CN117060468B - Energy storage peak shaving capacity optimization configuration method and system based on improved NSGA-II algorithm - Google Patents
Energy storage peak shaving capacity optimization configuration method and system based on improved NSGA-II algorithm Download PDFInfo
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Abstract
The application provides an energy storage peak shaving capacity optimization configuration method and system based on an improved NSGA-II algorithm, wherein the method comprises the following steps: constructing a plurality of objective functions for optimizing configuration of energy storage peak shaving capacity, wherein the objective functions comprise an energy storage system operation cost function constructed based on the energy storage life converted into battery discharge loss cost; constructing a plurality of constraint conditions for a plurality of objective functions; improving an NSGA-II algorithm based on a mixed crossover operator, and solving a plurality of objective functions by improving the NSGA-II algorithm to obtain a pareto optimal front edge solution; and determining an optimal solution of energy storage peak regulation capacity optimal configuration from the pareto optimal front solution through a multi-index gray target decision algorithm, and combining a plurality of constraint conditions, and calculating target power and target capacity of the energy storage system required configuration according to the optimal solution. According to the method, various factors are comprehensively considered, and on the basis of guaranteeing the energy storage service life, the peak regulation effect of the power system is improved, so that the energy storage peak regulation optimal configuration meets the actual requirements.
Description
Technical Field
The application relates to the technical field of energy storage of power systems, in particular to an energy storage peak shaving capacity optimization configuration method and system based on an improved NSGA-II algorithm.
Background
With the widespread use of renewable energy sources (e.g., solar energy, wind energy, etc.), current power systems are facing increasing peak shaving demands. Because of their intermittent and uncertain nature, renewable energy sources can have a large impact on the stability of the power system. Therefore, it is becoming increasingly important to rationally configure energy storage systems to achieve peak shaving effects.
The energy storage system plays an important role in the operation process of the power system, such as providing electric energy in the electricity consumption peak period, improving the stability and reliability of the power system and the like. There is therefore a need to efficiently configure the energy storage system to achieve optimal peak shaving.
In the related technology, the conventional energy storage peak shaving capacity optimization configuration scheme generally only focuses on a single target, and does not fully consider various factors, so that a large difference exists between an energy storage peak shaving optimization result and the actual running condition of the system, and the service life of the energy storage system is influenced. In addition, the related scheme has higher operation complexity, so that the peak shaving solving efficiency is lower.
Therefore, how to improve the energy storage peak shaving efficiency on the basis of guaranteeing the service life of the energy storage system is a problem to be solved at present.
Disclosure of Invention
The present application aims to solve at least one of the technical problems in the related art to some extent.
Therefore, the first object of the present application is to provide an improved NSGA-II algorithm-based energy storage peak shaving capacity optimization configuration method, which comprehensively considers multiple factors such as energy storage life, energy storage peak shaving effect, and economy, constructs two objective functions, introduces a hybrid crossover operator to improve the NSGA-II algorithm, enhances the global searching capability of the algorithm, improves the algorithm performance, and finally determines an optimal solution by using a multi-index gray target decision method, so that a better solution can be obtained in the multi-objective optimization problem.
The second aim of the application is to provide an energy storage peak shaving capacity optimizing configuration system based on an improved NSGA-II algorithm.
A third object of the present application is to propose a non-transitory computer readable storage medium.
In order to achieve the above object, a first aspect of the present application provides an energy storage and peak shaving capacity optimization configuration method based on an improved NSGA-II algorithm, the method comprising the steps of:
Constructing a plurality of objective functions for optimizing configuration of energy storage peak shaving capacity, wherein the objective functions comprise an energy storage system operation cost function constructed based on the energy storage life converted into battery discharge loss cost;
Constructing a plurality of constraint conditions for the plurality of objective functions;
improving a non-dominant ordering genetic NSGA-II algorithm based on a mixed crossover operator comprising a single-point crossover operator and a simulated binary crossover operator, and solving the plurality of objective functions by improving the NSGA-II algorithm to obtain a pareto optimal front edge solution;
and determining an optimal solution of energy storage peak regulation capacity optimization configuration from the pareto optimal front solution through a multi-index gray target decision algorithm, and combining the constraint conditions, and calculating target power and target capacity of the energy storage system required configuration according to the optimal solution.
Optionally, in one embodiment of the present application, the energy storage system operation cost function is an objective function established with energy storage system operation cost minimization, the energy storage system operation cost function being expressed by the following formula:
Wherein,
Wherein C inv,i represents the initial investment cost of the ith energy storage system, C op,i represents the running cost of the ith energy storage system, θi represents the attenuation factor of the ith energy storage system, li represents the index parameters of the ith energy storage system, including cycle number, operating time and capacity degradation,Represents the cost lost by the ith discharge of the battery, c es is the investment cost per unit capacity of the battery, N es is the battery capacity,/>To obtain the discharge amount of the i-th battery discharge consumption by integrating the influences of the discharge depth and the discharge rate, S R is the total discharge amount of the battery from the beginning to the end of the service life.
Optionally, in one embodiment of the present application, the plurality of constraints includes: energy storage power constraints, energy storage state of charge constraints, peak Gu Chalv constraints, energy storage capacity balance constraints, and system power balance constraints.
Optionally, in an embodiment of the present application, the solving the plurality of objective functions by improving an NSGA-II algorithm includes: initializing a population, and calculating an objective function value and an fitness value for each random solution in the initial population; non-dominant sorting is carried out on the initial population, the initial population is divided into different pareto grades, and elite individuals with preset proportion are selected from the initial population based on the fitness value and the pareto grades to directly enter the next generation population; selecting parent individuals for crossover and mutation from a population by a binary tournament selection method, and carrying out crossover operation on the parent individuals by the mixed crossover operator to generate offspring individuals; carrying out uniform mutation operation on the offspring individuals so as to generate random variation on the population, and combining the parent individuals and the mutated offspring individuals to generate a new population; non-dominant ranking is carried out on the new population again, and the fitness value of the new population and the crowding degree distance of each individual in the new population are calculated; selecting a preset number of individuals from the new population to form a next generation population based on a non-dominant ranking result and the crowding degree distance; judging whether a convergence condition is met, if so, outputting the current population as the pareto optimal front solution, and if not, returning to the parent individual selection operation to perform iterative operation until the convergence condition is met.
Optionally, in one embodiment of the present application, the intersecting the parent individual by the mixed intersection operator includes: when each crossing operation is performed, selecting a target operator from the single-point crossing operator and the simulated binary crossing operator according to a preset probability to perform the crossing operation on the parent individual; and for the child individuals to be repaired, repairing the corresponding operators by setting the out-of-range gene values as boundary values so as to repair the child individuals to be repaired until the constraint conditions are met.
Optionally, in an embodiment of the present application, the determining, by a multi-index gray target decision algorithm, an optimal solution of the energy storage peak shaving capacity optimization configuration from the pareto optimal front solution includes: setting an ash target, and carrying out normalization treatment on objective function values corresponding to each solution in the pareto optimal front solution; calculating the distance between each solution in the pareto optimal front solution after normalization treatment and the gray target; assigning a weight to each of the objective functions; and calculating the weighted distance of each solution based on the distance and the weight, and taking the solution with the smallest weighted distance as the optimal solution of the energy storage peak shaving capacity optimization configuration.
Optionally, in one embodiment of the present application, the distance includes: a distance between each objective function value corresponding to each solution and the gray target, the calculating a weighted distance for each solution based on the distance and the weight, comprising: for any solution, multiplying the weight of any objective function of any solution by the distance corresponding to any objective function to obtain the product of any objective function, and calculating the accumulated sum of the products of all objective functions corresponding to any solution.
In order to achieve the above object, the second aspect of the present application further provides an energy storage and peak shaving capacity optimization configuration system based on an improved NSGA-II algorithm, which comprises the following modules:
The first construction module is used for constructing a plurality of objective functions for energy storage peak shaving capacity optimization configuration, wherein the objective functions comprise an energy storage system operation cost function constructed based on the conversion of energy storage service life into battery discharge loss cost;
a first construction module for constructing a plurality of constraints for the plurality of objective functions;
The solving module is used for improving a non-dominant ordering genetic NSGA-II algorithm based on a mixed crossover operator comprising a single-point crossover operator and a simulated binary crossover operator, and solving the plurality of objective functions through the improved NSGA-II algorithm to obtain a pareto optimal front edge solution;
The configuration module is used for determining an optimal solution of energy storage peak regulation capacity optimization configuration from the pareto optimal front solution through a multi-index gray target decision algorithm, combining the constraint conditions, and calculating target power and target capacity of the energy storage system required to be configured according to the optimal solution.
Optionally, in one embodiment of the present application, the configuration module is specifically configured to: setting an ash target, and carrying out normalization treatment on objective function values corresponding to each solution in the pareto optimal front solution; calculating the distance between each solution in the pareto optimal front solution after normalization treatment and the gray target; assigning a weight to each of the objective functions; and calculating the weighted distance of each solution based on the distance and the weight, and taking the solution with the smallest weighted distance as the optimal solution of the energy storage peak shaving capacity optimization configuration.
In order to implement the above embodiment, the third aspect of the present application further provides a non-transitory computer readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the energy storage peak shaving capacity optimization configuration method based on the modified NSGA-II algorithm in the above embodiment.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects: according to the application, the optimal configuration strategy of the energy storage peak shaving capacity is formulated by considering a plurality of factors such as the energy storage service life, the energy storage peak shaving effect and the economy, and the optimal configuration is more in accordance with the actual demand on the basis of ensuring the energy storage service life by constructing the objective function with the maximum daily net income of the energy storage system and the minimum investment under the premise of considering the battery service life, so that the actual application value of the energy storage system in the power system is improved. In addition, the improved NSGA-II algorithm is adopted, so that the efficiency and the precision for solving the multi-objective optimization problem are improved, and compared with the traditional optimization algorithm, the improved NSGA-II algorithm disclosed by the application can better process the multi-objective problem, is beneficial to finding out the global optimal solution, and is better in energy storage effect and better in simulation of the real situation. In addition, the application combines various constraint conditions, ensures the feasibility and the practicability of the optimal configuration, ensures that the optimal configuration result better accords with the actual operation condition through the constraint conditions, and reduces the risk in the implementation process. In addition, the application is not limited to a specific type of energy storage system, can be properly adjusted according to actual requirements and different types of energy storage equipment, has stronger adaptability and flexibility, and can be widely applied to energy storage peak shaving capacity optimization configuration under various scenes and conditions.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of an energy storage peak shaving capacity optimizing configuration method based on an improved NSGA-II algorithm provided by the embodiment of the application;
FIG. 2 is a flow chart of a method for solving a plurality of objective functions by improving NSGA-II algorithm according to an embodiment of the present application;
FIG. 3 is a flowchart of a specific method for solving a plurality of objective functions by modifying the NSGA-II algorithm according to an embodiment of the present application;
FIG. 4 is a flowchart of an optimal solution determination method based on a multi-index gray target decision algorithm according to an embodiment of the present application;
FIG. 5 is a flowchart of a specific energy storage peak shaving capacity optimizing configuration method based on an improved NSGA-II algorithm according to an embodiment of the present application;
Fig. 6 is a schematic structural diagram of an energy storage peak shaving capacity optimizing configuration system based on an improved NSGA-II algorithm according to an embodiment of the present application.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
It should be noted that, in the related embodiment, the energy storage peak shaving capacity optimization configuration policy is mostly only aimed at the first objective, for example, the economy of energy storage peak shaving or the energy storage peak shaving effect, and the influence of the energy storage life is ignored. The energy storage life refers to the service life of the energy storage system and is influenced by factors such as the number of charge and discharge cycles, depth, temperature and the like of the energy storage system. The service life of the energy storage system is a key problem, and has important significance for long-term stable operation of the power system. Therefore, the energy storage peak shaving capacity optimizing configuration strategy provided by the application simultaneously considers the energy storage service life, the energy storage peak shaving effect and the economy.
Therefore, the application provides an energy storage peak shaving capacity optimizing configuration method and system based on an improved NSGA-II algorithm, which realizes the optimized energy storage system peak shaving design and realizes the optimal configuration of the energy storage system peak shaving through a more effective energy storage peak shaving capacity optimizing method.
The energy storage peak shaving capacity optimizing configuration method and system based on the improved NSGA-II algorithm provided by the embodiment of the invention are described in detail below with reference to the accompanying drawings.
Fig. 1 is a flowchart of an energy storage peak shaving capacity optimizing configuration method based on an improved NSGA-II algorithm according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps:
Step S101, constructing a plurality of objective functions for energy storage peak shaving capacity optimization configuration, wherein the objective functions comprise an energy storage system operation cost function constructed based on the energy storage life converted into the battery discharge loss cost.
Specifically, the application firstly builds a model of energy storage peak shaving capacity optimization configuration, and uses a plurality of objective functions as the model of energy storage peak shaving capacity optimization configuration. When a plurality of objective functions are constructed, the service life of the energy storage system, namely the factor of the energy storage service life, is fully considered, so that the subsequent optimal configuration analysis result is closer to reality.
In the implementation, the energy storage life is embodied in the form of the battery discharge loss cost, and then when the objective function is configured, the function is set by taking the lowest cost as one of the targets, and the lowest cost comprises the lowest battery discharge loss cost, so that the configured energy storage capacity optimization model is more reasonable.
In one embodiment of the application, the application can construct two objective functions, the first is to construct the objective function with the maximum net daily gain, the second is to embody the energy storage life in the form of the battery discharging loss cost on the premise of considering the energy storage life, and the objective function is constructed with the minimum investment cost of the energy storage system as the objective.
Specifically, in the present embodiment, the above-described maximum establishment objective function in net daily gain can be expressed by the following formula:
max{fl-c}＝(I1+I2+I3+I4)-(C1+C2-C3)
Wherein, I is daily gain of energy storage and peak shaving; i 1 is low-storage high-hair arbitrage benefit; i 2 is peak shaving compensation gain; i 3 is environmental benefit; i 4 is the yield of new energy consumption. C 1 is the daily construction cost of the energy storage system; c 2 is the daily operation maintenance cost of the energy storage system; and C 3 is the daily equipment residual value of the energy storage system.
The second objective function, namely the objective function established by the lowest investment cost of the energy storage system, is established by taking the lowest operation cost of the energy storage system as the objective function, and the operation cost function of the energy storage system can be expressed by the following formula:
Wherein,
Wherein C inv,i represents the initial investment cost of the ith energy storage system, C op,i represents the operating cost of the ith energy storage system,Represents the attenuation factor of the ith energy storage system, li represents the index parameters of the ith energy storage system, including cycle number, working time and capacity degradation,/>Represents the cost lost by the ith discharge of the battery, c es is the investment cost per unit capacity of the battery, N es is the battery capacity,/>To obtain the discharge amount of the i-th battery discharge consumption by integrating the influences of the discharge depth and the discharge rate, S R is the total discharge amount of the battery from the beginning to the end of the service life.
Step S102, constructing a plurality of constraint conditions for a plurality of objective functions.
Specifically, the application also constructs the constraint conditions corresponding to the objective function, so that in the subsequent process of optimizing configuration and solving, the objective function is optimized and limited by constructing a plurality of constraint conditions, and the solved is in a reasonable range.
In one embodiment of the present application, for the two objective functions constructed in the above embodiment, five constraint conditions may be correspondingly established, including an energy storage power constraint, an energy storage state of charge constraint, a peak Gu Chalv constraint, an energy storage capacity balance constraint, and a system power balance constraint.
Specifically, in the present embodiment, the above-described stored energy power constraint condition can be expressed by the following formula:
0≤Pf(t)≤Pp;0≤Pc(t)≤Pp;Pc(t)Pf(t)＝0
Wherein, P c(t)、Pf (t) is the power of charging and discharging the energy storage device from the power grid (energy storage charging power and energy storage discharging power for short) in the energy storage system at the moment t respectively.
The stored state of charge constraints described above may be expressed by the following formula:
SOCmin≤SOC≤SOCmax
wherein, SOC min and SOC max are the maximum state of charge and the minimum state of charge, respectively, of the energy storage system.
The peak Gu Chalv constraint above can be expressed by the following formula:
b＝(P1-P2)/P1≤K1
Wherein b is a peak Gu Chalv after energy storage and peak shaving; p1 and P2 are respectively the maximum load and the minimum load after the energy storage participates in peak shaving, and the unit is kW; k1 is the upper limit of the peak-valley difference rate.
The energy storage capacity balance constraint can be expressed by the following formula:
Wherein T is the total period number of a single scheduling day, and the meaning of the remaining parameters can refer to the above explanation about the same parameters, which will not be described in detail later.
The above system power balance constraints can be expressed by the following formula:
Wherein, P i is the i-th clean energy output; P 0 (t) is the load of the power grid before energy storage participates in peak shaving at the moment t; a c、af is the charging efficiency and discharging efficiency of the energy storage system respectively.
And step S103, improving a non-dominant ordering genetic NSGA-II algorithm based on a mixed crossover operator comprising a single-point crossover operator and a simulated binary crossover operator, and solving a plurality of objective functions through the improved NSGA-II algorithm to obtain a pareto optimal front edge solution.
Specifically, the application improves Non-dominant ordered genetic (Non-dominated sorting genetic algorithms, NSGA) -II algorithm through mixed crossover operator to obtain improved NSGA-II algorithm, solves objective function through the improved NSGA-II algorithm under the limitation of the constraint conditions, and searches for optimal solution.
It should be noted that, the improved algorithm of the application adopts elite strategy to prevent the genetic algorithm from losing excellent solutions in the evolution process, thereby improving the convergence rate and the quality of the solutions. In addition, as the improved algorithm introduces the mixed cross operator comprising the single-point cross operator and the simulated binary cross operator, the diversity of the population can be maintained in the searching process, so that a better pareto front solution can be found, and the diversity of the population and the performance of the algorithm are improved.
To more clearly illustrate the specific implementation of the present application for solving a plurality of objective functions by improving the NSGA-II algorithm, an exemplary method for solving an objective function based on the improved NSGA-II algorithm is set forth in one embodiment of the present application.
Fig. 2 is a flowchart of a method for solving a plurality of objective functions by modifying the NSGA-II algorithm according to an embodiment of the present application, as shown in fig. 2, the method includes the following steps:
step S201, initializing a population, and calculating an objective function value and an fitness value for each random solution in the initial population.
Specifically, an initial population is first initialized to generate an initial population, where the initial population includes N random solutions. Then, an objective function value and an fitness value are calculated for each random solution. In specific implementation, the numerical value of each random solution can be substituted into the objective function constructed in the above embodiment in sequence to perform operation, and each objective function value corresponding to each random solution can be obtained according to the operation result. The specific process of calculating the fitness value may refer to the fitness value calculation manner in the related art, and will not be described herein.
Step S202, non-dominant sorting is carried out on the initial population, the initial population is divided into different pareto grades, and elite individuals with preset proportions are selected from the initial population based on the fitness value and the pareto grades to directly enter the next generation population.
Specifically, the generated initial population rows are subjected to non-dominant sorting, the initial population rows are divided into different pareto grades, then an elite strategy is adopted, namely elite individuals with a certain proportion are selected from the current initial population to directly enter the next generation, the optimal individuals are reserved to directly enter a new generation population generated in the subsequent step through the elite strategy, and the optimal solutions of the population can be prevented from being lost in the iterative evolution process.
In specific implementation, when elite individuals are determined, as a possible implementation manner, the fitness value corresponding to each individual obtained in the above steps and various factors such as the initial pareto grade can be combined, and elite individuals can be selected from the individuals in the initial population through comprehensive comparison analysis.
Step S203, selecting parent individuals for crossover and mutation from the population by a binary tournament selection method, and performing crossover operation on the parent individuals by a mixed crossover operator to generate offspring individuals.
Specifically, the selection operation is performed on the remaining initial population, parent individuals for subsequent crossover operation and mutation operation are selected through a binary tournament selection method, and the number of the selected parent individuals can be determined according to factors such as actual solving accuracy.
Further, a mixed crossover operation is performed on selected parent individuals. The application provides a mixed crossover operator based on a single-point crossover operator and a simulated binary crossover operator, wherein the mixed crossover operator is introduced into a selected parent individual, and the parent individual is crossed by the mixed crossover operator to generate a child individual.
In one embodiment of the present application, the intersecting operation is performed on the parent individuals specifically by mixing intersection operators, including the following steps: and selecting a target operator from the single-point crossover operator and the simulated binary crossover operator according to the preset probability when each crossover operation is performed, and performing crossover operation on the parent individual. Specifically, during each crossing operation, selecting a target operator for performing the current crossing operation from the single-point crossing operator and the simulated binary crossing operator according to the preset probability, performing the current crossing operation through the target operator selected in real time, and finally generating a child individual.
And for the child individuals to be repaired, repairing the corresponding operators by setting the out-of-range gene value as a boundary value so as to repair the child individuals to be repaired until a plurality of constraint conditions are met. Specifically, for the solving of certain problems, the child individuals may need to repair to ensure that they meet the constraints of the problem, i.e., the constraints constructed in step S102 described above. The embodiment can be realized by setting the out-of-range gene value as a boundary value to repair the operator.
Step S204, carrying out uniform mutation operation on the child individuals so as to generate random variation on the population, and combining the parent individuals and the mutated child individuals to generate a new population.
Specifically, the embodiment further increases the diversity and searching capability of the population by uniformly mutating the offspring individuals, specifically, introducing uniform mutation operators into the offspring individuals so as to cause the population to randomly change, thereby achieving the purpose of increasing the diversity of the population.
Further, merging operation is carried out, and the parent individuals selected in the steps and the offspring individuals subjected to uniform mutation are merged to form a new population.
Step S205, non-dominant ranking is performed on the new population again, and the fitness value of the new population and the crowding distance of each individual in the new population are calculated.
Specifically, the new population formed in step S204 is subjected to non-dominant ranking again, and is classified into different pareto grades, and the process of performing non-dominant ranking specifically may be the same as the implementation manner of performing initial non-dominant ranking in step S202. And dividing the new population into updated different pareto grades through the non-dominant sorting of the time.
Further, the step of calculating the fitness value and the crowdedness distance is performed, the fitness value of the new population is calculated, and the crowdedness distance of each individual in the new population is calculated.
Step S206, selecting a preset number of individuals from the new population to form a next generation population based on the non-dominant ranking result and the crowdedness distance.
Specifically, an environment selection operation is performed. Specifically, based on the non-dominant ranking result in the last step, namely, different updated pareto grades into which the new population is divided, and the calculated crowding degree distance, comprehensive analysis is performed, and a preset number N of individuals are preferentially selected from the new population according to the analysis result to form a next generation population.
In one embodiment of the present application, the selected individuals from this step and the elite individuals determined in step S202 are combined to form a next generation population.
Step S207, judging whether a convergence condition is met, if so, outputting the current population as a pareto optimal front solution, and if not, returning to the parent individual selection operation to perform iterative operation until the convergence condition is met.
Specifically, the operation of checking the convergence condition is performed, specifically, whether the improved NSGA-II algorithm satisfies the convergence condition in the current state is judged. And if the convergence condition is met, outputting the new population generated in the current round as the solved pareto optimal front solution, and terminating the solving algorithm. If the convergence condition is not satisfied, returning to the step S203 to perform parent individual selection operation again, and sequentially repeating the above operations according to the sequence of the subsequent steps in the implementation of the method, so as to continue iteration, and returning to the iteration operation until the convergence condition is satisfied.
Based on the above embodiments, in order to facilitate a clearer understanding of a specific implementation procedure of the present application for solving a plurality of objective functions by improving the NSGA-II algorithm, a specific solving method is described below. Fig. 3 is a flowchart of a specific method for solving a plurality of objective functions by modifying NSGA-II algorithm according to an embodiment of the present application, as shown in fig. 3, the method includes the following steps:
Step S301, initializing a population.
Step S302, judging whether the maximum iteration number is reached, if so, outputting a result, and if not, executing step S303.
Step S303, calculating the fitness value of each individual.
Step S304, non-dominant sorting is performed on the population.
And S305, screening the optimal solution by adopting elite strategy.
Step S306, selecting operation.
Step S307, a hybrid crossover operation.
Step S308, mutation operation.
Step S309, merging operation, merging parent individuals and offspring individuals.
In step S310, the non-dominant ranking is updated.
Step S311, fitness value calculation and congestion degree distance calculation.
Step S312, the environment is selected, new individuals are selected to form the next generation population, and the process returns to step S302.
It should be noted that, the specific implementation manner of each step in the method of the present embodiment may refer to the related description in the foregoing embodiment, which is not repeated herein. In this embodiment, when it is determined that the algorithm does not meet the convergence condition, the operation may also return to the operation of initially calculating the fitness value of each individual, and loop iteration may be performed from the first step.
Therefore, according to the method for solving the objective function by improving the NSGA-II algorithm, the mixed crossover operator is introduced to improve the non-dominant sorting genetic algorithm (NSGA-II), so that the performance of the algorithm is improved, the diversity of the pareto front solution is also improved, the global optimizing capability of the algorithm is improved, and the obtained solution set is optimized. By improving the application of the algorithm, a more optimal solution can be obtained, and the energy storage of the corresponding configuration can achieve better balance among the service life of the energy storage, the energy storage peak shaving effect and the economy.
Step S104, determining an optimal solution of energy storage peak regulation capacity optimization configuration from the pareto optimal front solution through a multi-index gray target decision algorithm, and combining a plurality of constraint conditions, and calculating target power and target capacity of the energy storage system required configuration according to the optimal solution.
Specifically, after the pareto front solution is obtained according to the improved non-dominant sorting genetic algorithm, the optimal solution is determined by adopting a multi-index gray target decision method. And further, according to the determined optimal solution, the corresponding energy storage power and capacity are calculated by combining the constraint conditions such as the energy storage charge state and the power, namely the optimal power and the optimal energy storage peak regulation capacity of the energy storage system which participate in the peak regulation configuration of the power system.
In order to more clearly illustrate the specific implementation process of determining the optimal solution through the multi-index gray target decision algorithm, an exemplary description is given below of an optimal solution determining method based on the multi-index gray target decision algorithm according to an embodiment of the present application. Fig. 4 is a flowchart of an optimal solution determining method based on a multi-index gray target decision algorithm according to an embodiment of the present application, as shown in fig. 4, the method includes the following steps:
step S401, setting an ash target, and carrying out normalization processing on objective function values corresponding to each solution in the pareto optimal front solution.
Specifically, first, an gray target, i.e., an ideal target point is set, which represents the optimum value among all the objective functions constructed as described above, is determined. When an ideal target point is set, the characteristics of the problem to be solved, the relevant knowledge in the energy storage peak shaving field and the actual demand can be combined for determination.
Further, normalization processing is performed, and the objective function value of each solution in the pareto optimal front is performed. As a possible implementation manner, as described in the foregoing embodiment, when solving by the modified NSGA-ii algorithm, the objective function value may be calculated for each solution, each solution may correspond to a plurality of objective function values, and normalization is performed on the objective function value corresponding to each solution in this step. The normalization process can be specifically performed by the following formula:
Wherein, P is the normalized value, P 0 is the original value of the objective function value currently processed, P 1 is the minimum value, P 2 is the maximum value, and P 1 and P 2 are the minimum value and the maximum value of the corresponding objective function in the pareto optimal front respectively.
Step S402, calculating the distance between each solution in the pareto optimal front solution after normalization processing and the gray target.
Specifically, when calculating the distance, for each solution in the pareto optimal front, the distance between each solution and the gray target is calculated respectively. In particular embodiments, the distance may be calculated using Euclidean distance or other distance measurement methods.
As described above, each solution may correspond to a plurality of objective functions, and thus in one embodiment of the application, the calculated distance between each solution and the gray target includes: and the distance between each objective function value corresponding to each solution and the gray target.
Step S403 assigns a weight to each objective function.
Specifically, when determining the weights, a weight is assigned to each objective function constructed, indicating the relative importance of the objective functions. When the specific weight value of each function is determined, the specific weight value can be determined by combining the characteristics of the problem to be solved, the relevant knowledge in the energy storage peak shaving field and the actual requirement.
And step S404, calculating the weighted distance of each solution based on the distance and the weight, and taking the solution with the smallest weighted distance as the optimal solution of the energy storage peak shaving capacity optimization configuration.
Specifically, for each solution, a weighted distance is calculated according to the distance and the weight corresponding to each solution determined in the above steps. In one embodiment of the application, calculating a weighted distance for each solution based on the distance and the weights comprises: for any solution, multiplying the weight of any objective function of any solution by the distance corresponding to any objective function to obtain the product of any objective function, and calculating the accumulated sum of the products of all objective functions corresponding to any solution. That is, a specific method is to multiply each distance corresponding to one solution with a corresponding function weight and then sum, and calculate in this way for each solution.
When expressed by a formula, the calculation formula of the weighted distance is as follows:
where w i denotes the weight of the ith objective function and d i denotes the distance of the ith objective function value.
Further, an optimal solution is selected, and after the weighted distance values of the solutions are compared and sequenced, the solution with the smallest weighted distance is selected as the optimal solution. So that the selected optimal solution has the best overall performance among all objective functions.
Therefore, the multi-index gray target decision method is introduced to solve the optimal solution, and the optimal solution can be better selected by determining the optimal solution from the pareto optimal front by using the multi-index gray target decision method so as to realize a better decision effect.
In summary, according to the energy storage peak shaving capacity optimizing configuration method based on the improved NSGA-II algorithm, the energy storage service life, the energy storage peak shaving effect, the economical efficiency and other factors are considered to formulate an optimizing configuration strategy of the energy storage peak shaving capacity, and the energy storage system is enabled to be more in line with actual requirements on the basis of guaranteeing the energy storage service life by constructing an objective function with the largest daily net income of the energy storage system and the smallest investment on the premise of considering the battery service life, so that the practical application value of the energy storage system in an electric power system is improved. In addition, the method adopts an improved NSGA-II algorithm, so that the efficiency and the precision for solving the multi-objective optimization problem are improved, and compared with the traditional optimization algorithm, the improved NSGA-II algorithm adopted by the method can better process the multi-objective problem, is beneficial to finding out a global optimal solution, enables the configured energy storage effect to be better, and can simulate the real situation. In addition, the method combines various constraint conditions, ensures the feasibility and the practicability of the optimal configuration, ensures that the optimal configuration result better accords with the actual operation condition through the constraint conditions, and reduces the risk in the implementation process. In addition, the method is not limited to a specific type of energy storage system, can be properly adjusted according to actual requirements and different types of energy storage equipment, has strong adaptability and flexibility, and can be widely applied to energy storage peak shaving capacity optimization configuration under various scenes and conditions.
Based on the above embodiments, in order to facilitate a clearer understanding of a specific implementation flow of the energy storage peak shaving capacity optimizing configuration method based on the improved NSGA-II algorithm of the present application, a specific optimizing configuration method is described below. Fig. 5 is a flowchart of a specific energy storage peak shaving capacity optimization configuration method based on an improved NSGA-II algorithm according to an embodiment of the present application, as shown in fig. 5, where the method includes the following steps:
In step S501, required data is collected, including parameters of the energy storage battery, time-varying compliance of the distribution network, etc.
Step S502, an objective function model with the largest daily net income and the lowest investment cost of the energy storage system is established.
In step S503, constraint conditions of the system are established.
Step S504, introducing a mixed crossover operator to improve the NSGA-II algorithm.
Step S505, substituting the collected parameters into the improved algorithm to obtain the pareto optimal front edge solution.
Step S506, a multi-index gray target decision method is introduced to calculate an optimal solution in the pareto optimal front solution set.
And step S507, determining the optimal power and capacity of the stored energy according to the solving result and the constraint condition.
And step S508, outputting an energy storage peak regulation capacity optimization configuration scheme according to the optimal power and capacity of the energy storage.
It should be noted that, the specific implementation manner of each step in the method of the present embodiment may refer to the related description in the foregoing embodiment, which is not repeated herein.
In order to implement the above embodiment, the present application further provides an energy storage peak shaving capacity optimizing configuration system based on an improved NSGA-II algorithm, and fig. 6 is a schematic structural diagram of the energy storage peak shaving capacity optimizing configuration system based on the improved NSGA-II algorithm according to the embodiment of the present application, as shown in fig. 6, where the system includes a first building module 100, a second building module 200, a solving module 300 and a configuration module 400.
The first construction module is used for constructing a plurality of objective functions for energy storage peak regulation capacity optimization configuration, wherein the objective functions comprise an energy storage system operation cost function constructed based on the conversion of energy storage service life into battery discharge loss cost;
a first construction module for constructing a plurality of constraints for the plurality of objective functions;
The solving module is used for improving a non-dominant ordering genetic NSGA-II algorithm based on a mixed crossover operator comprising a single-point crossover operator and a simulated binary crossover operator, and solving the plurality of objective functions through the improved NSGA-II algorithm to obtain a pareto optimal front edge solution;
The configuration module is used for determining an optimal solution of energy storage peak regulation capacity optimization configuration from the pareto optimal front solution through a multi-index gray target decision algorithm, combining the constraint conditions, and calculating target power and target capacity of the energy storage system required to be configured according to the optimal solution.
Optionally, in one embodiment of the present application, the energy storage system operation cost function is an objective function established with energy storage system operation cost minimization, and the first construction module 100 is specifically configured to construct the energy storage system operation cost function by the following formula:
Wherein,
Wherein C inv,i represents the initial investment cost of the ith energy storage system, C op,i represents the running cost of the ith energy storage system, θi represents the attenuation factor of the ith energy storage system, li represents the index parameters of the ith energy storage system, including cycle number, operating time and capacity degradation,Represents the cost lost by the ith discharge of the battery, c es is the investment cost per unit capacity of the battery, N es is the battery capacity,/>To obtain the discharge amount of the i-th battery discharge consumption by integrating the influences of the discharge depth and the discharge rate, S R is the total discharge amount of the battery from the beginning to the end of the service life.
Optionally, in one embodiment of the present application, the solving module 300 is specifically configured to: initializing a population, and calculating an objective function value and an fitness value for each random solution in the initial population; non-dominant sorting is carried out on the initial population, the initial population is divided into different pareto grades, and elite individuals with preset proportion are selected from the initial population based on the fitness value and the pareto grades to directly enter the next generation population; selecting parent individuals for crossover and mutation from the population by a binary tournament selection method, and carrying out crossover operation on the parent individuals by a mixed crossover operator to generate offspring individuals; carrying out uniform mutation operation on the offspring individuals so as to generate random variation on the population, and combining the parent individuals and the mutated offspring individuals to generate a new population; non-dominant sorting is conducted on the new population again, and the fitness value of the new population and the crowding degree distance of each individual in the new population are calculated; selecting a preset number of individuals from the new population to form a next generation population based on the non-dominant ranking result and the crowding distance; judging whether the convergence condition is met, if so, outputting the current population as the pareto optimal front solution, and if not, returning to the parent individual selection operation for iterative operation until the convergence condition is met.
Optionally, in one embodiment of the present application, the solving module 300 is specifically configured to: during each crossing operation, selecting a target operator from a single-point crossing operator and a simulated binary crossing operator according to preset probability to perform crossing operation on the parent individual; and for the child individuals to be repaired, repairing the corresponding operators by setting the out-of-range gene value as a boundary value so as to repair the child individuals to be repaired until a plurality of constraint conditions are met.
Optionally, in one embodiment of the present application, the configuration module 400 is specifically configured to: setting an ash target, and carrying out normalization treatment on objective function values corresponding to each solution in the pareto optimal front solution; calculating the distance between each solution in the pareto optimal front solution after normalization treatment and the gray target; assigning a weight to each objective function; and calculating the weighted distance of each solution based on the distance and the weight, and taking the solution with the smallest weighted distance as the optimal solution of the energy storage peak shaving capacity optimization configuration.
Optionally, in one embodiment of the present application, the configuration module 400 is specifically configured to: for any solution, multiplying the weight of any objective function of any solution by the distance corresponding to any objective function to obtain the product of any objective function, and calculating the accumulated sum of the products of all objective functions corresponding to any solution.
It should be noted that the foregoing explanation of the embodiment of the energy storage peak shaving capacity optimizing configuration method based on the improved NSGA-II algorithm is also applicable to the system of this embodiment, and will not be repeated herein.
In summary, the energy storage peak shaving capacity optimizing configuration system based on the improved NSGA-II algorithm in the embodiment of the application also takes the energy storage service life, the energy storage peak shaving effect, the economy and other factors into consideration to formulate an optimizing configuration strategy of the energy storage peak shaving capacity, and by constructing an objective function with the largest daily net income of the energy storage system and the smallest investment under the premise of considering the service life of a battery, the optimizing configuration is more in accordance with the actual requirement on the basis of ensuring the service life of the energy storage, and the practical application value of the energy storage system in an electric power system is improved. And the system adopts an improved NSGA-II algorithm, so that the efficiency and the precision for solving the multi-objective optimization problem are improved, and compared with the traditional optimization algorithm, the system adopts the improved NSGA-II algorithm, so that the multi-objective problem can be better processed, the global optimal solution can be found, the configured energy storage effect is better, and the real situation can be simulated. In addition, the system combines various constraint conditions, ensures the feasibility and the practicability of the optimal configuration, ensures that the optimal configuration result better accords with the actual operation condition through the constraint conditions, and reduces the risk in the implementation process. In addition, the system is not limited to a specific type of energy storage system, can be properly adjusted according to actual requirements and different types of energy storage equipment, has strong adaptability and flexibility, and can be widely applied to energy storage peak shaving capacity optimization configuration under various scenes and conditions.
In order to implement the above embodiments, the present application further provides a non-transitory computer readable storage medium, on which a computer program is stored, where the computer program when executed by a processor implements the energy storage peak shaving capacity optimization configuration method based on the improved NSGA-II algorithm according to any one of the above embodiments.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (9)
1. The energy storage peak shaving capacity optimizing configuration method based on the improved NSGA-II algorithm is characterized by comprising the following steps of:
Constructing a plurality of objective functions for optimizing configuration of energy storage peak shaving capacity, wherein the objective functions comprise an energy storage system operation cost function constructed based on the energy storage life converted into battery discharge loss cost;
Constructing a plurality of constraint conditions for the plurality of objective functions;
improving a non-dominant ordering genetic NSGA-II algorithm based on a mixed crossover operator comprising a single-point crossover operator and a simulated binary crossover operator, and solving the plurality of objective functions by improving the NSGA-II algorithm to obtain a pareto optimal front edge solution;
Determining an optimal solution of energy storage peak shaving capacity optimal configuration from the pareto optimal front solution through a multi-index gray target decision algorithm, and combining the constraint conditions, and calculating target power and target capacity of the energy storage system required configuration according to the optimal solution;
the energy storage system operation cost function is an objective function established by minimizing the energy storage system operation cost, and the energy storage system operation cost function is expressed by the following formula:
Wherein,
Wherein C inv,i represents the initial investment cost of the ith energy storage system, C op,i represents the operating cost of the ith energy storage system,Represents the attenuation factor of the ith energy storage system, li represents the index parameters of the ith energy storage system, including cycle number, working time and capacity degradation,/>Represents the cost lost by the ith discharge of the battery, c es is the investment cost per unit capacity of the battery, N es is the battery capacity,/>To obtain the discharge amount of the i-th battery discharge consumption by integrating the influences of the discharge depth and the discharge rate, S R is the total discharge amount of the battery from the beginning to the end of the service life.
2. The method of claim 1, wherein the plurality of constraints comprise: energy storage power constraints, energy storage state of charge constraints, peak Gu Chalv constraints, energy storage capacity balance constraints, and system power balance constraints.
3. The method of claim 1, wherein said solving the plurality of objective functions by modifying the NSGA-II algorithm comprises:
initializing a population, and calculating an objective function value and an fitness value for each random solution in the initial population;
Non-dominant sorting is carried out on the initial population, the initial population is divided into different pareto grades, and elite individuals with preset proportion are selected from the initial population based on the fitness value and the pareto grades to directly enter the next generation population;
Selecting parent individuals for crossover and mutation from a population by a binary tournament selection method, and carrying out crossover operation on the parent individuals by the mixed crossover operator to generate offspring individuals;
carrying out uniform mutation operation on the offspring individuals so as to generate random variation on the population, and combining the parent individuals and the mutated offspring individuals to generate a new population;
non-dominant ranking is carried out on the new population again, and the fitness value of the new population and the crowding degree distance of each individual in the new population are calculated;
selecting a preset number of individuals from the new population to form a next generation population based on a non-dominant ranking result and the crowding degree distance;
Judging whether a convergence condition is met, if so, outputting the current population as the pareto optimal front solution, and if not, returning to the parent individual selection operation to perform iterative operation until the convergence condition is met.
4. A method according to claim 3, wherein said interleaving of said parent individuals by said hybrid interleaving operator comprises:
when each crossing operation is performed, selecting a target operator from the single-point crossing operator and the simulated binary crossing operator according to a preset probability to perform the crossing operation on the parent individual;
And for the child individuals to be repaired, repairing the corresponding operators by setting the out-of-range gene values as boundary values so as to repair the child individuals to be repaired until the constraint conditions are met.
5. The method according to claim 1, wherein the determining, by a multi-index gray target decision algorithm, an optimal solution for energy storage, peak shaving, capacity optimization configuration from the pareto optimal front solution comprises:
setting an ash target, and carrying out normalization treatment on objective function values corresponding to each solution in the pareto optimal front solution;
calculating the distance between each solution in the pareto optimal front solution after normalization treatment and the gray target;
Assigning a weight to each of the objective functions;
And calculating the weighted distance of each solution based on the distance and the weight, and taking the solution with the smallest weighted distance as the optimal solution of the energy storage peak shaving capacity optimization configuration.
6. The method of claim 5, wherein the distance between each solution and the gray target comprises: a distance between each objective function value corresponding to each solution and the gray target, the calculating a weighted distance for each solution based on the distance and the weight, comprising:
For any solution, multiplying the weight of any objective function of any solution by the distance corresponding to any objective function to obtain the product of any objective function, and calculating the accumulated sum of the products of all objective functions corresponding to any solution.
7. An energy storage peak shaving capacity optimizing configuration system based on an improved NSGA-II algorithm is characterized by comprising the following modules:
The system comprises a first construction module, a second construction module and a third construction module, wherein the first construction module is used for constructing a plurality of objective functions for energy storage peak shaving capacity optimization configuration, the objective functions comprise an energy storage system operation cost function constructed based on the energy storage life converted into battery discharge loss cost, the energy storage system operation cost function is an objective function established by minimizing the energy storage system operation cost, and the energy storage system operation cost function is expressed by the following formula:
Wherein,
Wherein C inv,i represents the initial investment cost of the ith energy storage system, C op,i represents the operating cost of the ith energy storage system,Represents the attenuation factor of the ith energy storage system, li represents the index parameters of the ith energy storage system, including cycle number, working time and capacity degradation,/>Represents the cost lost by the ith discharge of the battery, c es is the investment cost per unit capacity of the battery, N es is the battery capacity,/>Obtaining the discharge amount of the discharge consumption of the ith battery for the comprehensive influence of the discharge depth and the discharge rate, wherein S R is the total discharge amount of the battery in the process from the beginning of service to the end of service life;
a first construction module for constructing a plurality of constraints for the plurality of objective functions;
The solving module is used for improving a non-dominant ordering genetic NSGA-II algorithm based on a mixed crossover operator comprising a single-point crossover operator and a simulated binary crossover operator, and solving the plurality of objective functions through the improved NSGA-II algorithm to obtain a pareto optimal front edge solution;
The configuration module is used for determining an optimal solution of energy storage peak regulation capacity optimization configuration from the pareto optimal front solution through a multi-index gray target decision algorithm, combining the constraint conditions, and calculating target power and target capacity of the energy storage system required to be configured according to the optimal solution.
8. The system according to claim 7, wherein the configuration module is specifically configured to:
setting an ash target, and carrying out normalization treatment on objective function values corresponding to each solution in the pareto optimal front solution;
calculating the distance between each solution in the pareto optimal front solution after normalization treatment and the gray target;
Assigning a weight to each of the objective functions;
And calculating the weighted distance of each solution based on the distance and the weight, and taking the solution with the smallest weighted distance as the optimal solution of the energy storage peak shaving capacity optimization configuration.
9. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the energy storage peak shaving capacity optimizing configuration method based on the modified NSGA-II algorithm as claimed in any one of claims 1 to 6.
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