A planning method for photovoltaic energy storage grid-connected system based on hierarchical optimization

Abstract

The present invention provides a planning method for a photovoltaic energy storage grid-connected system based on hierarchical optimization, and the planning method includes: obtaining basic data of the photovoltaic energy storage grid-connected system; establishing a two-layer optimization model of the photovoltaic energy storage grid-connected system based on the hierarchical optimization concept, decomposing the optimization problem into a first-level problem and a second-level problem, and determining the constraints of the optimization problem; using the basic data as the input of the two-layer optimization model, solving the first-level problem according to the constraints, and obtaining the capacity configuration of photovoltaics and energy storage; obtaining the dual function after the relaxation of the two-layer optimization model, using the capacity configuration as the optimization boundary of the dual function, substituting it into the dual function and solving it, and obtaining the output of each power source. The planning method based on the hierarchical optimization system provided by the present invention can obtain the overall optimal solution for complex optimization problems of multiple regions and multiple power sources, and the solution speed is fast.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimization dispatching control, and in particular to a photovoltaic energy storage grid-connected system planning method based on hierarchical optimization. Background Art

[0002] At present, the scale of grid-connected renewable energy such as distributed photovoltaics continues to expand. Distributed photovoltaic output prediction and allocation has become one of the main development directions of new energy today. The complexity of the construction environment of distributed photovoltaic power station systems, the diversity of distributed power generation devices with power electronics, and the harshness of the operating environment are constantly increasing, which has put forward new requirements for the planning and operation technology of distributed photovoltaic systems.

[0003] Energy storage is one of the important measures to improve the flexibility of renewable energy power systems. Capacity planning of renewable energy such as photovoltaics and energy storage can solve the supply and demand balance problem of the "photovoltaic-grid-load-storage" system from the source, making the planning results more reasonable and reliable; the current conventional system optimization algorithms have problems such as over-reliance on initial values, easy to fall into local optimality, slow solution speed, etc. when facing large-scale complex optimization problems in multiple regions and multiple power sources, which affects the operating efficiency and safety of the power grid. Summary of the invention

[0004] Based on this, the purpose of the present invention is to provide a photovoltaic energy storage grid-connected system planning method based on hierarchical optimization to solve the technical problems existing in the prior art.

[0005] The present invention proposes a photovoltaic energy storage grid-connected system planning method based on hierarchical optimization, wherein the photovoltaic energy storage grid-connected system at least includes a photovoltaic module, a power grid module, a load module and an energy storage module.

[0006] The planning method includes:

[0007] S10, obtaining basic data of the photovoltaic energy storage grid-connected system, analyzing the power characteristics of photovoltaic power generation and non-renewable energy power generation on the power generation side of the power grid according to the basic data, and then obtaining the conventional output of each power source;

[0008] S20, establishing a two-level optimization model of the photovoltaic energy storage grid-connected system based on the hierarchical optimization concept, so as to decompose the optimization problem of the photovoltaic energy storage grid-connected system into a first-level problem and a second-level problem, and determine the constraint conditions of the optimization problem;

[0009] S30, using the basic data as input of the two-level optimization model, solving the first-level problem according to the constraint conditions, and obtaining the capacity configuration of photovoltaic and energy storage;

[0010] S40, relax the second-level problem to obtain the dual function of the relaxed two-layer optimization model, use the capacity configuration as the optimization boundary of the dual function, substitute it into the dual function and solve it to obtain the output of each power source.

[0011] Optionally, the basic data includes at least photovoltaic day-ahead forecast data, photovoltaic operation cost data, conventional unit day-ahead forecast data, electric energy cost data, energy storage element operation cost data, and user load day-ahead load data.

[0012] Optionally, the step of establishing a two-level optimization model of the photovoltaic energy storage grid-connected system based on the hierarchical optimization concept to decompose the optimization problem of the photovoltaic energy storage grid-connected system into a first-level problem and a second-level problem includes:

[0013] Based on the hierarchical optimization concept, the photovoltaic energy storage grid-connected system is divided, and the calculation domain of the optimization problem is decomposed into several independent subdomains, wherein the several independent subdomains are connected by interconnection lines;

[0014] The optimization problem of each subdomain is decomposed to obtain a two-level optimization problem. The first level problem is to determine the capacity configuration of photovoltaic and energy storage on the basis of ensuring the quality of power supply and satisfying the constraints of each power supply and system. The second level problem is to plan the scheme according to the capacity configuration, so as to realize the interactive power optimization calculation of the interconnection lines between subdomains.

[0015] Optionally, the step of using the basic data as input of the two-level optimization model, solving the first-level problem according to the constraint conditions, and obtaining the capacity configuration of photovoltaic and energy storage includes:

[0016] Initialize the system parameters and system boundary conditions of the two-layer optimization model based on the constraint conditions, and input the basic data into the two-layer optimization model;

[0017] Choose any scenario, solve the first-level problem of each subdomain using the BAS-GA algorithm, and determine whether the first-level problem has a solution;

[0018] If there is no solution, it is indicated that the problem has no solution and the system parameters and the system boundary conditions are adjusted to make a new judgment;

[0019] If there is a solution, the optimized capacity of each subdomain is obtained, and the optimized capacity of photovoltaic and energy storage is obtained according to the optimized capacity of each subdomain.

[0020] Optionally, in the optional scenario, the step of solving the first-level problem of each subdomain by using the BAS-GA algorithm includes:

[0021] Define the population size and number of population iterations of the BAS-GA algorithm;

[0022] The number of thermal power units, hydropower units, photovoltaic units, and energy storage units is converted from decimal numbers to binary numbers, and the converted binary numbers are coded by chromosomes, and each chromosome code has the same length;

[0023] Performing a population iteration operation, the population iteration operation comprising:

[0024] Convert the objective function value in the population into the corresponding fitness value. The objective function in the population is the expected cost under the constraints.

[0025] Select individuals within the population, use the single-point crossover method to perform a crossover operation on the population composed of binary numbers to increase the diversity of the population, and randomly select an individual in the population for forced mutation;

[0026] Select some individuals in the population, randomly increase or decrease the number of a certain power supply by 1, and keep the number of other power supplies unchanged. The changed power supplies are used as individuals in the BAS-GA algorithm to recalculate;

[0027] Calculate the fitness value of the population objective function after the change. If the fitness value of the individual after the change is smaller than the fitness value before the change, the individual after the change is used to replace the previous individual.

[0028] The above population iteration operation is repeated until the population iteration number is reached, and the optimized capacity of each power source in each sub-domain is obtained.

[0029] Optionally, the step of performing relaxation processing on the second-level problem to obtain the dual function of the relaxed two-level optimization model, substituting the capacity configuration into the dual function as the optimization boundary of the dual function and solving the dual function to obtain the output of each power source includes:

[0030] Set the initial value of the Lagrange factor , target error accuracy ;

[0031] Perform extreme value transformation on the second level problem and construct a dual function; based on the initial value of the Lagrangian factor solving the dual function;

[0032] Calculating the difference between the minimum comprehensive cost in the first level problem and the dual function;

[0033] If the difference is less than or equal to the target error accuracy , then get the initial value of the Lagrange factor The corresponding power output;

[0034] If the difference is greater than the target error accuracy , perform Lagrangian factor iteration according to the preset gradient direction, and solve the logarithmic function in sequence according to the iterated Lagrangian factor until the difference between the solution of the dual function and the minimum comprehensive cost is less than or equal to the target error accuracy , output the corresponding power output.

[0035] Optionally, the objective function of the two-layer optimization model is expressed as:

[0036] in, is the objective function of the first-level problem, is the objective function of the second-level problem;

[0037] Where N is the comprehensive cost, is the Lagrangian factor, is the proportion of photovoltaic power in the photovoltaic energy storage grid-connected system, is the total power, for The number of photovoltaic units in the scenario, for Photovoltaic power generation under the scenario, For the scene, It is a collection of photovoltaic output scenarios. is the power plant type, For photovoltaic power generation.

[0038] Optionally, the objective function of the first-level problem is expressed as:

[0039]

[0040] Where N is the comprehensive cost, is the expected result of the random scenario, It is a collection of photovoltaic output scenarios. For the scene The probability of occurrence, is the annual value of investment costs, It is the annual fixed operating cost;

[0041] The expression of the annual value of the investment cost is:

[0042]

[0043] The expression of the annual fixed operating cost is:

[0044]

[0045] Where n is the subdomain, is a collection of subdomains, For the collection of power plants to be installed and existing ones, At least thermal power units 、Hydrogen sets 、Photovoltaic units and energy storage units , For unit cost, is the number of units, is the capacity of a single machine, is the annual fixed operating expense rate.

[0046] Optionally, the constraint conditions include: power balance constraint, power balance constraint, tie line power constraint, photovoltaic output constraint, thermal power output constraint, hydropower output constraint, energy storage battery constraint, photovoltaic power proportion constraint;

[0047] The expression of the power balance constraint is:

[0048]

[0049] In the formula, is the set of generator types on the tie line, To provide power, is the power load, To plan the time period, For the moment, For the scene The adjustable load within the adjustable period;

[0050] The expression of the power balance constraint is:

[0051]

[0052] In the formula, For the amount of wasted electricity;

[0053] The expression of the tie line power constraint is:

[0054]

[0055] In the formula, , are the lower and upper limits of the tie line power, respectively. is the power of the generator set on the interconnection line, for any time;

[0056] The expression of the photovoltaic output constraint is:

[0057]

[0058] In the formula, To generate power for photovoltaic power, is the number of photovoltaic units, is the capacity of a single photovoltaic unit, is a random scene sequence of photovoltaic output;

[0059] The expression of the thermal power output constraint is:

[0060]

[0061] In the formula, , They represent the lower and upper limits of thermal power unit output respectively. To provide power for thermal power units, is the first binary variable, indicating the operating status of the thermal power unit, is the ramp time interval of the thermal power unit, and are the upward climbing rate and downward climbing rate of the thermal power unit, is the second binary variable, indicating the startup status of the thermal power unit, is the third binary variable, indicating the stop status of the thermal power unit. is the minimum start-up time of thermal power units, is the minimum stop time of the thermal power unit, The time interval between starting or stopping the thermal power unit;

[0062] The expression of the hydropower output constraint is:

[0063]

[0064] In the formula, , are the lower and upper limits of the hydropower unit output, Provide power for hydroelectric units;

[0065] The expression of the energy storage battery constraint is:

[0066]

[0067] In the formula, is the energy storage capacity, For charging status, In the discharge state, For charging efficiency, is the discharge efficiency, is the charging power, is the discharge power, is the energy storage change time interval, Self-discharge rate, is the lower limit of energy storage capacity, is the upper limit of energy storage capacity, is the energy storage duration, is the maximum energy storage duration, The maximum charging and discharging power of the energy storage battery;

[0068] The expression of the photovoltaic power ratio constraint is:

[0069]

[0070] In the formula, is the proportion of photovoltaic electricity.

[0071] Optionally, the dual function is expressed as:

[0072]

[0073] The expression of the Lagrangian factor iteration is:

[0074]

[0075] In the formula, is the number of Lagrangian factor iterations, is the iteration vector;

[0076] The expression of the iteration vector is:

[0077]

[0078] In the formula, for No. The iteration step size is for No. The gradient direction of the iteration, is the initial value of the dual function, for No. The value of the dual function at iterations, Total number of photovoltaic units, Power generation of photovoltaic units.

[0079] Compared with the prior art, the beneficial effects of the present invention are as follows: the photovoltaic energy storage grid-connected system planning method based on hierarchical optimization provided by the present invention first obtains the basic data of the photovoltaic energy storage grid-connected system, obtains the conventional output of each power source, and facilitates the subsequent power source output planning; based on the hierarchical optimization idea, a double-layer optimization model of the photovoltaic energy storage grid-connected system is established to decompose the optimization problem of the photovoltaic energy storage grid-connected system into a first-level problem and a second-level problem, determine the constraints of the optimization problem, input the obtained basic data into the double-layer optimization model, perform photovoltaic and energy storage capacity configuration, and obtain the actual output of each power source based on the capacity configuration as the calculation boundary. The photovoltaic energy storage grid-connected system planning method based on hierarchical optimization provided by the present invention decomposes large-scale complex problems into two-layer problems for optimization, facilitates the calculation and solution of subsequent optimization problems, and improves the solution speed and global target optimization.

[0080] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A schematic flow chart of a photovoltaic energy storage grid-connected system planning method based on hierarchical optimization provided in an embodiment of the present invention.

[0082] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0083] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0084] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0085] The embodiment of the present invention provides a photovoltaic energy storage grid-connected system planning method based on hierarchical optimization; it is applied to a "photovoltaic-grid-load-storage" system, wherein the photovoltaic energy storage grid-connected system comprises at least a photovoltaic module, a grid module, a load module and an energy storage module.

[0086] The planning method includes:

[0087] S10, obtaining basic data of the photovoltaic energy storage grid-connected system, analyzing the power characteristics of photovoltaic power generation and non-renewable energy power generation on the power generation side of the power grid according to the basic data, and then obtaining the conventional output of each power source;

[0088] Optionally, in specific implementation, the basic data includes at least photovoltaic day-ahead forecast data, photovoltaic operation cost data, conventional unit day-ahead forecast data, electric energy cost data, energy storage component operation cost data, and user load day-ahead load data. At the same time, the basic data is used to analyze the characteristics of power generation equipment on the power generation side of the power grid, obtain the typical power generation characteristics of photovoltaic power generation, energy storage units, and non-renewable energy, and obtain the output of each power source (generator set) in each period.

[0089] S20, establishing a two-level optimization model of the photovoltaic energy storage grid-connected system based on the hierarchical optimization concept, so as to decompose the optimization problem of the photovoltaic energy storage grid-connected system into a first-level problem and a second-level problem, and determine the constraint conditions of the optimization problem;

[0090] The steps of establishing a two-level optimization model of a photovoltaic energy storage grid-connected system based on the hierarchical optimization concept to decompose the optimization problem of the photovoltaic energy storage grid-connected system into a first-level problem and a second-level problem include:

[0091] Based on the hierarchical optimization concept, the photovoltaic energy storage grid-connected system is divided, and the calculation domain of the optimization problem is decomposed into several independent subdomains, wherein the several independent subdomains are connected by interconnection lines;

[0092] The optimization problem of each subdomain is decomposed to obtain a two-level optimization problem. The first level problem is to determine the capacity configuration of photovoltaic and energy storage on the basis of ensuring the quality of power supply and satisfying the constraints of each power supply and system. The second level problem is to plan the scheme according to the capacity configuration, so as to realize the interactive power optimization calculation of the interconnection lines between subdomains.

[0093] Optionally, the photovoltaic energy storage grid-connected system optimization model is essentially a complex planning problem with multiple power sources and multiple variables. It is very time-consuming and prone to local optimal or even unsolvable problems when solving the model. Therefore, in this embodiment, the photovoltaic energy storage grid-connected system is divided based on the hierarchical optimization idea, and the calculation domain of the problem is decomposed into several subdomains for separate solutions, and then integrated. The subdomain can be understood as a generator set, and each subdomain is connected by several interconnection lines. The subdomain can be determined according to the number of power sources; the optimization problem of several subdomains is decomposed into two layers of problems for solution. The first level problem is to determine the capacity configuration of photovoltaic and energy storage on the basis of ensuring the quality of power supply and satisfying the constraints of each power source and system. The second level problem is to plan the solution according to the capacity configuration given by the lower level problem, so as to realize the optimization calculation of the interconnection line interaction power between subdomains, and avoid the unbalanced power supply phenomenon that may occur when each subdomain is independently planned. Through this idea, different parameters can be selected according to the characteristics of different regions, so that the optimal solution obtained is more in line with the actual situation, and large-scale complex problems can be decomposed into multiple simple small problems, which is conducive to speeding up the calculation speed.

[0094] Furthermore, in the two-layer optimization model, the economic optimization is taken as the goal, and the reliability and the absorption constraints of photovoltaic and energy storage are considered; the expression of the objective function of the two-layer optimization model is:

[0095] in, is the objective function of the first-level problem, is the objective function of the second-level problem;

[0096] Where N is the comprehensive cost, is the Lagrangian factor, is the proportion of photovoltaic power in the photovoltaic energy storage grid-connected system, is the total power, for The number of photovoltaic units in the scenario, for Photovoltaic power generation under the scenario, For the scene, It is a collection of photovoltaic output scenarios. is the power plant type, For photovoltaic power generation.

[0097] The expression of the objective function of the first-level problem is:

[0098]

[0099] Where N is the comprehensive cost, is the expected result of the random scenario, It is a collection of photovoltaic output scenarios. For the scene The probability of occurrence, is the annual value of investment costs, It is the annual fixed operating cost;

[0100] The expression of the annual value of the investment cost is:

[0101]

[0102] The expression of the annual fixed operating cost is:

[0103]

[0104] Where n is the subdomain, is a collection of subdomains, For the collection of power plants to be installed and existing ones, At least thermal power units 、Hydrogen sets 、Photovoltaic units and energy storage units , For unit cost, is the number of units, is the capacity of a single machine, is the annual fixed operating expense rate.

[0105] The constraints include: power balance constraint, power balance constraint, tie line power constraint, photovoltaic output constraint, thermal power output constraint, hydropower output constraint, energy storage battery constraint, photovoltaic power proportion constraint;

[0106] The expression of the power balance constraint is:

[0107]

[0108] In the formula, is the set of generator types on the tie line, To provide power, is the power load, To plan the time period, For the moment, For the scene The adjustable load within the adjustable period;

[0109] The expression of the power balance constraint is:

[0110]

[0111] In the formula, For the amount of wasted electricity;

[0112] The expression of the tie line power constraint is:

[0113]

[0114] In the formula, , are the lower and upper limits of the tie line power, respectively. is the power of the generator set on the interconnection line, for any time;

[0115] The expression of the photovoltaic output constraint is:

[0116]

[0117] In the formula, To generate power for photovoltaic power, is the number of photovoltaic units, is the capacity of a single photovoltaic unit, is a random scene sequence of photovoltaic output;

[0118] The expression of the thermal power output constraint is:

[0119]

[0120] In the formula, , They represent the lower and upper limits of thermal power unit output respectively. To provide power for thermal power units, is the first binary variable, indicating the operating status of the thermal power unit, is the ramp time interval of the thermal power unit, and are the upward climbing rate and downward climbing rate of the thermal power unit, is the second binary variable, indicating the startup status of the thermal power unit, is the third binary variable, indicating the stop status of the thermal power unit. is the minimum start-up time of thermal power units, is the minimum stop time of the thermal power unit, The time interval between starting or stopping the thermal power unit;

[0121] The expression of the hydropower output constraint is:

[0122]

[0123] In the formula, , are the lower and upper limits of the hydropower unit output, Provide power for hydroelectric units;

[0124] The expression of the energy storage battery constraint is:

[0125]

[0126] In the formula, is the energy storage capacity, For charging status, In the discharge state, For charging efficiency, is the discharge efficiency, is the charging power, is the discharge power, is the energy storage change time interval, Self-discharge rate, is the lower limit of energy storage capacity, is the upper limit of energy storage capacity, is the energy storage duration, is the maximum energy storage duration, The maximum charging and discharging power of the energy storage battery;

[0127] The expression of the photovoltaic power ratio constraint is:

[0128]

[0129] In the formula, is the proportion of photovoltaic electricity.

[0130] S30, using the basic data as input of the two-level optimization model, solving the first-level problem according to the constraint conditions, and obtaining the capacity configuration of photovoltaic and energy storage;

[0131] The step of using the basic data as the input of the two-level optimization model, solving the first-level problem according to the constraint conditions, and obtaining the capacity configuration of photovoltaic and energy storage includes:

[0132] Initialize the system parameters and system boundary conditions of the two-layer optimization model based on the constraint conditions, and input the basic data into the two-layer optimization model;

[0133] Choose any scenario, solve the first-level problem of each subdomain using the BAS-GA algorithm, and determine whether the first-level problem has a solution;

[0134] If there is no solution, it is indicated that the problem has no solution and the system parameters and the system boundary conditions are adjusted to make a new judgment;

[0135] If there is a solution, the optimized capacity of each subdomain is obtained, and the optimized capacity of photovoltaic and energy storage is obtained according to the optimized capacity of each subdomain.

[0136] In the optional scenario, the steps of solving the first-level problem of each subdomain by using the BAS-GA algorithm include:

[0137] Define the population size and number of population iterations of the BAS-GA algorithm;

[0138] The number of thermal power units, hydropower units, photovoltaic units, and energy storage units is converted from decimal numbers to binary numbers, and the converted binary numbers are coded by chromosomes, and each chromosome code has the same length;

[0139] Performing a population iteration operation, the population iteration operation comprising:

[0140] Convert the objective function value in the population into the corresponding fitness value. The objective function in the population is the expected cost under the constraints.

[0141] Select individuals within the population, use the single-point crossover method to perform a crossover operation on the population composed of binary numbers to increase the diversity of the population, and randomly select an individual in the population for forced mutation;

[0142] Select some individuals in the population, randomly increase or decrease the number of a certain power supply by 1, and keep the number of other power supplies unchanged. The changed power supplies are used as individuals in the BAS-GA algorithm to recalculate;

[0143] Calculate the fitness value of the population objective function after the change. If the fitness value of the individual after the change is smaller than the fitness value before the change, the individual after the change is used to replace the previous individual.

[0144] The above population iteration operation is repeated until the population iteration number is reached, and the optimized capacity of each power source in each sub-domain is obtained.

[0145] Optionally, in this embodiment, the ‌BAS-GA algorithm‌ is a hybrid optimization algorithm that combines beetle whiskers (BAS) and genetics (GA); when solving, the population size can be defined as 100, the number of population iterations can be 180, the crossover probability can be 0.85, and the mutation probability can be 0.05; the encoding length of each chromosome can be 5, and the total length of the chromosome is 20; select individuals within the population, and use the single-point crossover method to exchange the gene fragments of two parent individuals at a randomly selected position (i.e., the crossover point). Through this single-point crossover operation, two new offspring individuals are obtained. These two offspring individuals inherit part of the genes of the parent individuals, but due to the crossover operation, the two offspring also introduce new gene combinations, thereby increasing the diversity of the population; this diversity helps the algorithm find better solutions in the subsequent evolution process. Whether an individual in the population should be mutated is determined based on the pre-set mutation probability. According to this mutation probability, no individual in the current population may be selected for mutation; therefore, in this embodiment, in order to ensure that the population maintains a certain diversity and evolutionary ability, a replacement strategy is adopted: randomly select an individual from the population for forced mutation; all power sources constitute the population individuals in the algorithm, so that the number of a certain power source changes, and the changed power source is recalculated as an individual in the beetle beard; after meeting the requirements, replacement iteration is performed to obtain the optimized capacity of each power source in each subdomain. Finally, the optimized capacity of photovoltaic and energy storage is obtained according to the optimized capacity of each subdomain.

[0146] S40, relax the second-level problem to obtain the dual function of the relaxed two-layer optimization model, use the capacity configuration as the optimization boundary of the dual function, substitute it into the dual function and solve it to obtain the output of each power source.

[0147] The step of performing relaxation processing on the second-level problem to obtain the dual function of the relaxed two-level optimization model, substituting the capacity configuration as the optimization boundary of the dual function into the dual function and solving it, and obtaining the output of each power source includes:

[0148] Set the initial value of the Lagrange factor , target error accuracy ;

[0149] Perform extreme value transformation on the second level problem and construct a dual function; based on the initial value of the Lagrangian factor solving the dual function;

[0150] Calculating the difference between the minimum comprehensive cost in the first level problem and the dual function;

[0151] If the difference is less than or equal to the target error accuracy , then get the initial value of the Lagrange factor The corresponding power output;

[0152] If the difference is greater than the target error accuracy , perform Lagrangian factor iteration according to the preset gradient direction, and solve the logarithmic function in sequence according to the iterated Lagrangian factor until the difference between the solution of the dual function and the minimum comprehensive cost is less than or equal to the target error accuracy , output the corresponding power output.

[0153] Optionally, the second-level problem is the photovoltaic consumption problem, and its objective function expression is:

[0154]

[0155] In the formula, is a constant, so the minimum value problem of the objective function can be converted into a maximum value problem, that is, maximizing the photovoltaic power. The expression after the maximum value conversion is:

[0156]

[0157] The expression of the dual function is:

[0158]

[0159] It is understandable that different When solving for power output, the comprehensive cost is variable, the power output is not less than the capacity configuration; when all constraints of the second-level problem are met, the dual function is considered to be only related to The relevant optimization problem is:

[0160]

[0161] The expression of the Lagrangian factor iteration is:

[0162]

[0163] In the formula, is the number of Lagrangian factor iterations, is the iteration vector;

[0164] The expression of the iteration vector is:

[0165]

[0166] In the formula, for No. The iteration step size is for No. The gradient direction of the iteration, is the initial value of the dual function, for No. The value of the dual function at iterations, Total number of photovoltaic units, Photovoltaic power generation.

[0167] The difference expression between the dual function and the minimum comprehensive cost is:

[0168]

[0169] when hour, At the end of the iteration, the target Lagrangian factor is obtained. The output of each power source corresponding to the end of iteration;

[0170] In this embodiment, a method based on introducing the Lagrangian factor is proposed. The improved beetle whiskers (BAS) genetic (GA) algorithm is -BAS-GA algorithm effectively solves the problems of slow solution speed and easy falling into local optimality of traditional genetic (GA) algorithm.

[0171] In summary, the photovoltaic energy storage grid-connected system planning method based on hierarchical optimization provided by the present invention first obtains the basic data of the photovoltaic energy storage grid-connected system, obtains the conventional output of each power source, and facilitates the subsequent power source output planning; based on the hierarchical optimization idea, a two-layer optimization model of the photovoltaic energy storage grid-connected system is established to decompose the optimization problem of the photovoltaic energy storage grid-connected system into a first-level problem and a second-level problem, determine the constraints of the optimization problem, input the obtained basic data into the two-layer optimization model, perform photovoltaic and energy storage capacity configuration, and obtain the actual output of each power source based on the capacity configuration as the calculation boundary. The photovoltaic energy storage grid-connected system planning method based on hierarchical optimization provided by the present invention decomposes large-scale complex problems into two-layer problems for optimization, facilitates the calculation and solution of subsequent optimization problems, and improves the solution speed and global target optimization.

[0172] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0173] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A photovoltaic energy storage grid-connected system planning method based on hierarchical optimization, characterized in that: The photovoltaic energy storage grid-connected system at least includes a photovoltaic module, a grid module, a load module and an energy storage module. The planning method includes: S10, obtaining basic data of the photovoltaic energy storage grid-connected system, analyzing the power characteristics of photovoltaic power generation and non-renewable energy power generation on the power generation side of the power grid according to the basic data, and then obtaining the conventional output of each power source; S20, establishing a two-level optimization model of the photovoltaic energy storage grid-connected system based on the hierarchical optimization concept, so as to decompose the optimization problem of the photovoltaic energy storage grid-connected system into a first-level problem and a second-level problem, and determine the constraint conditions of the optimization problem; S30, using the basic data as input of the two-level optimization model, solving the first-level problem according to the constraint conditions, and obtaining the capacity configuration of photovoltaic and energy storage; S40, performing relaxation processing on the second-level problem to obtain the dual function of the relaxed two-level optimization model, substituting the capacity configuration into the dual function as the optimization boundary of the dual function and solving the dual function to obtain the output of each power source; The steps of establishing a two-level optimization model of a photovoltaic energy storage grid-connected system based on the hierarchical optimization concept to decompose the optimization problem of the photovoltaic energy storage grid-connected system into a first-level problem and a second-level problem include: Based on the hierarchical optimization concept, the photovoltaic energy storage grid-connected system is divided, and the calculation domain of the optimization problem is decomposed into several independent subdomains, wherein the several independent subdomains are connected by interconnection lines; The optimization problem of each subdomain is decomposed to obtain two levels of optimization problems. The first level problem is to determine the capacity configuration of photovoltaic and energy storage on the basis of ensuring the quality of power supply and satisfying the constraints of each power supply and system. The second level problem is to plan the scheme according to the capacity configuration, so as to realize the interactive power optimization calculation of the interconnection lines between each subdomain. The step of using the basic data as the input of the two-level optimization model, solving the first-level problem according to the constraint conditions, and obtaining the capacity configuration of photovoltaic and energy storage includes: Initialize the system parameters and system boundary conditions of the two-layer optimization model based on the constraint conditions, and input the basic data into the two-layer optimization model; Choose any scenario, solve the first-level problem of each subdomain using the BAS-GA algorithm, and determine whether the first-level problem has a solution; If there is no solution, it is indicated that the problem has no solution and the system parameters and the system boundary conditions are adjusted to make a new judgment; If there is a solution, the optimized capacity of each subdomain is obtained, and the optimized capacity of photovoltaic and energy storage is obtained according to the optimized capacity of each subdomain.

2. The photovoltaic energy storage grid-connected system planning method based on hierarchical optimization according to claim 1 is characterized in that: The basic data includes at least photovoltaic day-ahead forecast data, photovoltaic operation cost data, conventional unit day-ahead forecast data, electric energy cost data, energy storage element operation cost data, and user load day-ahead load data.

3. The photovoltaic energy storage grid-connected system planning method based on hierarchical optimization according to claim 1 is characterized in that: In the optional scenario, the steps of solving the first-level problem of each subdomain by using the BAS-GA algorithm include: Define the population size and number of population iterations of the BAS-GA algorithm; The number of thermal power units, hydropower units, photovoltaic units, and energy storage units is converted from decimal numbers to binary numbers, and the converted binary numbers are coded by chromosomes, and each chromosome code has the same length; Performing a population iteration operation, the population iteration operation comprising: Convert the objective function value in the population into the corresponding fitness value. The objective function in the population is the expected cost under the constraints. Select individuals within the population, use the single-point crossover method to perform a crossover operation on the population composed of binary numbers to increase the diversity of the population, and randomly select an individual in the population for forced mutation; Select some individuals in the population, randomly increase or decrease the number of a certain power supply by 1, and keep the number of other power supplies unchanged. The changed power supplies are used as individuals in the BAS-GA algorithm to recalculate; Calculate the fitness value of the population objective function after the change. If the fitness value of the individual after the change is smaller than the fitness value before the change, the individual after the change is used to replace the previous individual. The above population iteration operation is repeated until the population iteration number is reached, and the optimized capacity of each power source in each sub-domain is obtained.

4. The photovoltaic energy storage grid-connected system planning method based on hierarchical optimization according to claim 3 is characterized in that: The step of performing relaxation processing on the second-level problem to obtain the dual function of the relaxed two-level optimization model, substituting the capacity configuration as the optimization boundary of the dual function into the dual function and solving it, and obtaining the output of each power source includes: Set the initial value of the Lagrange factor , target error accuracy ; Perform extreme value transformation on the second level problem and construct a dual function; based on the initial value of the Lagrangian factor solving the dual function; Calculating the difference between the minimum comprehensive cost in the first level problem and the dual function; If the difference is less than or equal to the target error accuracy , then get the initial value of the Lagrange factor The corresponding power output; If the difference is greater than the target error accuracy , perform Lagrangian factor iteration according to the preset gradient direction, and solve the logarithmic function in sequence according to the iterated Lagrangian factor until the difference between the solution of the dual function and the minimum comprehensive cost is less than or equal to the target error accuracy , output the corresponding power output.

5. The photovoltaic energy storage grid-connected system planning method based on hierarchical optimization according to claim 4 is characterized in that: The objective function of the two-layer optimization model is expressed as: in, is the objective function of the first-level problem, is the objective function of the second-level problem; Where N is the comprehensive cost, is the Lagrangian factor, is the proportion of photovoltaic power in the photovoltaic energy storage grid-connected system, is the total power, for The number of photovoltaic units in the scenario, for Photovoltaic power generation under the scenario, For the scene, It is a collection of photovoltaic output scenarios. is the power plant type, For photovoltaic power generation.

6. The photovoltaic energy storage grid-connected system planning method based on hierarchical optimization according to claim 5 is characterized in that: The expression of the objective function of the first-level problem is: Where N is the comprehensive cost, is the expected result of the random scenario, It is a collection of photovoltaic output scenarios. For the scene The probability of occurrence, is the annual value of investment costs, It is the annual fixed operating cost; The expression of the annual value of the investment cost is: The expression of the annual fixed operating cost is: Where n is the subdomain, is a collection of subdomains, For the collection of power plants to be installed and existing ones, At least thermal power units 、Hydrogen sets 、Photovoltaic units and energy storage units , For unit cost, is the number of units, is the capacity of a single machine, is the annual fixed operating expense rate.

7. The photovoltaic energy storage grid-connected system planning method based on hierarchical optimization according to claim 6 is characterized in that: The constraints include: power balance constraint, power balance constraint, tie line power constraint, photovoltaic output constraint, thermal power output constraint, hydropower output constraint, energy storage battery constraint, photovoltaic power proportion constraint; The expression of the power balance constraint is: In the formula, is the set of generator types on the tie line, To provide power, is the power load, To plan the time period, For the moment, For the scene The adjustable load within the adjustable period; The expression of the power balance constraint is: In the formula, For the amount of wasted electricity; The expression of the tie line power constraint is: In the formula, , are the lower and upper limits of the tie line power, respectively. is the power of the generator set on the interconnection line, for any time; The expression of the photovoltaic output constraint is: In the formula, To generate power for photovoltaic power, is the number of photovoltaic units, is the capacity of a single photovoltaic unit, is a random scene sequence of photovoltaic output; The expression of the thermal power output constraint is: In the formula, , They represent the lower and upper limits of thermal power unit output respectively. To provide power for thermal power units, is the first binary variable, indicating the operating status of the thermal power unit, is the ramp time interval of the thermal power unit, and are the upward climbing rate and downward climbing rate of the thermal power unit, is the second binary variable, indicating the startup status of the thermal power unit, is the third binary variable, indicating the stop status of the thermal power unit. is the minimum start-up time of thermal power units, is the minimum stop time of the thermal power unit, The time interval between starting or stopping the thermal power unit; The expression of the hydropower output constraint is: In the formula, , are the lower and upper limits of the hydropower unit output, Provide power for hydroelectric units; The expression of the energy storage battery constraint is: In the formula, is the energy storage capacity, For charging status, In the discharge state, For charging efficiency, is the discharge efficiency, is the charging power, is the discharge power, is the energy storage change time interval, Self-discharge rate, is the lower limit of energy storage capacity, is the upper limit of energy storage capacity, is the energy storage duration, is the maximum energy storage duration, The maximum charging and discharging power of the energy storage battery; The expression of the photovoltaic power ratio constraint is: In the formula, is the proportion of photovoltaic electricity.

8. The photovoltaic energy storage grid-connected system planning method based on hierarchical optimization according to claim 7 is characterized in that: The expression of the dual function is: The expression of the Lagrangian factor iteration is: In the formula, is the number of Lagrangian factor iterations, is the iteration vector; The expression of the iteration vector is: In the formula, for No. The iteration step size is for No. The gradient direction of the iteration, is the initial value of the dual function, for No. The value of the dual function at iterations, Total number of photovoltaic units, Photovoltaic power generation.

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