CN107704978A - Electric heating economic load dispatching method based on Pareto evolution and VIKOR methods - Google Patents
Electric heating economic load dispatching method based on Pareto evolution and VIKOR methods Download PDFInfo
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Abstract
The present invention discloses a kind of electric heating economic load dispatching method evolved based on Pareto with VIKOR methods, this method establishes economic model according to the output model of unit, the integrated economy Scheduling Optimization Model of constructing system optimized operation cost, it is characterized in that, propose and Pareto forward position is calculated based on intensity Pareto evolution algorithm, after Pareto forward position is obtained, compromise sequence is carried out to the individual in Pareto forward position using VIKOR methods, obtains the optimal solution of economic load dispatching.Consider the network constraint condition of the corresponding constraints of each subsystem, power system and therrmodynamic system in integrated system, and then targetedly on the premise of meeting to constrain reduce operating cost, improve energy utilization rate；Can abandoning wind consumption support be provided for region electric heating energy resource system, be advantageous to the economy of lifting region electric heating energy system call, reduce the operating cost of system to improve income.
Description
Technical Field
The invention relates to the field of comprehensive energy economy scheduling, in particular to an electric heating economy scheduling method based on pareto evolution and a VIKOR method.
Background
The comprehensive energy system is an important link for the generation, transmission, distribution, conversion, storage and consumption of energy, is a link for planning, construction and operation of a comprehensive energy region, and a key part for the economic operation of the comprehensive energy region on how to carry out scientific and reasonable economic dispatching of comprehensive energy. At present, large-scale wind abandon is caused and scheduling economy is poor due to a 'heat power fixing' mode in northern areas of China. Therefore, the economic dispatching model is reasonably established, so that each unit in the comprehensive energy region can play a role in assisting in dispatching in economic dispatching and the air volume is better consumed.
The research on the economic operation and wind power consumption of the multi-energy system mostly optimizes the output of each unit of the dispatching system by respectively taking the economy and the consumption wind curtailment as targets. The method is mainly characterized in that a grid-connected micro-grid comprising a fan, a photovoltaic cell, a cogeneration system, an electric boiler, heat storage, electricity storage, an electric vehicle and the like is taken as an example, a plurality of coordination scheduling models for wind curtailment are provided from the aspects of decoupling thermoelectric coupling constraint and improving the adjusting capacity of an electric power system, and different aspects such as system operation cost, environmental benefit, wind curtailment cost and the like are comprehensively considered in the considered optimization target.
However, although the current electric heating economic dispatching and wind power consumption have been advanced to some extent, there are some problems in the actual dispatching process. Firstly, wind power is consumed through reasonable scheduling, but the economy of the wind power is not quantized, and the two goals of wind abandonment and economic scheduling are not combined; secondly, the energy storage cycle life is prolonged by scheduling with the aim of reducing energy storage loss, but an economic description method of electricity energy storage cycle loss and heat energy storage heat dissipation loss is lacked, and the energy storage operation cost cannot be unified to the economic scheduling aim; thirdly, if the role of the electric vehicle is considered in the dispatching, the economy may be better, and the electric vehicle dispatching cost model is not established in the existing research. Fourthly, when aiming at the multi-objective optimization problem, the solution model mostly adopts a weighted summation method to convert a plurality of objective functions into a single objective function, and the method has great limitation. In consideration of the importance of wind power consumption in economic dispatching of a comprehensive energy region, a brand-new economic dispatching method of a regional electric heating comprehensive energy system is provided, so that the economy of wind power can be quantified, and wind abandonment and economic dispatching are combined; expanding an economic dispatching target and adding energy storage operation cost; and the electric automobile is added in the dispatching process, so that the economical efficiency of the system is improved. And further, the system can better absorb the abandoned wind and improve the scheduling economy.
Disclosure of Invention
The invention aims to overcome at least one defect in the prior art, and provides an electric heating economic dispatching method based on the pareto evolution and VIKOR method, which can economically dispatch regional electric heating comprehensive energy systems from the perspective of coordinating the output of each unit by an electric heating combined system, and considers the corresponding constraint conditions of each subsystem in the comprehensive system and the network constraint conditions of an electric power system and a thermodynamic system, thereby pertinently reducing the operation cost and improving the energy utilization rate on the premise of meeting the constraint.
In order to solve the technical problems, the technical scheme of the invention is as follows:
an electric heating economic dispatching method based on pareto evolution and a VIKOR method is characterized in that an economic model is established according to an output model of each unit, an integrated economic dispatching optimization model of the optimal operation cost of a system is established, a pareto front edge is calculated based on an intensity pareto evolution algorithm, after the pareto front edge is obtained, individuals in the pareto front edge are subjected to compromise sequencing by the aid of the VIKOR method, and the optimal solution of economic dispatching is obtained.
In a preferred embodiment, the intensity pareto evolutionary algorithm comprises the following steps:
s1: initializing the current generation population G: and generating a decision vector x of the comprehensive energy system in a coding mode, wherein the decision vector x is used for representing the starting and stopping states of different units in the comprehensive energy system, and a variable of 0-1 is used for representing whether each unit is started or not. Since different decision variables x may not satisfy the constraint condition, x needs to be checked to screen out an unqualified decision vector.
S2: carrying out cross mutation operation: calculating the fitness of the current-generation population, determining the crossing and variation of individuals according to the fitness, and carrying out crossing variation on the codes of the initial current-generation population G to form a new population Q; and carrying out the objective function value of the individuals in the Q population.
S3: searching for non-inferior solutions: comparing all sub-target functions of all individuals in the new population Q to find out all non-inferior solutions of the new population Q; denoted as set C. And merging C into elite population G + In (3), duplicate individuals are eliminated.
S4: calculating a fitness function; the fitness of each population is calculated using the following formula:
elite population G + The fitness function of the intermediate individual i is as follows:
in the formulas (6) and (7), z is an individual dominated by i in the contemporary population G; p is the total number of individuals for z; c ti And C tz The values of the t-th objective function for individual i and individual z, respectively.
The fitness function of the individual j in the contemporary population G is:
in the formula (8), k is an elite population G + Any of (a); z is a radical of formula 1 For the individuals dominating i in the contemporary population G, q is z 1 Total number of individuals.
S5, population mixing: elimination of new population Q and elite population G + Repeating the individuals of (1), separating the elite population G + And copying the new generation population G 'into the population Q to form a new generation population G'.
S6, ending iteration: g = g +1, if g<g max And returning to the step S2, otherwise, ending, and finally obtaining the elite population G +, which is the pareto frontier.
In a preferred scheme, after the pareto frontier is obtained, a VIKOR method can be used to find an optimal solution in the pareto frontier, which obtains a solution of mutual compromise of schemes by maximizing the group benefit and minimizing the individual loss, and the solution is the optimal economic dispatching method that we need to find;
the VIKOR method is based on the method of pj -aggregation function developed by metric:
in the formula (1), p is more than or equal to 1 and less than or equal to infinity, j =1,2, … and m.
Suppose the alternative is A i Alternative one has a total of n, i.e. n solutions in the pareto frontier; evaluation index of C j The evaluation indexes are m in total.
The specific steps for solving are as follows:
y1: determining the weight: before the evaluation is performed, the weights, i.e. ω, have to be determined j A value of (d);
y2: determining an evaluation value: let alternatives A i At the corresponding evaluation criterion C j The lower evaluation value is f ij ；
Y3: calculating a positive ideal solution and a negative ideal solution:
y4: calculating comprehensive evaluation optimal solution S of candidate scheme i And scheme comprehensive evaluation of the worst solution R i And plan benefit value Q i
Q i ＝ν(S i -S * )/(S - -S * )+(1-ν)(R i -R * )/(R - -R * ) (5)
Wherein S is * For all S i Minimum value of (1), R * Is all R i Minimum value of (1); s - For all S i Maximum value of (1), R - Is all R i V is a decision mechanism coefficient;
y5: from S i ，R i ，Q i The three sorting lists sort the alternative schemes, and the scheme is better when the numerical value is smaller;
y6: determining an optimal solution: the alternatives are arranged according to the scheme benefit value Q i The alternative scheme of sorting, the first two is A 1 And A 2 If A is 1 The following two conditions are satisfied simultaneously: (1) q (A) 2 )-Q(A 1 ) More than or equal to 1/(n-1); (2) scheme A 1 Then according to S i ，R i Ordering to obtain the optimal solution of the scheme;
if the above two conditions can be satisfied simultaneously, the scheme A 1 Is the optimal solution;
if the above two conditions cannot be satisfied simultaneously, two situations are divided: first, if the condition (2) is not satisfied, the compromise is A 1 And A 2 (ii) a Second, if the condition (1) is not satisfied, the compromise is A 1 To A j Wherein A is j Is Q (A) j )-Q(A 1 )&And (1/(n-1) determining the maximum j value.
In a preferred scheme, the weight of each evaluation index is determined by adopting an entropy method.
In a preferred embodiment, the decision mechanism coefficient is 0.5.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: an electric heating economic dispatching method based on pareto evolution and VIKOR methods can economically dispatch regional electric heating comprehensive energy systems from the perspective of coordinating the output of each unit by an electric heating combined system, and considers the corresponding constraint conditions of each subsystem in the comprehensive system and the network constraint conditions of an electric power system and a thermodynamic system, thereby reducing the operation cost and improving the energy utilization rate on the premise of meeting the constraint pertinently; the support can be provided for the wind abandoning and absorption of the regional electric heating energy system, the economy of the regional electric heating energy system scheduling is favorably improved, and the running cost of the system is reduced to improve the income.
Drawings
Fig. 1 is a flowchart of economic dispatch performed by a system according to embodiment 1 of the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
First, an economic model is established based on the output model of each unit
The regional electric heating comprehensive energy system mainly comprises a cogeneration system, a fan, an electric boiler, an electric energy storage unit, a heat energy storage unit, an electric vehicle and other units, wherein all the units are in flow connection through energy transmission of a heat supply network and a power grid to supply electric heating loads to regions.
The economic model of each unit, i.e., the cost model of each unit in the system, is as follows.
1) Cogeneration system fuel cost
In the formula (9), c h0 And c e0 Respectively the average heating cost and the average power generation cost of the cogeneration unit; delta t is scheduling time length; c. C f Is the unit coal cost; p cog，i (t) and H cog，i (t) electric power and thermal power output by the ith cogeneration unit, respectively; eta tp,h Efficiency of heat supply to the units, eta tp,e The generating efficiency of the unit is obtained.
Controllable unit start-stop cost
In the formula (10), N CG The total number of the controllable units; u shape i The start-stop state of the unit i can be controlled within a time period t when the value of (t) is 0 or 1;is the one-time starting cost of the controllable unit i.
2) Environmental pollution cost of cogeneration unit
The coal-fired cogeneration will emit NOx, SO2, CO and CO2 gases, and the pollution cost of each power generation unit is:
in the formula (11), rho ij Discharging factors of j types of gas for the cogeneration unit i; gamma ray 1j The environmental value of j-type gas; gamma ray 2j A pollution penalty for class j gas emissions.
3) Cost of waste wind
In formula (12): n is a radical of W The total number of the fans is; p is WT，i (t) the output power of the ith fan at the moment t; p is WI，i (t) is the grid-connected power of the ith fan at the moment t; c WT Is the unit wind power cost.
The unit wind power cost model is as follows:
in formula (13), N W The number of the fans; p WN The method is characterized in that the method is that a single fan generates power fully, and the power is considered to be equal to the installed capacity of the fan; c. C w0 Investment for constructing unit capacity for the fan; rho is the net residual value rate of the fan; t is a unit of Wmax The annual power generation equivalent full load hours of the fan; b is subsidy Is a subsidy for national energy conservation and emission reduction. f (Y) can be represented as：
f(Y)＝(1+β CPI ) Y-1 C WME (14)
In the formula (14), Y is the running time of the fan; beta is a beta CPI Is the price rising coefficient; c WME Which is the annual operating cost of the fan.
4) Thermal energy storage loss cost
In the formula (15), H HS (t) thermal energy storage capacity for time period t; k is a radical of LOSS The heat loss rate of heat storage and heat dissipation; q st (t)、Q ex (t) and k st 、k ex Respectively the heat absorption and discharge power and efficiency in the time period t. k is a radical of formula LOSS 1%/h, k st 、k ex And taking 90 percent.
5) Cost of electrical energy storage loss
The electrical energy storage loss cost is two parts, one part is the battery life loss cost, and the other part is the electrical transmission loss cost.
Cost of battery life loss
In the formula (16), mu 1 Is an adjustment factor; c ES-invest One-time purchase cost for the storage battery; e SB Is the battery capacity; p st 、P ex And delta st ，δ ex The charge and discharge power and efficiency, respectively.
Cost of electrical transmission loss C ES2 (t)＝c e0 ((1-δ st )P st (t)+(1/δ ex -1)P ex (t)) (17)
In the formula (17), P xt (t)、P ex (t) and δ st ，δ ex Charging and discharging power and efficiency are respectively in a time period t; c e0 The average power generation cost of the cogeneration unit is obtained.
6) Maintenance cost of electric boiler
The electric boiler is a device for directly converting electric energy into heat energy, only maintenance cost exists in the operation process, and a cost model can be represented by an equation (18):
in the formula (18), K i A maintenance cost coefficient for the i-th equipment; p i And (t) is the output power of the ith type equipment at the moment t.
7) Electric vehicle battery unit electric quantity circulation loss cost:
in the formula (19), C EV-invest A one-time purchase cost for the battery; mu.s 2 Is a comprehensive correction coefficient; n is a radical of EV Indicating the SOC of the battery of the electric vehicle EVmin Charging to SOC EVmax Maximum number of cycles; e EV Is the battery capacity; SOC Evmin Indicating the minimum State of Charge, SOC, of an electric vehicle Evmax And the maximum charge state of the electric vehicle is represented.
Secondly, constructing an integrated economic dispatching optimization model of the optimal operation cost of the system
The economic dispatching goal of the comprehensive energy system is to ensure that the daily operation cost of the whole society is the lowest by reasonably arranging the output of each unit under the condition of meeting the system operation constraint by combining the cost models of each unit. In the running cost, the loss cost and the maintenance cost of each unit are included, and the environmental pollution cost, the wind abandoning cost and the start-stop cost of the controllable unit are also included due to the consideration of the environmental protection benefit. The objective function is as follows:
n in the formulae (20) and (21) T Is the total number of scheduling periods; c ME And (t) is the operation and maintenance cost of each unit in the time period t. The unit maintenance cost can be expressed as:
in formula (22): n is a radical of M Is the total number of equipment units; k i A maintenance cost coefficient for the ith equipment; p i And (t) is the output power of the ith type equipment at the moment t.
The constraint conditions of the model need to consider the corresponding constraint conditions of each subsystem in the integrated system and the network constraint conditions of the power system and the thermodynamic system.
The electric power balance is constrained as follows:
P cog (t)+P WI (t)+P eve (t)+P ex (t)＝P L (t)+P evc (t)+P st (t)+P EB (t) (23)
in the formula (23), P L (t) is the electrical load of the system at time t; p WI And (t) wind power consumed by the system at the moment t.
The thermal power balance is constrained as follows:
Q ds (t)+Q ex (t)+Q EB (t)＝Q L (t) (24)
in formula (24), Q ds (t) a direct heat supply part aiming at heat load in the heat power output by the cogeneration unit at the moment t; q L (t) represents the thermal load of the system at time t.
Other constraints include CHP power supply/hot output interval constraints, controlled unit output ramp constraints, energy storage capacity constraints, capacity constraints for electric vehicle batteries, energy storage device power transmission constraints, electric vehicle battery power transmission constraints, electric boiler output constraints, power flow constraints for power and heating power grid transmission branches, and the like.
And thirdly, calculating the pareto front edge based on an intensity pareto evolutionary algorithm, and after the pareto front edge is obtained, carrying out compromise sequencing on individuals in the pareto front edge by using a VIKOR method to obtain the optimal solution of economic scheduling.
As shown in fig. 1, the steps of the intensity pareto evolutionary algorithm are as follows:
s1: initializing the current generation population G: and generating a decision vector x of the comprehensive energy system in a coding mode, wherein the decision vector x is used for representing the starting and stopping states of different units in the comprehensive energy system, and a variable of 0-1 is used for representing whether each unit is started or not. Since different decision variables x may not satisfy the constraint condition, x needs to be checked to screen out an unqualified decision vector.
S2: carrying out cross mutation operation: calculating the fitness of the current-generation population, determining the crossing and variation of individuals according to the fitness, and carrying out crossing variation on the codes of the initial current-generation population G to form a new population Q; and performing objective function values of individuals in the Q population according to equations (20) (21).
S3: searching for non-inferior solutions: comparing all sub-target functions of all individuals in the new population Q to find out all non-inferior solutions of the new population Q; denoted as set C. And merging C into elite population G + In (3), duplicate individuals are eliminated.
S4: calculating a fitness function; the fitness of each population is calculated using the following formula:
elite population G + The fitness function of the intermediate individual i is as follows:
in the formulas (6) and (7), z is an individual dominated by i in the contemporary population G; p is the total number of individuals for z; c ti And C tz The values of the t-th objective function for individual i and individual z, respectively.
The fitness function of the individual j in the contemporary population G is:
in formula (8), k is an elite population G + Any of (a); z is a radical of 1 For individuals dominating i in the contemporary population G, q is z 1 Total number of individuals.
S5, population mixing: elimination of new population Q and elite population G + Repeating the individuals of (1), separating the elite population G + And copying the new gene into a population Q to form a new contemporary population G'.
S6, ending iteration: g = g +1, if g<g max Returning to the step S2, otherwise ending to finally obtain the elite population G + I.e. the pareto frontier.
In a specific implementation process, after the pareto front is obtained, a VIKOR method can be used to obtain an optimal solution in the pareto front, which obtains a solution of mutual compromise of each scheme by maximizing the population benefit and minimizing the individual loss, and the solution is the optimal economic dispatching method that we need to solve;
the VIKOR method is based on the method of pj -aggregation function developed by metric:
in the formula (1), p is more than or equal to 1 and less than or equal to infinity, j =1,2, …, m.
Suppose the alternative is A i Alternative one has a total of n, i.e. n solutions in the pareto frontier; evaluation index of C j The evaluation indexes are m in total.
The specific steps for solving are as follows:
y1: determining the weight: before the evaluation is performed, the weight, i.e. ω, has to be determined j A value of (d);
y2: determining an evaluation value: let alternatives A i In phaseCorresponding evaluation criteria C j An evaluation value of f ij ；
Y3: calculating a positive ideal solution and a negative ideal solution:
y4: calculating comprehensive evaluation optimal solution S of candidate scheme i And comprehensively evaluating the worst solution R by using a scheme i And plan benefit value Q i
Q i ＝ν(S i -S * )/(S - -S * )+(1-ν)(R i -R * )/(R - -R * ) (5)
Wherein S is * For all S i Minimum value of (1), R * Is all R i Minimum value of (1); s. the - For all S i Maximum value of (1), R - Is all R i V is a decision mechanism coefficient;
y5: from S i ，R i ，Q i The three sorting lists sort the alternative schemes, and the scheme is better when the numerical value is smaller;
y6: determining an optimal solution: the alternatives are arranged according to the scheme benefit value Q i The alternative of ordering, the top two bits are A 1 And A 2 If A is 1 The following two conditions are satisfied simultaneously: (1) q (A) 2 )-Q(A 1 ) More than or equal to 1/(n-1); (2) scheme A 1 Then according to S i ，R i Ordering to obtain the optimal solution of the scheme;
if the above two conditions can be satisfied simultaneously, then scheme A 1 Is the optimal solution;
if the above two conditions cannot be satisfied simultaneously, two situations are divided: first, if the condition (2) is not satisfied, the compromise is A 1 And A 2 (ii) a Second, if the condition (1) is not satisfied, the compromises are A1 to A j Wherein A is j Is Q (A) j )-Q(A 1 )&And (1/(n-1) determining the maximum value of j.
In the specific implementation process, the weight of each evaluation index is determined by adopting an entropy method.
In the specific implementation, the decision mechanism coefficient is 0.5.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (5)
1. An electric heating economic dispatching method based on pareto evolution and a VIKOR method is characterized in that an economic model is established according to a power model of each unit, an integrated economic dispatching optimization model of the optimal operation cost of a system is established, the pareto front edge is calculated based on an intensity pareto evolution algorithm, after the pareto front edge is obtained, individuals in the pareto front edge are subjected to compromise sequencing by the aid of the VIKOR method, and the optimal solution of economic dispatching is obtained.
2. The method for electrothermal economic dispatch based on pareto evolution and VIKOR method according to claim 1 characterized in that the steps of the intensity pareto evolution algorithm are as follows:
s1: initializing a current generation population G;
s2: carrying out cross mutation operation: calculating the fitness of the current-generation population, determining the crossing and variation of individuals according to the fitness, and carrying out crossing variation on the codes of the initial current-generation population G to form a new population Q;
s3: searching for non-inferior solutions: comparing all the sub-objective functions of all the individuals in the new population Q to find out all the non-inferior solutions of the new population Q;
s4: calculating a fitness function;
s5, population mixing: elimination of new population Q and elite population G + Repeating the individuals of (1), separating the elite population G + And copying the new generation population G 'into the population Q to form a new generation population G'.
S6, ending iteration: g = g +1, if g<g max Returning to the step S2, otherwise ending to finally obtain the elite population G + I.e. the pareto frontier.
3. The method for electrothermal economic dispatch based on pareto evolution and VIKOR method as claimed in claim 1, wherein after obtaining the pareto frontier, the VIKOR method can be used to find the optimal solution in the pareto frontier, which is a solution of mutual compromise between each scheme by maximizing the population benefit and minimizing the individual loss, and this solution is the optimal economic dispatch method we need to solve;
the VIKOR method is based on the method of pj -aggregation function developed by metric:
in the formula (1), p is more than or equal to 1 and less than or equal to infinity, j =1,2, …, m.
Suppose the alternative is A i Alternative one has a total of n, i.e. n solutions in the pareto frontier; evaluation index of C j The evaluation indexes are m in total.
The specific steps for solving are as follows:
y1: determining the weight: before the evaluation is performed, the weight, i.e. ω, has to be determined j A value of (d);
y2: determining an evaluation value: let alternatives A i In the corresponding evaluation criterion C j An evaluation value of f ij ；
Y3: calculating a positive ideal solution and a negative ideal solution:
y4: calculating comprehensive evaluation optimal solution S of candidate scheme i And scheme comprehensive evaluation of the worst solution R i And a scheme benefit value Q i
Wherein S is * For all S i Minimum value of (1), R * Is all R i Minimum value of (1); s - For all S i Maximum value of (1), R - Is all R i V is a decision mechanism coefficient;
y5: from S i ，R i ，Q i The three sorting lists sort the alternative schemes, and the scheme is better when the numerical value is smaller;
y6: determining an optimal solution: the alternatives are arranged according to the scheme benefit value Q i The alternative of ordering, the top two bits are A 1 And A 2 If A is 1 The following two conditions are satisfied simultaneously: (1) q (A) 2 )-Q(A 1 ) More than or equal to 1/(n-1); (2) scheme A 1 Then according to S i ，R i Ordering to obtain the optimal solution of the scheme;
if the above two conditions can be satisfied simultaneously, then scheme A 1 Is the optimal solution;
if the above two conditions cannot be satisfied simultaneously, two situations are divided: first, if the condition (2) is not satisfied, the compromise is A 1 And A 2 (ii) a Second, if the condition (1) is not satisfied, the compromise is A 1 To A j Wherein A is j Is Q (A) j )-Q(A 1 )&And (1/(n-1) determining the maximum value of j.
4. The method for electric heating economy scheduling based on pareto evolution and VIKOR method according to claim 3, wherein the weight of each evaluation index is determined by an entropy method.
5. The method of claim 3, wherein the decision-making mechanism coefficient is 0.5.
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| CN113268931B (en) * | 2021-06-11 | 2022-11-29 | 云南电网有限责任公司电力科学研究院 | Method and system for reconstructing photovoltaic array based on multi-target slime optimization algorithm |
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