Fire, light and storage complementary active power control method and system

Abstract

The invention discloses a flame-light storage complementary active power control method and system, and relates to power system control, and the method comprises the steps: constructing a flame-light storage hierarchical scheduling optimization model, and enabling an upper optimization model of flame-light storage hierarchical scheduling to be used for minimizing the operation characteristic parameters of a flame-light storage complementary system, the lower-layer optimization model is used for minimizing the operation cost of the firelight storage complementary system; calculating an upper-layer optimization model to obtain the optimal active power distribution and the optimal charging and discharging strategy of the flame-light-storage complementary system, and generating a corresponding equivalent load curve; and the lower-layer optimization model takes the equivalent load curve as a constraint, and calculates an optimal configuration strategy of the operation cost and the output power of the flame-light-storage complementary system so as to perform power control of the minimum operation cost of the flame-light-storage complementary system. The system operation efficiency can be improved, and the system operation stability is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power system control, and in particular to a method and system for controlling active power of a thermal, solar and energy storage system. Background Art

[0002] With the rapid development of renewable energy, especially photovoltaic power generation, how to effectively utilize PV power and ensure stable power system operation has become a major challenge facing current power system dispatch. Traditional power system dispatch methods, which primarily rely on conventional energy sources such as thermal power, are unable to adapt to the volatility and uncertainty of PV power generation. Therefore, integrating PV power generation with thermal power and energy storage systems to build a thermal-solar-storage complementary system and developing corresponding active power control systems are of great significance for improving the power system's clean energy absorption capacity and achieving stable and efficient power system operation. As a new power system architecture, research on control system methods for the thermal-solar-storage complementary system is continuously developing. Currently, the following main control methods exist: rule-based control methods such as proportional-integral-derivative (PID) control and fuzzy control; optimization-based control methods such as dynamic programming (DP), linear programming (LP), quadratic programming (QP), and heuristic optimization algorithms; artificial intelligence-based control methods such as artificial neural networks (ANN) and deep learning (DL); chaos optimization algorithms such as genetic algorithms (GA), particle swarm optimization (PSO), and ant colony optimization (ACO); and hybrid control methods.

[0003] Choosing an appropriate control method requires considering factors such as the system's scale and complexity, the priority of control objectives, the system's dynamic characteristics, computing resources, and real-time requirements. Existing scheduling models mostly employ simple scheduling strategies that fail to effectively balance the volatility of photovoltaic power generation with the stability of the power system, resulting in high curtailment rates and high system operating costs. Some optimization algorithms struggle to handle the large-scale, multi-objective, and multi-constrained optimization of thermal-solar-storage complementary systems, leading to poor optimization results that fail to meet practical application requirements. Furthermore, existing control systems fail to fully consider the operational characteristics of thermal power plants, energy storage, and other equipment, resulting in high system operating costs and difficulty maximizing economic benefits. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a method and system for controlling active power of a thermal, solar and energy storage system complementary system to solve the problems mentioned in the background art.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a method for controlling active power of a fire-solar-storage complementary system, including:

[0007] Constructing a fire-solar-storage layered scheduling optimization model, wherein the upper layer optimization model of the fire-solar-storage layered scheduling is used to minimize the operating characteristic parameters of the fire-solar-storage complementary system, and the lower layer optimization model is used to minimize the operating cost of the fire-solar-storage complementary system;

[0008] Calculating the upper optimization model to obtain the optimal active power distribution and optimal charging and discharging strategy of the thermal-solar-storage complementary system, and generating a corresponding equivalent load curve;

[0009] The lower optimization model uses the equivalent load curve as a constraint to calculate the optimal configuration strategy of the operating cost and output power of the thermal-solar-storage complementary system, so as to perform power control to minimize the operating cost of the thermal-solar-storage complementary system.

[0010] As a preferred solution of the thermal-solar-storage complementary active power control method described in the present invention, the operating characteristic parameters include average power deviation and net load fluctuation, and the upper-level optimization model of the thermal-solar-storage stratified scheduling is used to minimize the operating characteristic parameters of the thermal-solar-storage complementary system. The constraints of the upper-level optimization model include: the maximum charging and discharging power and charge state of the energy storage system, the power adjustment speed of the thermal power generation system, the power adjustment speed of the photovoltaic power generation system, and the combined output constraints of thermal power and photovoltaic power generation.

[0011] As an optimal solution of the thermal-solar-storage complementary active power control method described in the present invention, the constraints of the lower-level optimization model used to minimize the operating costs of the thermal-solar-storage complementary system include: power balance constraints of thermal power, photovoltaic and energy storage systems, output constraints of thermal power units and ramp rate constraints of thermal power units.

[0012] As a preferred solution of the active power control method for the thermal-solar-storage complementary system of the present invention, wherein: calculating the upper-level optimization model to obtain the optimal active power distribution and the optimal charging and discharging strategy of the thermal-solar-storage complementary system includes: defining the decision variables of the upper-level optimization model as the solution space of the algorithm, each solution representing a feasible solution to the objective function of the upper-level optimization model;

[0013] Initialize the entire population K, randomly generate K candidate solutions in the D-dimensional search space, and each dimension value of each solution is within the preset lower bound L j and upper bound U j , the maximum number of iterations is Maxlter;

[0014] Divide the entire population into two subpopulations, and adjust the size of the corresponding subpopulation according to the number of improved solutions in each subpopulation;

[0015] For each solution, a candidate solution is generated based on the trial vector generation strategy according to the subpopulation to which it belongs;

[0016] Calculate the objective function value of each newly generated candidate solution and compare it with the fitness value of the current optimal solution. Keep the solution with the largest fitness value and update the population.

[0017] The population partition calculation and update steps are traversed until the maximum number of iterations is reached, and the optimal solution for the active power distribution and energy storage output of the upper-layer optimization model of thermal-solar-storage layer scheduling is obtained.

[0018] As an optimal solution of the thermal-photovoltaic-storage complementary active power control method described in the present invention, the decision variables of the upper optimization model include: the planned active power of the photovoltaic power generation system, the planned active power of the thermal power generation system, the planned active power of the energy storage system, and the discharge power and charging power of the energy storage system.

[0019] As a preferred solution of the active power control method of the thermal-solar-storage complementary system of the present invention, the optimal configuration strategy for calculating the operating cost and output power of the thermal-solar-storage complementary system includes:

[0020] Initialize the population solution Ni and randomly generate Ni candidate solutions in the Di-dimensional search space. Each candidate solution is described as: im =(x im,1 ,x im,2 ,…,x im,Di ), where x im Optimize the decision variables of the model at the lower level;

[0021] Calculate the fitness value of each candidate solution, sort all candidate solutions according to the fitness value, select the top 10% individuals in the fitness value as the initial elite candidate solutions and store them in the elite pool;

[0022] At the beginning of each iteration, the panic coefficient is calculated according to the current number of iterations and the maximum number of iterations to adjust the update direction of the candidate solution;

[0023] When the current number of iterations is not greater than half of the maximum number of iterations, the population is divided into three sub-populations according to the fitness value of each candidate solution, namely the calm group, the herd group and the panic group;

[0024] Based on the candidate solutions updated by the three subpopulations, the fitness values ​​of the updated candidate solutions are calculated and sorted, and the top 10% to 20% of excellent individuals are selected as position solutions and stored in the elite pool.

[0025] As a preferred solution of the fire-solar-storage complementary active power control method of the present invention, it further includes: when the number of iterations is greater than half of the maximum number of iterations, all individual candidate solutions are classified into a calm group population, and the individual optimizes the optimal solution position by approaching the elite pool candidate solution, and the position in the elite pool represents the potential position solution and the iterative historical optimal solution position;

[0026] The fitness of each updated individual is recalculated in each round of iteration. If the fitness value of the candidate solution position after update is better than the fitness value of the previous position, the individual position is updated; otherwise, the original candidate solution position is retained until the maximum number of iterations is reached and the optimal solution for the operating cost distribution and output power distribution of the fire-solar-storage complementary system is output.

[0027] In a second aspect, the present invention provides a fire-solar-storage complementary active power control system, comprising:

[0028] A model building module is used to build a fire-solar-reservoir layered scheduling optimization model, wherein the upper layer optimization model of the fire-solar-reservoir layered scheduling is used to minimize the operating characteristic parameters of the fire-solar-reservoir complementary system, and the lower layer optimization model is used to minimize the operating cost of the fire-solar-reservoir complementary system;

[0029] A first optimization module is used to calculate the upper optimization model, obtain the optimal active power distribution and optimal charging and discharging strategy of the thermal-solar-storage complementary system, and generate a corresponding equivalent load curve;

[0030] The second optimization module is used for the lower optimization model to calculate the optimal configuration strategy of the operating cost and output power of the thermal, solar and storage complementary system with the equivalent load curve as the constraint, so as to perform power control to minimize the operating cost of the thermal, solar and storage complementary system.

[0031] In a third aspect, the present invention provides an electronic device, comprising:

[0032] memory and processor;

[0033] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for controlling the complementary active power of fire, solar and energy storage are realized.

[0034] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for controlling complementary active power of thermal, solar and energy storage.

[0035] Compared with the existing technology, the beneficial effects of the present invention are as follows: the present invention constructs a layered scheduling optimization model for thermal, solar and storage systems, the upper optimization model minimizes the average power deviation and net load fluctuation of the complementary system, and the lower optimization model minimizes the operating cost of the system; then an improved multi-objective sine-cosine optimization algorithm based on multiple test vectors is used to find the optimal solution for the active power distribution and energy storage output of the thermal, solar and storage complementary system and generate the corresponding equivalent load curve; the equivalent load curve is brought into the lower optimization scheduling model as a constraint condition, and an improved escape optimization algorithm is used to find the optimal solution for the operating cost distribution and output power distribution of the thermal, solar and storage complementary system, and finally the power control of the thermal, solar and storage complementary system to minimize the operating cost is realized, the system operating efficiency is improved, and the stability of the system operation is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0037] Figure 1 A schematic diagram of a method flow of a method and system for controlling active power of a fire-solar-storage complementary system according to an embodiment of the present invention;

[0038] Figure 2 This is a flow chart of a thermal-solar-storage layered scheduling optimization model for a thermal-solar-storage complementary active power control method and system according to one embodiment of the present invention;

[0039] Figure 3 This is a flow chart of an improved multi-objective sine-cosine optimization algorithm based on multiple test vectors for a method and system for controlling active power of a fire-solar-storage complementary system according to an embodiment of the present invention;

[0040] Figure 4 This is a flow chart of an improved escape optimization algorithm for a method and system for controlling active power of a fire-solar-storage complementary system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0041] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0042] Example 1, reference Figures 1 to 4, is an embodiment of the present invention, which provides a method for controlling the complementary active power of fire, solar and energy storage, such as Figure 1 Shown, including:

[0043] S100: Constructing a fire-solar-storage layered scheduling optimization model. The upper layer optimization model of the fire-solar-storage layered scheduling is used to minimize the operating characteristic parameters of the fire-solar-storage complementary system, and the lower layer optimization model is used to minimize the operating cost of the fire-solar-storage complementary system.

[0044] S200: Calculate the upper optimization model to obtain the optimal active power distribution and optimal charging and discharging strategy of the thermal-solar-storage complementary system, and generate the corresponding equivalent load curve;

[0045] S300: The lower optimization model uses the equivalent load curve as a constraint to calculate the optimal configuration strategy for the operating cost and output power of the thermal-solar-storage complementary system, so as to perform power control to minimize the operating cost of the thermal-solar-storage complementary system.

[0046] It should be noted that the upper-level optimization of the thermal-photovoltaic-storage layered scheduling optimization model focuses on active power balance, while the lower-level optimization focuses on output power distribution and equipment operating costs. The upper-level optimization model minimizes the average power deviation and net load fluctuation of the complementary system, and reasonably allocates the power generation and storage capacity of thermal power, photovoltaic power, and energy storage to reduce system fluctuations and absorb the volatility of photovoltaic power generation, so as to achieve active power balance in each time period and ensure stable system operation. The optimization results output by the upper-level model include active power distribution and energy storage output in each time period. Based on this, an equivalent load curve is generated and passed to the lower-level optimization model. The goal of the lower-level optimization model is to minimize the operating cost of the system. Based on the equivalent load curve obtained by the upper-level optimization model, the output power of each generator set is further refined and distributed, the operating characteristics of the equipment are considered to reduce costs and ensure equipment safety, and the operation scheduling of the generator set is optimized, such as Figure 2 As shown:

[0047] In an embodiment of the present application, the operating characteristic parameters include average power deviation and net load fluctuation. The upper-level optimization model of thermal-solar-storage layered scheduling is used to minimize the operating characteristic parameters of the thermal-solar-storage complementary system. The constraints of the upper-level optimization model include: the maximum charging and discharging power and charge state of the energy storage system, the power adjustment speed of the thermal power generation system, the power adjustment speed of the photovoltaic power generation system, and the combined output constraints of thermal power and photovoltaic power generation.

[0048] In an embodiment of the present application, the constraints used by the lower-level optimization model to minimize the operating costs of the thermal-photovoltaic-storage complementary system include: power balance constraints of thermal power, photovoltaic and energy storage systems, output constraints of thermal power units, and ramp rate constraints of thermal power units.

[0049] In an optional embodiment, assuming that the total number of optimization periods of the thermal-solar-storage complementary system is N, the sum of the deviations between the planned active power of each component (thermal power generation, photovoltaic power generation, and energy storage) and the planned power of the grid per unit time is the objective function F1(x), which is expressed as:

[0050]

[0051] Where i represents the optimization period index, i∈[1,2,…,N]; P sch (i) represents the system planned active power in time period i; P pv_sch (i) represents the planned active power of the photovoltaic power generation system in time period i; P f_sch (i) represents the planned active power of the thermal power generation system in time period i; P b_sch (i) represents the planned active power of the energy storage system in time period i, and is negative when the energy storage is discharging and positive when the energy storage is charging.

[0052] By adjusting the output of thermal power units, photovoltaic and energy storage systems, the net load fluctuation of the entire system is minimized. The objective function F2(x) is established to represent the net load fluctuation variance of the complementary system, which is expressed as:

[0053]

[0054] Among them, P net,i represents the net load in time period i; represents the average net load in period i; P load (i) represents the load in time period i; η d ,η c Respectively represent the discharge efficiency and charging efficiency of the energy storage system; P d,i 、P c,i They represent the discharge power and charging power of the energy storage system respectively.

[0055] In a preferred embodiment, the goal of the upper-level optimization model is to ensure the stable and efficient operation of the solar-thermal-storage complementary system. This application adopts the construction of the objective function G1 to minimize the average power deviation and net load fluctuation of the system. The weight coefficients ω1 and ω2 are introduced to adjust the emphasis on the average power deviation and net load fluctuation, respectively. In view of the importance of real-time control of active power, this application focuses more on reducing the average power deviation of the complementary system. Therefore, the weight coefficient ω1 is given a larger value to emphasize the priority of the average power deviation in the objective function.

[0056] Specifically, the optimized objective function is expressed as:

[0057] minG1=ω1F1(x)+ω2F2(x)

[0058] Wherein, ω1 represents the weight coefficient of average power deviation; ω2 represents the weight coefficient of net load fluctuation.

[0059] In an optional embodiment, the constraints of the upper optimization model are:

[0060] Assuming that the state of charge of the energy storage system at time period i is represented as SOC(i), the constraints on the discharge and charge states and capacity of the energy storage station are as follows:

[0061]

[0062] SOC min <SOC(i)<SOC max

[0063] in, Indicates the maximum discharge power of the energy storage system; Indicates the maximum charging power of the energy storage system; SOC max Indicates the maximum capacity of the energy storage system; SOC min Indicates the minimum power of the energy storage system.

[0064] Specifically, the maximum charging and discharging power of the energy storage system are expressed as:

[0065]

[0066] Among them, P rated represents the rated power of the energy storage system; SOC(i) represents the state of charge of the energy storage system in time period i; SOC low Indicates the low power threshold of the energy storage system; SOC high Indicates the high power threshold of the energy storage system.

[0067] In an optional embodiment, the constraints of the thermal power generation system are expressed as follows:

[0068]

[0069] Among them, P f_pre (i) represents the predicted active power of the thermal power generation system in time period i; V f (i) represents the speed of the thermal power generation system in time period i; V f_dmax Indicates the maximum value of the speed at which the thermal power generation system adjusts its active power downward; V f_upmax Indicates the maximum value of the speed at which the thermal power generation system adjusts its active power upward.

[0070] In an optional embodiment, the constraints of the photovoltaic power generation system are expressed as:

[0071]

[0072] Among them, P pv_pre (i) represents the predicted active power of the photovoltaic power generation system in time period i; V pv (i) represents the speed of the photovoltaic power generation system in time period i; V pv_dmax Indicates the maximum value of the speed at which the photovoltaic power generation system adjusts its active power downward; V pv_upmax Indicates the maximum value of the speed at which the photovoltaic power generation system adjusts its active power upward.

[0073] In an optional embodiment, the combined output constraint of thermal power and photovoltaic power is expressed as:

[0074]

[0075] in, Indicates the grid-connected power of thermal power; Indicates the grid-connected power of photovoltaic power.

[0076] In an optional embodiment, the goal of the lower optimization model is to comprehensively consider the efficient and stable operation of the complementary system and minimize the operating cost, with the minimum system operating cost as the objective function, which is expressed as:

[0077]

[0078] Among them, G2 represents the operating cost of the thermal-solar-storage complementary system; represents the operating cost of thermal power generation system; represents the operating cost of the energy storage system; Indicates the penalty cost for abandoning fire and light.

[0079] Furthermore, the operating cost of a thermal power generation system is mainly composed of fuel cost and ramp cost, which can be expressed as:

[0080]

[0081] Among them, N g Indicates the total number of thermal power units; represents the fuel cost of the t-th thermal power unit at time i; represents the ramping cost of the t-th thermal power unit at time i; χ represents the unit price of fuel; a t 、b t 、c t represents the operating cost coefficient of the t-th thermal power unit; represents the output power of the t-th thermal power unit at time i; γ t Represents the ramping cost coefficient of the t-th thermal power unit.

[0082] Furthermore, the operating cost of the energy storage system is mainly composed of the operating cost and depreciation cost of the energy storage device, which can be expressed as:

[0083]

[0084] Among them, μ soc Represents the operating cost coefficient of the energy storage system.

[0085] Furthermore, the penalty cost for abandoning power is expressed as:

[0086]

[0087] Among them, σ f represents the penalty cost coefficient for abandoning fire; σ pv P represents the penalty cost coefficient for abandoning light; f_pre,i represents the predicted output power of the thermal power generation system at time i; P pv_pre,i represents the predicted output power of the photovoltaic power generation system at time i; P f,i represents the output power of the thermal power generation system in time period i; P pv,i represents the output power of the photovoltaic power generation system in time period i.

[0088] In an optional embodiment, the constraints of the lower-level optimization scheduling model are:

[0089] Without considering system network losses, the sum of the output power of thermal power, photovoltaic power, and energy storage systems should be equal to the real-time load. The specific system power balance constraints are as follows:

[0090]

[0091] In an optional embodiment, the output constraint condition of the thermal power unit is:

[0092]

[0093] Among them, u gti It represents the 0-1 variable when the t-th thermal power unit outputs at time i, that is, only when u gti =1, the output constraint of thermal power units is considered; They represent the minimum and maximum output power of the thermal power unit respectively.

[0094] In an optional embodiment, the thermal power unit ramp rate constraint condition is:

[0095]

[0096] Among them, u gt(i-1) represents a 0-1 variable when the t-th thermal power unit outputs at time i-1; represents the output power of the t-th thermal power unit at time i-1; r gt,up 、r gt,down Indicates the upper limit of the ramp-up rate and the lower limit of the ramp-down rate of the t-th thermal power unit.

[0097] In an optional embodiment, the minimum start-stop time constraint of the thermal power unit is:

[0098]

[0099] Among them, N on 、N off They represent the maximum continuous start-up time and the maximum continuous shutdown time of thermal power unit t respectively.

[0100] In an embodiment of the present application, calculating the upper-level optimization model to obtain the optimal active power distribution and the optimal charging and discharging strategy of the thermal-solar-storage hybrid system includes: defining the decision variables of the upper-level optimization model as the solution space of the algorithm, each solution representing a feasible solution to the objective function of the upper-level optimization model;

[0101] Initialize the entire population K, randomly generate K candidate solutions in the D-dimensional search space, and each dimension value of each solution is within the preset lower bound L j and upper bound U j , the maximum number of iterations is Maxlter;

[0102] Divide the entire population into two subpopulations, and adjust the size of the corresponding subpopulation according to the number of improved solutions in each subpopulation;

[0103] For each solution, a candidate solution is generated based on the trial vector generation strategy according to the subpopulation to which it belongs;

[0104] Calculate the objective function value of each newly generated candidate solution and compare it with the fitness value of the current optimal solution. Keep the solution with the largest fitness value and update the population.

[0105] The population partition calculation and update steps are traversed until the maximum number of iterations is reached, and the optimal solution for the active power distribution and energy storage output of the upper-layer optimization model of thermal-solar-storage layer scheduling is obtained.

[0106] In an embodiment of the present application, the decision variables of the upper optimization model include: the planned active power of the photovoltaic power generation system, the planned active power of the thermal power generation system, the planned active power of the energy storage system, and the discharge power and charging power of the energy storage system.

[0107] In an optional embodiment, the upper-level optimization model for the thermal-solar-storage layer scheduling is constructed, and an improved multi-objective sine-cosine based multi-trial vector (MTV-SCA) optimization algorithm is used to find the optimal solution for the active power distribution and energy storage output of the thermal-solar-storage complementary system. The decision variables considered in the upper-level optimization model for the thermal-solar-storage layer scheduling mainly include the active power distribution and energy storage output in each period, namely: P sch (i) P pv_sch (i) P f_sch (i) P b_sch (i) P d,i 、P c,i .

[0108] In order to better find the optimal solution of the model, the above original problem is simplified and rewritten, and the decision variable is x ie , then the expression of the decision variables of the upper optimization model of the thermal-solar-reservoir stratified scheduling is:

[0109] x ie =[P sch (i),P pv_sch (i),P f_sch (i),P b_sch (i),P d,i ,P c,i ]

[0110] Furthermore, assuming that there is no coupling relationship between the variables in the objective function and the corresponding linear constraints only involve the internal variables of the corresponding thermal-solar-storage complementary system, an improved multi-objective sine-cosine based on multi-trial vector (MTV-SCA) optimization algorithm is used to solve the optimal solution of active power distribution and energy storage output of the thermal-solar-storage stratified scheduling optimization model.

[0111] In a preferred embodiment, the decision variables are defined as the solution space of the algorithm, and each solution represents a feasible solution to the objective function of the hierarchical optimization model. This application uses an improved multi-objective (MTV-SCA) optimization algorithm to solve the optimal value of the decision variables. The flow chart is as follows: Figure 3 The specific implementation process is as follows:

[0112] A1: Introduce tent mapping to initialize the population.

[0113] First, initialize the entire population K. Randomly generate K candidate solutions in the D-dimensional search space and ensure that each dimension value of each solution is within the specified lower bound L j and upper bound U j , the maximum number of iterations is Maxlter. The population initialization formula is as follows:

[0114] x ie,je =L je +(Uje -L je )×rand(0,1)

[0115] Among them, x ie,je represents the initial value of the je-th dimension of the ie-th candidate solution, ie∈[1,K]; rand(0,1) represents a random number uniformly distributed in the interval [0,1]; L je represents the lower bound of the je-th dimension, je∈[1,D]; U je Represents the upper bound of the je-th dimension.

[0116] Furthermore, this application introduces Tent mapping to generate chaotic sequences and initialize the population so that the initial solutions are distributed as evenly as possible in the solution space. It can be expressed as:

[0117]

[0118] Where rand' represents the chaotic random sequence after the introduction of Tent mapping; ε represents the Tent mapping parameter.

[0119] Furthermore, the population is updated as follows:

[0120] x ie,je =L je +(U je -L je )×rand'

[0121] Create a K×D matrix X to store the location information of K candidate solutions. The specific expression formula of matrix X is:

[0122]

[0123] Furthermore, each dimension value is stored in the matrix X0 as the initial population, and the fitness function f(X ie (t)) is used to evaluate the quality of candidate solutions. Iter represents the number of iterations. The fitness value of each solution in the initial population is calculated, and the objective function value corresponding to each solution is calculated and stored in the matrix F. The initial population X0 is sorted according to the elite non-dominated sorting method, and the non-dominated solution sorting and Pareto efficient frontier are found. The crowding distance of each frontier is calculated, which is expressed as:

[0124]

[0125] Among them, CD ie,je represents the crowding distance of the je-th dimension of the ie-th solution; f je,max 、f je,min Indicates the maximum and minimum values ​​of the je-th dimension of the objective function value; f ie+1,je represents the objective function value of the je-th dimension of the ie+1-th solution; fie-1,je Represents the objective function value of the je-th dimension of the ie-1-th solution.

[0126] Record the optimal fitness value and corresponding position of the current population, and set the optimal solution of the current iter iteration to

[0127] A2: Divide the population into two sub-populations SC-TVP and Pool-TVP.

[0128] The population is divided into two subpopulations: SC-TVP subpopulation and Pool-TVP subpopulation, and its size is allocated according to the number of improved solutions of each subpopulation. The ratio of improved solutions of each subpopulation is expressed as:

[0129]

[0130] Among them, ImpRate SC-TVP ImpRate Pool-TVP are the improved solution ratios of the SC-TVP subpopulation and the Pool-TVP subpopulation respectively; K ImprovedsolutionsbySC-TVP , K ImprovedsolutionsbyPool-TVP Respectively represent the number of improved solutions found by the two subpopulations in the last iteration; K SC-TVP , K Pool-TVP Represent the sizes of the two subpopulations respectively; K FEs Indicates the number of function evaluations performed on each candidate solution in the previous iteration.

[0131] Furthermore, the ratio of the improved solutions of the two subpopulations is compared, and the subpopulation size is allocated according to the ratio, which is expressed as:

[0132] If ImpRate SC-TVP >ImpRate Pool-TVP then

[0133] K SC-TVP =K Pool-TVP =λ×K

[0134] If ImpRate SC-TVP <ImpRate Pool-TVP then

[0135] K SC-TVP =(λ×K) / 2,K Pool-TVP =(λ×K)+(λ×K) / 2

[0136] Among them, λ represents the coefficient of difference between subpopulation sizes; K represents the population size.

[0137] Furthermore, according to the calculated subpopulation sizes, SC-TVP and Pool-TVP subpopulations are created, denoted as XSC and XPool, respectively.

[0138] As the number of iterations increases, each solution X ie The position information of will be adjusted by the SC-TVP subpopulation and the Pool-TVP subpopulation respectively.

[0139] A3: Generate multiple trial vectors to update the population solution

[0140] In the multi-trial vector generation step, a candidate solution is generated for each solution. For each solution, an appropriate TVP is selected based on the subpopulation (SC-TVP or Pool-TVP) to which it belongs to generate a candidate trial vector. The MTV-SCA algorithm includes four different search strategies: S1-TVP, S2-TVP, S3-TVP, and S4-TVP, each with different objectives.

[0141] Furthermore, in each iteration iter, the SC-TVP subpopulation The ieth solution in will generate a candidate solution, expressed as:

[0142]

[0143] in, represents the candidate solution generated by the ie-th solution in the SC-TVP subpopulation after the iter+1-th iteration; represents the position information of the ieth solution in the SC-TVP subpopulation after the iterth iteration; r1 represents the parameter that controls the search direction and distance, represents the constant 0.5, iter represents the current number of iterations, and Maxlter represents the maximum number of iterations; r2 and r3 represent parameters randomly generated in the interval [0,1]; r4 represents the parameter used to determine whether to use the sine function or the cosine function; Indicates the optimal solution position of the current iter iteration.

[0144] In an optional embodiment, the S1-TVP search strategy:

[0145] By combining the position information between the current optimal solution and the randomly selected solution, and using the Chebyshev function and random parameters to control the generation of the trial vector of the ie-th solution of the subpopulation XS1, the specific implementation method is as follows:

[0146]

[0147] in, represents the trial vector of the ieth solution belonging to the subpopulation XS1 in the iter+1th iteration; Chebyshev(iter) represents the value of the Chebyshev function in the iterth iteration; represents four individual solutions randomly selected from the current population X in the iter-th iteration; rand represents a random number.

[0148] Furthermore, the candidate trial vector of the ieth solution is expressed as:

[0149]

[0150] in, represents the candidate solution test vector generated by the ieth solution of the S1-TVP subpopulation in the iter+1th iteration; M ie 、 Respectively represent the corresponding values ​​of the ieth solution in the transformation matrix M and its inverse matrix; represents the ieth solution of the subpopulation XS1 at the iter+1th iteration.

[0151] In another optional embodiment, the S2-TVP search strategy:

[0152] The specific implementation of the trial vector of the ieth solution belonging to the subpopulation XS2 is as follows:

[0153]

[0154] in, It represents the trial vector of the ieth solution belonging to the subpopulation XS2 in the iter+1th iteration.

[0155] The candidate trial vector of the ieth solution is expressed as:

[0156]

[0157] in, represents the candidate solution trial vector generated by the ieth solution of the S2-TVP subpopulation in the iter+1th iteration; represents the ieth solution of the subpopulation XS1 at the iter+1th iteration.

[0158] In another optional embodiment, the S3-TVP search strategy:

[0159] The candidate trial vector of the ieth solution is expressed as:

[0160]

[0161] in, It represents the candidate solution trial vector generated by the ieth solution of the S3-TVP subpopulation in the iter+1th iteration; It represents the ieth solution of the subpopulation XS3 in the iter+1th iteration; sinusoidal(iter) represents the sine coefficient used in the iterth iteration, which is used to adjust the search radius and direction.

[0162] In another optional embodiment, the S4-TVP search strategy:

[0163]

[0164] in, It represents the candidate solution trial vector generated by the ieth solution of the S4-TVP subpopulation in the iter+1th iteration; represents the ieth solution of the subpopulation XS4 in the iter+1th iteration; cos(·) represents the cosine function; sin(·) represents the sine function.

[0165] A4: Population evaluation and update, introducing the optimal solution reverse learning strategy.

[0166] Next, we evaluate each objective function value in the target solution space and sort them according to the elitist non-dominated sorting and crowding distance. We calculate the fitness value of each candidate solution and compare it with the fitness value of the current optimal solution. Solutions with better fitness values ​​are retained and the population is updated.

[0167]

[0168] in, Represents the fitness value of the ieth candidate solution; Indicates the fitness value of the current optimal solution.

[0169] Furthermore, in the present invention, the optimal solution reverse learning strategy is introduced to find the optimal solution, balancing the global search ability and local search ability of the algorithm, which is expressed as:

[0170]

[0171] in, represents the optimal solution of introducing the optimal position reverse learning strategy; L best,je 、U best,je Indicates the lower and upper bounds of the current optimal solution. The updated population is The Pareto solution is used to generate a new population.

[0172] A5: Repeat operations A2 to A4 until the maximum number of iterations is reached, and the final Pareto optimal solution is obtained, that is, the optimal solution for active power distribution and energy storage output of the upper-level optimization model of thermal, solar and energy storage layer scheduling.

[0173] In the embodiment of the present application, the optimal configuration strategy for calculating the operating cost and output power of the thermal-solar-storage complementary system includes:

[0174] Initialize the population solution Ni and randomly generate Ni candidate solutions in the Di-dimensional search space. Each candidate solution is described as: im =(x im,1 ,x im,2 ,…,x im,Di ), where x im Optimize the decision variables of the model at the lower level;

[0175] Calculate the fitness value of each candidate solution, sort all candidate solutions according to the fitness value, select the top 10% individuals in the fitness value as the initial elite candidate solutions and store them in the elite pool;

[0176] At the beginning of each iteration, the panic coefficient is calculated according to the current number of iterations and the maximum number of iterations to adjust the update direction of the candidate solution;

[0177] When the current number of iterations is not greater than half of the maximum number of iterations, the population is divided into three sub-populations according to the fitness value of each candidate solution, namely the calm group, the herd group and the panic group;

[0178] Based on the candidate solutions updated by the three subpopulations, the fitness values ​​of the updated candidate solutions are calculated and sorted, and the top 10% to 20% of excellent individuals are selected as position solutions and stored in the elite pool.

[0179] In the embodiment of the present application, the method further includes: when the number of iterations is greater than half of the maximum number of iterations, all individual candidate solutions are classified into the calm group population.

[0180] Individuals optimize their optimal solution positions by moving closer to the candidate solutions in the elite pool. The positions in the elite pool represent potential position solutions and the iterative historical optimal solution positions.

[0181] The fitness of each updated individual is recalculated in each round of iteration. If the fitness value of the candidate solution position after update is better than the fitness value of the previous position, the individual position is updated; otherwise, the original candidate solution position is retained until the maximum number of iterations is reached and the optimal solution for the operating cost distribution and output power distribution of the fire-solar-storage complementary system is output.

[0182] In an optional embodiment, a lower-level optimization model for the stratified scheduling of thermal, solar, and reservoir systems is constructed, and an improved escape-from-situation (ESC) optimization algorithm is used to find the optimal solution for the operating cost allocation and output power allocation of the thermal, solar, and reservoir complementary system. The decision variables considered in the lower-level optimization model for the stratified scheduling of thermal, solar, and reservoir systems mainly include the output power allocation of each system, including: P f,i 、P pv,i .

[0183] Simplify and rewrite the original problem above, and let the decision variable be x im , then the expression of the decision variables of the lower-layer optimization model of the fire-photovoltaic-reservoir layer scheduling is as follows:

[0184]

[0185] Furthermore, an improved escape-from-storage (ESC) optimization algorithm is used to find the optimal solution for the operating cost allocation and output power allocation of the lower-level optimization model for pyro-photovoltaic-reservoir stratification scheduling. The decision variables are defined as the solution space of the algorithm, and each solution represents a feasible solution to the objective function of the lower-level optimization model.

[0186] In a preferred embodiment, an improved escape-from-situation (ESC) optimization algorithm is used to solve the optimal value of the decision variable. Figure 4 The specific implementation process is as follows:

[0187] B1: Initialize the population.

[0188] Initialize a population solution Ni and randomly generate Ni candidate solutions in the Di-dimensional search space. Each candidate solution is described as: x im =(x im,1 ,x im,2 ,…,x im,Di ). The specific expression of the value of the im-th individual in the j-th dimension is as follows:

[0189] x im,jm =lb jm +r im,jm ×(ub jm -lb jm ),r im,jm ~U(0,1)

[0190] Among them, x im,jm represents the im-th candidate solution in the j-th dimension, im=1,2,…,Ni; lb jm 、ub jm Respectively represent the lower and upper bounds of the j-th dimension, jm=1,2,…,Di; r im,jm Represents a random variable uniformly distributed between [0,1]; U(0,1) represents a random variable uniformly distributed between [0,1].

[0191] In an optional embodiment, a Halton sequence is introduced to improve population initialization and enhance the diversity and uniformity of the initial population. The specific implementation formula is as follows:

[0192] x im,jm =lb jm +(ub jm -lb jm )×Y jm

[0193] Among them, Y jm Represents a random number generated by a Halton sequence.

[0194] After improving the initialization group, the fitness function Fit is introduced im Evaluate the fitness value of each candidate solution, expressed as:

[0195] Fit im =f(x im )

[0196] Furthermore, each candidate solution is sorted according to its fitness value, and the top 10% individuals with the best fitness value are taken as the initial elite candidate solutions and stored in the elite pool E, which is expressed as:

[0197] E={x (1) ,x (2) ,…,x (exist)}

[0198] Among them, E represents the elite storage pool; exist represents the number of optimal potential solutions among the candidate solution individuals, and serves as the judgment condition for subsequent iterations.

[0199] B2: Introducing the panic coefficient Pe(t e ) adjusts the update direction of candidate solutions.

[0200] At each iteration t e At the beginning, calculate the panic coefficient Pe(t e ), expressed as:

[0201]

[0202] Among them, Pe(t e ) indicates that e The panic coefficient size at the iteration; T e Indicates the maximum number of iterations.

[0203] In a preferred embodiment, the adaptive inertia weight is introduced to optimize the panic coefficient Pe(t e ), the specific implementation formula is as follows:

[0204]

[0205] Where b represents the weight control coefficient; φ represents the control search smoothness parameter.

[0206] B3: Candidate solution exploration phase.

[0207] Furthermore, in the exploration phase, at the current iteration number t e Less than or equal to When T e Indicates the maximum number of iterations. The population is divided according to the fitness value of each candidate solution, and is divided into three groups: calm group c e 、Follow the crowd e and panic group p e Among them, the calm group accounts for 15% of the population solution; the herd group accounts for 35% of the population solution; and the panic group accounts for 50% of the population solution. The specific position update formula in the exploration phase is as follows:

[0208] (1) Assume that the center position of the solution individual of the calm group is C jm , the position update of the calm group subpopulation is expressed as:

[0209]

[0210] in, represents the updated position of the im-th candidate solution in the ji-th dimension; C jm Represents the center position of the calm group subpopulation in the jith dimension; m1 represents a Bernoulli random variable 0 or 1, and when it is 0, the individual is not updated; w1 represents the adaptive Levy weight control coefficient; represents the random position of the jmth dimension in the calm group subpopulation;

[0211] Furthermore, the random position v of the calm group subpopulation ce,jm is expressed as:

[0212]

[0213] in, represents a randomly generated position within the random boundary of the jm-th dimension of the calm group subpopulation; ε jm Indicates a smaller adjustment value, z jm Randomly distributed values ​​between (0,1) are used to prevent individuals from staying completely on the boundary.

[0214] Specifically, ε jm Expressed as:

[0215]

[0216] Further, The specific calculation formula is as follows:

[0217]

[0218] in, They represent the minimum and maximum values ​​of the jmth dimension in the calm group subpopulation respectively.

[0219] (2) The position update of the subpopulation of the group is expressed as:

[0220]

[0221] in, represents the position of an individual randomly selected in the panic group subpopulation; w2 represents another adaptive Levy weight control coefficient for the crowd group subpopulation; represents a random position in the jm-th dimension of the subpopulation from the crowd; m2 represents a Bernoulli random variable 0 or 1 for the subpopulation from the crowd.

[0222] Furthermore, the random positions of the subpopulations are is expressed as:

[0223]

[0224] in, represents a randomly generated position within the random boundary of the jm-th dimension of the subpopulation of the crowd group.

[0225] Further, The specific calculation formula is as follows:

[0226]

[0227] in, They represent the minimum and maximum values ​​of the jmth dimension in the subpopulation of the crowd group respectively.

[0228] (3) The updated position of the panic group subpopulation is expressed as:

[0229]

[0230] Among them, E jm represents the position of an individual randomly selected from the elite pool in the jmth dimension; x rand,jm represents an individual randomly selected from all individuals in the jmth dimension; represents the random position of the panic group subpopulation in the jmth dimension.

[0231] Furthermore, the random positions of the panic group subpopulation is expressed as:

[0232]

[0233] in, represents a randomly generated position within the random boundary of the jm-th dimension of the panic group subpopulation.

[0234] Further, The specific calculation formula is as follows:

[0235]

[0236] in, They represent the minimum and maximum values ​​of the jmth dimension in the panic group subpopulation, respectively.

[0237] Furthermore, the adaptive Levy weight control coefficient w jm is expressed as:

[0238]

[0239] Among them, u jm Represents a normal distribution N(0,σ 2 ) random variable; v jm represents a random variable that obeys the standard normal distribution N(0,1); β represents the dynamic parameter; σ represents the control coefficient; represents the gamma function.

[0240] Specifically, the calculation of dynamic parameters is as follows:

[0241]

[0242] Among them, β base It represents the initial value of β, which can be 1.5.

[0243] According to the candidate solutions updated by the three subpopulations, calculate The fitness values ​​of the individuals are sorted and the top 10% to 20% individuals are selected as the better position solutions and stored in the elite pool E.

[0244] B4: Candidate solution development stage

[0245] Furthermore, in the development phase, when the number of iterations t e Greater than At this time, all individual candidate solutions are classified into the calm group population, and their positions are fine-tuned according to the optimal solution found so far. Individuals refine the optimal solution position by moving closer to the elite pool candidate solution. The position in the elite pool represents the potential position solution and the optimal solution position found in the previous iteration.

[0246] Furthermore, the candidate solution position update in the development phase is expressed as:

[0247]

[0248] B5: Fitness evaluation and elite pool update.

[0249] In each iteration, the fitness of each updated individual is recalculated. A greedy strategy is used to retain the better solution in the old position and the new position, which is expressed as:

[0250]

[0251] Among them, x' im Indicates the latest position of the candidate solution individual; Indicates the fitness value of the candidate solution after the position is updated; Fit im The fitness value of the candidate solution before the position is updated.

[0252] If the candidate solution's position is updated, the fitness value Than the fitness value of the previous position Fit im Better, then the individual's position is updated to Otherwise, the original candidate solution position is retained until the maximum number of iterations is completed and the optimal solution position is output, that is, the optimal solution for the operation cost distribution and output power distribution of the thermal-solar-storage complementary system, which is specifically expressed as follows:

[0253]

[0254] in, represents the fitness value of the optimal candidate solution; Indicates the optimal solution.

[0255] It should be noted that the improved multi-objective sine-cosine optimization algorithm based on multiple test vectors is used to find the optimal solution for the active power distribution and energy storage output of the thermal-solar-storage complementary system, and the improved escape optimization algorithm is used to find the optimal solution for the operating cost distribution and output power distribution of the lower-level optimization model, and then bring them into the thermal-solar-storage layered scheduling optimization model to achieve stable and efficient operation of the thermal-solar-storage complementary system and minimize operating costs.

[0256] Example 2: The above embodiment is an illustrative scheme of a method for controlling active power of a fire-solar-storage complementary system. It should be noted that the technical scheme of this fire-solar-storage complementary system and the technical scheme of the above-mentioned fire-solar-storage complementary system are based on the same concept. For details not described in detail in the technical scheme of the fire-solar-storage complementary system in this embodiment, please refer to the description of the technical scheme of the above-mentioned fire-solar-storage complementary system.

[0257] In this embodiment, a fire-solar-energy-storage complementary active power control system includes:

[0258] A model building module is used to construct a fire-solar-reservoir stratified scheduling optimization model. The upper-layer optimization model of the fire-solar-reservoir stratified scheduling is used to minimize the operating characteristic parameters of the fire-solar-reservoir complementary system, and the lower-layer optimization model is used to minimize the operating cost of the fire-solar-reservoir complementary system.

[0259] The first optimization module is used to calculate the upper optimization model, obtain the optimal active power distribution and optimal charging and discharging strategy of the thermal-solar-storage complementary system, and generate the corresponding equivalent load curve;

[0260] The second optimization module is used in the lower optimization model to calculate the optimal configuration strategy of the operating cost and output power of the thermal-solar-storage complementary system with the equivalent load curve as the constraint, so as to perform power control to minimize the operating cost of the thermal-solar-storage complementary system.

[0261] Embodiment 3: This embodiment further provides an electronic device applicable to the method for controlling active power of a fire-solar-storage complementary system, including:

[0262] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method for controlling the complementary active power of fire, solar power and storage as proposed in the above embodiment.

[0263] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for controlling the complementary active power of fire, solar power and energy storage proposed in the above embodiment is implemented.

[0264] The storage medium proposed in this embodiment and the method for realizing complementary active power control of fire, solar and storage proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0265] Through the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented with the help of software and necessary general-purpose hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a computer's floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0266] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for controlling active power of a fire-solar-storage complementary system, characterized in that: include: Constructing a fire-solar-storage layered scheduling optimization model, wherein the upper layer optimization model of the fire-solar-storage layered scheduling is used to minimize the operating characteristic parameters of the fire-solar-storage complementary system, and the lower layer optimization model is used to minimize the operating cost of the fire-solar-storage complementary system; Calculating the upper optimization model to obtain the optimal active power distribution and optimal charging and discharging strategy of the thermal-solar-storage complementary system, and generating a corresponding equivalent load curve; The lower optimization model uses the equivalent load curve as a constraint to calculate the optimal configuration strategy of the operating cost and output power of the thermal-solar-storage complementary system, so as to perform power control to minimize the operating cost of the thermal-solar-storage complementary system.

2. The method for controlling active power of fire-solar-storage complementary power system according to claim 1, characterized in that: The operating characteristic parameters include average power deviation and net load fluctuation. The upper-level optimization model of the thermal-solar-storage layered scheduling is used to minimize the operating characteristic parameters of the thermal-solar-storage complementary system. The constraints of the upper-level optimization model include: the maximum charging and discharging power and charge state of the energy storage system, the power adjustment speed of the thermal power generation system, the power adjustment speed of the photovoltaic power generation system, and the combined output constraints of thermal power and photovoltaic power generation.

3. The method for controlling active power of fire-solar-storage complementary power system according to claim 2, characterized in that: The constraints used by the lower-level optimization model to minimize the operating costs of the thermal-solar-storage complementary system include: power balance constraints of thermal power, photovoltaic and energy storage systems, output constraints of thermal power units, and ramp rate constraints of thermal power units.

4. The method for controlling active power of a fire-solar-storage complementary system as claimed in claim 2 or 3, characterized in that: Calculating the upper optimization model to obtain the optimal active power distribution and the optimal charging and discharging strategy of the thermal-solar-storage hybrid system includes: defining the decision variables of the upper optimization model as the solution space of the algorithm, each solution representing a feasible solution to the objective function of the upper optimization model; Initialize the entire population K, randomly generate K candidate solutions in the D-dimensional search space, and each dimension value of each solution is within the preset lower bound L j and upper bound U j , the maximum number of iterations is Maxlter; Divide the entire population into two subpopulations, and adjust the size of the corresponding subpopulation according to the number of improved solutions in each subpopulation; For each solution, a candidate solution is generated based on the trial vector generation strategy according to the subpopulation to which it belongs; Calculate the objective function value of each newly generated candidate solution and compare it with the fitness value of the current optimal solution. Keep the solution with the largest fitness value and update the population. The population partition calculation and update steps are traversed until the maximum number of iterations is reached, and the optimal solution for the active power distribution and energy storage output of the upper-layer optimization model of thermal-solar-storage layer scheduling is obtained.

5. The method for controlling active power of fire-solar-storage complementary power system according to claim 4, characterized in that: The decision variables of the upper optimization model include: the planned active power of the photovoltaic power generation system, the planned active power of the thermal power generation system, the planned active power of the energy storage system, and the discharge power and charging power of the energy storage system.

6. The method for controlling active power of fire-solar-storage complementary power system according to claim 5, characterized in that: The optimal configuration strategy for calculating the operating cost and output power of the thermal-solar-storage hybrid system includes: Initialize the population solution Ni and randomly generate Ni candidate solutions in the Di-dimensional search space. Each candidate solution is described as: im =(x im,1 ,x im,2 ,…,x im,Di ), where x im Optimize the decision variables of the model at the lower level; Calculate the fitness value of each candidate solution, sort all candidate solutions according to the fitness value, select the top 10% individuals in the fitness value as the initial elite candidate solutions and store them in the elite pool; At the beginning of each iteration, the panic coefficient is calculated according to the current number of iterations and the maximum number of iterations to adjust the update direction of the candidate solution; When the current number of iterations is not greater than half of the maximum number of iterations, the population is divided into three sub-populations according to the fitness value of each candidate solution, namely the calm group, the herd group and the panic group; Based on the candidate solutions updated by the three subpopulations, the fitness values ​​of the updated candidate solutions are calculated and sorted, and the top 10% to 20% of excellent individuals are selected as position solutions and stored in the elite pool.

7. The method for controlling active power of fire-solar-storage complementary power system according to claim 6, characterized in that: Also includes: When the number of iterations is greater than half the maximum number of iterations, all individual candidate solutions are classified into the calm group population, and individuals optimize the optimal solution position by moving closer to the elite pool candidate solution. The position in the elite pool represents the potential position solution and the iterative historical optimal solution position; The fitness of each updated individual is recalculated in each iteration. If the fitness value of the candidate solution position after update is better than the fitness value of the previous position, the individual position is updated; Otherwise, the original candidate solution position is retained until the maximum number of iterations is completed and the optimal solution for the operation cost distribution and output power distribution of the thermal-solar-storage complementary system is output.

8. A fire-solar-storage complementary active power control system, characterized in that: include: A model building module is used to build a fire-solar-reservoir layered scheduling optimization model, wherein the upper layer optimization model of the fire-solar-reservoir layered scheduling is used to minimize the operating characteristic parameters of the fire-solar-reservoir complementary system, and the lower layer optimization model is used to minimize the operating cost of the fire-solar-reservoir complementary system; A first optimization module is used to calculate the upper optimization model, obtain the optimal active power distribution and optimal charging and discharging strategy of the thermal-solar-storage complementary system, and generate a corresponding equivalent load curve; The second optimization module is used for the lower optimization model to calculate the optimal configuration strategy of the operating cost and output power of the thermal, solar and storage complementary system with the equivalent load curve as the constraint, so as to perform power control to minimize the operating cost of the thermal, solar and storage complementary system.

9. An electronic device comprising: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for controlling the complementary active power of thermal, solar and energy storage described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for controlling the complementary active power of thermal, solar and energy storage as described in any one of claims 1 to 7.

Images