CN113971510A - Integrated energy system planning method based on improved Jaya algorithm - Google Patents

Integrated energy system planning method based on improved Jaya algorithm Download PDF

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CN113971510A
CN113971510A CN202111217477.1A CN202111217477A CN113971510A CN 113971510 A CN113971510 A CN 113971510A CN 202111217477 A CN202111217477 A CN 202111217477A CN 113971510 A CN113971510 A CN 113971510A
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李顺昕
赵敏
聂文海
周毅
赵一男
杨敏
杨金刚
李莉
全璐瑶
李笑蓉
傅守强
孙海波
丁健民
陈丹
刘志雄
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Beijing Jingyan Electric Power Engineering Design Co ltd
State Grid Corp of China SGCC
Economic and Technological Research Institute of State Grid Hebei Electric Power Co Ltd
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State Grid Corp of China SGCC
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Abstract

The invention provides an integrated energy system planning method based on an improved Jaya algorithm, which comprises the following steps: determining an energy integration system planning model target, wherein the planning target comprises: minimum annual operating cost, construction investment cost, equipment maintenance cost, system operation cost and environmental cost; the system composition mode and the energy supply structure of the comprehensive energy sources; an improved method for improving the solving precision and the coding capability of the Jaya algorithm; a method for solving an integrated energy system by applying an improved Jaya algorithm. The optimization planning result of the comprehensive energy system can be solved more accurately, so that the planning result has more practical application value.

Description

Integrated energy system planning method based on improved Jaya algorithm
Technical Field
The invention relates to an optimization method for comprehensive energy system planning based on an intelligent algorithm, in particular to an integrated energy system planning method based on an improved Jaya algorithm.
Background
The comprehensive energy system can effectively improve the absorption capacity of renewable energy sources in a complementary and coordinated mode, and reduce the operation cost of the system. In addition, the integrated energy system can also coordinate the operation of different energy sources. However, with the continuous development of the integrated energy system, more and more energy devices are connected to the integrated energy system, so that the uncertainty factor of the integrated energy system is increased. The coupling of multiple energy sources has become more complex, presenting many challenges to the construction planning and system operation of integrated energy systems.
Proper selection and configuration of the system structure and energy device capacity of the integrated energy system is necessary. As it is critical to ensure sustainable economic, efficient and environmental operation of the system. The corresponding planning algorithm needs to process a large amount of equipment parameters and load data in the planning process of the comprehensive energy system. Due to the large calculation amount, the calculation efficiency and the calculation precision of the scheme cannot be further improved. Therefore, an intelligent algorithm with higher computational efficiency and accuracy is needed to solve the planning scheme of the integrated energy system.
Disclosure of Invention
Aiming at the problem that when an optimal configuration scheme is searched for by the comprehensive energy system, an accurate optimal scheme is difficult to find quickly and efficiently in a large-scale calculation process, the invention constructs a comprehensive energy system planning model considering multiple constraint conditions of system construction investment, operation maintenance, power balance and environmental protection cost, combines a nonlinear mutation operator, provides an improved Jaya algorithm, and optimizes and solves equipment selection and capacity configuration, thereby achieving the purposes of effectively reducing the annual operation cost of the system and improving the precision of the solution.
The technical scheme of the invention is realized as follows:
an integrated energy system planning method based on an improved Jaya algorithm is characterized by comprising the following steps:
calculating a planning model of the integrated energy system, wherein an objective function of the planning model is the lowest annual operation cost of the integrated energy system, and the planning model comprises the following steps: construction investment cost and equipment maintenance cost, system operation cost and environmental cost, and the target function is described by formula (1):
minCtot＝Cn+Ct+Cp+Cen (1)
the construction investment cost is determined by the type and the rated capacity of equipment, the construction cost of the whole life cycle of each device in the system is amortized to each year by utilizing the discount rate, and the model is as follows:
Figure BDA0003311236820000021
wherein SkIs the rated capacity, omega, of the device kkIs the unit investment cost of the equipment k, alphakThe service life of the equipment k is shown, and r is the discount rate;
the equipment maintenance cost is determined by the construction investment cost, and is calculated according to 3 percent of the construction investment cost:
Ct＝0.03Cn (3)
in the operation process of the comprehensive energy system, the comprehensive energy system interacts with an upstream power grid or a natural gas network to meet the normal operation and load requirements of the comprehensive energy system; the system operation cost comprises the upstream power grid electricity purchasing cost and the natural gas purchasing cost:
Cp＝Ce+Cg (4)
Figure BDA0003311236820000031
Figure BDA0003311236820000032
wherein C iseTo purchase house cost, CgFor natural gas procurement costs, EtThe amount of electricity purchased by the integrated energy system at time t, m is the selling price of the upstream power grid, GiFor plant natural gas consumption, RTRQThe natural gas combustion heat value is shown, and p is the natural gas price;
the environmental cost is the equivalent carbon emission cost of the gas turbine and the power grid; the types are as follows:
Figure BDA0003311236820000033
wherein,
Figure BDA0003311236820000034
and
Figure BDA0003311236820000035
carbon emissions of unit energy consumed by the gas turbine and the power grid, respectively; ft GTAnd Ft gRespectively the electric quantity of the gas turbine and the electric network, mu and sigma respectively the carbon dioxide emission coefficient when electricity and gas are used,
Figure BDA0003311236820000036
is the unit carbon emission cost;
the power balance equation is
Pg,t+PPV,t+PWT,t+Pgrid,t+PES_d,t＝PL,t+PES_c,t (8)
Wherein, Pg，t、PPv,t、PWR,tThe output powers of the gas turbine, the photovoltaic and the fan respectively; pgrid,tAmount of electricity purchased for an integrated energy system, PES_d,tDischarge power for energy storage, PL,tThe demand of the user on the power load; pES_c,tCharging power for storing energy;
the heat (cold) equilibrium equation is
Pg,t+PPV,t+PWT,t+Pgrid,t+PES_d,t＝PL,t+PES_c,t (9)
Wherein HGB,t、HEB,t、HHU,tRespectively the thermal power of the gas boiler, the electric boiler and the waste heat recovery device, HHS_d,tAs heat power of a heat source, HL,tFor the user's heat load demand, HHS_c,tThe heat storage power of the heat source is obtained by dividing the above equationOutside the beam, the following inequality constraints are satisfied according to the characteristics of each piece of equipment in the comprehensive energy system;
QAC,t+QEC,t＝QL,t (10)
QAC,t、QEC,tcooling power for ac and electric refrigeration respectively;
the upper and lower limits of the normal working range of the electric, hot and cold output of each device are as follows:
Pi,min≤Pi,t≤Pi,max (11)
Hj,min≤Hj,t≤Hj,max (12)
Qk,min≤Qk,t≤Qk,max (13)
wherein, Pi,imaxAnd Pi,maxRespectively minimum and maximum power, P, of devices in the systemi,tElectrical power of the apparatus during Δ t, Hj,minAnd Hj,maxMinimum and maximum heating power, H, respectively, of the system equipmentj,tIs the thermal output power of the equipment during Δ t; qk,minAnd Qk，maxMinimum and maximum cooling power devices for the system, Qk,tIs the cold output of the plant during Δ t;
and solving the optimal plan of the comprehensive energy system by using the cost model, the energy balance equation and the equipment constraint condition of each equipment and combining an improved Jaya algorithm.
Further:
the power supply of the comprehensive energy system consists of photovoltaic and fan renewable energy, and a gas turbine is used as auxiliary power supply equipment;
the comprehensive energy system planning model takes an upstream power grid as supplement of a power supply and is configured with energy storage so as to flexibly absorb renewable energy;
the heat energy of the comprehensive energy system is provided by waste heat recovery devices of a gas boiler and an electric boiler, and the required heat energy is provided for users;
the integrated energy system adopts electric refrigeration and absorption refrigeration to meet the cooling load requirement.
Further:
the improved Jaya algorithm adopts a nonlinear mutation operator; the basic principle of the Jaya algorithm is that an individual approaches to an optimal solution in the solving process and is far away from a worst solution in the solving process; let Xi,j,kThe value of the kth variable of the kth candidate solution in the first iteration, and then the value in the (i +1) th iteration is modified as follows:
Xi+1,j,k＝Xi,j,k+ri,j,1(Xi,j,best-|Xi,j,k|)-ri,j,2(Xi,j,worst-|Xi,j,k|) (14)
wherein, Xi,j,bestAnd Ci,j,worstRespectively, the optimum and worst value, X, of the variable k in the iterationi+1,j,kTo update the value, ri,j,1And ri,j,2Is the first iteration interval [0,1 ]]Two random numbers of the jth variable;
the next generation solution generated by the modified Jaya algorithm is represented by the current solution X according to equation (14)i,j,kCurrent global optimal solution Xi,j,bestWorst solution Xi,j,worstDecide if the algorithm converges prematurely, Xi,jMust be a locally optimal solution; in different iteration stages, the evolution direction of the population is adjusted, the probability of premature convergence is reduced, and the performance of the algorithm is improved; it is proposed to rewrite equation (14) using a nonlinear mutation operator as follows:
Xi+1,j,k+Xi,j,k+βi,j[ri,j,1(Xi,j,best-|Xi,j,k|)-ri,j,2(Xi,j,worst-|Xi,j,k|)] (15)
Figure BDA0003311236820000051
△fi＝{|fi,1-fi,best|,|fi,2-fi,best|,…,|fi,1-fi,best|} (17)
Figure BDA0003311236820000052
the next generation solution generated by the improved Jaya algorithm is jointly determined by the current solution, the current overall optimal solution and the worst solution;
the improved Jaya algorithm can adjust the evolution direction of the population at different iteration stages, reduce the probability of premature convergence, improve the global search capability of the algorithm and shorten the capacity allocation optimization time; the planning cost solution obtained by the improved Jaya algorithm is superior to particle swarm and genetic algorithm; in 600 iterations, although the genetic algorithm obtains a capacity of some units better than the improved result of the Jaya algorithm, the lowest energy cost cannot be guaranteed, and therefore the running cost of the system is increased; meanwhile, as can be seen from the iteration curve, the improved Jaya algorithm has better global search capability in the aspect of solving a planning model; the PSO algorithm has poor local searching capability, when the iteration is carried out for 450 times, the optimal solution appears, the optimal solution enters a local minimum value, and the premature phenomenon appears; the comparative analysis shows that the improved Jaya algorithm has better convergence performance and premature convergence resistance when the comprehensive energy system is planned.
Further:
firstly, initializing the population scale, the variable number, the optimal solution space and the maximum iteration number of the Jaya algorithm, then carrying out iterative computation according to formulas (15) to (18), wherein the computation result of each step of iteration comprises the maximum value and the minimum value of a mutation operator, the overall fitness and the population fitness, storing the optimal solution after each iteration is completed, comparing the optimal solution of the next step with the current optimal solution, preferentially storing the optimal solution until the given iteration number is reached, completing the iteration process, finding the optimal solution of the problem by the Jaya algorithm, and obtaining the planning scheme of the comprehensive energy system by improving the Jaya algorithm and applying the Jaya algorithm in the planning of the comprehensive energy system.
Compared with the prior art, the invention has the obvious effects that:
1. in the technical scheme of the invention, when the comprehensive energy system is planned by utilizing the proposed Jaya algorithm, the optimal scheme of the comprehensive energy system is jointly determined by a total scheme and a local scheme; the premise that the overall scheme of the comprehensive energy system achieves the optimal effect is that each local scheme also achieves the optimal solution, namely the comprehensive energy system not only achieves the optimal effect on the planning results of the operation cost, the construction cost and the equipment maintenance cost, but also has the optimal planning effect on the system operation cost and the environmental cost.
2. In the technical scheme of the invention, each cost of the comprehensive energy system can be adjusted through the dynamic coefficient, so that a dynamic balance relationship is kept among the project costs, the situation that the overall scheme can reach the optimum but the individual project cost exceeds the expected cost is prevented from occurring in the planning result, the adaptability of the optimal planning scheme of the comprehensive energy system is enhanced, the minimum annual operation cost, the construction investment cost, the equipment maintenance cost, the system operation cost and the environment cost are managed in a balanced manner, and the planning result has practical application value.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1: the embodiment of the invention provides a structural schematic diagram of a comprehensive energy system in an integrated energy system planning method based on an improved Jaya algorithm.
FIG. 2: in the embodiment of the invention, a workflow diagram of an improved Jaya algorithm in an integrated energy system planning method based on the improved Jaya algorithm is provided.
FIG. 3: the embodiment of the invention provides a relation graph of algorithm iteration times and objective function values in an integrated energy system planning method based on an improved Jaya algorithm.
FIG. 4: the embodiment of the invention provides a finishing flow chart of an integrated energy system planning method based on an improved Jaya algorithm.
Detailed Description
Reference will now be made in detail to the embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein: all identical or similar elements and variables in the present invention mean identical or similar elements and variables. This embodiment is only one specific example of the application of the present invention, and should not be construed as limiting the present invention.
The invention provides an integrated energy system planning method based on an improved Jaya algorithm, which aims to efficiently and accurately obtain a planning scheme of a comprehensive energy system by improving the Jaya algorithm and applying the Jaya algorithm to the planning of the comprehensive energy system.
The integrated energy system planning method based on the improved Jaya algorithm is suitable for analyzing and solving the optimal configuration scheme of the comprehensive energy system shown in the figure 1, renewable energy sources such as photovoltaic and draught fan provide system power, a gas turbine is used as auxiliary power supply equipment, an upstream power grid is used as supplement of the power supply, energy storage is configured, and the renewable energy sources are flexibly absorbed. The heat energy is provided by the waste heat recovery devices of the gas boiler and the electric boiler, and the required heat energy can be reliably provided for users. Electric refrigeration, absorption refrigeration and other forms are used to meet cooling load requirements.
The integrated energy system planning method based on the improved Jaya algorithm considers the lowest annual operation cost of the comprehensive energy system in the objective function of the planning model. Annual operating costs include construction investment costs and equipment maintenance costs, system operating costs and environmental costs. Firstly, information is collected according to energy planning requirements, and annual operation cost C is calculatedtotComprises the following steps:
minCtot＝Cn+Ct+Cp+Cen (1)
wherein, CtotTo annual operating costs, CnFor construction investment costs, CtFor equipment maintenance costs, CpFor the system operating cost, CenIs an environmental cost.
Construction investment costs are determined by the type and rated capacity of the equipment, and the total lifecycle construction costs for each device in the system are amortized to each year by the current rate. The investment cost model is constructed as follows:
Figure BDA0003311236820000081
wherein SkIs the rated capacity, omega, of the device kkIs the unit investment cost of the equipment k, alphakFor the use of devices kThe lifetime, r, is the presentation rate.
The equipment maintenance cost can be determined by the construction investment cost, calculated as 3% of the construction investment cost, as shown in formula (3):
Ct＝0.03Cn (3)
and in the operation process of the integrated energy system, the integrated energy system interacts with an upstream power grid or a natural gas network to meet the normal operation and load requirements of the integrated energy system. The system operation cost comprises the upstream power grid electricity purchasing cost and the natural gas power grid natural gas purchasing cost, and is calculated as follows:
Cp＝Ce+Cg (4)
Figure BDA0003311236820000091
Figure BDA0003311236820000092
wherein C iseTo purchase house cost, CgFor the purchase cost of natural gas, EtThe amount of electricity purchased by the integrated energy system at time t, m is the selling price of the upstream power grid, GiFor plant natural gas consumption, RTRQIs the combustion heat value of natural gas, and p is the price of natural gas.
The environmental cost is the equivalent carbon emission cost of the gas turbine and the power grid. The model is calculated as follows:
Figure BDA0003311236820000093
wherein,
Figure BDA0003311236820000094
and
Figure BDA0003311236820000095
carbon emissions of unit energy consumed by the gas turbine and the power grid, respectively;
Figure BDA0003311236820000096
and
Figure BDA0003311236820000097
respectively the electric quantity of the gas turbine and the electric network, mu and sigma respectively the carbon dioxide emission coefficient when electricity and gas are used,
Figure BDA0003311236820000098
is the unit carbon emission cost.
The power balance equation is
Pg,t+PPV,t+PWT,t+Pgrid,t+PES_d,t＝PL,t+PES_c,t (8)
Wherein, Pg，t、PPv,t、PWR,tThe output powers of the gas turbine, the photovoltaic and the fan respectively; pgrid,tAmount of electricity purchased for an integrated energy system, PES_d,tDischarge power for energy storage, PL,tThe demand of the user on the power load; pES_c,tAnd charging power for storing energy.
The heat (cold) equilibrium equation is
Pg,t+PPV,t+PWT,t+Pgrid,t+PES_d,t＝PL,t+PES_c,t (9)
Wherein HGB,t、HEB,t、HHU,tRespectively the thermal power of the gas boiler, the electric boiler and the waste heat recovery device, HHS_d,tAs heat power of a heat source, HL,tFor the user's heat load demand, HHS_c,tIn addition to the above equality constraints, the following inequality constraints on cooling power should be satisfied according to the characteristics of each piece of equipment in the integrated energy system:
QAC,t+QEC,t＝QL,t (10)
QAC,t、QEC,tcooling power for ac and electric refrigeration, respectively.
The normal operating ranges of the electrical, hot and cold outputs of each device are as follows:
Pi,min≤Pi,t≤Pi,max (11)
Hj,min≤Hj,t≤Hj,max (12)
Qk,min≤Qk,t≤Qk,max (13)
wherein, Pi,imaxAnd Pi,maxMinimum and maximum power, P, respectively, of devices in the systemi,tElectrical power of the apparatus during Δ t period, Hj,minAnd Hj,maxMinimum heating power and maximum heating power of the system equipment, Hj,tIs the thermal output power of the device during Δ t; qk,minAnd Qk，maxMinimum and maximum cooling power devices for the system, Qk,tIs the cold output power of the plant during Δ t.
And solving the optimal planning model of the comprehensive energy system by using the cost model, the energy balance equation and the equipment constraint conditions of each equipment and combining the improved Jaya algorithm to obtain a planning scheme of the comprehensive energy system.
The basic principle of the Jaya algorithm is that it always tries to approach the optimal solution and moves away from the worst solution in the solution process. Let Xi,j,kThe value of the kth variable of the kth candidate solution in the first iteration, and then the value in the (i +1) th iteration is modified as shown in equation (14) below:
Xi+1,j,k＝Xi,j,k+ri,j,1(Xi,j,best-|Xi,j,k|)-ri,j,2(Xi,j,worst-|Xi,j,k|) (14)
wherein, Xi,j,bestAnd Ci,j,worstRespectively, the optimum and worst value, X, of the variable k in the iterationi+1,j,kFor the updated value of the variable k in the iteration, ri,j,1And ri,j,2Is the first iteration interval [0,1 ]]Two random numbers of the jth variable in (c). If X isi+1,j,kThe objective function can be solved better and then X is acceptedi+1,j,k. Like other bionic intelligence algorithms, the algorithm also has the problem of premature convergence. In the early stage of algorithm optimization, if an individual is in the populationThe fitness in (a) is far better than the overall fitness of the current population, then the individual can quickly occupy a large proportion of the population. It leads to a rapid decrease in population diversity and a decrease in the overall exploratory capacity of the population. Finally, the algorithm converges prematurely to a locally optimal solution.
FIG. 2 is a flowchart of the algorithm execution of the present invention, and the next generation solution generated by the modified Jaya algorithm proposed by the present invention is represented by the current solution X according to equation (14)i,j,kCurrent global optimal solution Xi,j,bestWorst solution Xi,j,worstDecide if the algorithm converges prematurely, Xi,jMust be a locally optimal solution. Therefore, the evolution direction of the population can be adjusted at different iteration stages, the probability of premature convergence is reduced, and the performance of the algorithm is improved. Therefore, the present invention proposes to rewrite equation (14) with the nonlinear mutation operator as shown in equation (15):
Xi+1,j,k+Xi,j,k+βi,j[ri,j,1(Xi,j,best-|Xi,j,k|)-ri,j,2(Xi,j,worst-|Xi,j,k|)] (15)
Figure BDA0003311236820000111
△fi＝{|fi,1-fi,best|,|fi,2-fi,best|,…,|fi,1-fi,best|} (17)
Figure BDA0003311236820000121
wherein, betai,jFor the mutation operator of the jth population in the first iteration, betamaxAnd betaminMaximum and minimum values of mutation operators, respectively, fi,jFitness of the jth population in the first iteration, fiIs the fitness of the optimal population at the first iteration, Δ fiIs a fitness set of N populations at the first iteration, NiterIs the maximum number of iterations, and k is the number of iterations.
To illustrate the advantages of the integrated energy system planning method based on the improved Jaya Algorithm in the present invention, fig. 3 shows the planning results of the improved Jaya Algorithm proposed by the present invention, and the traditional Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The improved Jaya algorithm in the invention obtains a planning cost solution superior to particle swarm and genetic algorithms. In 600 iterations, while the genetic algorithm achieves a capacity of some units that is better than the improved results of the Jaya algorithm, the genetic algorithm does not guarantee the lowest energy cost, thereby increasing the operating cost of the system. Meanwhile, as can be seen from the iteration curve, the improved Jaya algorithm has better global search capability in the aspect of solving the planning model. The PSO algorithm has poor local searching capability, and when the PSO algorithm is iterated for 450 times, the PSO algorithm has an optimal solution, enters a local minimum value and has an early maturing phenomenon. The comparative analysis shows that the improved Jaya algorithm has better convergence performance and premature convergence resistance when integrated energy system planning is carried out.

Claims (4)

1. An integrated energy system planning method based on an improved Jaya algorithm is characterized by comprising the following steps:
the objective function of the planning model is the lowest annual operating cost of the integrated energy system, and comprises the following steps: construction investment cost and equipment maintenance cost, system operation cost and environmental cost, and the target function is described by formula (1):
minCtot＝Cn+Ct+Cp+Cen (1)
the construction investment cost is determined by the type and the rated capacity of equipment, the construction cost of the whole life cycle of each device in the system is amortized to each year by utilizing the discount rate, and the model is as follows:
Figure FDA0003311236810000011
wherein SkIs the rated capacity, omega, of the device kkIs the unit investment cost of the equipment k, alphakFor the service life of the apparatus k, rThe current rate is the current rate;
the equipment maintenance cost is determined by the construction investment cost, and is calculated according to 3 percent of the construction investment cost:
Ct＝0.03Cn (3)
in the operation process of the comprehensive energy system, the comprehensive energy system interacts with an upstream power grid or a natural gas network to meet the normal operation and load requirements of the comprehensive energy system; the system operation cost comprises the upstream power grid electricity purchasing cost and the natural gas purchasing cost:
Cp＝Ce+Cg (4)
Figure FDA0003311236810000012
Figure FDA0003311236810000013
wherein C iseTo purchase electricity cost, CgFor natural gas procurement costs, EtThe amount of electricity purchased by the integrated energy system at time t, m is the selling price of the upstream power grid, GiFor plant natural gas consumption, RTRQThe natural gas combustion heat value is shown, and p is the natural gas price;
the environmental cost is the equivalent carbon emission cost of the gas turbine and the power grid; the types are as follows:
Figure FDA0003311236810000021
wherein,
Figure FDA0003311236810000022
and
Figure FDA0003311236810000023
carbon emissions of unit energy consumed by the gas turbine and the power grid, respectively; ft GTAnd Ft gRespectively the electric quantity of the gas turbine and the electric network, mu and sigma respectively the carbon dioxide emission coefficient when electricity and gas are used,
Figure FDA0003311236810000024
is the unit carbon emission cost;
the power balance equation is
Pg,t+PPV,t+PWT,t+Pgrid,t+PES_d,t＝PL,t+PES_c,t (8)
Wherein, Pg，t、PPv,t、PWR,tThe output powers of the gas turbine, the photovoltaic and the fan respectively; pgrid,tAmount of electricity purchased for an integrated energy system, PES_d,tDischarge power for energy storage, PL,tThe demand of the user on the power load; pES_c,tCharging power for storing energy;
the heat (cold) equilibrium equation is
Pg,t+PPV,t+PWT,t+Pgrid,t+PES_d,t＝PL,t+PES_c,t (9)
Wherein HGB,t、HEB,t、HHU,tRespectively the thermal power of the gas boiler, the electric boiler and the waste heat recovery device, HHS_d,tAs heat power of a heat source, HL,tFor the user's heat load demand, HHS_c,tThe heat storage power of the heat source meets the following inequality constraint according to the characteristics of each piece of equipment in the comprehensive energy system in addition to the equality constraint;
QAC,t+QEC,t＝QL,t (10)
QAC,t、QEC,tcooling power for ac and electric refrigeration respectively;
the upper and lower limits of the normal working range of the electric, hot and cold output of each device are as follows:
Pi,min≤Pi,t≤Pi,max (11)
Hj,min≤Hj,t≤Hj,max (12)
Qk,min≤Qk,t≤Qk,max (13)
wherein, Pi,imaxAnd Pi,maxRespectively minimum and maximum power, P, of devices in the systemi,tElectrical power of the apparatus during Δ t, Hj,minAnd Hj,maxMinimum and maximum heating power, H, respectively, of the system equipmentj,tIs the thermal output power of the equipment during Δ t; qk,minAnd Qk，maxMinimum and maximum cooling power devices for the system, Qk,tIs the cold output of the plant during Δ t;
and solving the optimal plan of the comprehensive energy system by using the cost model, the energy balance equation and the equipment constraint condition of each equipment and combining an improved Jaya algorithm.
2. The integrated energy system planning method based on the improved Jaya algorithm of claim 1, wherein:
the power supply of the comprehensive energy system consists of photovoltaic and fan renewable energy, and a gas turbine is used as auxiliary power supply equipment;
the comprehensive energy system planning model takes an upstream power grid as supplement of a power supply and is configured with energy storage so as to flexibly absorb renewable energy;
the heat energy of the comprehensive energy system is provided by waste heat recovery devices of a gas boiler and an electric boiler, and the required heat energy is provided for users;
the integrated energy system adopts electric refrigeration and absorption refrigeration to meet the cooling load requirement.
3. The integrated energy system planning method based on the improved Jaya algorithm of claim 1, wherein:
the improved Jaya algorithm adopts a nonlinear mutation operator, and the individual Jaya algorithm approaches to an optimal solution in the solving process and is far away from a worst solution in the solving process; the next generation solution generated by the modified Jaya algorithm is represented by the current solution Xi,j,kCurrent global optimal solution Xi,j,bestWorst solution Xi,j,worstDecide if the algorithm converges prematurely, Xi,jMust be a locally optimal solution; in different iteration stages, the evolution direction of the population is adjusted, the premature convergence probability is reduced, the performance of the algorithm is improved, and the nonlinear mutation operator is proposed to be used as follows:
Xi+1,j,k+Xi,j,k+βi,j[ri,j,1(Xi,j,best-|Xi,j,k|)-ri,j,2(Xi,j,worst-|Xi,j,k|)] (14)
Figure FDA0003311236810000041
△fi＝{|fi,1-fi,best|,|fi,2-fi,best|,…,|fi,1-fi,best|} (16)
Figure FDA0003311236810000042
the next generation solution generated by the improved Jaya algorithm is jointly determined by the current solution, the current overall optimal solution and the worst solution.
4. The method of integrated energy system planning based on the modified Jaya algorithm of claim 3, the method comprising:
firstly, initializing the population scale, the variable number, the optimal solution space and the maximum iteration number of the Jaya algorithm, then carrying out iterative computation according to formulas (14) to (17), wherein the computation result of each step of iteration comprises the maximum value and the minimum value of a mutation operator, the overall fitness and the population fitness, storing the optimal solution after each iteration is completed, comparing the optimal solution of the next step with the current optimal solution, preferentially storing the optimal solution until the given iteration number is reached, completing the iteration process, finding the optimal solution of the problem by the Jaya algorithm, and obtaining the planning scheme of the comprehensive energy system by improving the Jaya algorithm and applying the Jaya algorithm in the planning of the comprehensive energy system.
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