1、单亲遗传算法对于在梯级水力发系统中多级电站的经济分配方法的改进作用 毕业论文外文翻译外文资料原文An improved partheno genetic algorithm for multi-objective economic dispatch in cascaded hydropower systemsAbstract：The multi-objective economic dispatch (MOED) problem in cascaded hydropower systems is a complicated nonlinear optimization problem with
2、 a group of complex constraints. In this paper, an improved partheno genetic algorithm (IPGA) for resolving the MOED problem in hydropower energy systems based on the non-uniform mutation operator is proposed. In the new algorithm, the crossover operator is removed and only mutation operation is mad
3、e, which makes it simpler than GA in the genetic operations and not generate invalid offspring during evolution. With the help of incorporating greedy selection idea into the non-uniform mutation operator, IPGA searches the solution space uniformly at the early stage and very locally at the later st
4、age, which makes it avoid the random blind jumping and stay at the promising solution areas. Finally, the proposed algorithm is applied to a realistic hydropower energy system with two giant scale cascaded hydropower plants in China. Compared with other algorithms, the results obtained using IPGA ve
5、rify its superiority in both efficienc.Key words: multi-objective optimization; Improved partheno genetic algorithm; Non-uniform mutation operator. Cascade hydropower station group of. Introduction Optimization of multi-objective economic dispatch (MOED) in cascaded hydropower systems is one of the
6、most complicated issues in water resources management as it typically involves trade-offs. For example, a single multipurpose reservoir, which not only serves hydropower but also navigation, its dispatcher may wish to maximize benefits from hydropower generation, while releasing sufficient water for
7、 navigation to satisfy the demands. However, a higher profit from hydropower generation would conflict with the navigation releases, that is to say, any improvement of one objective can be achieved only at the expense of another. The curve or surface (for more than 2 objectives), describing the opti
8、mal trade-off solutions between the objectives, is known as the Pareto front. In real life, most of reservoir systems serve multiple purposes and they are multi-objective in nature. Due to the dispatch rules for a joint operation of cascade reservoirs enable to develop the capacity of hydropower gen
9、eration, the MOED problem in cascaded hydropower systems becomes an active research area in recent years. The goal of MOED in cascaded hydropower systems is to determine the water discharge process of all hydropower stations during the scheduling period in order to maximize the total benefit while f
10、ulfilling various actual water demands and other complicated constraints simultaneously. Because of the complex power and hydraulic relations between cascaded hydropower systems, the multi-objective optimal operation of cascade hydropower stations is a large scale, dynamic, and strong coupling nonli
11、near problem, which involves many variables, such as, inflow, storage, discharge, water level, water head, output and generated energy. So far, different methods to solve the MOED problem have been proposed and discussed by many researchers. The traditional methods, such as, mixed integer linear pro
12、gramming (MILP), Lagrange relaxation (LR), nonlinear programming (NLP), dynamic programming (DP) and progressive optimality algorithm (POA), have been widely applied in the past. And many achievements have also been obtained. Nevertheless, all of the methods listed above exist some shortcomings whic
13、h make them less efficient and even difficult in searching for the optimal solution. When MILP is employed to solve the MOED problem, linearization of the multi-objective functions and constraints could deviate the original problem, which makes the non-inferior solution inaccuracy. For NLP methodolo
14、gy, some approximate approaches are adopted to deal with discontinuous, non-differentiable and non-convex multi-objective functions, and these methods are computational expensive. Lagrange multipliers updating strategy has an unfavorable influence on the efficiency of LR, which makes the stability o
15、f solution poor. Though DP method can resolve the optimal operation of a single multipurpose reservoir, it suffers from the curse of the dimensionality which makes the computation time increase dramatically when the dimension of cascaded hydropower systems increases. POA is widely used in the multi-
16、objective optimal operation of cascaded hydropower plants, but it is sensitive with the initial solution, which generally shrinks the searching area and traps it in the local Pareto optimal front easily. Recently, there has been an increasing interest in adaptive heuristic search algorithms modeled
17、from the biologically motivated adaptive systems, for solving the MOED problem, because of their powerful global searching capacity, such as genetic algorithm (GA), ant colony optimization (ACO) algorithm, particle swarm optimization (PSO) algorithm, and artificial bee colony (ABC) algorithm. Howeve
18、r, being same with the classical methods, all these heuristics stochastic search algorithms mentioned above have their drawbacks too. For example, sometimes they may suffer from premature convergence, and the searching ability of these algorithms is sensitive to parameter settings. GA is one of the
19、most promising techniques in natural adaptive system field of evolutionary algorithm paradigm and has received great attention, because of its flexibility and effectiveness for optimizing complex systems. But sometimes, the traditional GA may produce a violating offspring in the cross-over operation
20、, which has a negative effect on the efficiency of GA by reason of having to restore the violating offspring or adopt a penalty function in the objective functions to discard the violating offspring. In order to overcome this disadvantage, partheno genetic algorithm (PGA) is proposed as an improved
21、GA. In GA, the crossover is always seen as the major operator and the mutation operator just plays an assistant role. But in PGA, only the mutation operation is made and no invalid offspring is generated. According to the partheno genetic schema theorems proposed in literature, we find that high ran
22、k schemas in the subsequent generation decrease exponentially though their fitness values are more optimal than the average one in the population, which may cause the high rank schemas that match the global optimal individual to be deserted fast before the global optimal individual is found. It seem
23、s that PGA runs away from the area where the global optimal individual lies though search nears the area, which reduces the searching efficiency of PGA. In order to overcome this shortcoming and avoid the undesirable tendency, this paper presents an improved partheno genetic algorithm (IPGA) based o
24、n the non-uniform mutation operator to solve the MOED problem in cascaded hydropower systems. Meanwhile, the greedy idea and the idea of mutating a single component of the individual vector rather than modifying all the components are incorporated into IPGA in order to avoid possible random jumps an
25、d to ensure the algorithm to stay at the promising area just found by the search engine for the better optimal individual. The rest of the paper is organized as follows. Section Numerical model describes the model of the MOED problem in cascaded hydropower systems. Section Improved partheno genetic
26、algorithm presents the IPGA and introduces its realization process in detail. Section Implementation of IPGA for economic dispatch in cascade hydropower systems evaluates the proposed algorithm and compares it with different algorithms using the data from a hydropower energy system with two cascade
27、reservoirs in China. Finally, we conclude this paper in Section Conclusions.Numerical model The economic dispatch of cascaded hydropower plants is a typical problem in the optimal fields of hydropower energy system. Usually, the operation purpose is to maximize the total power generation benefits or
28、 total electricity production of cascaded hydropower plants during the scheduling period by determining the optimal process of water discharge value of hydropower stations, under the condition of satisfying all constraints. However, with the development of social economy in China, change of power co
29、nsumption structure requires most large-capacity units to be involved in peak load regulation of electricity grid. In order to guide optimal operation of cascade reservoirs and give full play to capacity benefits of cascade hydropower stations, a mathematical model is established based on the princi
30、ples of (1) maximum total electricity production and (2) maximum total least output of the cascaded hydropower plants in this paper. The spatial coupling of a hydropower energy system with two cascade reservoirs is shown in, and the objective functions and constraints of aforementioned mathematical
31、model are expressed as follows: Obviously, this is a multi-objective optimization problem. Weighted approach and constraint method are usually employed by many researchers to handle it in water resources management. For the constraint method, all objectives except the major one are constrained to sp
32、ecific values to yield a Pareto optimal solution. With increment of the specific values, the model is run again to find another Pareto optimal solution until the trade-off relationship between the objectives is sufficiently represented. In weighted method, all objectives are incorporated into one ob
33、jective function simultaneously by different weights, and a different set of weights is adopted in each run of the optimization model for the optimal trade-off solution. However, the constraint method has been used more for the MOED problem in a single multipurpose reservoir or cascaded hydropower systems by various researchers. Because when the non-inferior
