A Distribution Network Energy Storage Planning Method and Terminal Based on Adaptive Evolution

Abstract

The present invention discloses a method and a terminal for distribution network energy storage planning based on adaptive evolution. An initial population of distribution energy storage planning solutions is generated by the method based on adaptive evolution, and the initial population is adjusted according to the range constraint conditions and the number of nodes of each individual in the initial population, and the population is iterated according to the adaptive adjustment parameters; optimal power flow analysis is used to screen the iterated population, and economic analysis is performed on the screened individuals, and then the individual fitness is calculated. The optimal individual is selected in each iteration, and in this way, the distribution network energy storage planning solution corresponding to the optimal individual is output after the iteration is completed, so as to adapt to complex models and improve the solution accuracy and speed. Compared with the traditional energy storage planning method, the present invention is more detailed and reliable in economic analysis and solution method, and can effectively reduce the investment in energy storage and the operation loss of the system under the conditions of multiple time and space and high proportion of new energy.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage optimization configuration of distribution networks, and particularly relates to a distribution network energy storage planning method and terminal based on adaptive evolution. Background Art

[0002] In recent years, with the vigorous development of energy storage technology, the investment loss of energy storage power stations has been continuously decreasing, and the application of energy storage power stations has become more and more extensive. For areas with wind and solar power sources, energy storage power stations can effectively alleviate the problem of wind and solar power consumption, reduce network losses, and improve economic benefits. Therefore, studying the site selection, capacity determination and energy management of energy storage power stations to achieve the optimization of the access and operation of energy storage power stations has strong theoretical significance and practical value.

[0003] The energy storage scheduling and planning problem belongs to a typical non-linear programming problem with multiple constraints. As the proportion of renewable energy in the power system power supply continues to increase, the energy storage system for assisting its consumption will also continue to be deployed and developed, and the complexity of its model and objective function is increasing day by day. The common methods for solving the optimization scheduling and planning model include genetic algorithm, particle swarm algorithm, mixed integer programming, etc. Due to the existence of the operation constraints of energy storage and related network constraints, it is difficult to linearize through relaxation and other mathematical methods, and it is difficult to handle the increasingly complex non-linear programming model. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: to provide a distribution network energy storage planning method and terminal based on adaptive evolution, which can obtain the optimal distribution network energy storage planning scheme based on the adaptive evolution method and improve the reliability of energy storage planning.

[0005] In order to solve the above technical problem, the technical solution adopted by the present invention is:

[0006] A distribution network energy storage planning method based on adaptive evolution, comprising the steps of:

[0007] Generating an initial population of distribution network energy storage planning scheme individuals based on adaptive evolution, adjusting the initial population through range constraint conditions and the number of nodes of each individual in the initial population to obtain a population of distribution network energy storage planning schemes, and iterating the population according to adaptive adjustment parameters;

[0008] Using optimal power flow analysis to screen the population after each iteration, performing economic analysis on the screened individuals, and obtaining the fitness of the individuals based on the analysis results;

[0009] Obtaining the best fitness and its corresponding optimal individual in each iteration, determining whether to retain the optimal individual of the current iteration based on the best fitness of the previous iteration, and updating the adaptive adjustment parameters;

[0010] The iteration ends when the maximum number of iterations is reached or the iteration convergence condition is satisfied, and the distribution network energy storage planning scheme corresponding to the optimal individual is output.

[0011] To solve the above technical problems, another technical solution adopted by the present invention is as follows:

[0012] A distribution network energy storage planning terminal based on adaptive evolution includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, each step of the above-mentioned distribution network energy storage planning method based on adaptive evolution is implemented.

[0013] The beneficial effects of the present invention are as follows: The initial population of the distribution network energy storage planning scheme is generated by the method based on adaptive evolution, and the initial population is adjusted according to the range constraint conditions and the number of nodes of each individual in the initial population, and the population is iterated according to the adaptive adjustment parameters; the optimal power flow analysis is used to screen the iterated population, and the economic analysis is carried out on the screened individuals, and then the individual fitness is calculated. The optimal individual is selected for each iteration. In this way, the distribution network energy storage planning scheme corresponding to the optimal individual is output after the iteration is completed, so as to adapt to complex models and improve the solution accuracy and speed. Compared with the traditional energy storage planning method, the adaptive evolution algorithm of the present invention has universality and does not require the linearization process of traditional linear programming optimization. Its binary optimization mechanism is applicable to the complex planning model of energy storage, and is more detailed and reliable in economic analysis and solution methods, and can effectively reduce the investment in energy storage and the system operation loss under the conditions of multiple time and space and high proportion of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flowchart of a distribution network energy storage planning method based on adaptive evolution according to an embodiment of the present invention;

[0015] Figure 2 It is a schematic diagram of a distribution network energy storage planning terminal based on adaptive evolution according to an embodiment of the present invention;

[0016] Figure 3 It is a deployment diagram of energy storage in the IEEE-33 standard distribution network according to an embodiment of the present invention.

[0017] Label description:

[0018] 1. A distribution network energy storage planning terminal based on adaptive evolution; 2. Memory; 3. Processor. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To describe in detail the technical content, achieved objectives and effects of the present invention, the following is described in conjunction with the embodiments and accompanied by the drawings.

[0020] Please refer to Figure 1 , an embodiment of the present invention provides a method for planning a distribution network energy storage based on adaptive evolution, including the steps of:

[0021] Generating an initial population of individuals of the distribution network energy storage planning scheme based on adaptive evolution, adjusting the initial population through range constraint conditions and the number of nodes of each individual in the initial population to obtain a population of the distribution network energy storage planning scheme, and iterating the population according to the adaptive adjustment parameters;

[0022] Using optimal power flow analysis to screen the population after each iteration, performing economic analysis on the screened individuals, and obtaining the fitness of the individuals based on the analysis results;

[0023] Obtaining the best fitness and its corresponding optimal individual in each iteration, determining whether to retain the optimal individual of the current iteration based on the best fitness of the previous iteration, and updating the adaptive adjustment parameters;

[0024] Ending the iteration when the maximum number of iterations is reached or the iteration convergence condition is satisfied, and outputting the distribution network energy storage planning scheme corresponding to the optimal individual.

[0025] As can be seen from the above description, the beneficial effects of the present invention are as follows: An initial population of the distribution network energy storage planning scheme is generated based on the method of adaptive evolution, and the initial population is adjusted through range constraint conditions and the number of nodes of each individual in the initial population, and the population is iterated according to the adaptive adjustment parameters; Using optimal power flow analysis to screen the population after iteration, and performing economic analysis on the screened individuals, and then calculating the individual fitness, and selecting the optimal individual in each iteration. In this way, the optimal distribution network energy storage planning scheme corresponding to the individual is output after the iteration is completed, so as to adapt to complex models and improve the solution accuracy and speed. Compared with the traditional energy storage planning method, the adaptive evolution algorithm of the present invention has universality and does not require the linearization process of traditional linear programming optimization. Its binary optimization mechanism is applicable to the complex planning model of energy storage, and is more detailed and reliable in economic analysis and solution methods, and can effectively reduce the investment in energy storage and the operation loss of the system under the conditions of multi-time space and high proportion of new energy.

[0026] Further, the generating the initial population of individuals of the distribution network energy storage planning scheme based on adaptive evolution includes:

[0027] Generating an initial population, the initial population includes N p chromosomes, the chromosomes represent an individual of the distribution network energy storage planning scheme, and 2u optimization variables are randomly generated within the range of satisfying the energy storage installation node constraint and the output constraint for each of the chromosomes;

[0028] Each chromosome in the initial population is represented as C i = [c1, c2, ..., c u , p u+1 , p u+2 ..., p 2u , where c i represents the node number selected by the i-th energy storage planning scheme, and the parameter p i represents the capacity size corresponding to the i-th energy storage planning scheme, satisfying the energy storage installation node constraint c i ∈ [2, N c , the output constraint p i ∈ [1, N p , N c and N p represent the number of distribution network nodes and the maximum capacity allowed for energy storage installation, respectively.

[0029] As can be seen from the above description, each chromosome in the population represents a solution to the energy storage planning in the distribution network planning. The optimization variables in the chromosome, that is, genes, have a mapping relationship with the investment plans and related economies of the corresponding energy storages. Under this setting and corresponding constraints, each individual in the population can correspond to a unique distribution network energy storage planning scheme.

[0030] Furthermore, the adjustment of the initial population by the range constraint conditions and the number of nodes of each individual in the initial population includes:

[0031] Removing the individuals of the distribution network energy storage planning scheme that contain the same number of nodes through the range constraint conditions and the number of nodes of each individual in the initial population.

[0032] As can be seen from the above description, removing the individuals of the distribution network energy storage planning scheme that contain the same number of nodes can ensure an energy storage planning scheme corresponding to one node.

[0033] Furthermore, before screening the population after each iteration using the optimal power flow analysis, it includes:

[0034] Establishing the objective function of the optimal power flow analysis model:

[0035]

[0036] In the formula, represents the power loss, represents the new energy penalty loss;

[0037]

[0038] In the formula, represents the operation and maintenance loss of new energy, Indicates the system power generation loss of the lower layer, Indicates the network loss;

[0039]

[0040] In the formula, C W 、C PV Respectively represent the operation and maintenance loss coefficient of wind power generation and photovoltaic power generation, Represents the actual output of wind power dispatching at time t in the Tth year, Represents the actual output of photovoltaic dispatching at time t in the Tth year;

[0041]

[0042]

[0043] In the formula, Represents the operating cost of the conventional thermal power unit in the Tth year, Represents the start-up cost of the conventional thermal power unit in the Tth year, u it Represents the operating state of the ith conventional thermal power unit at time t, u it =0 indicates that the unit is stopped, otherwise it is running, a i 、b i 、c i Represents the operation loss coefficient of the ith conventional thermal power unit, s i Represents the single start-up cost of the ith conventional thermal power unit;

[0044]

[0045] In the formula, K Pw,j Represents the penalty loss coefficient of the jth wind power plant, Represents the actual dispatched output of the jth power plant, Represents the output estimated according to the wind power probability density function of the jth power plant in advance;

[0046]

[0047] In the formula, K Ps,k Represents the penalty loss coefficient of the kth photovoltaic power plant, Represents the pre-estimated photovoltaic power generation; Represents the actual output value of photovoltaic in the lower layer dispatching model;

[0048]

[0049] In the formula, Represents the penalty loss coefficient of new energy consumption at time T, It represents the surplus after the new energy supplies the load at a certain penetration rate at time T. It represents the amount of wind and light absorbed by the energy storage participating in the new energy absorption.

[0050] As can be seen from the above description, in the optimal power flow analysis model, a penalty term is added to the existing optimization objective loss term to optimize the problems of new energy consumption and fluctuation.

[0051] Furthermore, the adaptive adjustment parameter includes a hybrid fitness, and the hybrid fitness includes the fitness of the population and the auxiliary fitness.

[0052] Calculate the fitness f(C i ):

[0053]

[0054] In the formula, P BESS represents the total planned and operating loss of the energy storage, P allen represents the operating loss term of the distribution network, P punish represents the penalty term.

[0055] If C i in the population can meet the power system operation constraint conditions of the optimal power flow during the current operation of the distribution network system, then let the auxiliary fitness V(C i ) = 1, otherwise, let the auxiliary fitness V(C i ) = 0.

[0056] As can be seen from the above description, calculating the fitness and auxiliary fitness of the population can facilitate the screening of population individuals during subsequent iterations.

[0057] Furthermore, the power system operation constraint conditions include:

[0058] The power system operation constraint is:

[0059]

[0060]

[0061]

[0062] In the formula, it includes the distribution network power flow constraint, power balance constraint, and node voltage upper and lower limit constraints. respectively represent the active power injection and reactive power injection of the i-th node at time t; respectively represent the voltages of node i and node j at time t; G i,j , B i,j , respectively represent the conductance, susceptance of the branch with nodes i and j as the starting and ending points, and the voltage phase difference angle at time t; respectively represent the total active and reactive power generation of the power grid at time t; represent the total active and reactive power of the electrical load at time t; represent the active and reactive power of the network loss at time t; U imin , ω imin respectively represent the lower limit value of the voltage and amplitude of node i; U imax , ω imax respectively represent the upper limit value of the voltage and amplitude of node i.

[0063] As can be seen from the above description, by setting the operation constraint conditions of the power system, individuals that do not meet the operation constraint conditions of the power system can be immediately eliminated, facilitating the rapid screening of individuals in the population and improving the population iteration efficiency.

[0064] Furthermore, the economic analysis of the selected individuals includes:

[0065] Calculate the total construction and operation and maintenance loss of energy storage, the loss of energy storage system capacity construction, the loss of energy storage system operation and maintenance, and the loss of energy storage land acquisition and development for the selected individual of the distribution network energy storage planning scheme;

[0066] The total construction and operation and maintenance loss of energy storage is:

[0067]

[0068] In the formula, represents the loss of energy storage system capacity construction, represents the loss of energy storage system operation and maintenance, represents the loss of energy storage land acquisition and development;

[0069]

[0070] In the formula, represents the investment and construction loss of the energy storage battery per unit capacity, represents the actual grid-connected capacity of the jth energy storage power station, r0 represents the discount rate, and y represents the pre-operation years of energy storage;

[0071]

[0072] In the formula, L represents the number of energy storage devices, λ BESS,dis represents the discharge loss coefficient of the energy storage system, λ BESS,ch represents the charge loss coefficient of the energy storage system, p dis,l,T,t represents the real-time discharge power of the lth energy storage device of the energy storage system, p ch,l,T,t represents the real-time charge power of the energy storage device;

[0073]

[0074] In the formula, represents the partition coefficient, representing the price of this area, and α represents the coefficient related to the energy storage investment loss.

[0075] As can be seen from the above description, in terms of the loss, the loss of the energy storage system capacity construction, the operation and maintenance loss of the energy storage system, and the loss of energy storage land expropriation and development are considered. Among them, the energy storage replacement purchase, life, and the price differences in the deployment and development of different land areas are considered in the loss, which improves the comprehensiveness of the economic analysis.

[0076] Please refer to Figure 2 , another embodiment of the present invention provides a distribution network energy storage planning terminal based on adaptive evolution, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, each step of the above-mentioned distribution network energy storage planning method based on adaptive evolution is implemented.

[0077] The above-mentioned distribution network energy storage planning method and terminal of the present invention are applicable to solving the difficulties of optimizing the planning and configuration of energy storage with the goal of peak shaving and valley filling and new energy consumption under the background of large-scale renewable energy access to the distribution network. The following is illustrated through specific embodiments:

[0078] Embodiment 1

[0079] Please refer to Figure 1 , a distribution network energy storage planning method based on adaptive evolution, includes the steps:

[0080] S1. Generate an initial population of distribution network energy storage planning scheme individuals based on adaptive evolution, adjust the initial population through range constraint conditions and the number of nodes of each individual in the initial population to obtain a population of distribution network energy storage planning schemes, and iterate the population according to the adaptive adjustment parameter.

[0081] S11. Generate an initial population, where the initial population includes N p chromosomes, and each chromosome represents an individual of a distribution network energy storage planning scheme. The chromosomes are randomly generated with 2u optimization variables within the range that satisfies the energy storage installation node constraint and the output constraint;

[0082] Each chromosome in the initial population is represented as C i = [c1, c2,..., c u , p u+1 , p u+2 ..., p 2u , where ci Denote the node number selected by the \(i\)-th energy storage planning scheme. Please refer to Figure 3 , the maximum node number in this embodiment is 33, and the parameter \(p\) i Denote the capacity corresponding to the \(i\)-th energy storage planning scheme, which satisfies the energy storage installation node constraint \(c\) i \(\in[2, N]\) c , the output constraint \(p\) i \(\in[1, N]\) p , \(N\) c and \(N\) p respectively represent the number of distribution network nodes and the maximum capacity allowed for energy storage installation.

[0083] S12. Through the range constraint conditions and the node numbers of each individual in the initial population, remove the individuals of the distribution network energy storage planning scheme that contain the same node numbers, and supplement new individuals to ensure that the number of energy storage installations at each node is 1.

[0084] Among them, each chromosome in the population represents a solution to the energy storage planning in the distribution network planning. The genes in the chromosome have a mapping relationship with the investment schemes and related economies of their corresponding energy storages. Under this setting and corresponding constraints, each individual in the population corresponds to a unique distribution network energy storage planning scheme.

[0085] S13. Iterate the population according to the adaptive adjustment parameters.

[0086] Among them, the adaptive adjustment parameters include hybrid fitness, double adaptive crossover rate, and adaptive mutation operator. The hybrid fitness includes the fitness of the population and the auxiliary fitness.

[0087] S131. Calculate the fitness \(f(C\) i ):

[0088]

[0089] In the formula, \(P\) BESS represents the total planning and operation loss of the energy storage, \(P\) allen represents the distribution network operation loss item, and \(P\) punish represents the penalty item;

[0090] If \(C\) i in the population can meet the power system operation constraint conditions of the optimal power flow during the operation of the current distribution network system, then let the auxiliary fitness \(V(C\) i ) = 1, otherwise, let the auxiliary fitness \(V(C\) i ) = 0.

[0091] Among them, the power system operation constraints are:

[0092]

[0093]

[0094]

[0095] Among them, it includes power flow constraints of the distribution network, power balance constraints, and upper and lower limits of node voltage constraints; respectively represent the active power injection and reactive power injection of the i-th node at time t; respectively represent the voltages of node i and node j at time t; G i,j 、B i,j 、 respectively represent the conductance, susceptance and voltage phase difference angle at time t of the branch with node i and node j as the starting and ending points; respectively represent the total active power and reactive power generation of the power grid at time t; represents the total active power and reactive power of the electrical load at time t; represents the active power and reactive power of the network loss at time t; U imin 、ω imin respectively represent the lower limit value of the voltage amplitude of node i; U imax 、ω imax respectively represent the upper limit value of the voltage amplitude of node i.

[0096] S132. The double adaptive crossover rate is: Mark the individual with the best fitness value in the iteration generation t as C bf (t), where this individual can generate offspring with other individuals in the population with a probability of PC elite preferably, and the remaining individuals will generate corresponding offspring with a probability of PC sta according to the standard genetic algorithm crossover steps.

[0097] S133. Use the adaptive mutation operators σ n (t) and σ m (t) to implement the function of the improved adaptive evolutionary algorithm. The specific principle is as follows:

[0098]

[0099]

[0100]

[0101]

[0102] Among them, ω, ε, ρ respectively represent the auxiliary parameters for implementing adaptation, avefitness(t) represents the average fitness of all populations at the t-th generation during the iteration process, N toleRecord the cumulative number of iterations in which the marker fails to obtain a better fitness. In the adaptive mutation operator, σ n (t) characterizes the stability of the entire population, and σ m (t) is used to adjust the gene mutation rate in the next iteration. When the improved adaptive evolutionary algorithm is difficult to provide a better fitness value during continuous iterations, the mutation rate will increase actively. The continuously increasing mutation rate is restricted within a preset limit value ε (ε ∈ (0, 1)). When it is easy to reach a better fitness value during the iteration process, the mutation rate will be set to zero actively. Thus, the number of genes that need to mutate in the t-th generation of iteration and the mutation rate in the t-th generation of iteration can be expressed by the following formulas:

[0103] N mut (t) = N b ×σ n (t);

[0104] P mut (t) = σ m (t);

[0105] In the formula, N b is the number of genes to be planned in the distribution network.

[0106] S2. Use optimal power flow analysis to screen the population after each iteration, conduct economic analysis on the screened individuals, and obtain the fitness of the individuals based on the analysis results.

[0107] S21. Establish the objective function of the optimal power flow analysis model:

[0108]

[0109] In the formula, represents the power loss, represents the new energy penalty loss;

[0110]

[0111] In the formula, represents the operation and maintenance loss of new energy, represents the system power generation loss of the lower layer, represents the network loss;

[0112]

[0113] In the formula, C W and C PV respectively represent the operation and maintenance loss coefficient of wind power generation and photovoltaic power generation, represents the actual output of wind power dispatch at time t in the T-th year, represents the actual output of photovoltaic dispatch at time t in the T-th year;

[0114]

[0115]

[0116] In the formula, represents the operating cost of a conventional thermal power unit in the T-th year, represents the start-up cost of a conventional thermal power unit in the T-th year, u it represents the operating state of the i-th conventional thermal power unit at time t, u it =0 indicates that the unit is out of operation, otherwise it is in operation, a i , b i , c i represent the operating loss coefficient of the i-th conventional thermal power unit, s i represents the single start-up cost of the i-th conventional thermal power unit;

[0117]

[0118] In the formula, K Pw,j represents the penalty loss coefficient of the j-th wind power plant, represents the actual dispatched output of the j-th power plant, represents the output predicted according to the wind power probability density function of the j-th power plant in advance;

[0119]

[0120] In the formula, K Ps,k represents the penalty loss coefficient of the k-th photovoltaic power plant, represents the pre-estimated photovoltaic power generation; represents the actual output value of the photovoltaic in the lower-level dispatching model;

[0121]

[0122] In the formula, represents the penalty loss coefficient of new energy consumption at time T, represents the surplus of new energy after supplying the load at a certain penetration rate at time T, represents the amount of wind and light for energy storage to participate in new energy absorption.

[0123] S22. Conduct an economic analysis on the individuals screened by using optimal power flow analysis.

[0124] Specifically, calculate the total construction and operation and maintenance losses of energy storage, the construction loss of energy storage system capacity, the operation and maintenance loss of energy storage system, and the loss of land acquisition and development of energy storage for the individual of the distribution network energy storage planning scheme after screening;

[0125] The total construction and operation and maintenance losses of energy storage are:

[0126]

[0127] In the formula, represents the loss of energy storage system capacity construction, represents the operation and maintenance loss of the energy storage system, represents the loss of land expropriation and development for energy storage;

[0128]

[0129] In the formula, represents the investment and construction loss of the energy storage battery per unit capacity, represents the actual grid-connected capacity of the j-th energy storage power station, r0 represents the discount rate, and y represents the pre-operation years of the energy storage;

[0130]

[0131] In the formula, L represents the number of energy storage devices, λ BESS,dis represents the discharge loss coefficient of the energy storage system, λ BESS,ch represents the charge loss coefficient of the energy storage system, p dis,l,T,t represents the real-time discharge power of the l-th energy storage device in the energy storage system, p ch,l,T,t represents the real-time charge power of the energy storage device;

[0132]

[0133] In the formula, represents the zoning coefficient, representing the price of this area, and α represents the coefficient related to the energy storage investment loss.

[0134] S3. Obtain the best fitness and its corresponding optimal individual in each iteration, determine whether to retain the optimal individual of the current iteration based on the best fitness of the previous iteration, and update the adaptive adjustment parameter.

[0135] Specifically, determine the specific distribution network energy storage planning combination corresponding to each individual in the population, and the AC optimal power flow analysis model performs a total of 5 power system optimal power flow analyses on each individual separately on an annual basis.

[0136] Individuals that cannot meet the power system operation constraint conditions will be immediately eliminated, and then the adaptive evolutionary algorithm module will randomly generate new individuals according to relevant constraints. After determining that the newly generated individual can meet the power system operation constraint conditions through power flow analysis, it will directly replace the eliminated individual.

[0137] After the individuals that meet the operation constraint conditions of the power system perform the AC optimal power flow analysis, calculate the total power loss under the distribution network structure represented by the individual, calculate the total line loss of the distribution network over the entire life cycle based on the power flow analysis results, and obtain the fitness of each individual based on the analysis results.

[0138] Obtain the best fitness value and its corresponding individual in the current iteration, compare it with the best fitness value in the previous iteration, and perform the replacement or retention of the optimal individual. Relying on the calculation results of this generation and the previous generation, update the sizes of their respective adaptive crossover and mutation operators, and generate a new set of self-adaptively adjusted operators for the next iteration.

[0139] S4. End the iteration when the maximum number of iterations is reached or the iteration convergence condition is met, and output the distribution network energy storage planning scheme corresponding to the optimal individual.

[0140] Therefore, through the distribution network energy storage planning method of this embodiment, the new energy consumption penalty and the uncertainty penalty power loss are proposed as the economic analysis of new energy. The civil engineering loss of energy storage and the construction scale loss are separated and respectively corresponding to the economy of site selection and capacity determination, so as to achieve multi-space optimization analysis. Considering the long-term planning, a reasonable load growth rate is required. In terms of the solution method, an improved adaptive evolutionary algorithm is proposed to adapt to complex models, so as to improve the solution accuracy and speed. Compared with the traditional energy storage planning method, this method is more detailed and reliable in economic analysis and solution method, and can effectively reduce the investment in energy storage and the operation power loss of the system in the case of multi-time-space and high proportion of new energy.

[0141] Embodiment 2

[0142] Please refer to Figure 2 , a distribution network energy storage planning terminal 1 based on adaptive evolution, including a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it realizes each step of a distribution network energy storage planning method based on adaptive evolution in Embodiment 1.

[0143] In summary, a distribution network energy storage planning method and terminal based on adaptive evolution provided by the present invention generate an initial population of distribution energy storage planning solutions based on the adaptive evolution method, adjust the initial population through range constraint conditions and the number of nodes of each individual in the initial population, and perform iteration on the population according to the adaptive adjustment parameter; use optimal power flow analysis to screen the iterated population, perform economic analysis on the screened individuals, and then calculate the individual fitness. The optimal individual is selected in each iteration, and in this way, the distribution network energy storage planning solution corresponding to the optimal individual is output after the iteration is completed, so as to adapt to complex models and improve the solution accuracy and speed. Compared with the traditional energy storage planning method, the present invention is more detailed and reliable in economic analysis and solution method, and can effectively reduce the investment in energy storage and the operation loss of the system in the case of multi-time space and high proportion of new energy.

[0144] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in the relevant technical field, shall be equally included in the patent protection scope of the present invention.

Claims

1. A method for energy storage planning of a distribution network based on adaptive evolution, characterized in that, Including the steps: Based on adaptive evolution, generate an initial population of individuals of the distribution network energy storage planning scheme. Adjust the initial population through range constraint conditions and the number of nodes of each individual in the initial population to obtain a population of the distribution network energy storage planning scheme, and iterate the population according to the adaptive adjustment parameters; Use optimal power flow analysis to screen the population after each iteration, conduct economic analysis on the screened individuals, and obtain the fitness of the individuals based on the analysis results; Obtain the best fitness and its corresponding optimal individual in each iteration, determine whether to retain the optimal individual of the current iteration based on the best fitness of the previous iteration, and update the adaptive adjustment parameters; End the iteration when the maximum number of iterations is reached or the iteration convergence condition is satisfied, and output the distribution network energy storage planning scheme corresponding to the optimal individual; Before using the optimal power flow analysis to screen the population after each iteration, it includes: Establish the objective function of the optimal power flow analysis model: ； In the formula, represents the power loss; represents the new energy penalty loss; ； Wherein, represents the operation and maintenance loss of new energy, represents the power generation loss of the lower-layer system, represents the network loss; ； Wherein, C W and C PV respectively represent the operation and maintenance loss coefficient of wind power generation and photovoltaic power generation, represents the actual output of wind power dispatching at the t-th moment in the T-th year, represents the actual output of photovoltaic dispatching at the t-th moment in the T-th year; ； ； In the formula, represents the operating cost of the conventional thermal power unit in the T-th year, represents the start-up cost of the conventional thermal power unit in the T-th year, u it represents the operating status of the i-th conventional thermal power unit at time t, u it =0 indicates that the unit is out of service, otherwise it is in operation, a i and b i and c i represent the operating loss coefficient of the i-th conventional thermal power unit, s i represents the single start-up cost of the i-th conventional thermal power unit; ； Wherein, K Pw,j represents the penalty loss coefficient of the j-th wind power plant, represents the actual dispatched output of the j-th power plant, represents the output of the j-th power plant predicted according to the wind power probability density function in advance; ； In the formula, K Ps,k represents the penalty loss coefficient of the k-th photovoltaic power plant, represents the pre-estimated photovoltaic power generation; represents the actual output value of the photovoltaic in the lower-layer scheduling model; ； In the formula, represents the penalty loss coefficient of new energy consumption at time T, represents the surplus of new energy after supplying the load at a certain penetration rate at time T, represents the wind and light volume of energy storage participating in new energy absorption.

2. The method for planning the energy storage of a distribution network based on adaptive evolution according to claim 1, wherein, The generation of the initial population of individuals of the distribution network energy storage planning scheme based on adaptive evolution includes: Generate an initial population, where the initial population includes N p chromosomes. Each chromosome represents an individual of a distribution network energy storage planning scheme, and 2u optimization variables are randomly generated for each chromosome within the range that satisfies the energy storage installation node constraints and output constraints. Each chromosome in the initial population is represented as , where c i represents the node number selected for the i-th energy storage planning scheme, and the parameter p i represents the capacity corresponding to the i-th energy storage planning scheme, satisfying the energy storage installation node constraint c i ∈[2, N c , satisfying the output constraint p i ∈[1, N p , N c and N p represent the number of distribution network nodes and the maximum allowable capacity of energy storage installation, respectively.

3. A method for energy storage planning of a distribution network based on adaptive evolution according to claim 1, characterized in that, The adjustment of the initial population through range constraint conditions and the number of nodes of each individual in the initial population includes: Through range constraint conditions and the number of nodes of each individual in the initial population, remove the individuals of the distribution network energy storage planning scheme that contain the same number of nodes.

4. A method for planning energy storage in a distribution network based on adaptive evolution according to claim 2, characterized in that, The adaptive adjustment parameters include a hybrid fitness, and the hybrid fitness includes the fitness of the population and an auxiliary fitness; Calculate the fitness of the population f ( C i ): ； In the formula, represents the total planned and operating losses of energy storage, represents the item of operating losses of the distribution network, represents the penalty term; If in the population C i can meet the power system operation constraint conditions of the optimal power flow during the operation of the current distribution network system, then let the auxiliary fitness V ( C i ) = 1, otherwise, let the auxiliary fitness V ( C i ) = 0.

5. A method for planning energy storage in a distribution network based on adaptive evolution according to claim 4, characterized in that, The operation constraint conditions of the power system include: The power system operation constraint is: ； ； ； Among them, it includes power flow constraints of the distribution network, power balance constraints, and upper and lower limits of node voltages; and respectively represent the active power injection and reactive power injection of the i-th node at time t; and respectively represent the voltages of node i and node j at time t; G i,j and B i,j and respectively represent the conductance, susceptance, and voltage phase difference angle at time t of the branch with node i and node j as the start and end points; and respectively represent the total active power and reactive power generation of the power grid at time t; and represent the total active power and reactive power of the electrical load at time t; and represent the active power and reactive power of the network loss at time t; U imin and respectively represent the lower limit value of the voltage and amplitude of node i; U imax and respectively represent the upper limit value of the voltage and amplitude of node i.

6. The method for planning a distribution network energy storage based on adaptive evolution according to claim 1, characterized in that The economic analysis of the screened individuals includes: Calculate the total construction and operation and maintenance losses of energy storage, the construction losses of the energy storage system capacity, the operation and maintenance losses of the energy storage system, and the losses of land expropriation and development of the energy storage for the screened individuals of the distribution network energy storage planning scheme; The total construction and operation and maintenance losses of the energy storage are: ； In the formula, represents the loss of energy storage system capacity construction, represents the operation and maintenance loss of the energy storage system, represents the loss of land expropriation and development for energy storage; ； Wherein, represents the investment and construction loss of energy storage batteries per unit capacity, represents the actual grid-connected capacity of the j-th energy storage power station, r 0 represents the discount rate, y represents the pre-operation years of energy storage; ； Wherein, L represents the number of energy storage devices, λ BESS,dis represents the discharge loss coefficient of the energy storage system, λ BESS,ch represents the charge loss coefficient of the energy storage system, p dis,l,T,t represents the real-time discharge power of the l-th energy storage device in the energy storage system, p ch,l,T,t represents the real-time charge power of the energy storage device; ； In the formula, represents the zoning coefficient, which represents the price of this lot, represents the coefficient related to the energy storage investment loss.

7. A distribution network energy storage planning terminal based on adaptive evolution, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a distribution network energy storage planning method according to any one of claims 1 to 6 above.

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