during period t, T represents the time scale of the planning scenario for one day, represents the annualized investment cost of distributed photovoltaics, represents the operation and maintenance cost of distributed photovoltaics, represents the annualized investment cost of the distributed battery energy storage system, represents the operation and maintenance cost of the distributed battery energy storage system, γ represents the bank interest rate, and y DPV Represents the service life of distributed photovoltaics, N DPV represents the number of distributed photovoltaics, represents the unit capacity configuration cost of distributed photovoltaics, represents the rated power of the mth distributed photovoltaic, represents the operation and maintenance cost of distributed photovoltaics, Indicates the grid-connected subsidy for distributed photovoltaics, represents the output power of the mth distributed photovoltaic power station in time period t, y DBESS Represents the service life of the distributed battery energy storage system, N DBESS represents the number of distributed battery energy storage systems, represents the installation cost of the power of the distributed battery energy storage system, represents the rated power of the nth distributed battery energy storage system, represents the installation cost of the capacity of the distributed battery energy storage system, represents the rated capacity of the nth distributed battery energy storage system, represents the output power of the nth distributed battery energy storage system in time period t, represents the maintenance cost of the distributed battery energy storage system, N bus Indicates the number of nodes in the distribution network, U i,t represents the voltage value of node i at time period t, represents the average voltage at node i, N b Indicates the number of branches in the distribution network, Represents the line loss of line b in time period t.

[0021] Furthermore, the voltage deviation range is constrained as follows:

[0022] U i,min ≤U i,t ≤U i,max ;

[0023] Where U i,min represents the upper limit of the voltage deviation of node i, U i,max represents the lower limit of the voltage deviation of node i;

[0024] The power balance constraint is:

[0025]

[0026] Where P i,t represents the active power of node i in period t;

[0027] The power flow constraint is:

[0028]

[0029] Where U j,t represents the voltage value of node j at time period t, G ij represents the conductance between node i and node j, θ ij represents the voltage phase angle between node i and node j, B ij represents the susceptance between node i and node j, Q i,t represents the reactive power of node i in period t;

[0030] The charging and discharging constraints of the distributed battery energy storage system are:

[0031]

[0032] SoC n,min ≤SoC n,t ≤SoC n,max ;

[0033] |SoC n,0 -SoC n,T |≤σ;

[0034]

[0035] Where, SoC n,min Represents the lower limit of the state of charge of the nth distributed battery energy storage system, SoC n,t Indicates the charging state of the nth distributed battery energy storage system in period t, SoC n,maxRepresents the upper limit of the state of charge of the nth distributed battery energy storage system, SoC n,0 Indicates the charging state of the nth distributed battery energy storage system in the initial period, SoC n,T represents the charging state of the nth distributed battery energy storage system at the end of the period, σ represents the maximum deviation of the charging state of the distributed battery energy storage system at the beginning and the end, represents the output power of the nth distributed battery energy storage system in time period τ, η represents the charging efficiency of the distributed battery energy storage system, η1 represents the first charging efficiency of the distributed battery energy storage system, and η2 represents the second charging efficiency of the distributed battery energy storage system;

[0036] The cluster distribution constraints of the distributed photovoltaic and distributed battery energy storage system are:

[0037]

[0038] Where N c represents the number of clusters in the distribution network, represents the number of distributed photovoltaics in cluster c, represents the number of distributed battery energy storage systems in cluster c.

[0039] Furthermore, the improved third-generation non-dominated genetic algorithm is used to solve the joint planning model of the distributed photovoltaic and distributed battery energy storage system, and the installation location and capacity of the distributed photovoltaic and distributed battery energy storage system are obtained, including:

[0040] A population is initialized by using a logical chaos mapping combined with a hybrid initialization method, wherein the population includes chromosomes, and the chromosomes include the installation location and capacity of distributed photovoltaics and the installation location and capacity of distributed battery energy storage systems;

[0041] Performing segmented integer encoding on the chromosome to obtain an encoded chromosome;

[0042] performing crossover and mutation operations on the encoded chromosome;

[0043] After the chromosomes that have completed the crossover and mutation operations are combined with the parent generation, a fast non-dominated sorting of feasible solutions is performed;

[0044] Adopting the adaptive local search strategy based on the confidence interval upper bound algorithm to perform local search, the Pareto planning solution set is obtained;

[0045] Selecting the best compromise solution from the Pareto planning solution set by using a near-ideal solution ranking method;

[0046] The optimal compromise solution is decoded using a decoding method based on greedy insertion to obtain the installation location and capacity of the distributed photovoltaic and distributed battery energy storage systems.

[0047] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0048] A multi-objective joint planning device for a distribution network, comprising:

[0049] The model building module is used to establish a joint planning model for distributed photovoltaic and distributed battery energy storage systems by minimizing the annual average comprehensive cost of the distribution network, voltage fluctuations, and network losses as a multi-objective function;

[0050] A solution module, used to solve the joint planning model of the distributed photovoltaic and distributed battery energy storage system using an improved third-generation non-dominated genetic algorithm to obtain the installation location and capacity of the distributed photovoltaic and distributed battery energy storage system; and

[0051] The improved third generation non-dominated genetic algorithm uses a logical chaos map combined with a hybrid initialization method to initialize the population, and uses an adaptive local search strategy based on a confidence interval upper bound algorithm to perform local search.

[0052] Furthermore, the model building module includes:

[0053] Objective function establishment module, used to establish multi-objective functions to minimize the annual average comprehensive cost of the distribution network, voltage fluctuations and network losses;

[0054] Constraint establishment module, used to establish voltage deviation range constraints, power balance constraints, power flow constraints, charging and discharging constraints of distributed battery energy storage systems, and cluster distribution constraints of distributed photovoltaic and distributed battery energy storage systems;

[0055] A model generation module is used to generate a joint planning model of a distributed photovoltaic and distributed battery energy storage system according to the multi-objective function, the voltage deviation range constraint, the power balance constraint, the power flow constraint, the charging and discharging constraint of the distributed battery energy storage system, and the cluster distribution constraint of the distributed photovoltaic and distributed battery energy storage system.

[0056] Furthermore, the objective function establishment module establishes a multi-objective function to minimize the annual average comprehensive cost of the distribution network, voltage fluctuation and network loss, including:

[0057] minF=min{F C ,F U ,F P};

[0058]

[0059]

[0060] In the formula, F represents the multi-objective function, F C represents the average annual comprehensive cost of the distribution network, F U Indicates voltage fluctuation, F P Indicates network loss, p e,t represents the time-of-use electricity price within time period t, P L,t represents the amount of electricity purchased from the main grid during period t, T represents the time scale of the planning scenario for one day, represents the annualized investment cost of distributed photovoltaics, represents the operation and maintenance cost of distributed photovoltaics, represents the annualized investment cost of the distributed battery energy storage system, represents the operation and maintenance cost of the distributed battery energy storage system, γ represents the bank interest rate, and y DPV Represents the service life of distributed photovoltaics, N DPV represents the number of distributed photovoltaics, represents the unit capacity configuration cost of distributed photovoltaics, represents the rated power of the mth distributed photovoltaic, represents the operation and maintenance cost of distributed photovoltaics, Indicates the grid-connected subsidy for distributed photovoltaics, represents the output power of the mth distributed photovoltaic power station in time period t, y DBESS Represents the service life of the distributed battery energy storage system, N DBESS represents the number of distributed battery energy storage systems, represents the installation cost of the power of the distributed battery energy storage system, represents the rated power of the nth distributed battery energy storage system, represents the installation cost of the capacity of the distributed battery energy storage system, represents the rated capacity of the nth distributed battery energy storage system, represents the output power of the nth distributed battery energy storage system in time period t, represents the maintenance cost of the distributed battery energy storage system, N bus Indicates the number of nodes in the distribution network, U i,t represents the voltage value of node i at time period t, represents the average voltage at node i, N b Indicates the number of branches in the distribution network, Represents the line loss of line b in time period t.

[0061] Furthermore, the voltage deviation range constraint in the constraint establishment module is:

[0062] U i,min ≤U i,t ≤U i,max ;

[0063] Where U i,min represents the upper limit of the voltage deviation of node i, U i,max represents the lower limit of the voltage deviation of node i;

[0064] The power balance constraint in the constraint establishment module is:

[0065]

[0066] Where P i,t represents the active power of node i in period t;

[0067] The power flow constraint in the constraint establishment module is:

[0068]

[0069] Where U j,t represents the voltage value of node j at time period t, G ij represents the conductance between node i and node j, θ ij represents the voltage phase angle between node i and node j, B ij represents the susceptance between node i and node j, Q i,t represents the reactive power of node i in period t;

[0070] The charging and discharging constraints of the distributed battery energy storage system in the constraint establishment module are:

[0071]

[0072] SoC n,min ≤SoC n,t ≤SoC n,max ;

[0073] |SoC n,0 -SoC n,T |≤σ;

[0074]

[0075] Where, SoC n,min Represents the lower limit of the state of charge of the nth distributed battery energy storage system, SoC n,t Indicates the charging state of the nth distributed battery energy storage system in period t, SoC n,max Represents the upper limit of the state of charge of the nth distributed battery energy storage system, SoC n,0 Indicates the charging state of the nth distributed battery energy storage system in the initial period, SoC n,T represents the charging state of the nth distributed battery energy storage system at the end of the period, σ represents the maximum deviation of the charging state of the distributed battery energy storage system at the beginning and the end, represents the output power of the nth distributed battery energy storage system in time period τ, η represents the charging efficiency of the distributed battery energy storage system, η1 represents the first charging efficiency of the distributed battery energy storage system, and η2 represents the second charging efficiency of the distributed battery energy storage system;

[0076] The cluster distribution constraint of the distributed photovoltaic and distributed battery energy storage system in the constraint establishment module is:

[0077]

[0078] Where N c represents the number of clusters in the distribution network, represents the number of distributed photovoltaics in cluster c, represents the number of distributed battery energy storage systems in cluster c.

[0079] Furthermore, the solution module includes:

[0080] An initialization module, used to initialize a population by using a logical chaotic mapping combined with a hybrid initialization method, wherein the population includes chromosomes, and the chromosomes include the installation location and capacity of distributed photovoltaics and the installation location and capacity of distributed battery energy storage systems;

[0081] An encoding module, used for performing segmented integer encoding on the chromosome to obtain an encoded chromosome;

[0082] A crossover and mutation module, used for performing crossover and mutation operations on the encoded chromosome;

[0083] The sorting module is used to combine the chromosomes that have completed the crossover and mutation operations with the parent generation, and then perform fast non-dominated sorting of feasible solutions;

[0084] A local search module is used to perform local search using an adaptive local search strategy based on a confidence interval upper bound algorithm to obtain a Pareto planning solution set;

[0085] An optimal compromise solution selection module, used to select the optimal compromise solution from the Pareto planning solution set by using an approximate ideal solution sorting method;

[0086] A decoding module is used to decode the optimal compromise solution by using a decoding method based on greedy insertion to obtain the installation location and capacity of the distributed photovoltaic and distributed battery energy storage systems.

[0087] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0088] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements each step of the above-mentioned distribution network multi-objective joint planning method.

[0089] The beneficial effects of the present invention are: taking minimizing the annual average comprehensive cost of the distribution network, voltage fluctuations and network losses as multi-objective functions, a joint planning model of distributed photovoltaic and distributed battery energy storage systems is established, which is more efficient and comprehensive, and an improved third-generation non-dominated genetic algorithm (NSGA-III) is used to solve the joint planning model of distributed photovoltaic and distributed battery energy storage systems. The improved third-generation non-dominated genetic algorithm can realize parallel processing and balanced optimization of multiple optimization objectives, improve population diversity, and enhance the search ability of the algorithm, and solve the optimal installation location and capacity of distributed photovoltaic and distributed battery energy storage systems, thereby improving the overall operation efficiency of the system, and effectively suppressing grid volatility and optimizing resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 A flow chart of the steps of a distribution network multi-objective joint planning method according to an embodiment of the present invention;

[0091] Figure 2 It is a structural schematic diagram of a distribution network multi-objective joint planning device according to an embodiment of the present invention;

[0092] Figure 3 This is a schematic diagram of initializing a logical chaotic map in a multi-objective joint planning method for a distribution network according to an embodiment of the present invention;

[0093] Figure 4 A schematic diagram of crossover operations in a multi-objective joint planning method for a distribution network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0094] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0095] Please refer to Figure 1 , a multi-objective joint planning method for distribution network, comprising the steps of:

[0096] A joint planning model for distributed photovoltaic and distributed battery energy storage systems is established with the multi-objective function of minimizing the annual average comprehensive cost of the distribution network, voltage fluctuations, and network losses.

[0097] Using an improved third-generation non-dominated genetic algorithm to solve the joint planning model of the distributed photovoltaic and distributed battery energy storage systems, the installation location and capacity of the distributed photovoltaic and distributed battery energy storage systems are obtained;

[0098] The improved third generation non-dominated genetic algorithm uses a logical chaos map combined with a hybrid initialization method to initialize the population, and uses an adaptive local search strategy based on a confidence interval upper bound algorithm to perform local search.

[0099] From the above description, it can be seen that the beneficial effects of the present invention are: taking the minimization of the annual average comprehensive cost of the distribution network, voltage fluctuations and network losses as multi-objective functions, a joint planning model of distributed photovoltaic and distributed battery energy storage systems is established, which is more efficient and comprehensive, and an improved third-generation non-dominated genetic algorithm is used to solve the joint planning model of distributed photovoltaic and distributed battery energy storage systems. The improved third-generation non-dominated genetic algorithm can realize parallel processing and balanced optimization of multiple optimization objectives, improve population diversity, and enhance the search ability of the algorithm, and solve the optimal installation location and capacity of distributed photovoltaic and distributed battery energy storage systems, thereby improving the overall operation efficiency of the system, and effectively suppressing grid volatility and optimizing resource utilization.

[0100] Furthermore, the joint planning model of distributed photovoltaic and distributed battery energy storage systems is established by taking minimization of the annual average comprehensive cost of the distribution network, voltage fluctuation and network loss as a multi-objective function, including:

[0101] A multi-objective function is established to minimize the annual average comprehensive cost of the distribution network, voltage fluctuations, and network losses;

[0102] Establish voltage deviation range constraints, power balance constraints, power flow constraints, charging and discharging constraints of distributed battery energy storage systems, and cluster distribution constraints of distributed photovoltaic and distributed battery energy storage systems;

[0103] A joint planning model of a distributed photovoltaic and distributed battery energy storage system is generated according to the multi-objective function, the voltage deviation range constraint, the power balance constraint, the power flow constraint, the charging and discharging constraint of the distributed battery energy storage system, and the cluster distribution constraint of the distributed photovoltaic and distributed battery energy storage system.

[0104] From the above description, it can be seen that a joint planning model of distributed photovoltaic and distributed battery energy storage systems is generated according to multi-objective functions, voltage deviation range constraints, power balance constraints, flow constraints, charging and discharging constraints of distributed battery energy storage systems, and cluster distribution constraints of distributed photovoltaic and distributed battery energy storage systems. The joint planning model of distributed photovoltaic and distributed battery energy storage systems not only overcomes the limitations caused by the singleness of existing optimization objectives, the long calculation time and convergence problems brought by the two-layer optimization model, and the lack of comprehensive consideration of the specific control strategy of the distributed battery energy storage system, but also provides new ideas and methods for realizing the efficient, safe and economical grid connection of distributed power sources.

[0105] Furthermore, the multi-objective function established by minimizing the annual average comprehensive cost of the distribution network, voltage fluctuation and network loss includes:

[0106] minF=min{F C ,F U ,F P};

[0107]

[0108]

[0109] In the formula, F represents the multi-objective function, F C represents the average annual comprehensive cost of the distribution network, F U Indicates voltage fluctuation, F P Indicates network loss, p e,t represents the time-of-use electricity price within time period t, P L,t represents the amount of electricity purchased from the main grid during period t, T represents the time scale of the planning scenario for one day, represents the annualized investment cost of distributed photovoltaics, represents the operation and maintenance cost of distributed photovoltaics, represents the annualized investment cost of the distributed battery energy storage system, represents the operation and maintenance cost of the distributed battery energy storage system, γ represents the bank interest rate, and y DPV Represents the service life of distributed photovoltaics, N DPV represents the number of distributed photovoltaics, represents the unit capacity configuration cost of distributed photovoltaics, represents the rated power of the mth distributed photovoltaic, represents the operation and maintenance cost of distributed photovoltaics, Indicates the grid-connected subsidy for distributed photovoltaics, represents the output power of the mth distributed photovoltaic power station in time period t, y DBESS Represents the service life of the distributed battery energy storage system, N DBESS represents the number of distributed battery energy storage systems, represents the installation cost of the power of the distributed battery energy storage system, represents the rated power of the nth distributed battery energy storage system, represents the installation cost of the capacity of the distributed battery energy storage system, represents the rated capacity of the nth distributed battery energy storage system, represents the output power of the nth distributed battery energy storage system in time period t, represents the maintenance cost of the distributed battery energy storage system, N bus Indicates the number of nodes in the distribution network, U i,trepresents the voltage value of node i at time period t, represents the average voltage at node i, N b Indicates the number of branches in the distribution network, Represents the line loss of line b in time period t.

[0110] From the above description, it can be seen that a multi-objective function is established to minimize the annual average comprehensive cost, voltage fluctuation and network loss of the distribution network, so as to achieve a win-win situation of economic benefits and environmental protection while ensuring the safe and stable operation of the distribution network.

[0111] Furthermore, the voltage deviation range is constrained as follows:

[0112] U i,min ≤U i,t ≤U i,max ;

[0113] Where U i,min represents the upper limit of the voltage deviation of node i, U i,max represents the lower limit of the voltage deviation of node i;

[0114] The power balance constraint is:

[0115]

[0116] Where P i,t represents the active power of node i in period t;

[0117] The power flow constraint is:

[0118]

[0119] Where U j,t represents the voltage value of node j at time period t, G ij represents the conductance between node i and node j, θ ij represents the voltage phase angle between node i and node j, B ij represents the susceptance between node i and node j, Q i,t represents the reactive power of node i in period t;

[0120] The charging and discharging constraints of the distributed battery energy storage system are:

[0121]

[0122] SoC n,min ≤SoC n,t ≤SoC n,max ;

[0123] |SoC n,0 -SoC n,T |≤σ;

[0124]

[0125] Where, SoC n,min Represents the lower limit of the state of charge of the nth distributed battery energy storage system, SoC n,t Indicates the charging state of the nth distributed battery energy storage system in period t, SoC n,max Represents the upper limit of the state of charge of the nth distributed battery energy storage system, SoC n,0 Indicates the charging state of the nth distributed battery energy storage system in the initial period, SoC n,T represents the charging state of the nth distributed battery energy storage system at the end of the period, σ represents the maximum deviation of the charging state of the distributed battery energy storage system at the beginning and the end, represents the output power of the nth distributed battery energy storage system in time period τ, η represents the charging efficiency of the distributed battery energy storage system, η1 represents the first charging efficiency of the distributed battery energy storage system, and η2 represents the second charging efficiency of the distributed battery energy storage system;

[0126] The cluster distribution constraints of the distributed photovoltaic and distributed battery energy storage system are:

[0127]

[0128] Where N c represents the number of clusters in the distribution network, represents the number of distributed photovoltaics in cluster c, represents the number of distributed battery energy storage systems in cluster c.

[0129] From the above description, it can be seen that by establishing voltage deviation range constraints, power balance constraints, power flow constraints, charging and discharging constraints of distributed battery energy storage systems, and cluster distribution constraints of distributed photovoltaic and distributed battery energy storage systems, the safe and stable operation of the distribution network can be ensured.

[0130] Furthermore, the improved third-generation non-dominated genetic algorithm is used to solve the joint planning model of the distributed photovoltaic and distributed battery energy storage system, and the installation location and capacity of the distributed photovoltaic and distributed battery energy storage system are obtained, including:

[0131] A population is initialized by using a logical chaos mapping combined with a hybrid initialization method, wherein the population includes chromosomes, and the chromosomes include the installation location and capacity of distributed photovoltaics and the installation location and capacity of distributed battery energy storage systems;

[0132] Performing segmented integer encoding on the chromosome to obtain an encoded chromosome;

[0133] performing crossover and mutation operations on the encoded chromosome;

[0134] After the chromosomes that have completed the crossover and mutation operations are combined with the parent generation, a fast non-dominated sorting of feasible solutions is performed;

[0135] Adopting the adaptive local search strategy based on the confidence interval upper bound algorithm to perform local search, the Pareto planning solution set is obtained;

[0136] Selecting the best compromise solution from the Pareto planning solution set by using a near-ideal solution ranking method;

[0137] The optimal compromise solution is decoded using a decoding method based on greedy insertion to obtain the installation location and capacity of the distributed photovoltaic and distributed battery energy storage systems.

[0138] From the above description, it can be seen that the use of logical chaotic mapping combined with the hybrid initialization method to initialize the population can effectively enhance the diversity of the population. The adaptive local search strategy based on the confidence interval upper bound algorithm is used for local search, which improves the algorithm search capability. The encoding and decoding strategy can effectively cope with the diversity and heterogeneity of decision variables, which not only ensures the economy and feasibility of the planning scheme, but also takes into account the efficiency and flexibility of the system operation.

[0139] Please refer to Figure 2 , a distribution network multi-objective joint planning device, comprising:

[0140] The model building module is used to establish a joint planning model for distributed photovoltaic and distributed battery energy storage systems by minimizing the annual average comprehensive cost of the distribution network, voltage fluctuations, and network losses as a multi-objective function;

[0141] A solution module, used to solve the joint planning model of the distributed photovoltaic and distributed battery energy storage system using an improved third-generation non-dominated genetic algorithm to obtain the installation location and capacity of the distributed photovoltaic and distributed battery energy storage system; and

[0142] The improved third generation non-dominated genetic algorithm uses a logical chaos map combined with a hybrid initialization method to initialize the population, and uses an adaptive local search strategy based on a confidence interval upper bound algorithm to perform local search.

[0143] From the above description, it can be seen that the beneficial effects of the present invention are: taking the minimization of the annual average comprehensive cost of the distribution network, voltage fluctuations and network losses as multi-objective functions, a joint planning model of distributed photovoltaic and distributed battery energy storage systems is established, which is more efficient and comprehensive, and an improved third-generation non-dominated genetic algorithm is used to solve the joint planning model of distributed photovoltaic and distributed battery energy storage systems. The improved third-generation non-dominated genetic algorithm can realize parallel processing and balanced optimization of multiple optimization objectives, improve population diversity, and enhance the search ability of the algorithm, and solve the optimal installation location and capacity of distributed photovoltaic and distributed battery energy storage systems, thereby improving the overall operation efficiency of the system, and effectively suppressing grid volatility and optimizing resource utilization.

[0144] Furthermore, the model building module includes:

[0145] Objective function establishment module, used to establish multi-objective functions to minimize the annual average comprehensive cost of the distribution network, voltage fluctuations and network losses;

[0146] Constraint establishment module, used to establish voltage deviation range constraints, power balance constraints, power flow constraints, charging and discharging constraints of distributed battery energy storage systems, and cluster distribution constraints of distributed photovoltaic and distributed battery energy storage systems;

[0147] A model generation module is used to generate a joint planning model of a distributed photovoltaic and distributed battery energy storage system according to the multi-objective function, the voltage deviation range constraint, the power balance constraint, the power flow constraint, the charging and discharging constraint of the distributed battery energy storage system, and the cluster distribution constraint of the distributed photovoltaic and distributed battery energy storage system.

[0148] From the above description, it can be seen that a joint planning model of distributed photovoltaic and distributed battery energy storage systems is generated according to multi-objective functions, voltage deviation range constraints, power balance constraints, flow constraints, charging and discharging constraints of distributed battery energy storage systems, and cluster distribution constraints of distributed photovoltaic and distributed battery energy storage systems. The joint planning model of distributed photovoltaic and distributed battery energy storage systems not only overcomes the limitations caused by the singleness of existing optimization objectives, the long calculation time and convergence problems brought by the two-layer optimization model, and the lack of comprehensive consideration of the specific control strategy of the distributed battery energy storage system, but also provides new ideas and methods for realizing the efficient, safe and economical grid connection of distributed power sources.

[0149] Furthermore, the objective function establishment module establishes a multi-objective function to minimize the annual average comprehensive cost of the distribution network, voltage fluctuation and network loss, including:

[0150] minF=min{F C ,F U ,F P};

[0151]

[0152]

[0153] In the formula, F represents the multi-objective function, F C represents the average annual comprehensive cost of the distribution network, F U Indicates voltage fluctuation, F P Indicates network loss, p e,t represents the time-of-use electricity price within time period t, P L,t represents the amount of electricity purchased from the main grid during period t, T represents the time scale of the planning scenario for one day, represents the annualized investment cost of distributed photovoltaics, represents the operation and maintenance cost of distributed photovoltaics, represents the annualized investment cost of the distributed battery energy storage system, represents the operation and maintenance cost of the distributed battery energy storage system, γ represents the bank interest rate, and y DPV Represents the service life of distributed photovoltaics, N DPV represents the number of distributed photovoltaics, represents the unit capacity configuration cost of distributed photovoltaics, represents the rated power of the mth distributed photovoltaic, represents the operation and maintenance cost of distributed photovoltaics, Indicates the grid-connected subsidy for distributed photovoltaics, represents the output power of the mth distributed photovoltaic power station in time period t, y DBESS Represents the service life of the distributed battery energy storage system, N DBESS represents the number of distributed battery energy storage systems, represents the installation cost of the power of the distributed battery energy storage system, represents the rated power of the nth distributed battery energy storage system, represents the installation cost of the capacity of the distributed battery energy storage system, represents the rated capacity of the nth distributed battery energy storage system, represents the output power of the nth distributed battery energy storage system in time period t, represents the maintenance cost of the distributed battery energy storage system, N bus Indicates the number of nodes in the distribution network, U i,t represents the voltage value of node i at time period t, represents the average voltage at node i, N b Indicates the number of branches in the distribution network, Represents the line loss of line b in time period t.

[0154] From the above description, it can be seen that a multi-objective function is established to minimize the annual average comprehensive cost, voltage fluctuation and network loss of the distribution network, so as to achieve a win-win situation of economic benefits and environmental protection while ensuring the safe and stable operation of the distribution network.

[0155] Furthermore, the voltage deviation range constraint in the constraint establishment module is:

[0156] U i,min ≤U i,t ≤U i,max ;

[0157] Where U i,min represents the upper limit of the voltage deviation of node i, U i,max represents the lower limit of the voltage deviation of node i;

[0158] The power balance constraint in the constraint establishment module is:

[0159]

[0160] Where P i,t represents the active power of node i in period t;

[0161] The power flow constraint in the constraint establishment module is:

[0162]

[0163] Where U j,t represents the voltage value of node j at time period t, G ij represents the conductance between node i and node j, θ ij represents the voltage phase angle between node i and node j, B ij represents the susceptance between node i and node j, Q i,t represents the reactive power of node i in period t;

[0164] The charging and discharging constraints of the distributed battery energy storage system in the constraint establishment module are:

[0165]

[0166] SoC n,min ≤SoC n,t ≤SoC n,max ;

[0167] |SoC n,0 -SoC n,T |≤σ;

[0168]

[0169] Where, SoC n,minRepresents the lower limit of the state of charge of the nth distributed battery energy storage system, SoC n,t Indicates the charging state of the nth distributed battery energy storage system in period t, SoC n,max Represents the upper limit of the state of charge of the nth distributed battery energy storage system, SoC n,0 Indicates the charging state of the nth distributed battery energy storage system in the initial period, SoC n,T represents the charging state of the nth distributed battery energy storage system at the end of the period, σ represents the maximum deviation of the charging state of the distributed battery energy storage system at the beginning and the end, represents the output power of the nth distributed battery energy storage system in time period τ, η represents the charging efficiency of the distributed battery energy storage system, η1 represents the first charging efficiency of the distributed battery energy storage system, and η2 represents the second charging efficiency of the distributed battery energy storage system;

[0170] The cluster distribution constraint of the distributed photovoltaic and distributed battery energy storage system in the constraint establishment module is:

[0171]

[0172]

[0173] Where N c represents the number of clusters in the distribution network, represents the number of distributed photovoltaics in cluster c, represents the number of distributed battery energy storage systems in cluster c.

[0174] From the above description, it can be seen that by establishing voltage deviation range constraints, power balance constraints, power flow constraints, charging and discharging constraints of distributed battery energy storage systems, and cluster distribution constraints of distributed photovoltaic and distributed battery energy storage systems, the safe and stable operation of the distribution network can be ensured.

[0175] Furthermore, the solution module includes:

[0176] An initialization module, used to initialize a population by using a logical chaotic mapping combined with a hybrid initialization method, wherein the population includes chromosomes, and the chromosomes include the installation location and capacity of distributed photovoltaics and the installation location and capacity of distributed battery energy storage systems;

[0177] An encoding module, used for performing segmented integer encoding on the chromosome to obtain an encoded chromosome;

[0178] A crossover and mutation module, used for performing crossover and mutation operations on the encoded chromosome;

[0179] The sorting module is used to combine the chromosomes that have completed the crossover and mutation operations with the parent generation, and then perform fast non-dominated sorting of feasible solutions;

[0180] A local search module is used to perform local search using an adaptive local search strategy based on a confidence interval upper bound algorithm to obtain a Pareto planning solution set;

[0181] An optimal compromise solution selection module, used to select the optimal compromise solution from the Pareto planning solution set using the Topology of Nearly Ideal Solutions (TOPSIS) method;

[0182] A decoding module is used to decode the optimal compromise solution by using a decoding method based on greedy insertion to obtain the installation location and capacity of the distributed photovoltaic and distributed battery energy storage systems.

[0183] From the above description, it can be seen that the use of logical chaotic mapping combined with the hybrid initialization method to initialize the population can effectively enhance the diversity of the population. The adaptive local search strategy based on the confidence interval upper bound algorithm is used for local search, which improves the algorithm search capability. The encoding and decoding strategy can effectively cope with the diversity and heterogeneity of decision variables, which not only ensures the economy and feasibility of the planning scheme, but also takes into account the efficiency and flexibility of the system operation.

[0184] Another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the computer program implements each step in the above-mentioned distribution network multi-objective joint planning method.

[0185] The above-mentioned distribution network multi-objective joint planning method, device and readable storage medium of the present invention can be applied to the joint planning scenario of distributed photovoltaic and battery energy storage systems, which is described below through specific implementation methods:

[0186] Please refer to Figure 1 , Figure 3 and Figure 4 , Embodiment 1 of the present invention is:

[0187] A multi-objective joint planning method for a distribution network comprises the following steps:

[0188] S1. Taking minimizing the annual average comprehensive cost of the distribution network, voltage fluctuation and network loss as the multi-objective function, a joint planning model of distributed photovoltaic (DPV) and distributed battery energy storage system (DBESS) is established, which specifically includes S11-S13:

[0189] S11. Establish a multi-objective function to minimize the average annual comprehensive cost of the distribution network, voltage fluctuations and network losses. Specifically:

[0190] minF=min{F C ,F U,F P};

[0191]

[0192] In the formula, F represents the multi-objective function, F C represents the average annual comprehensive cost of the distribution network, F U Indicates voltage fluctuation, F P Indicates network loss, p e,t represents the time-of-use electricity price within time period t, P L,t represents the amount of electricity purchased from the main grid during period t, T represents the time scale of the planning scenario for one day, represents the annualized investment cost of distributed photovoltaics, represents the operation and maintenance cost of distributed photovoltaics, represents the annualized investment cost of the distributed battery energy storage system, represents the operation and maintenance cost of the distributed battery energy storage system, γ represents the bank interest rate, and y DPV Represents the service life of distributed photovoltaics, N DPV represents the number of distributed photovoltaics, represents the unit capacity configuration cost of distributed photovoltaics, represents the rated power of the mth distributed photovoltaic, represents the operation and maintenance cost of distributed photovoltaics, Indicates the grid-connected subsidy for distributed photovoltaics, represents the output power of the mth distributed photovoltaic power station in time period t, y DBESS Represents the service life of the distributed battery energy storage system, N DBESS represents the number of distributed battery energy storage systems, represents the installation cost of the power of the distributed battery energy storage system, represents the rated power of the nth distributed battery energy storage system, represents the installation cost of the capacity of the distributed battery energy storage system, represents the rated capacity of the nth distributed battery energy storage system, represents the output power of the nth distributed battery energy storage system in time period t, represents the maintenance cost of the distributed battery energy storage system, N bus Indicates the number of nodes in the distribution network, U i,t represents the voltage value of node i at time period t, represents the average voltage at node i, N b Indicates the number of branches in the distribution network, Represents the line loss of line b in time period t.

[0193] Although the access of distributed photovoltaic power to the distribution network has brought positive changes to the energy supply, it may also cause a series of challenges, including increased voltage fluctuations, increased network losses, and reversal of the flow direction, all of which pose a potential threat to the stable operation of the distribution network. To solve these problems, incorporating distributed battery energy storage systems into the planning scope is seen as an effective strategy. However, the deployment of distributed battery energy storage systems requires high purchase costs and reasonable power and capacity configurations, which need to be carefully weighed. Globally, in order to effectively alleviate the peak load pressure of the power grid and promote the efficient use of energy, the time-of-use electricity price system is gradually being promoted. In this context, the energy delay characteristics of the distributed battery energy storage system can be fully utilized, which can not only flexibly charge and discharge according to electricity price fluctuations, but also help reduce the overall power purchase cost of the distribution network and maximize economic benefits.

[0194] Therefore, a multi-objective function is established to minimize the annual average comprehensive cost, voltage fluctuation and network loss of the distribution network. The goal of minimizing the annual average comprehensive cost of the distribution network aims to minimize the total operating cost of the distribution network within one year, including equipment investment, operation and maintenance costs and electricity purchase costs, by optimizing resource allocation, including the installation location, capacity and operation strategy of distributed photovoltaic and distributed battery energy storage systems, thereby improving the economy of the power grid; the goal of minimizing voltage fluctuation focuses on ensuring that the voltage level of each node in the distribution network remains within a safe and stable range, reducing the voltage fluctuation caused by distributed photovoltaic access, maintaining the voltage stability of the power grid, and avoiding equipment damage or power supply interruption caused by voltage exceeding the limit; the goal of minimizing network loss is committed to effectively reducing the energy loss of the power grid in the process of transmitting and distributing electric energy by optimizing the network structure, adjusting power distribution and utilizing the regulation capacity of distributed battery energy storage systems, improving energy utilization efficiency and reducing negative impacts on the environment.

[0195] The multi-objective function of the present invention can achieve a win-win situation of economic benefits and environmental protection while ensuring the safe and stable operation of the distribution network.

[0196] S12. Establish voltage deviation range constraints, power balance constraints, flow constraints, charging and discharging constraints of distributed battery energy storage systems, and cluster distribution constraints of distributed photovoltaic and distributed battery energy storage systems.

[0197] Wherein, the voltage deviation range constraint is:

[0198] U i,min ≤U i,t ≤U i,max ;

[0199] Where U i,min represents the upper limit of the voltage deviation of node i, U i,max represents the lower limit of the voltage deviation of node i;

[0200] The power balance constraint is:

[0201]

[0202] Where P i,t represents the active power of node i in period t;

[0203] The power flow constraint is:

[0204]

[0205] Where U j,t represents the voltage value of node j at time period t, G ij represents the conductance between node i and node j, θ ij represents the voltage phase angle between node i and node j, B ij represents the susceptance between node i and node j, Q i,t represents the reactive power of node i in period t;

[0206] The charging and discharging constraints of the distributed battery energy storage system are:

[0207]

[0208] SoC n,min ≤SoC n,t ≤SoC n,max ;

[0209] |SoC n,0 -SoC n,T |≤σ;

[0210]

[0211] Where, SoC n,min Represents the lower limit of the state of charge of the nth distributed battery energy storage system, SoC n,t Indicates the charging state of the nth distributed battery energy storage system in period t, SoC n,max Represents the upper limit of the state of charge of the nth distributed battery energy storage system, SoC n,0 Indicates the charging state of the nth distributed battery energy storage system in the initial period, SoC n,T represents the charging state of the nth distributed battery energy storage system at the end of the period, σ represents the maximum deviation of the charging state of the distributed battery energy storage system at the beginning and the end, represents the output power of the nth distributed battery energy storage system in time period τ, η represents the charging efficiency of the distributed battery energy storage system, η1 represents the first charging efficiency of the distributed battery energy storage system, and η2 represents the second charging efficiency of the distributed battery energy storage system; in an optional implementation, σ takes a value of 0.1.

[0212] Among them, in order to improve the optimization efficiency, the rated capacity of the distributed battery energy storage system is calculated by the maximum cumulative charging and discharging power and the maximum single charging and discharging power, specifically:

[0213]

[0214] In the formula, E′ n Indicates the maximum cumulative charge and discharge power, P n,τ represents the charging and discharging power in the τth period, Indicates the maximum single charge and discharge power, P n,T It represents the charging and discharging power in the Tth period, and Δt represents the time scale of the t period.

[0215] Calculating the rated capacity of the distributed battery energy storage system in the above way is conducive to accurately calculating and optimizing the rated power and capacity configuration of the distributed battery energy storage system. At the same time, the real-time output of the distributed battery energy storage system is taken into consideration as a key decision variable, thereby achieving effective connection and coordinated optimization from long-term planning to short-term operation, and effectively integrating and optimizing variables across different time scales.

[0216] The cluster distribution constraints of the distributed photovoltaic and distributed battery energy storage system are:

[0217]

[0218]

[0219] Where N c represents the number of clusters in the distribution network, represents the number of distributed photovoltaics in cluster c, represents the number of distributed battery energy storage systems in cluster c. In an optional implementation, and The value is 3.

[0220] S13. Generate a joint planning model of distributed photovoltaic and distributed battery energy storage systems according to the multi-objective function, the voltage deviation range constraint, the power balance constraint, the power flow constraint, the charging and discharging constraint of the distributed battery energy storage system, and the cluster distribution constraint of the distributed photovoltaic and distributed battery energy storage systems.

[0221] S2. Use an improved third-generation non-dominated genetic algorithm to solve the joint planning model of the distributed photovoltaic and distributed battery energy storage system to obtain the installation location and capacity of the distributed photovoltaic and distributed battery energy storage system; wherein the improved third-generation non-dominated genetic algorithm uses a logical chaos mapping combined with a hybrid initialization method to initialize the population, and uses an adaptive local search strategy based on a confidence interval upper bound algorithm to perform local search, specifically including S21-S27:

[0222] S21. Initialize the population by combining logical chaos mapping with a hybrid initialization method, wherein the population includes chromosomes, and the chromosomes include the installation location and capacity of distributed photovoltaics and the installation location and capacity of distributed battery energy storage systems.

[0223] Chaos refers to a type of nonlinear system whose initial conditions are extremely sensitive and whose trajectories exhibit unpredictable irregular behaviors. Among various chaotic systems, logical chaos mapping is the most typical, such as Figure 3 As shown, the expression is:

[0224] x(t+1)=μx(t)[1-x(t)];

[0225] In the formula, x(t+1) represents the population proportion at time t+1, μ represents the Logistic parameter, which ranges from [0,4] and is used to control the dynamic behavior of the mapping, and x(t) represents the population proportion at time t, that is, the proportion of the population to the maximum possible population size at time t. When the value of μ is in the range of [1,4], the mapping will exhibit chaotic behavior, that is, the final state of x(t) will not repeat, but will take a certain interval with equal probability.

[0226] Using logical chaotic mapping combined with hybrid initialization method to initialize the population can enrich the diversity of the population.

[0227] S22, performing segmented integer encoding on the chromosome to obtain an encoded chromosome, specifically:

[0228]

[0229] In the formula, represents the installation location of the mth distributed photovoltaic, Indicates the installation location of the nth distributed battery energy storage system.

[0230] S23, performing crossover and mutation operations on the encoded chromosome.

[0231] Specifically, due to the four-segment coding of the installation location and capacity of distributed photovoltaics and the installation location and capacity of distributed battery energy storage systems, it is necessary to design four genetic operations respectively. It is necessary to ensure that each bit is legal. Therefore, a unified crossover strategy (UX) is adopted. First, an array with a length equal to the total number of operations is randomly generated. The elements in the array are 0 or 1. The array corresponds to the chromosome one by one, and the code at the position corresponding to 1 in the parent chromosome is exchanged. The crossover operation not only needs to ensure that each position is legal, but also requires that the number of occurrences of each integer remains unchanged. Therefore, IPOX crossover (a commonly used crossover method in genetic algorithms) is adopted. First, a job set S is randomly generated, and the operands of S in parent generation 1 are directly copied to the corresponding positions of the offspring. Then, the operands in parent generation 2 that do not exist in S are filled in the remaining positions of the offspring chromosome in sequence, such as Figure 4 The mutation operation of chromosomes is relatively simple. The installation location and capacity of distributed photovoltaics only need to regenerate a legal number at the mutation location. The installation location and capacity of distributed battery energy storage systems will randomly select several locations, disrupt the codes at these locations and fill them in order to complete the crossover and mutation operations.

[0232] S24. After the chromosomes that have completed the crossover and mutation operations are combined with the parent generation, a fast non-dominated sorting of feasible solutions is performed.

[0233] S25. Adopt an adaptive local search strategy based on the confidence interval upper bound algorithm to perform local search and obtain the Pareto planning solution set.

[0234] Among them, as the number of group iterations increases or the test cases change, the probability of different local search methods finding new non-dominated solutions is also different. The confidence interval upper bound (UCB) algorithm is introduced. The expression is as follows: when the newly generated solution dominates the old solution obtained by the local search method, the probability of using this local search method will increase.

[0235]

[0236] In the formula, UCB i represents the probability of the newly generated solution in the local search method, n r It represents the number of times that the solution of the local search method dominates the old solution, n represents the number of times the local search method is selected, N represents the total number of local searches, and c represents the weight variable.

[0237] S26. Selecting the optimal compromise solution from the Pareto planning solution set using a method of sorting solutions close to the ideal solution.

[0238] S27. Decode the optimal compromise solution using a decoding method based on greedy insertion to obtain the installation location and capacity of the distributed photovoltaic and distributed battery energy storage systems.

[0239] The planning results include the installation location and capacity of the above-mentioned distributed photovoltaic and distributed battery energy storage systems. In order to comprehensively evaluate the planning results from the perspective of economy and safety, in an optional implementation, it also includes: selecting the power self-balancing rate indicator, the average daily electricity purchase cost indicator and the capacity penetration rate indicator to evaluate the planning results.

[0240] Wherein, the power self-balancing rate index is:

[0241]

[0242] In the formula, S D Indicates the power self-balancing rate, T DPV The time scale representing the planning scenario of distributed PV for one day;

[0243] The average daily electricity purchase cost indicator is:

[0244]

[0245] In the formula, S loss represents the average daily electricity purchase cost;

[0246] The capacity penetration index is:

[0247]

[0248] In the formula, S CP Represents capacity penetration.

[0249] S D It mainly evaluates the ability of distributed photovoltaic and distributed battery energy storage systems to coordinate the supply of distribution network needs. D The closer to 1 the better, S CP It is defined as the ratio of the maximum combined output of distributed photovoltaic and distributed battery energy storage systems to the maximum load of the distribution network. It is used to reflect the saturation level and limit of distributed photovoltaic installed capacity in the distribution network.

[0250] The present invention solves the complex problem of coupling variables of different time scales in joint planning by constructing a joint planning model and an efficient coding strategy, and supports the scientific planning and intelligent operation of distributed energy systems. The proposed joint planning method not only significantly improves the access capacity of distributed photovoltaics, but also greatly improves the self-balancing rate of distributed energy storage. At the same time, by optimizing the scheduling of distributed energy storage systems, voltage fluctuations and network losses are effectively reduced, the peak-to-valley difference of net load is significantly reduced, and the overall operation efficiency of the power grid is improved. The joint planning model of the above-mentioned distributed photovoltaic and distributed battery energy storage systems can be flexibly applied to actual scenarios with more complex and diverse renewable energy sources and loads. In addition, the model is solved by an improved third-generation non-dominated genetic algorithm, and the population diversity is enhanced by introducing a logical chaos map combined with a hybrid initialization method. In addition, the algorithm also uses a local search method based on a confidence interval upper bound algorithm to improve the algorithm's search capability.

[0251] Please refer to Figure 2 , Embodiment 2 of the present invention is:

[0252] A multi-objective joint planning device for a distribution network, comprising:

[0253] The model building module is used to establish a joint planning model for distributed photovoltaic and distributed battery energy storage systems by minimizing the annual average comprehensive cost of the distribution network, voltage fluctuations, and network losses as a multi-objective function;

[0254] A solution module, used to solve the joint planning model of the distributed photovoltaic and distributed battery energy storage system using an improved third-generation non-dominated genetic algorithm to obtain the installation location and capacity of the distributed photovoltaic and distributed battery energy storage system; and

[0255] The improved third generation non-dominated genetic algorithm uses a logical chaos map combined with a hybrid initialization method to initialize the population, and uses an adaptive local search strategy based on a confidence interval upper bound algorithm to perform local search.

[0256] Furthermore, the model building module includes:

[0257] Objective function establishment module, used to establish multi-objective functions to minimize the annual average comprehensive cost of the distribution network, voltage fluctuations and network losses;

[0258] Constraint establishment module, used to establish voltage deviation range constraints, power balance constraints, power flow constraints, charging and discharging constraints of distributed battery energy storage systems, and cluster distribution constraints of distributed photovoltaic and distributed battery energy storage systems;

[0259] A model generation module is used to generate a joint planning model of a distributed photovoltaic and distributed battery energy storage system according to the multi-objective function, the voltage deviation range constraint, the power balance constraint, the power flow constraint, the charging and discharging constraint of the distributed battery energy storage system, and the cluster distribution constraint of the distributed photovoltaic and distributed battery energy storage system.

[0260] Furthermore, the objective function establishment module establishes a multi-objective function to minimize the annual average comprehensive cost of the distribution network, voltage fluctuation and network loss, including:

[0261] minF=min{F C ,F U ,F P};

[0262]

[0263] In the formula, F represents the multi-objective function, F C represents the average annual comprehensive cost of the distribution network, F U Indicates voltage fluctuation, F P Indicates network loss, p e,t represents the time-of-use electricity price within time period t, P L,t represents the amount of electricity purchased from the main grid during period t, T represents the time scale of the planning scenario for one day, represents the annualized investment cost of distributed photovoltaics, represents the operation and maintenance cost of distributed photovoltaics, represents the annualized investment cost of the distributed battery energy storage system, represents the operation and maintenance cost of the distributed battery energy storage system, γ represents the bank interest rate, and y DPV Represents the service life of distributed photovoltaics, N DPV represents the number of distributed photovoltaics, represents the unit capacity configuration cost of distributed photovoltaics, represents the rated power of the mth distributed photovoltaic, represents the operation and maintenance cost of distributed photovoltaics, Indicates the grid-connected subsidy for distributed photovoltaics, represents the output power of the mth distributed photovoltaic power station in time period t, y DBESS Represents the service life of the distributed battery energy storage system, N DBESS represents the number of distributed battery energy storage systems, represents the installation cost of the power of the distributed battery energy storage system, represents the rated power of the nth distributed battery energy storage system, represents the installation cost of the capacity of the distributed battery energy storage system, represents the rated capacity of the nth distributed battery energy storage system, represents the output power of the nth distributed battery energy storage system in time period t, represents the maintenance cost of the distributed battery energy storage system, N bus Indicates the number of nodes in the distribution network, U i,t represents the voltage value of node i at time period t, represents the average voltage at node i, N b Indicates the number of branches in the distribution network, Represents the line loss of line b in time period t.

[0264] Furthermore, the voltage deviation range constraint in the constraint establishment module is:

[0265] U i,min ≤U i,t ≤U i,max ;

[0266] Where U i,min represents the upper limit of the voltage deviation of node i, U i,max represents the lower limit of the voltage deviation of node i;

[0267] The power balance constraint in the constraint establishment module is:

[0268]

[0269] Where P i,t represents the active power of node i in period t;

[0270] The power flow constraint in the constraint establishment module is:

[0271]

[0272] Where U j,t represents the voltage value of node j at time period t, G ij represents the conductance between node i and node j, θ ij represents the voltage phase angle between node i and node j, B ij represents the susceptance between node i and node j, Q i,t represents the reactive power of node i in period t;

[0273] The charging and discharging constraints of the distributed battery energy storage system in the constraint establishment module are:

[0274]

[0275] SoC n,min ≤SoC n,t ≤SoC n,max ;

[0276] |SoC n,0 -SoC n,T |≤σ;

[0277]

[0278] Where, SoC n,min Represents the lower limit of the state of charge of the nth distributed battery energy storage system, SoC n,t Indicates the charging state of the nth distributed battery energy storage system in period t, SoC n,max Represents the upper limit of the state of charge of the nth distributed battery energy storage system, SoC n,0 Indicates the charging state of the nth distributed battery energy storage system in the initial period, SoC n,T represents the charging state of the nth distributed battery energy storage system at the end of the period, σ represents the maximum deviation of the charging state of the distributed battery energy storage system at the beginning and the end, represents the output power of the nth distributed battery energy storage system in time period τ, η represents the charging efficiency of the distributed battery energy storage system, η1 represents the first charging efficiency of the distributed battery energy storage system, and η2 represents the second charging efficiency of the distributed battery energy storage system;

[0279] The cluster distribution constraint of the distributed photovoltaic and distributed battery energy storage system in the constraint establishment module is:

[0280]

[0281] Where N c represents the number of clusters in the distribution network, represents the number of distributed photovoltaics in cluster c, represents the number of distributed battery energy storage systems in cluster c.

[0282] Furthermore, the solution module includes:

[0283] An initialization module, used to initialize a population by using a logical chaotic mapping combined with a hybrid initialization method, wherein the population includes chromosomes, and the chromosomes include the installation location and capacity of distributed photovoltaics and the installation location and capacity of distributed battery energy storage systems;

[0284] An encoding module, used for performing segmented integer encoding on the chromosome to obtain an encoded chromosome;

[0285] A crossover and mutation module, used for performing crossover and mutation operations on the encoded chromosome;

[0286] The sorting module is used to combine the chromosomes that have completed the crossover and mutation operations with the parent generation, and then perform fast non-dominated sorting of feasible solutions;

[0287] A local search module is used to perform local search using an adaptive local search strategy based on a confidence interval upper bound algorithm to obtain a Pareto planning solution set;

[0288] An optimal compromise solution selection module, used to select the optimal compromise solution from the Pareto planning solution set by using an approximate ideal solution sorting method;

[0289] A decoding module is used to decode the optimal compromise solution by using a decoding method based on greedy insertion to obtain the installation location and capacity of the distributed photovoltaic and distributed battery energy storage systems.

[0290] Embodiment 3 of the present invention is:

[0291] A computer-readable storage medium stores a computer program, which, when executed by a processor, can implement each step of the distribution network multi-objective joint planning method in Example 1.

[0292] In summary, the present invention provides a distribution network multi-objective joint planning method, device and readable storage medium, which takes minimizing the annual average comprehensive cost of the distribution network, voltage fluctuation and network loss as the multi-objective function, establishes a joint planning model of distributed photovoltaic and distributed battery energy storage systems, which is more efficient and comprehensive, and uses an improved third-generation non-dominated genetic algorithm to solve the joint planning model of distributed photovoltaic and distributed battery energy storage systems. The improved third-generation non-dominated genetic algorithm can realize parallel processing and balanced optimization of multiple optimization objectives, improve population diversity, and enhance the search ability of the algorithm, and solve the optimal installation location and capacity of the distributed photovoltaic and distributed battery energy storage systems, thereby improving the overall operation efficiency of the system, and effectively suppressing the volatility of the power grid and optimizing resource utilization; in addition, the encoding and decoding strategy can effectively cope with the diversity and heterogeneity of decision variables, which not only ensures the economy and feasibility of the planning scheme, but also takes into account the efficiency and flexibility of the system operation.

[0293] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0294] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0295] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0296] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0297] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0298] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A multi-objective joint planning method for a distribution network, characterized in that: Includes steps: A joint planning model for distributed photovoltaic and distributed battery energy storage systems is established with the multi-objective function of minimizing the annual average comprehensive cost of the distribution network, voltage fluctuations, and network losses. Using an improved third-generation non-dominated genetic algorithm to solve the joint planning model of the distributed photovoltaic and distributed battery energy storage systems, the installation location and capacity of the distributed photovoltaic and distributed battery energy storage systems are obtained; The improved third generation non-dominated genetic algorithm uses a logical chaos map combined with a hybrid initialization method to initialize the population, and uses an adaptive local search strategy based on a confidence interval upper bound algorithm to perform local search.

2. A distribution network multi-objective joint planning method according to claim 1, characterized in that: The joint planning model of distributed photovoltaic and distributed battery energy storage systems is established by minimizing the annual average comprehensive cost of the distribution network, voltage fluctuations and network losses as a multi-objective function, including: A multi-objective function is established to minimize the annual average comprehensive cost of the distribution network, voltage fluctuations, and network losses; Establish voltage deviation range constraints, power balance constraints, power flow constraints, charging and discharging constraints of distributed battery energy storage systems, and cluster distribution constraints of distributed photovoltaic and distributed battery energy storage systems; A joint planning model of a distributed photovoltaic and distributed battery energy storage system is generated according to the multi-objective function, the voltage deviation range constraint, the power balance constraint, the power flow constraint, the charging and discharging constraint of the distributed battery energy storage system, and the cluster distribution constraint of the distributed photovoltaic and distributed battery energy storage system.

3. A distribution network multi-objective joint planning method according to claim 2, characterized in that: The multi-objective function established by minimizing the annual average comprehensive cost of the distribution network, voltage fluctuation and network loss includes: minF=min{F C ,F U ,F P }; In the formula, F represents the multi-objective function, F C represents the average annual comprehensive cost of the distribution network, F U Indicates voltage fluctuation, F P Indicates network loss, p e,t represents the time-of-use electricity price within time period t, P L,t represents the amount of electricity purchased from the main grid during period t, T represents the time scale of the planning scenario for one day, represents the annualized investment cost of distributed photovoltaics, represents the operation and maintenance cost of distributed photovoltaics, represents the annualized investment cost of the distributed battery energy storage system, represents the operation and maintenance cost of the distributed battery energy storage system, γ represents the bank interest rate, and y DPV Represents the service life of distributed photovoltaics, N DPV represents the number of distributed photovoltaics, represents the unit capacity configuration cost of distributed photovoltaics, represents the rated power of the mth distributed photovoltaic, represents the operation and maintenance cost of distributed photovoltaics, Indicates the grid-connected subsidy for distributed photovoltaics, represents the output power of the mth distributed photovoltaic power station in time period t, y DBESS Represents the service life of the distributed battery energy storage system, N DBESS represents the number of distributed battery energy storage systems, represents the installation cost of the power of the distributed battery energy storage system, represents the rated power of the nth distributed battery energy storage system, represents the installation cost of the capacity of the distributed battery energy storage system, represents the rated capacity of the nth distributed battery energy storage system, represents the output power of the nth distributed battery energy storage system in time period t, represents the maintenance cost of the distributed battery energy storage system, N bus Indicates the number of nodes in the distribution network, U i,t represents the voltage value of node i at time period t, represents the average voltage at node i, N b Indicates the number of branches in the distribution network, Represents the line loss of line b in time period t.

4. A distribution network multi-objective joint planning method according to claim 3, characterized in that: The voltage deviation range constraint is: IN i,min ≤U i,t ≤U i,max ; Where U i,min represents the upper limit of the voltage deviation of node i, U i,max represents the lower limit of the voltage deviation of node i; The power balance constraint is: Where P i,t represents the active power of node i in period t; The power flow constraint is: Where U j,t represents the voltage value of node j at time period t, G ij represents the conductance between node i and node j, θ ij represents the voltage phase angle between node i and node j, B ij represents the susceptance between node i and node j, Q i,t represents the reactive power of node i in period t; The charging and discharging constraints of the distributed battery energy storage system are: SoC n,min ≤SoC n,t ≤SoC n,max ; |SoC n,0 -SoC n,T |≤σ; Where, SoC n,min Represents the lower limit of the state of charge of the nth distributed battery energy storage system, SoC n,t Indicates the charging state of the nth distributed battery energy storage system in period t, SoC n,max Represents the upper limit of the state of charge of the nth distributed battery energy storage system, SoC n,0 Indicates the charging state of the nth distributed battery energy storage system in the initial period, SoC n,T represents the charging state of the nth distributed battery energy storage system at the end of the period, σ represents the maximum deviation of the charging state of the distributed battery energy storage system at the beginning and the end, represents the output power of the nth distributed battery energy storage system in time period τ, η represents the charging efficiency of the distributed battery energy storage system, η1 represents the first charging efficiency of the distributed battery energy storage system, and η2 represents the second charging efficiency of the distributed battery energy storage system; The cluster distribution constraints of the distributed photovoltaic and distributed battery energy storage system are: Where N c represents the number of clusters in the distribution network, represents the number of distributed photovoltaics in cluster c, represents the number of distributed battery energy storage systems in cluster c.

5. A distribution network multi-objective joint planning method according to claim 1, characterized in that: The improved third-generation non-dominated genetic algorithm is used to solve the joint planning model of the distributed photovoltaic and distributed battery energy storage system, and the installation location and capacity of the distributed photovoltaic and distributed battery energy storage system are obtained, including: A population is initialized by using a logical chaos mapping combined with a hybrid initialization method, wherein the population includes chromosomes, and the chromosomes include the installation location and capacity of distributed photovoltaics and the installation location and capacity of distributed battery energy storage systems; Performing segmented integer encoding on the chromosome to obtain an encoded chromosome; performing crossover and mutation operations on the encoded chromosome; After the chromosomes that have completed the crossover and mutation operations are combined with the parent generation, a fast non-dominated sorting of feasible solutions is performed; Adopting the adaptive local search strategy based on the confidence interval upper bound algorithm to perform local search, the Pareto planning solution set is obtained; Selecting the best compromise solution from the Pareto planning solution set by using a near-ideal solution ranking method; The optimal compromise solution is decoded using a decoding method based on greedy insertion to obtain the installation location and capacity of the distributed photovoltaic and distributed battery energy storage systems.

6. A distribution network multi-objective joint planning device, comprising: The model building module is used to establish a joint planning model for distributed photovoltaic and distributed battery energy storage systems by minimizing the annual average comprehensive cost of the distribution network, voltage fluctuations, and network losses as a multi-objective function; A solution module is used to solve the joint planning model of the distributed photovoltaic and distributed battery energy storage system using an improved third-generation non-dominated genetic algorithm to obtain the installation location and capacity of the distributed photovoltaic and distributed battery energy storage system; as well as The improved third generation non-dominated genetic algorithm uses a logical chaos map combined with a hybrid initialization method to initialize the population, and uses an adaptive local search strategy based on a confidence interval upper bound algorithm to perform local search.

7. A distribution network multi-objective joint planning device according to claim 6, characterized in that: The model building module includes: Objective function establishment module, used to establish multi-objective functions to minimize the annual average comprehensive cost of the distribution network, voltage fluctuations and network losses; Constraint establishment module, used to establish voltage deviation range constraints, power balance constraints, power flow constraints, charging and discharging constraints of distributed battery energy storage systems, and cluster distribution constraints of distributed photovoltaic and distributed battery energy storage systems; A model generation module is used to generate a joint planning model of a distributed photovoltaic and distributed battery energy storage system according to the multi-objective function, the voltage deviation range constraint, the power balance constraint, the power flow constraint, the charging and discharging constraint of the distributed battery energy storage system, and the cluster distribution constraint of the distributed photovoltaic and distributed battery energy storage system.

8. A distribution network multi-objective joint planning device according to claim 7, characterized in that: The objective function establishment module includes establishing a multi-objective function to minimize the annual average comprehensive cost of the distribution network, voltage fluctuations and network losses: minF=min{F C ,F U ,F P }; In the formula, F represents the multi-objective function, F C represents the average annual comprehensive cost of the distribution network, F U Indicates voltage fluctuation, F P Indicates network loss, p e,t represents the time-of-use electricity price within time period t, P L,t represents the amount of electricity purchased from the main grid during period t, T represents the time scale of the planning scenario for one day, represents the annualized investment cost of distributed photovoltaics, represents the operation and maintenance cost of distributed photovoltaics, represents the annualized investment cost of the distributed battery energy storage system, represents the operation and maintenance cost of the distributed battery energy storage system, γ represents the bank interest rate, and y DPV Represents the service life of distributed photovoltaics, N DPV represents the number of distributed photovoltaics, represents the unit capacity configuration cost of distributed photovoltaics, represents the rated power of the mth distributed photovoltaic, represents the operation and maintenance cost of distributed photovoltaics, Indicates the grid-connected subsidy for distributed photovoltaics, represents the output power of the mth distributed photovoltaic power station in time period t, y DBESS Represents the service life of the distributed battery energy storage system, N DBESS represents the number of distributed battery energy storage systems, represents the installation cost of the power of the distributed battery energy storage system, represents the rated power of the nth distributed battery energy storage system, represents the installation cost of the capacity of the distributed battery energy storage system, represents the rated capacity of the nth distributed battery energy storage system, represents the output power of the nth distributed battery energy storage system in time period t, represents the maintenance cost of the distributed battery energy storage system, N bus Indicates the number of nodes in the distribution network, U i,t represents the voltage value of node i at time period t, represents the average voltage at node i, N b Indicates the number of branches in the distribution network, Represents the line loss of line b in time period t.

9. A distribution network multi-objective joint planning device according to claim 8, characterized in that: The voltage deviation range constraint in the constraint establishment module is: IN i,min ≤U i,t ≤U i,max ; Where U i,min represents the upper limit of the voltage deviation of node i, U i,max represents the lower limit of the voltage deviation of node i; The power balance constraint in the constraint establishment module is: Where P i,t represents the active power of node i in period t; The power flow constraint in the constraint establishment module is: Where U j,t represents the voltage value of node j at time period t, G ij represents the conductance between node i and node j, θ ij represents the voltage phase angle between node i and node j, B ij represents the susceptance between node i and node j, Q i,t represents the reactive power of node i in period t; The charging and discharging constraints of the distributed battery energy storage system in the constraint establishment module are: SoC n,min ≤SoC n,t ≤SoC n,max ; |SoC n,0 -SoC n,T |≤σ; Where, SoC n,min Represents the lower limit of the state of charge of the nth distributed battery energy storage system, SoC n,t Indicates the charging state of the nth distributed battery energy storage system in period t, SoC n,max Represents the upper limit of the state of charge of the nth distributed battery energy storage system, SoC n,0 Indicates the charging state of the nth distributed battery energy storage system in the initial period, SoC n,T represents the charging state of the nth distributed battery energy storage system at the end of the period, σ represents the maximum deviation of the charging state of the distributed battery energy storage system at the beginning and the end, represents the output power of the nth distributed battery energy storage system in time period τ, η represents the charging efficiency of the distributed battery energy storage system, η1 represents the first charging efficiency of the distributed battery energy storage system, and η2 represents the second charging efficiency of the distributed battery energy storage system; The cluster distribution constraint of the distributed photovoltaic and distributed battery energy storage system in the constraint establishment module is: Where N c represents the number of clusters in the distribution network, represents the number of distributed photovoltaics in cluster c, represents the number of distributed battery energy storage systems in cluster c.

10. A distribution network multi-objective joint planning device according to claim 6, characterized in that: The solution module includes: An initialization module, used to initialize a population by using a logical chaotic mapping combined with a hybrid initialization method, wherein the population includes chromosomes, and the chromosomes include the installation location and capacity of distributed photovoltaics and the installation location and capacity of distributed battery energy storage systems; An encoding module, used for performing segmented integer encoding on the chromosome to obtain an encoded chromosome; A crossover and mutation module, used for performing crossover and mutation operations on the encoded chromosome; The sorting module is used to combine the chromosomes that have completed the crossover and mutation operations with the parent generation, and then perform fast non-dominated sorting of feasible solutions; A local search module is used to perform local search using an adaptive local search strategy based on a confidence interval upper bound algorithm to obtain a Pareto planning solution set; An optimal compromise solution selection module, used to select the optimal compromise solution from the Pareto planning solution set by using an approximate ideal solution sorting method; A decoding module is used to decode the optimal compromise solution by using a decoding method based on greedy insertion to obtain the installation location and capacity of the distributed photovoltaic and distributed battery energy storage systems.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step in a distribution network multi-objective joint planning method as described in any one of claims 1 to 5 is implemented.

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