Economic dispatch problem lies at the kernel among different issues in GTCC units’ operation, which is about minimizing the fuel consumption for a period of operation so as to accomplish optimal load dispatch among u...Economic dispatch problem lies at the kernel among different issues in GTCC units’ operation, which is about minimizing the fuel consumption for a period of operation so as to accomplish optimal load dispatch among units. This paper has analyzed the load dispatch model of gas turbine combined-cycle (GTCC) units and utilizes a quantum genetic algorithm to optimize the solution of the model. The performance of gas turbine combined-cycle units varies with many factors and this directly leads to variation of model parameters. To solve the dispatch problem, variable constraints are adopted to correct the parameters influenced by ambient conditions. In the simulation, comparison of dispatch models for GTCC units considering and not considering the influence of ambient conditions indicates that it is necessary to adopt variable constraints for the dispatch model of GTCC units. To optimize the solution of the model, a Quantum Genetic Algorithm is used considering its advantages in searching performance. QGA combines the quantum theory with evolutionary theory of genetic algorithm. It is a new kind of intelligence algorithm which has been successfully employed in optimization problems. Utilizing quantum code, quantum gate and so on, QGA shows flexibility, high convergent rate, and global optimal capacity and so on. Simulations were performed by building up models and optimizing the solutions of the models by QGA. QGA shows better effect than equal micro incremental method used in the previous literature. The operational economy is proved by the results obtained by QGA. It can be concluded that QGA is quite effective in optimizing economic dispatch problem of GTCC units.展开更多
The hydro unit economic load dispatch （ELD） is of great importance in energy conservation and emission reduction. Dynamic programming （DP） and genetic algorithm （GA） are two representative algorithms for solving...The hydro unit economic load dispatch （ELD） is of great importance in energy conservation and emission reduction. Dynamic programming （DP） and genetic algorithm （GA） are two representative algorithms for solving ELD problems. The goal of this study was to examine the performance of DP and GA while they were applied to ELD. We established numerical experiments to conduct performance comparisons between DP and GA with two given schemes. The schemes included comparing the CPU time of the algorithms when they had the same solution quality, and comparing the solution quality when they had the same CPU time. The numerical experiments were applied to the Three Gorges Reservoir in China, which is equipped with 26 hydro generation units. We found the relation between the performance of algorithms and the number of units through experiments. Results show that GA is adept at searching for optimal solutions in low-dimensional cases. In some cases, such as with a number of units of less than 10, GA＇s performance is superior to that of a coarse-grid DP. However, GA loses its superiority in high-dimensional cases. DP is powerful in obtaining stable and high-quality solutions. Its performance can be maintained even while searching over a large solution space. Nevertheless, due to its exhaustive enumerating nature, it costs excess time in low-dimensional cases.展开更多
In this paper, a multiple population genetic algorithm (MPGA) is proposed to solve the problem of optimal load dispatch of gas turbine generation units. By introducing multiple populations on the basis of Standard Gen...In this paper, a multiple population genetic algorithm (MPGA) is proposed to solve the problem of optimal load dispatch of gas turbine generation units. By introducing multiple populations on the basis of Standard Genetic Algorithm (SGA), connecting each population through immigrant operator and preserving the best individuals of every generation through elite strategy, MPGA can enhance the efficiency in obtaining the global optimal solution. In this paper, MPGA is applied to optimize the load dispatch of 3×390MW gas turbine units. The results of MPGA calculation are compared with that of equal micro incremental method and AGC instruction. MPGA shows the best performance of optimization under different load conditions. The amount of saved gas consumption in the calculation is up to 2337.45m3N/h, which indicates that the load dispatch optimization of gas turbine units via MPGA approach can be effective.展开更多
How to solve unit commitment and load dispatch of power system by genetic algorithms is discussed in this paper. A combination encoding scheme of binary encoding and floating number encoding and corresponding genetic ...How to solve unit commitment and load dispatch of power system by genetic algorithms is discussed in this paper. A combination encoding scheme of binary encoding and floating number encoding and corresponding genetic operators are developed. Meanwhile a contract mapping genetic algorithm is used to enhance traditional GA’s convergence. The result of a practical example shows that this algorithm is effective.展开更多
文摘Economic dispatch problem lies at the kernel among different issues in GTCC units’ operation, which is about minimizing the fuel consumption for a period of operation so as to accomplish optimal load dispatch among units. This paper has analyzed the load dispatch model of gas turbine combined-cycle (GTCC) units and utilizes a quantum genetic algorithm to optimize the solution of the model. The performance of gas turbine combined-cycle units varies with many factors and this directly leads to variation of model parameters. To solve the dispatch problem, variable constraints are adopted to correct the parameters influenced by ambient conditions. In the simulation, comparison of dispatch models for GTCC units considering and not considering the influence of ambient conditions indicates that it is necessary to adopt variable constraints for the dispatch model of GTCC units. To optimize the solution of the model, a Quantum Genetic Algorithm is used considering its advantages in searching performance. QGA combines the quantum theory with evolutionary theory of genetic algorithm. It is a new kind of intelligence algorithm which has been successfully employed in optimization problems. Utilizing quantum code, quantum gate and so on, QGA shows flexibility, high convergent rate, and global optimal capacity and so on. Simulations were performed by building up models and optimizing the solutions of the models by QGA. QGA shows better effect than equal micro incremental method used in the previous literature. The operational economy is proved by the results obtained by QGA. It can be concluded that QGA is quite effective in optimizing economic dispatch problem of GTCC units.
基金supported by the National Basic Research Program of China(973 Program,Grant No.2013CB036406)the National Natural Science Foundation of China(Grant No.51179044)the Research Innovation Program for College Graduates in Jiangsu Province of China(Grant No.CXZZ12-0242)
文摘The hydro unit economic load dispatch （ELD） is of great importance in energy conservation and emission reduction. Dynamic programming （DP） and genetic algorithm （GA） are two representative algorithms for solving ELD problems. The goal of this study was to examine the performance of DP and GA while they were applied to ELD. We established numerical experiments to conduct performance comparisons between DP and GA with two given schemes. The schemes included comparing the CPU time of the algorithms when they had the same solution quality, and comparing the solution quality when they had the same CPU time. The numerical experiments were applied to the Three Gorges Reservoir in China, which is equipped with 26 hydro generation units. We found the relation between the performance of algorithms and the number of units through experiments. Results show that GA is adept at searching for optimal solutions in low-dimensional cases. In some cases, such as with a number of units of less than 10, GA＇s performance is superior to that of a coarse-grid DP. However, GA loses its superiority in high-dimensional cases. DP is powerful in obtaining stable and high-quality solutions. Its performance can be maintained even while searching over a large solution space. Nevertheless, due to its exhaustive enumerating nature, it costs excess time in low-dimensional cases.
文摘In this paper, a multiple population genetic algorithm (MPGA) is proposed to solve the problem of optimal load dispatch of gas turbine generation units. By introducing multiple populations on the basis of Standard Genetic Algorithm (SGA), connecting each population through immigrant operator and preserving the best individuals of every generation through elite strategy, MPGA can enhance the efficiency in obtaining the global optimal solution. In this paper, MPGA is applied to optimize the load dispatch of 3×390MW gas turbine units. The results of MPGA calculation are compared with that of equal micro incremental method and AGC instruction. MPGA shows the best performance of optimization under different load conditions. The amount of saved gas consumption in the calculation is up to 2337.45m3N/h, which indicates that the load dispatch optimization of gas turbine units via MPGA approach can be effective.
文摘How to solve unit commitment and load dispatch of power system by genetic algorithms is discussed in this paper. A combination encoding scheme of binary encoding and floating number encoding and corresponding genetic operators are developed. Meanwhile a contract mapping genetic algorithm is used to enhance traditional GA’s convergence. The result of a practical example shows that this algorithm is effective.
