Distributed energy storage economic optimization configuration method for improving voltage quality of power distribution network

Abstract

The invention discloses a distributed energy storage economic optimization configuration method for improving the voltage quality of a power distribution network. The method comprises the following steps: S1, constructing an energy storage optimization model based on multiple targets; establishing a target function, carrying out unified dimension conversion, and carrying out weight distribution on each target in the target function; establishing a target function constraint condition; s2, performing iterative solution on the energy storage optimization model based on a genetic algorithm, and outputting a minimum value of an objective function; according to the method, multiple economic factors and voltage quality indexes of power distribution network operation are analyzed, various constraint conditions are considered, the energy storage optimization model is established, the model is solved by adopting the improved genetic algorithm, the calculation precision is improved, the system operation cost is reduced, and the voltage quality is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed energy storage optimization, and in particular relates to a distributed energy storage economic optimization configuration method for improving the voltage quality of a distribution network. Background Art

[0002] The distribution network is the basic network responsible for power distribution in the power system and is the core link in the entire power system that is directly connected to power users. This means that the planning, management and operation of the distribution network have a direct impact on the voltage quality of users.

[0003] Voltage quality is one of the important indicators for measuring power quality in power systems. It represents the deviation between the actual voltage and the ideal voltage in the power system. This indicator can effectively show whether the power delivered by the power supply company to the power user terminal through the transmission and distribution process is qualified. The level of voltage quality is largely related to the safety, economy and efficiency of power supply. The demand of power users for high voltage quality is also constantly increasing. Many automated and intelligent equipment require higher voltage quality to meet operating conditions.

[0004] To alleviate power shortages during peak hours, energy storage devices are integrated into distribution networks. These devices offer a fast energy response and the advantages of low storage and high generation. However, with the large-scale integration of energy storage systems, their randomness and intermittency also lead to the complexity of distribution operations. The location, capacity, and scheduling of energy storage devices not only significantly impact the voltage quality of the distribution network, but also directly affect the economic efficiency of its operation.

[0005] Therefore, in order to solve the above problems, it is necessary to develop a distributed energy storage economic optimization configuration method to improve the voltage quality of the distribution network. Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a distributed energy storage economic optimization configuration method for improving the voltage quality of the distribution network. It analyzes multiple economic factors and voltage quality indicators of the distribution network operation, takes into account various constraints, establishes an energy storage optimization model, and uses an improved genetic algorithm to solve the model, thereby improving calculation accuracy, reducing system operating costs, and improving voltage quality.

[0007] The object of the present invention is achieved as follows: a distributed energy storage economic optimization configuration method for improving the voltage quality of the distribution network comprises the following steps:

[0008] S1. Construct a multi-objective energy storage optimization model;

[0009] S11, establishing an objective function with investment cost, line loss cost, user main network electricity purchase cost and system node voltage deviation as minimum optimization targets;

[0010] minf=aG1+bG2+cG3+dαΔU

[0011] , where G1 is the energy storage investment cost, G2 is the line loss cost, G3 is the main grid electricity purchase cost, ΔU is the system node voltage deviation, a, b, c, d are the weight coefficients of each target, satisfying a+b+c+d=1, and α is the dimensional unified conversion coefficient;

[0012] S111. Performing a unified dimensional conversion on the system node voltage deviation in the objective function;

[0013] S112, assigning weights to the objectives in the objective function to obtain weight coefficients for the objectives;

[0014] S12, establishing objective function constraints based on power balance, energy storage system power, node voltage, and candidate node installation;

[0015] S2, iteratively solving the energy storage optimization model based on genetic algorithm;

[0016] S21. Obtain an initial population of the genetic algorithm, determine the encoding method of the genetic algorithm, and set the population size and maximum number of iterations;

[0017] S22, decoding each individual in the initial population, and calculating the fitness value of the individual according to the objective function in step S1;

[0018] S23, judging whether the individual fitness value meets the iteration termination condition, if the maximum number of iterations has been reached, decoding the optimal individual with the fitness value, otherwise continue the iteration;

[0019] S24, calculating the crossover probability and mutation probability of this iteration;

[0020] S25, perform selection, crossover and mutation operations to generate a new generation population, and return to step S22 to re-execute the iteration;

[0021] S26. When the optimal individual with the best fitness value is obtained after iteration and the maximum number of iterations is reached, the minimum value of the objective function is output.

[0022] Furthermore, the energy storage investment cost G1 in step S11 is expressed as:

[0023]

[0024] , where β is the discount factor, τ is the annual interest rate, y is the energy storage service life, k is the number of installed energy storage coefficients, C pess﹣k is the unit power cost of the energy storage system, P esskis the rated power capacity of the kth energy storage system, C eess﹣k is the unit capacity cost of the energy storage system, E essk is the rated energy capacity of the kth energy storage system.

[0025] Furthermore, the line loss cost G2 in step S11 is expressed as:

[0026]

[0027] , where C ep is the unit line loss cost, Δt is the time period length, Δt is 1h, P lossj,t is the active power loss of the j-th branch at time t.

[0028] Furthermore, the main grid electricity purchase cost G3 in step S11 is expressed as:

[0029] G3=G buy -G ess

[0030]

[0031]

[0032] , where G buy G is the electricity purchase cost when energy storage is not connected. ess The power purchase fee of the main grid is mediated by the energy storage system. When the energy storage system charges and stores the energy, the power is negative, and when the stored energy is released, the power is positive. ebuy is the peak-valley unit electricity price, P load,t represents the total system load when no energy storage is connected at time t, Δt is the time period length, Δt is 1h, P ess,k,t is the rated power capacity of the kth energy storage system at time t.

[0033] Furthermore, the system node voltage deviation ΔU in step S11 is expressed as:

[0034]

[0035] , where N bus is the number of system nodes, U m,t is the voltage at node m at time t, U N Indicates the node voltage rating during the inspection period.

[0036] Furthermore, the dimensional uniform conversion ratio coefficient α during the dimensional uniform conversion in step S111 is expressed as:

[0037] α=C ep Dω

[0038] , where C ep is the unit line loss cost, D is the number of days in a year, and ω is the conversion coefficient between active power loss and node voltage deviation.

[0039] Furthermore, in step S112, the analytic hierarchy process is used to assign weights to each target.

[0040] Furthermore, the power balance constraint in step S12 is expressed as:

[0041]

[0042] , where P m , Q m are the injected active and reactive power of node m, R m-1 , Q m-1 are the resistance and reactance between node m-1 and node m, PL m 、QL m are the active and reactive loads of node m, U m-1 is the node voltage, x m is a binary 0-1 decision variable, x m A value of 1 indicates that node m is connected to the energy storage system, and a value of 0 indicates that node m is not connected to the energy storage system;

[0043] The power constraint of the energy storage system in step S12 is expressed as:

[0044] P ess,min ≤P essk ≤P ess,max

[0045] , where P ess,min 、P ess,max are the lower and upper limits of the installed power capacity of the energy storage system respectively;

[0046] The node voltage constraint in step S12 is expressed as:

[0047] U min ≤U m,t ≤U max

[0048] , where U min 、U max They are the node voltage amplitude U in period t m,t the lower and upper limits of

[0049] The installation constraints of the selected nodes in step S12 are expressed as:

[0050]

[0051] , where N is the system's candidate installation node, x mis a binary 0-1 decision variable, N ess is the number of energy storage devices connected to the system.

[0052] Furthermore, the crossover probability in step S24 is expressed as:

[0053]

[0054] , where p cmin is the minimum crossover probability, N is the population capacity, f i is the fitness value of individual i, f avg is the average fitness value of all individuals in the population, p ca is the crossover probability adjustment parameter.

[0055] Furthermore, the mutation probability in step S24 is expressed as:

[0056]

[0057] , where p mmax is the maximum mutation probability, N is the population capacity, f i is the fitness value of individual i, f avg is the average fitness value of all individuals in the population, p ma is the mutation probability adjustment parameter.

[0058] Due to the adoption of the above technical solution, the beneficial effects of the present invention are: analyzing the investment cost of distribution network operation, line loss cost, user main network electricity purchase cost and voltage quality index, establishing a minimum optimization objective function, and considering various constraints, establishing an energy storage optimization model, and using an improved genetic algorithm to solve the model. By increasing the mutation probability and enhancing the local search capability of the population, the optimal solution is quickly found, the algorithm convergence is accelerated, the calculation accuracy is improved, the system operation cost is reduced, and the voltage quality is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0060] The technical solution of the present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0061] like Figure 1 As shown, a distributed energy storage economic optimization configuration method for improving the voltage quality of the distribution network includes the following steps:

[0062] S1. Construct a multi-objective energy storage optimization model.

[0063] S11, establishing an objective function with investment cost, line loss cost, user main network electricity purchase cost and system node voltage deviation as minimum optimization targets;

[0064] minf=aG1+bG2+cG3+dαΔU

[0065] , where G1 is the energy storage investment cost, G2 is the line loss cost, G3 is the main grid electricity purchase cost, ΔU is the system node voltage deviation, a, b, c, d are the weight coefficients of each target, satisfying a+b+c+d=1, and α is the dimensional unified conversion ratio coefficient.

[0066] Preferably, the energy storage investment cost G1 in step S11 is expressed as:

[0067]

[0068] , where β is the discount factor, τ is the annual interest rate, y is the energy storage service life, k is the number of installed energy storage coefficients, C pess﹣k is the unit power cost of the energy storage system, P essk is the rated power capacity of the kth energy storage system, C eess﹣k is the unit capacity cost of the energy storage system, E essk is the rated energy capacity of the kth energy storage system.

[0069] The line loss cost G2 in step S11 is expressed as:

[0070]

[0071] , where C ep is the unit line loss cost, Δt is the time period length, Δt is 1h, P lossj,t is the active power loss of the j-th branch at time t.

[0072] The main grid electricity purchase cost G3 in step S11 is expressed as:

[0073] G3=G buy -G ess

[0074]

[0075] , where G buy G is the electricity purchase cost when energy storage is not connected. ess The power purchase fee of the main grid is mediated by the energy storage system. When the energy storage system charges and stores the energy, the power is negative, and when the stored energy is released, the power is positive. ebuy is the peak-valley unit electricity price, P load,trepresents the total system load when no energy storage is connected at time t, Δt is the time period length, Δt is 1h, P ess,k,t is the rated power capacity of the kth energy storage system at time t.

[0076] The system node voltage deviation ΔU in step S11 is expressed as:

[0077]

[0078] , where N bus is the number of system nodes, U m,t is the voltage at node m at time t, U N Indicates the node voltage rating during the inspection period.

[0079] S111. Perform a unified dimensional conversion on the system node voltage deviation in the objective function.

[0080] Preferably, the dimensional unified conversion ratio coefficient α when performing the dimensional unified conversion in step S111 is expressed as:

[0081] α=C ep Dω

[0082] , where C ep is the unit line loss cost, D is the number of days in a year, and ω is the conversion coefficient between active power loss and node voltage deviation.

[0083] S112. Assign weights to each objective in the objective function to obtain a weight coefficient for each objective.

[0084] Preferably, in step S112, the analytic hierarchy process is used to assign weights to each target.

[0085] S12. Establish objective function constraints based on power balance, energy storage system power, node voltage, and candidate node installation.

[0086] Preferably, the power balance constraint in step S12 is expressed as:

[0087]

[0088] , where P m , Q m are the injected active and reactive power of node m, R m-1 , Q m-1 are the resistance and reactance between node m-1 and node m, PL m 、QL m are the active and reactive loads of node m, U m-1 is the node voltage, x m is a binary 0-1 decision variable, x mA value of 1 indicates that node m is connected to the energy storage system, and a value of 0 indicates that node m is not connected to the energy storage system.

[0089] Preferably, the energy storage system power constraint in step S12 is expressed as:

[0090] P ess,min ≤P essk ≤P ess,max

[0091] , where P ess,min 、P ess,max are the lower limit and upper limit of the installed power capacity of the energy storage system respectively. Preferably, the node voltage constraint in step S12 is expressed as:

[0092] U min ≤U m,t ≤U max

[0093] , where U min 、U max They are the node voltage amplitude U in period t m,t lower and upper limits.

[0094] Preferably, the installation constraints of the selected nodes in step S12 are expressed as:

[0095]

[0096] , where N is the system's candidate installation node, x m is a binary 0-1 decision variable, N ess is the number of energy storage devices connected to the system.

[0097] S2. Iteratively solve the energy storage optimization model based on genetic algorithm.

[0098] S21. Obtain the initial population of the genetic algorithm, determine the encoding method of the genetic algorithm, set the population size and the maximum number of iterations, preferably,.

[0099] S22. Decode each individual in the initial population and calculate the fitness value of the individual according to the objective function in step S1.

[0100] S23. Determine whether the individual fitness value meets the iteration termination condition. If the maximum number of iterations has been reached, decode the optimal individual with the fitness value, otherwise continue the iteration.

[0101] S24. Calculate the crossover probability and mutation probability of this iteration.

[0102] Preferably, the crossover probability in step S24 is expressed as:

[0103]

[0104] , where p cmin is the minimum crossover probability, N is the population capacity, f i is the fitness value of individual i, f avg is the average fitness value of all individuals in the population, p ca is the crossover probability adjustment parameter.

[0105] Preferably, the mutation probability in step S24 is expressed as:

[0106]

[0107] , where p mmax is the maximum mutation probability, N is the population capacity, f i is the fitness value of individual i, f avg is the average fitness value of all individuals in the population, p ma is the mutation probability adjustment parameter.

[0108] S25. Perform selection, crossover, and mutation operations to generate a new generation of populations, and return to step S22 to re-execute iteration.

[0109] S26. When the optimal individual with the best fitness value is obtained after iteration and the maximum number of iterations is reached, the minimum value of the objective function is output.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A distributed energy storage economic optimization configuration method for improving distribution network voltage quality, characterized by: The following steps are involved: S1. Construct a multi-objective energy storage optimization model; S11, establishing an objective function with investment cost, line loss cost, user main network electricity purchase cost and system node voltage deviation as minimum optimization targets; minf=aG1+bG2+cG3+dαΔU, Where G1 is the energy storage investment cost, G2 is the line loss cost, G3 is the main grid electricity purchase cost, ΔU is the system node voltage deviation, a, b, c, d are the weight coefficients of each target, satisfying a + b + c + d = 1, and α is the dimensional unified conversion coefficient; S111. Performing a unified dimensional conversion on the system node voltage deviation in the objective function; S112, assigning weights to the objectives in the objective function to obtain weight coefficients for the objectives; S12, establishing objective function constraints based on power balance, energy storage system power, node voltage, and candidate node installation; S2, iteratively solving the energy storage optimization model based on genetic algorithm; S21. Obtain an initial population of the genetic algorithm, determine the encoding method of the genetic algorithm, and set the population size and maximum number of iterations; S22, decoding each individual in the initial population, and calculating the fitness value of the individual according to the objective function in step S1; S23, judging whether the individual fitness value meets the iteration termination condition, if the maximum number of iterations has been reached, decoding the optimal individual with the fitness value, otherwise continue the iteration; S24, calculating the crossover probability and mutation probability of this iteration; S25, perform selection, crossover and mutation operations to generate a new generation population, and return to step S22 to re-execute the iteration; S26. When the optimal individual with the best fitness value is obtained after iteration and the maximum number of iterations is reached, the minimum value of the objective function is output.

2. A distributed energy storage economic optimization configuration method for improving distribution network voltage quality according to claim 1, characterized in that: The energy storage investment cost G1 in step S11 is expressed as: ， Where β is the discount factor, τ is the annual interest rate, y is the energy storage service life, k is the number of installed energy storage coefficients, C pess﹣k is the unit power cost of the energy storage system, P essk is the rated power capacity of the kth energy storage system, C eess﹣k is the unit capacity cost of the energy storage system, E essk is the rated energy capacity of the kth energy storage system.

3. The method for economic optimization configuration of distributed energy storage for improving the voltage quality of a distribution network according to claim 1, characterized in that: The line loss cost G2 in step S11 is expressed as: ， Where C ep is the unit line loss cost, Δt is the time period length, Δt is 1h, P lossj,t is the active power loss of the j-th branch at time t.

4. The method for economic optimization configuration of distributed energy storage for improving the voltage quality of a distribution network according to claim 1, characterized in that: The main grid electricity purchase cost G3 in step S11 is expressed as: G3=G buy -G ess ， Where G buy G is the electricity purchase cost when energy storage is not connected. ess The power purchase fee of the main grid is mediated by the energy storage system. When the energy storage system charges and stores the energy, the power is negative, and when the stored energy is released, the power is positive. ebuy is the peak-valley unit electricity price, P load,t represents the total system load when no energy storage is connected at time t, Δt is the time period length, Δt is 1h, P ess,k,t is the rated power capacity of the kth energy storage system at time t.

5. The method for economic optimization configuration of distributed energy storage for improving the voltage quality of a distribution network according to claim 1, characterized in that: The system node voltage deviation ΔU in step S11 is expressed as: ， Where N bus is the number of system nodes, U m,t is the voltage at node m at time t, U N Indicates the node voltage rating during the inspection period.

6. The method for economic optimization configuration of distributed energy storage for improving the voltage quality of a distribution network according to claim 1, characterized in that: When performing the dimensional uniform conversion in step S111, the dimensional uniform conversion ratio coefficient α is expressed as: α=C ep Yes, Where C ep is the unit line loss cost, D is the number of days in a year, and ω is the conversion coefficient between active power loss and node voltage deviation.

7. The method for economic optimization configuration of distributed energy storage for improving the voltage quality of a distribution network according to claim 1, characterized in that: In step S112, the analytic hierarchy process is used to assign weights to each target.

8. The method for economic optimization configuration of distributed energy storage for improving the voltage quality of a distribution network according to claim 1, characterized in that: The power balance constraint in step S12 is expressed as: ， Where, P m , Q m are the injected active and reactive power of node m, R m-1 , Q m-1 are the resistance and reactance between node m-1 and node m, PL m 、QL m are the active and reactive loads of node m, U m-1 is the node voltage, x m is a binary 0-1 decision variable, x m A value of 1 indicates that node m is connected to the energy storage system, and a value of 0 indicates that node m is not connected to the energy storage system; The power constraint of the energy storage system in step S12 is expressed as: P ess,min ≤P essk ≤P ess,max , Where, P ess,min 、P ess,max are the lower and upper limits of the installed power capacity of the energy storage system respectively; The node voltage constraint in step S12 is expressed as: IN min ≤U m,t ≤U max , Where U min 、U max They are the node voltage amplitude U in period t m,t the lower and upper limits of The installation constraints of the selected nodes in step S12 are expressed as: ， Where N is the system's candidate installation node, x m is a binary 0-1 decision variable, N ess is the number of energy storage devices connected to the system.

9. The method for economic optimization configuration of distributed energy storage for improving voltage quality of distribution network according to claim 1, characterized in that: The crossover probability in step S24 is expressed as: ， Where p cmin is the minimum crossover probability, N is the population capacity, f i is the fitness value of individual i, f avg is the average fitness value of all individuals in the population, p ca is the crossover probability adjustment parameter.

10. The method for economic optimization configuration of distributed energy storage for improving the voltage quality of a distribution network according to claim 1, characterized in that: The mutation probability in step S24 is expressed as: ， Where p mmax is the maximum mutation probability, N is the population capacity, f i is the fitness value of individual i, f avg is the average fitness value of all individuals in the population, p ma is the mutation probability adjustment parameter.

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