CN113392513A - Multi-objective optimization method, device and terminal for combined cooling, heating and power system - Google Patents
Multi-objective optimization method, device and terminal for combined cooling, heating and power system Download PDFInfo
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Abstract
The invention is suitable for the technical field of power systems, and particularly relates to a multi-objective optimization method, a multi-objective optimization device and a multi-objective optimization terminal for a combined cooling heating and power system, wherein the method comprises the following steps: constructing a multi-objective optimization function and constraint conditions thereof; performing iterative computation on the multi-objective optimization function based on the constraint conditions and the improved gull algorithm to obtain a plurality of non-dominated solutions; in each iteration process, improving a gull algorithm to calculate each objective function value corresponding to different gull individuals, selecting a non-dominant solution from solutions corresponding to the gull individuals according to the objective function values to store, and updating the positions of the gull individuals according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions; and selecting an optimal solution from a plurality of non-dominant solutions based on a good-bad solution distance method to optimize the target combined cooling heating and power system. The invention can optimize and solve the combined cooling heating and power system and improve the comprehensive performance of the system.
Description
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to a multi-objective optimization method, device and terminal for a combined cooling heating and power system.
Background
Along with the rapid increase of world economy, energy consumption of countries in the world is increasing day by day, and in order to find a clean energy supply mode with high energy utilization rate, a combined cooling heating and power system for supplying cold and heat to the system by using waste heat generated in the power generation process of a generator is gradually concerned.
The combined cooling heating and power system is used as a poly-generation energy supply system, the structural form of the combined cooling and heating and power system is flexible and various, and the selection of the type and the capacity of equipment has great influence on the comprehensive performance of the system. At present, the capacity of each device is still set by the load peak value in the design scheme of the combined cooling heating and power system, so that the capacity of the established combined cooling heating and power system cannot be fully utilized, the investment cost is increased, and the economy is poor, so that the combined cooling heating and power system needs to be optimized in operation.
However, the inventor of the present application finds that, with the development of distributed power generation technology, devices such as wind power and photovoltaic are gradually introduced into a combined cooling, heating and power system, so that the system structure is more complicated. Moreover, in order to suppress the influence of the output fluctuation of the renewable energy power generation device on the system, an energy storage device is usually introduced into the system, thereby further increasing the difficulty of optimization solution. Considering that the types of related devices in the combined cooling, heating and power system are more, and the three types of cooling, heating and power have a stronger coupling relationship, how to optimize and solve the combined cooling, heating and power system, and improve the comprehensive performance of the system becomes a problem which needs to be solved urgently.
Disclosure of Invention
In view of this, embodiments of the present invention provide a multi-objective optimization method, device and terminal for a combined cooling, heating and power system, so as to perform optimization solution on the combined cooling, heating and power system, and improve the comprehensive performance of the system.
The first aspect of the embodiments of the present invention provides a multi-objective optimization method for a combined cooling, heating and power system, including:
constructing a multi-objective optimization function by taking the equipment capacity in a target combined cooling heating and power system as a decision variable and taking the minimum operation cost, the minimum energy consumption and the minimum environmental influence as targets;
constructing a constraint condition of the multi-objective optimization function, and performing iterative computation on the multi-objective optimization function based on the constraint condition and an improved gull algorithm to obtain a plurality of non-dominated solutions; in each iteration process, improving a gull algorithm to calculate each objective function value corresponding to different gull individuals, selecting a non-dominant solution from solutions corresponding to the gull individuals according to the objective function values to store the non-dominant solution, and updating the positions of the gull individuals according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions;
and selecting an optimal solution from the multiple non-dominated solutions based on a good-bad solution distance method, and optimizing the target combined cooling heating and power system according to the optimal solution.
A second aspect of the embodiments of the present invention provides a multi-objective optimization device for a combined cooling, heating and power system, including:
the system comprises a first processing module, a second processing module and a third processing module, wherein the first processing module is used for constructing a multi-objective optimization function by taking the equipment capacity in a target combined cooling heating and power system as a decision variable and taking the minimum operation cost, the minimum energy consumption and the minimum environmental influence as targets;
the second processing module is used for constructing constraint conditions of the multi-objective optimization function;
the third processing module is used for carrying out iterative computation on the multi-objective optimization function based on the constraint conditions and the improved gull algorithm to obtain a plurality of non-dominated solutions; in each iteration process, improving a gull algorithm to calculate each objective function value corresponding to different gull individuals, selecting a non-dominant solution from solutions corresponding to the gull individuals according to the objective function values to store the non-dominant solution, and updating the positions of the gull individuals according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions;
and the optimization module is used for selecting an optimal solution from the non-dominated solutions based on a good-bad solution distance method and optimizing the target combined cooling heating and power system according to the optimal solution.
A third aspect of the embodiments of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the multi-objective optimization method for a combined cooling and heating and power system as described above when executing the computer program.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the multi-objective optimization method for a combined cooling, heating and power system are implemented.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
according to the invention, by establishing the multi-objective optimization function and the constraint conditions thereof with the goals of minimum operation cost, minimum energy consumption and minimum environmental influence, the optimal solution of the combined cooling, heating and power system under the three objective functions is calculated through the improved gull algorithm and the good-bad solution distance method, the operation cost and the fuel consumption of the combined cooling, heating and power system are favorably reduced, and the emission of greenhouse gases is reduced. Specifically, a constraint non-dominance ordering and external archive mechanism is introduced into the traditional gull algorithm, namely, the gull algorithm is improved to calculate each objective function value corresponding to different gull individuals, and a non-dominance solution is selected from each solution according to the objective function values to be stored, so that a series of solutions which meet the constraint and have a strong dominance relationship are obtained; and updating the positions of the individual gulls according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions, so that the obtained solutions are uniformly distributed in the solution space. The improved gull algorithm can optimize a plurality of targets simultaneously, and compared with the existing optimization algorithm, the improved gull algorithm can effectively reduce random clustering of the obtained solutions and obtain a solution set with uniform distribution. Further, an optimal solution is selected from a plurality of non-dominated solutions based on a good-bad solution distance method, and the target combined cooling heating and power system is optimized according to the optimal solution. The invention can optimize and solve the combined cooling heating and power system and improve the comprehensive performance of the system.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow chart of an implementation of a multi-objective optimization method for a combined cooling, heating and power system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a detailed optimization process provided by an embodiment of the invention;
FIG. 3 is a graph of ambient temperature and solar radiation intensity data provided by an embodiment of the present invention;
FIG. 4 is a graph of user load demand data provided by an embodiment of the present invention;
FIG. 5 is a schematic non-dominated solution of the output of the improved gull algorithm provided by embodiments of the present invention;
FIG. 6 is a schematic diagram of power balance of a system provided by an embodiment of the invention;
FIG. 7 is a schematic diagram of the system for balancing the cooling energy and the heat energy provided by the embodiment of the invention;
FIG. 8 is a schematic diagram of a multi-objective optimization device of the combined cooling, heating and power system provided by the embodiment of the invention;
fig. 9 is a schematic diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Along with the rapid increase of world economy, energy consumption of countries in the world is increasing day by day, and in order to find a clean energy supply mode with high energy utilization rate, a combined cooling heating and power system for supplying cold and heat to the system by using waste heat generated in the power generation process of a generator is gradually concerned. The combined cooling heating and power system not only can reduce the electric energy loss on the line in the transmission process by arranging the power generation unit near the user side, but also can meet the diversified energy demand of users for heat supply and refrigeration of the users by comprehensively utilizing the waste heat in the power generation process through various devices, thereby improving the energy utilization efficiency, relieving the energy crisis to a certain extent and reducing the emission of greenhouse gases. However, the combined cooling heating and power system as a poly-generation energy supply system has flexible and various structural forms, and the selection of the type and the capacity of the equipment has great influence on the comprehensive performance of the system. At present, the design scheme of the combined cooling heating and power system still sets the capacity of each device at the peak load value, so that the capacity of the built combined cooling heating and power system device cannot be fully utilized, the investment cost is increased, and the economical efficiency is poor. Therefore, the operation optimization of the combined cooling heating and power system is needed.
The embodiment of the invention provides a multi-objective optimization method for a combined cooling heating and power system, which comprises the following steps of:
and S101, constructing a multi-objective optimization function by taking the equipment capacity in the target combined cooling heating and power system as a decision variable and taking the minimum operation cost, the minimum energy consumption and the minimum environmental influence as targets.
In the embodiment of the invention, the specific construction process of the multi-objective optimization function is as follows.
(1) Establishing mathematical models of all devices in target combined cooling heating and power system
The combined cooling heating and power system usually comprises photovoltaic cells, a micro gas turbine, a waste heat recovery device, a heat storage tank, a storage battery, an adsorption refrigerator, an electric refrigerator, a gas boiler and other equipment.
The mathematical model of the photovoltaic cell may be:
in the formula, PPVIs the output power of the photovoltaic cell, NPVIs the installation capacity of the photovoltaic cell, GPVAnd TPVRepresenting the intensity of the radiation received by the photovoltaic panel and the surface temperature, GSTCAnd TSTCIndicating the intensity of radiation received by the photovoltaic panel and the ambient temperature under standard test conditions, and alpha represents the temperature coefficient.
The mathematical model of the micro gas turbine and the waste heat recovery device may be:
Pmt＝Fmt·ηp,mt
Hmt＝Fmt·(1-ηp,mt)
Hhr＝Hmt·ηhr
in the formula, PmtRepresenting the micro gas turbine output electric power, FmtRepresenting the fuel consumption, eta, of a micro-gas turbinep,mtRepresenting the efficiency of electric energy conversion, etahrRepresents the heat energy recovery efficiency of the waste heat recovery device, HmtAnd HhrShowing the output heat energy of the micro gas turbine and the waste heat recovery device.
The mathematical model of the heat storage tank may be:
in the formula,
and
representing the amount of heat stored by the heat storage tank at time t and time t-1,
and
representing the amount of heat absorbed and released by the thermal storage tank at time t, eta, respectivelyhst,loss、ηhst,inAnd ηhst,outThe heat loss rate, the heat absorption efficiency and the heat release efficiency of the heat storage tank are respectively expressed.
The mathematical model of the battery may be:
in the formula,
and
respectively representing the electric quantity stored by the storage battery at the time t and the time t-1,
and
representing the absorption and release of electric energy, eta, respectively, by the accumulator at time tbat,loss、ηbat,inAnd ηbat,outRespectively representing the battery loss rate, the charging efficiency and the discharging efficiency.
Mathematical models for adsorption chillers and electric chillers can be:
Cac＝Hac·COPac
Cec＝Pec·COPec
in the formula, CacAnd CecRespectively representing the refrigerating capacities, H, of the adsorption refrigerator and the electric refrigeratoracRepresenting the heat energy consumed by the adsorption refrigerator, PecRepresenting the electrical energy, COP, consumed by an electric refrigeratoracAnd COPecEnergy efficiency coefficients of the adsorption refrigerator and the electric refrigerator respectively.
The mathematical model of the gas boiler may be:
Hgb＝Fgb·ηgb
in the formula, HgbRepresenting heat production of the gas boiler, FgbIndicating fuel consumption, eta, of gas-fired boilersgbIndicating the heat generation efficiency of the gas boiler.
(2) Determining decision variables
In order to optimize the combined cooling heating and power system, the capacity of the equipment is selected as a decision variable:
X＝[NPV,NMT,Ngrid,Nbat,Nhst,Ngb]
in the formula, NPV、NMT、Nbat、NhstAnd NgbRespectively representing the installation capacities of the photovoltaic cell, the micro gas turbine, the storage battery, the heat storage tank and the gas boiler, NgridAnd represents the upper limit of the power purchase of the system to the power grid.
(3) Establishing multi-objective optimization function of combined cooling heating and power system
In order to optimize the overall performance of the combined cooling heating and power system, objective functions are respectively established in the aspects of system operation cost, energy consumption and environmental influence according to the equipment model and the decision variables, and the three objective functions are optimized simultaneously, so that the aims of reducing the operation cost, the energy consumption and the pollutant emission are fulfilled.
The cost objective function may be:
in the formula, CostCCHPRepresents the system operating cost, NkThe capacity of the installation of the device is represented,
representing the amount of electricity purchased from the power grid when the system is short of generating electricity, i representing interest rate, n representing the service life of the equipment, CkRepresents the equipment investment cost, CeRepresents the electricity purchase price of the power grid,
indicating consumption of fuel by the plant, CFThe price of the fuel is indicated and,
and
representing the energy wasted by the system during operation, λpAnd λhRepresenting an energy waste penalty factor.
The energy objective function may be:
in the formula，
And
respectively, the energy consumption of the micro gas turbine, the gas boiler and the power grid in the system, and o represents the operation time of the system.
The environment objective function may be:
in the formula, CDECCHPIndicating CO during operation of the system2Discharging the waste water, and discharging the waste water,
and
and the equivalent emission coefficient of the power grid and the combined cooling heating and power system is represented.
And S102, constructing constraint conditions of the multi-objective optimization function.
In the embodiment of the invention, the constraint conditions mainly comprise an energy supply and demand balance equation constraint, a slope rate constraint of equipment and an equipment capacity constraint. Wherein the energy supply and demand balance equality constraints comprise an electrical balance constraint, a thermal balance constraint and a cold balance constraint.
The electrical balance constraint may be:
in the formula,
and
respectively representing the shortage and waste of the electric energy at the time t,
representing the power demand of the user at time t.
The thermal equilibrium constraint may be:
in the formula,
and
respectively representing the shortage and waste of heat energy at the moment t,
representing the thermal energy demand of the user at time t.
The cold balance constraint may be:
in the formula,
indicating the cooling load demand of the user.
The ramp rate constraint of the device may be:
in the formula,
and
respectively representing the upper limits of the ramp rates of the heat storage tank and the storage battery.
The equipment capacity constraints are shown in table 1:
TABLE 1 Equipment Capacity constraint Table
Step S103, carrying out iterative computation on the multi-objective optimization function based on constraint conditions and an improved gull algorithm to obtain a plurality of non-dominated solutions; in each iteration process, the gull algorithm is improved to calculate each objective function value corresponding to different gull individuals, non-dominant solutions are selected from solutions corresponding to the gull individuals according to the objective function values to be stored, and the positions of the gull individuals are updated according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions.
In the embodiment of the invention, in order to solve a multi-objective optimization function, the traditional gull algorithm is improved, and a constraint non-dominated sorting and external archive mechanism is introduced into the gull algorithm, namely, the improved gull algorithm calculates each objective function value corresponding to different gull individuals, and selects a non-dominated solution from each solution according to the objective function values for storage, so that a series of solutions which meet the constraint and have a strong domination relationship are obtained; and updating the positions of the individual gulls according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions, so that the obtained solutions are uniformly distributed in the solution space. The improved gull algorithm can optimize a plurality of targets simultaneously, and compared with the existing optimization algorithm, the improved gull algorithm can effectively reduce random clustering of the obtained solutions and obtain a solution set with uniform distribution.
And S104, selecting an optimal solution from the non-dominated solutions based on a good-bad solution distance method, and optimizing the target combined cooling heating and power system according to the optimal solution.
According to the invention, by establishing the multi-objective optimization function and the constraint conditions thereof with the goals of minimum operation cost, minimum energy consumption and minimum environmental influence, the optimal solution of the combined cooling, heating and power system under the three objective functions is calculated through the improved gull algorithm and the good-bad solution distance method, the operation cost and the fuel consumption of the combined cooling, heating and power system are favorably reduced, and the emission of greenhouse gases is reduced. Specifically, a constraint non-dominance ordering and external archive mechanism is introduced into the traditional gull algorithm, namely, the gull algorithm is improved to calculate each objective function value corresponding to different gull individuals, and a non-dominance solution is selected from each solution according to the objective function values to be stored, so that a series of solutions which meet the constraint and have a strong dominance relationship are obtained; and updating the positions of the individual gulls according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions, so that the obtained solutions are uniformly distributed in the solution space. The improved gull algorithm can optimize a plurality of targets simultaneously, and compared with the existing optimization algorithm, the improved gull algorithm can effectively reduce random clustering of the obtained solutions and obtain a solution set with uniform distribution. Further, an optimal solution is selected from a plurality of non-dominated solutions based on a good-bad solution distance method, and the target combined cooling heating and power system is optimized according to the optimal solution. The invention can optimize and solve the combined cooling heating and power system and improve the comprehensive performance of the system.
Optionally, as a possible implementation manner, in step S103, iterative computation is performed on the multi-objective optimization function based on the constraint condition and the improved gull algorithm to obtain a plurality of non-dominated solutions, which may be detailed as:
step S1031, setting improved gull algorithm parameters, and initializing positions of individual gulls; wherein the position of each gull individual is the equipment capacity, namely a solution of the multi-objective optimization function;
step S1032, judging whether the position of the individual gull exceeds a threshold or not according to the constraint condition, and if not, calculating each objective function value corresponding to the individual gull;
step S1033, selecting a non-dominant solution from the solutions corresponding to all gull individuals according to the objective function value, and storing the non-dominant solution;
step S1034, selecting the non-dominated solution with the lowest crowdedness from the stored non-dominated solutions as a prey position, and updating the position of the individual gull according to the prey position;
and S1035, repeatedly executing the steps S1032-S1034 until the preset maximum iteration number is reached, and obtaining a plurality of stored non-dominated solutions.
In the embodiment of the invention, the population position of the improved gull algorithm is initialized, the population size N is 100, and the maximum iteration times Max iter500, setting the maximum storage quantity of the files as 100, and setting the individual movement behavior control parameter f of the gulleSet to 2. In each iteration process, whether the individual gull exceeds the bound is judged according to the equipment capacity constraint shown in table 1, each objective function value corresponding to the individual gull is calculated, the non-dominant solution is selected from the solutions according to the objective function value and stored, the non-dominant solution with the lowest crowdedness in the file is selected as a prey position, and the individual gull performs migration behavior and attack behavior to approach the prey position to complete the position updating process. And if the maximum iteration number is reached, the termination condition is met, and the stored non-dominated solution is used as a compromise solution of the combined cooling heating and power system under three targets of cost, energy and environment. Wherein, the choice of the prey position of the improved seagull optimization algorithm can be realized by a roulette method, and the input probability is calculated by Pi＝m/SiM is a constant greater than 1, SiThe number of solutions in the ith non-dominant solution neighborhood. When the non-dominant solution to be deleted is selected by using the roulette method, the input probability is PiThe reciprocal of (c).
Specifically, the gull individual can avoid collision with other individuals in the migration behavior, and the individual position is updated according to the optimal position. The position updating formula of the gull individual during migration is as follows:
wherein A represents the movement behavior of individual gull in the search space, feThe variable A being controllableThe frequency of use; t represents the current iteration number; max (maximum of ten)iterRepresenting the maximum number of iterations;
representing a new position that will not collide with other gull individuals during the t-th iteration;
indicating the direction of the optimal position;
representing the distance between the gull individual and the optimal individual;
representing a location of a current individual;
representing the current optimal gull individual position (prey position), and selecting the non-dominant solution with low crowdedness in the external file by the gull individual in each iterative calculation through a roulette method; in the multi-objective optimization problem of the combined cooling heating and power system, the position parameter of the individual gull represents an alternative solution of the problem, the dimension of each alternative solution is 6, and
representing the device capacity of each type of device in the t-th iteration when the problem is solved using the improved gull optimization algorithm.
Specifically, in the attack behavior, the individual gull makes spiral motion in the air by changing the angle and speed continuously. The position updating formula when the gull individual executes the attack behavior is as follows:
wherein u ', v ' and w ' represent the motion behaviors of the individual gull when attacking a prey in a three-dimensional space, r represents the radius of each spiral coil, alpha represents the flight angle of the gull when doing spiral motion, h and k are constants defining the spiral shape, e is the base number of a natural logarithm, and alpha is a random number in the range of [0,2 pi ].
Optionally, as a possible implementation, the method for determining the non-dominant solution may be:
calculating constraint violation quantities corresponding to the solutions, and judging the types of the solutions according to the constraint violation quantities; the type comprises a feasible solution and an infeasible solution, the solution with the constraint violation quantity of 0 is the feasible solution, and the solution with the constraint violation quantity of more than 0 is the infeasible solution;
determining a domination relation among all feasible solutions; wherein, for any two feasible solutions x and y, if satisfied
Then x is considered to dominate y; f. ofi(x) For the ith objective function value, f, corresponding to the feasible solution xi(y) is the ith objective function value corresponding to the feasible solution y;
and determining non-dominant solutions in the solutions based on the constraint violation quantities, the types of the solutions and the dominant relationship among the feasible solutions.
Optionally, as a possible implementation manner, the constraint violation quantity corresponding to each solution may be calculated by the following formula:
in the formula, p is the number of inequality constraints in the constraint condition,<gi(x)>a constraint violation quantity representing the ith inequality constraint, if gi(x) Less than or equal to 0<gi(x)>0, if gi(x) If greater than 0<gi(x)>＝|gi(x) I, m is the number of equality constraints in the constraint condition, | hj(x) I represents the constraint violation quantity of the jth equality constraint, and the constraint violation quantity is the constraint violation quantityDifference of each solution from the constraint.
Optionally, as a possible implementation manner, the non-dominant solution in each solution is determined based on the constraint violation quantity, the type of each solution, and the dominant relationship between each feasible solution, which may be detailed as:
if all the solutions are infeasible solutions, determining the infeasible solution with the minimum constraint violation amount as a non-dominated solution;
and if feasible solutions exist in the solutions, determining the feasible solutions which are not dominated by any other feasible solutions as non-dominated solutions.
Optionally, as a possible implementation, the non-dominant solution is stored, which may be detailed as:
judging the domination relationship between the newly obtained non-domination solution and the stored non-domination solution;
if the newly obtained non-dominated solution and each stored non-dominated solution have no domination relationship, storing the newly obtained non-dominated solution;
if the newly obtained non-dominated solution is dominated by at least one stored non-dominated solution, ignoring the newly obtained non-dominated solution;
and if the newly obtained non-dominated solution dominates at least one stored non-dominated solution, storing the newly obtained non-dominated solution and deleting all the stored non-dominated solutions dominated by the newly obtained non-dominated solution.
In the embodiment of the invention, a constraint non-dominated sorting mechanism and an external archive mechanism are introduced into a traditional gull algorithm, the constraint non-dominated sorting mechanism combines a constraint violation quantity with a non-dominated sorting process to obtain a series of solutions which meet constraints and have a strong dominance relation, the non-dominated solution obtained by the constraint non-dominated sorting is stored in an external archive in each iteration, and meanwhile, the solution in the archive is continuously updated according to an update rule, so that the obtained solution is uniformly distributed in a solution space. The improved gull algorithm can optimize a plurality of targets simultaneously, and compared with the existing optimization algorithm, the improved gull algorithm can effectively reduce random clustering of the obtained solutions and obtain a solution set with uniform distribution.
In addition, when the non-dominant solution stored in the archive exceeds the number limit, the congestion degree of each non-dominant solution in the archive may be checked, and the non-dominant solution with a high congestion degree may be deleted.
Optionally, as a possible implementation manner, selecting an optimal solution from a plurality of non-dominated solutions based on a good-poor solution distance method may be detailed as follows:
selecting the minimum objective function value of each objective function as an ideal point and selecting the maximum objective function value of each objective function as a negative ideal point from a plurality of non-dominated solutions;
respectively calculating Euclidean distances between each non-dominated solution and an ideal point and between each non-dominated solution and a negative ideal point according to the objective function value of each objective function corresponding to each non-dominated solution;
computing the desirability of each non-dominated solution
Selecting a non-dominated solution with the maximum ideal degree as an optimal solution; wherein,
is the euclidean distance between the ith non-dominated solution and the ideal point,
is the euclidean distance between the ith non-dominated solution and the negative ideal point.
In an embodiment of the present invention, the ideal point and the negative ideal point may be determined according to the following formula:
further, the euclidean distance between each non-dominated solution and the ideal point may be calculated according to:
finally, the desirability of each non-dominant solution may be calculated according to:
in the formula, ri,jA jth objective function value representing an ith non-dominated solution,
and
as a function of the maximum and minimum values for all non-dominated solutions,
and
representing the ideal point and the negative ideal point,
and
representing the distance, Z, between the non-dominated solution and the ideal point and the negative ideal pointiRepresenting the ideality of the ith non-dominant solution, where ZiThe non-dominated solution with the maximum value is the optimal solution of the multi-objective optimization problem of the combined cooling heating and power system.
In the embodiment of the invention, after the optimal solution is obtained, the sub-supply systems of power supply by a power grid, heat supply of a gas boiler and cold supply of an electric refrigerator are used as comparison systems, and the Cost Saving Rate (CSR), the Primary Energy Saving Rate (PESR), the Carbon Dioxide Emission Reduction Rate (CDERR) and the system energy efficiency (eta) are selectedCCHP) The system performance is evaluated as an evaluation index, and the corresponding calculation formula is as follows:
in the formula, CostSPAnd CostCCHPRespectively representing the operating costs of the separate supply system and the combined cooling, heating and power systemSPAnd FuelCCHPRespectively representing the fuel consumption, CDE, of the separate supply system and the combined cooling, heating and power systemSPAnd CDECCHPRespectively represents the carbon dioxide emission of the separate supply system and the combined cooling heating and power system.
In addition, in the embodiment of the present invention, it is considered that the heat load in the system is preferentially satisfied in the conventional electric heating strategy, and the electric load in the system is preferentially satisfied in the conventional electric heating strategy, but there is energy waste in both of the conventional strategies. Therefore, in order to reduce energy waste in the system, a hybrid strategy based on the conversion between two strategies based on the energy storage state of the heat storage tank is provided based on the traditional heat-based and electricity-based strategy, namely, the combined cooling and heating system executes the heat-based strategy when sufficient heat energy is stored in the heat storage tank, so that the heat load in the system is met preferentially, the heat energy in the heat storage tank is used preferentially, and redundant electric energy is absorbed by a storage battery in the operation process; on the contrary, when the stored heat in the heat storage tank is lower than the lowest limit, an electric heat fixing strategy is executed, the electric load in the system is preferably met, the redundant heat generated in the running process of the system is absorbed by the heat storage tank until the stored heat reaches the upper limit, and the system executes the electric heat fixing strategy again; during operation, the storage battery is charged and discharged in a balanced manner according to the electric energy of the system, so that the combined cooling, heating and power system is switched between two traditional strategies according to the energy storage state of the heat storage tank, and the waste of energy during operation is reduced.
Based on the above steps, the present invention provides a more detailed schematic diagram of the optimization process, which is shown in fig. 2.
Illustratively, a Matlab program is compiled according to the multi-objective optimization method of the combined cooling heating and power system, and the feasibility of the method is verified.
Specifically, according to the data input by the data graph of the ambient temperature and the solar radiation intensity shown in fig. 3 and the data input by the data graph of the user load demand shown in fig. 4, the improved gull optimization algorithm outputs the trade-off (non-dominant) solution of the combined cooling, heating and power system cost, energy and environment under the electric heating, heating and power and hybrid strategies as shown in fig. 5. It can be seen that the output of the improved gull optimization algorithm is uniformly distributed in the solution space, and the hybrid strategy proposed by the present invention is closer to the origin of coordinates in fig. 5, which illustrates that the hybrid strategy is reducing the operating cost, fuel consumption and CO2The method has more advantages in the aspect of emission, and proves that the improved gull optimization algorithm is very effective in optimizing the combined cooling, heating and power system. Meanwhile, the output of each device in the electric energy balance diagram and the cold energy and heat energy balance diagram of the system under the mixed strategy of the combined cooling, heating and power system in fig. 6 and 7 can reliably meet the cooling, heating and power load of the device, and the constraint non-dominated sorting mechanism is proved to effectively eliminate solutions which do not meet the constraint condition, so that any solution output by the improved gull optimization algorithm can be used as a design scheme of the combined cooling, heating and power system.
The solution marked by the five-pointed star in fig. 5 is the optimal solution decided by using the optimal-solution distance method, the system performance under the optimal solution is evaluated by using a plurality of evaluation indexes, and the index values are counted and shown in table 2.
TABLE 2 statistical table of system performance evaluation indexes
As can be seen from Table 2, the CSR of the combined cooling heating and power system under the mixed strategy reaches-9.7965, which is greater than the two traditional strategies of electricity heating and electricity heating; in the aspect of PESR, the hybrid strategy can fully utilize the product produced in the running process of the system through energy storage equipment such as a heat storage tank, a storage battery and the likeThe generated redundant energy is generated, so the PESR of the system under the mixed strategy is 31.10 percent; the hybrid strategy achieves the compromise effect of two traditional strategies of electricity-based heat determination and heat-based electricity determination in the aspect of CDERR; at etaCCHPIn the aspect, the energy storage device in the hybrid strategy collects and utilizes the redundant electric energy and heat energy generated in the system operation process, so the energy efficiency of the system under the hybrid strategy is 55.60%, and the evaluation indexes show that the hybrid strategy provided by the invention has more advantages in the aspects of saving cost, reducing fuel consumption and improving the energy efficiency of the system. Further, the method is effective in optimizing the combined cooling heating and power system under the situation of complicated constraint conditions and a multi-objective optimization model.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The embodiment of the present invention further provides a multi-objective optimization device for a combined cooling heating and power system, as shown in fig. 8, the device 80 includes:
the first processing module 81 is configured to construct a multi-objective optimization function with the device capacity in the target combined cooling heating and power system as a decision variable and with the goals of minimum operation cost, minimum energy consumption and minimum environmental impact.
And the second processing module 82 is used for constructing the constraint conditions of the multi-objective optimization function.
The third processing module 83 is configured to perform iterative computation on the multi-objective optimization function based on the constraint condition and the improved gull algorithm to obtain a plurality of non-dominated solutions; in each iteration process, the gull algorithm is improved to calculate each objective function value corresponding to different gull individuals, non-dominant solutions are selected from solutions corresponding to the gull individuals according to the objective function values to be stored, and the positions of the gull individuals are updated according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions.
And the optimizing module 84 is configured to select an optimal solution from the multiple non-dominated solutions based on a good-bad solution distance method, and optimize the target combined cooling heating and power system according to the optimal solution.
Optionally, as a possible implementation manner, the third processing module 83 is configured to execute steps S1031 to S1035 in the combined cooling, heating and power system multi-objective optimization method.
Optionally, as a possible implementation manner, the third processing module 83 is configured to calculate constraint violation quantities corresponding to the solutions, and determine the type of each solution according to the constraint violation quantities; the type comprises a feasible solution and an infeasible solution, the solution with the constraint violation quantity of 0 is the feasible solution, and the solution with the constraint violation quantity of more than 0 is the infeasible solution; determining a domination relation among all feasible solutions; wherein, for any two feasible solutions x and y, if satisfied
Then x is considered to dominate y; f. ofi(x) For the ith objective function value, f, corresponding to the feasible solution xi(y) is the ith objective function value corresponding to the feasible solution y; and determining non-dominant solutions in the solutions based on the constraint violation quantities, the types of the solutions and the dominant relationship among the feasible solutions.
Optionally, as a possible implementation manner, the third processing module 83 is configured to calculate constraint violations corresponding to the solutions according to the following formula:
in the formula, p is the number of inequality constraints in the constraint condition,<gi(x)>a constraint violation quantity representing the ith inequality constraint, if gi(x) Less than or equal to 0<gi(x)>0, if gi(x) If greater than 0<gi(x)>＝|gi(x) I, m is the number of equality constraints in the constraint condition, | hj(x) And | represents the constraint violation of the jth equality constraint.
Optionally, as a possible implementation manner, the third processing module 83 is configured to determine, if each solution is an infeasible solution, the infeasible solution with the minimum constraint violation amount as a non-dominated solution; and if feasible solutions exist in the solutions, determining the feasible solutions which are not dominated by any other feasible solutions as non-dominated solutions.
Optionally, as a possible implementation manner, the third processing module 83 is configured to determine a dominance relationship between a newly obtained non-dominance solution and a stored non-dominance solution; if the newly obtained non-dominated solution and each stored non-dominated solution have no domination relationship, storing the newly obtained non-dominated solution; if the newly obtained non-dominated solution is dominated by at least one stored non-dominated solution, ignoring the newly obtained non-dominated solution; and if the newly obtained non-dominated solution dominates at least one stored non-dominated solution, storing the newly obtained non-dominated solution and deleting all the stored non-dominated solutions dominated by the newly obtained non-dominated solution.
Optionally, as a possible implementation, the optimization module 84 is configured to select, from a plurality of non-dominated solutions, a minimum objective function value of each objective function as an ideal point, and select a maximum objective function value of each objective function as a negative ideal point; respectively calculating Euclidean distances between each non-dominated solution and an ideal point and between each non-dominated solution and a negative ideal point according to the objective function value of each objective function corresponding to each non-dominated solution; computing the desirability of each non-dominated solution
Selecting a non-dominated solution with the maximum ideal degree as an optimal solution; wherein,
is the euclidean distance between the ith non-dominated solution and the ideal point,
is the euclidean distance between the ith non-dominated solution and the negative ideal point.
Fig. 9 is a schematic diagram of a terminal 90 according to an embodiment of the present invention. As shown in fig. 9, the terminal 90 of this embodiment includes: a processor 91, a memory 92 and a computer program 93 stored in the memory 92 and executable on the processor 91. When the processor 91 executes the computer program 93, the steps in the multi-objective optimization method for combined cooling heating and power system described above, such as steps S101 to S104 shown in fig. 1, are implemented. Alternatively, the processor 91, when executing the computer program 93, implements the functions of the respective modules in the above-described respective apparatus embodiments, for example, the functions of the modules 81 to 84 shown in fig. 8.
Illustratively, the computer program 93 may be divided into one or more modules/units, which are stored in the memory 92 and executed by the processor 91 to implement the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 93 in the terminal 90. For example, the computer program 93 may be divided into a first processing module 81, a second processing module 82, a third processing module 83, and an optimization module 84 (a module in a virtual device), and the specific functions of each module are as follows:
the first processing module 81 is configured to construct a multi-objective optimization function with the device capacity in the target combined cooling heating and power system as a decision variable and with the goals of minimum operation cost, minimum energy consumption and minimum environmental impact.
And the second processing module 82 is used for constructing the constraint conditions of the multi-objective optimization function.
The third processing module 83 is configured to perform iterative computation on the multi-objective optimization function based on the constraint condition and the improved gull algorithm to obtain a plurality of non-dominated solutions; in each iteration process, the gull algorithm is improved to calculate each objective function value corresponding to different gull individuals, non-dominant solutions are selected from solutions corresponding to the gull individuals according to the objective function values to be stored, and the positions of the gull individuals are updated according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions.
And the optimizing module 84 is configured to select an optimal solution from the multiple non-dominated solutions based on a good-bad solution distance method, and optimize the target combined cooling heating and power system according to the optimal solution.
The terminal 90 may be a computing device such as a desktop computer, a notebook, a palm top computer, and a cloud server. The terminal 90 may include, but is not limited to, a processor 91, a memory 92. Those skilled in the art will appreciate that fig. 9 is merely an example of a terminal 90 and does not constitute a limitation of the terminal 90, and may include more or less components than those shown, or combine certain components, or different components, e.g., the terminal 90 may also include input-output devices, network access devices, buses, etc.
The Processor 91 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 92 may be an internal storage unit of the terminal 90, such as a hard disk or a memory of the terminal 90. The memory 92 may also be an external storage device of the terminal 90, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the terminal 90. Further, the memory 92 may also include both internal and external memory storage devices of the terminal 90. The memory 92 is used for storing computer programs and other programs and data required by the terminal 90. The memory 92 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other ways. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the steps of the above-described embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, in accordance with legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunications signals.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A multi-objective optimization method for a combined cooling heating and power system is characterized by comprising the following steps:
constructing a multi-objective optimization function by taking the equipment capacity in a target combined cooling heating and power system as a decision variable and taking the minimum operation cost, the minimum energy consumption and the minimum environmental influence as targets;
constructing a constraint condition of the multi-objective optimization function, and performing iterative computation on the multi-objective optimization function based on the constraint condition and an improved gull algorithm to obtain a plurality of non-dominated solutions; in each iteration process, the improved gull algorithm calculates each objective function value corresponding to different gull individuals, selects a non-dominant solution from solutions corresponding to the gull individuals according to the objective function values to store, and updates the positions of the gull individuals according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions;
and selecting an optimal solution from the non-dominated solutions based on a good-bad solution distance method, and optimizing the target combined cooling heating and power system according to the optimal solution.
2. The combined cooling, heating and power system multi-objective optimization method of claim 1, wherein the iterative computation of the multi-objective optimization function based on the constraint conditions and the improved gull algorithm to obtain a plurality of non-dominated solutions comprises:
step S1031, setting improved gull algorithm parameters, and initializing positions of individual gulls; wherein the position of each gull individual is the equipment capacity, namely a solution of the multi-objective optimization function;
step S1032, judging whether the position of the individual gull exceeds the threshold or not according to the constraint condition, and if not, calculating each objective function value corresponding to the individual gull;
step S1033, selecting a non-dominant solution from the solutions corresponding to all gull individuals according to the objective function value, and storing the non-dominant solution;
step S1034, selecting the non-dominated solution with the lowest crowdedness from the stored non-dominated solutions as a prey position, and updating the position of the individual gull according to the prey position;
and S1035, repeatedly executing the steps S1032-S1034 until the preset maximum iteration number is reached, and obtaining a plurality of stored non-dominated solutions.
3. The combined cooling, heating and power system multi-objective optimization method of claim 1, wherein the determination method of the non-dominant solution is as follows:
calculating constraint violation quantities corresponding to the solutions, and judging the type of each solution according to the constraint violation quantities; wherein the type comprises a feasible solution and an infeasible solution, the solution with the constraint violation quantity of 0 is the feasible solution, and the solution with the constraint violation quantity of more than 0 is the infeasible solution;
determining a domination relation among all feasible solutions; wherein, for any two feasible solutions x and y, if satisfied
Then x is considered to dominate y; f. ofi(x) For the ith objective function value, f, corresponding to the feasible solution xi(y) is the ith objective function value corresponding to the feasible solution y;
and determining non-dominant solutions in the solutions based on the constraint violation quantities, the types of the solutions and the dominant relationship among the feasible solutions.
4. The multi-objective optimization method for the combined cooling heating and power system as claimed in claim 3, wherein the constraint violations corresponding to each solution are calculated by the following formula:
in the formula, p is a constraint conditionThe number of the inequality constraints in the middle,<gi(x)>a constraint violation quantity representing the ith inequality constraint, if gi(x) Less than or equal to 0<gi(x)>0, if gi(x) If greater than 0<gi(x)>＝|gi(x) I, m is the number of equality constraints in the constraint condition, | hj(x) And | represents the constraint violation of the jth equality constraint.
5. The multi-objective optimization method for the combined cooling heating and power system according to claim 3, wherein the determining of the non-dominant solution in the solutions based on the constraint violation quantity, the type of the solutions and the dominant relationship among the feasible solutions comprises:
if all the solutions are infeasible solutions, determining the infeasible solution with the minimum constraint violation amount as a non-dominated solution;
and if feasible solutions exist in the solutions, determining the feasible solutions which are not dominated by any other feasible solutions as non-dominated solutions.
6. The multi-objective optimization method for a combined cooling, heating and power system according to any one of claims 1 to 5, wherein storing the non-dominant solution comprises:
judging the domination relationship between the newly obtained non-domination solution and the stored non-domination solution;
if the newly obtained non-dominated solution and each stored non-dominated solution have no domination relationship, storing the newly obtained non-dominated solution;
if the newly obtained non-dominated solution is dominated by at least one stored non-dominated solution, ignoring the newly obtained non-dominated solution;
and if the newly obtained non-dominated solution dominates at least one stored non-dominated solution, storing the newly obtained non-dominated solution and deleting all the stored non-dominated solutions dominated by the newly obtained non-dominated solution.
7. The multi-objective optimization method for the combined cooling heating and power system according to any one of claims 1 to 5, wherein selecting an optimal solution from the non-dominated solutions based on a good-bad solution distance method comprises:
selecting the minimum objective function value of each objective function as an ideal point and selecting the maximum objective function value of each objective function as a negative ideal point from the non-dominated solutions;
respectively calculating Euclidean distances between each non-dominated solution and an ideal point and between each non-dominated solution and a negative ideal point according to the objective function value of each objective function corresponding to each non-dominated solution;
computing the desirability of each non-dominated solution
Selecting a non-dominated solution with the maximum ideal degree as an optimal solution; wherein,
is the euclidean distance between the ith non-dominated solution and the ideal point,
is the euclidean distance between the ith non-dominated solution and the negative ideal point.
8. The utility model provides a combined cooling heating and power system multi-objective optimization device which characterized in that includes:
the system comprises a first processing module, a second processing module and a third processing module, wherein the first processing module is used for constructing a multi-objective optimization function by taking the equipment capacity in a target combined cooling heating and power system as a decision variable and taking the minimum operation cost, the minimum energy consumption and the minimum environmental influence as targets;
the second processing module is used for constructing constraint conditions of the multi-objective optimization function;
the third processing module is used for carrying out iterative computation on the multi-target optimization function based on the constraint conditions and the improved gull algorithm to obtain a plurality of non-dominated solutions; in each iteration process, the improved gull algorithm calculates each objective function value corresponding to different gull individuals, selects a non-dominant solution from solutions corresponding to the gull individuals according to the objective function values to store, and updates the positions of the gull individuals according to the non-dominant solution with the lowest crowdedness in the stored non-dominant solutions;
and the optimization module is used for selecting an optimal solution from the non-dominated solutions based on a good-bad solution distance method and optimizing the target combined cooling heating and power system according to the optimal solution.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114037354A (en) * | 2021-12-06 | 2022-02-11 | 河北师范大学 | Method for capacity optimization configuration of combined cooling heating and power system |
| CN114611822A (en) * | 2022-03-22 | 2022-06-10 | 华北电力大学 | Operation optimization method, device, terminal and storage medium for comprehensive energy heating system |
| CN114638170A (en) * | 2022-03-31 | 2022-06-17 | 河北工业大学 | Electric energy metering device fault diagnosis method and system based on data mining technology |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170308968A1 (en) * | 2014-10-21 | 2017-10-26 | Qin Zhou | System, method and apparatus for capacity determination for micro grid and tangible computer readable medium |
| CN109165788A (en) * | 2018-09-04 | 2019-01-08 | 河北工业大学 | A kind of optimization method of cooling heating and power generation system |
| CN110333660A (en) * | 2019-07-29 | 2019-10-15 | 西安科技大学 | A kind of cooling heating and power generation system Multipurpose Optimal Method |
| CN110705776A (en) * | 2019-09-27 | 2020-01-17 | 中冶赛迪电气技术有限公司 | Energy optimization scheduling method |
| CN111429301A (en) * | 2020-03-26 | 2020-07-17 | 中国科学技术大学 | Collaborative optimization processing method and device for capacity configuration and operation strategy |
| CN111445107A (en) * | 2020-03-02 | 2020-07-24 | 山东大学 | Multi-objective optimization configuration method for cold-heat-power combined supply type micro-grid |
| CN112508391A (en) * | 2020-12-04 | 2021-03-16 | 国网辽宁省电力有限公司经济技术研究院 | Comprehensive energy system planning method for cooperation of industrial park cluster and regional power grid |
| CN112784484A (en) * | 2021-01-19 | 2021-05-11 | 国网江苏省电力有限公司经济技术研究院 | Multi-objective optimization method and optimization system for regional comprehensive energy system |
-
2021
- 2021-05-28 CN CN202110594587.3A patent/CN113392513B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170308968A1 (en) * | 2014-10-21 | 2017-10-26 | Qin Zhou | System, method and apparatus for capacity determination for micro grid and tangible computer readable medium |
| CN109165788A (en) * | 2018-09-04 | 2019-01-08 | 河北工业大学 | A kind of optimization method of cooling heating and power generation system |
| CN110333660A (en) * | 2019-07-29 | 2019-10-15 | 西安科技大学 | A kind of cooling heating and power generation system Multipurpose Optimal Method |
| CN110705776A (en) * | 2019-09-27 | 2020-01-17 | 中冶赛迪电气技术有限公司 | Energy optimization scheduling method |
| CN111445107A (en) * | 2020-03-02 | 2020-07-24 | 山东大学 | Multi-objective optimization configuration method for cold-heat-power combined supply type micro-grid |
| CN111429301A (en) * | 2020-03-26 | 2020-07-17 | 中国科学技术大学 | Collaborative optimization processing method and device for capacity configuration and operation strategy |
| CN112508391A (en) * | 2020-12-04 | 2021-03-16 | 国网辽宁省电力有限公司经济技术研究院 | Comprehensive energy system planning method for cooperation of industrial park cluster and regional power grid |
| CN112784484A (en) * | 2021-01-19 | 2021-05-11 | 国网江苏省电力有限公司经济技术研究院 | Multi-objective optimization method and optimization system for regional comprehensive energy system |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114037354A (en) * | 2021-12-06 | 2022-02-11 | 河北师范大学 | Method for capacity optimization configuration of combined cooling heating and power system |
| CN114037354B (en) * | 2021-12-06 | 2024-06-04 | 河北师范大学 | Capacity optimization configuration method for combined cooling heating power system |
| CN114611822A (en) * | 2022-03-22 | 2022-06-10 | 华北电力大学 | Operation optimization method, device, terminal and storage medium for comprehensive energy heating system |
| CN114611822B (en) * | 2022-03-22 | 2024-07-19 | 华北电力大学 | Operation optimization method, device, terminal and storage medium for comprehensive energy heating system |
| CN114638170A (en) * | 2022-03-31 | 2022-06-17 | 河北工业大学 | Electric energy metering device fault diagnosis method and system based on data mining technology |
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