Application of PSO for Optimization of Power Systems Under UncertaintyGRIN Verlag, 2010 - 168 pages Doctoral Thesis / Dissertation from the year 2009 in the subject Electrotechnology, grade: 1.0, University of Duisburg-Essen (Institute of Electrical Power Systems), course: Electrical Engineering, language: English, abstract: The primary objective of this dissertation is to develop a black box optimization tool. The algorithm should be able to solve complex nonlinear, multimodal, discontinuous and mixed-integer power system optimization problems without any model reduction. Although there are many computational intelligence (CI) based algorithms which can handle these problems, they require intense human intervention in the form of parameter tuning, selection of a suitable algorithm for a given problem etc. The idea here is to develop an algorithm that works relatively well on a variety of problems with minimum human effort. The most significant optimization task in the power system operation is the scheduling of various generation resources (Unit Commitment, UC). The current practice used in UC modelling is the binary approach. This modelling results in a high dimension problem. This in turn leads to increased computational effort and decreased efficiency of the algorithm. A duty cycle based modelling proposed in this thesis results in 80 percent reduction in the problem dimension. The stern uptime and downtime requirements are also included in the modelling. Therefore, the search process mostly starts in a feasible solution space. From the investigations on a benchmark problem, it was found that the new modelling results in high quality solutions along with improved convergence. The final focus of this thesis is to investigate the impact of unpredictable nature of demand and renewable generation on the power system operation. These quantities should be treated as a stochastic processes evolving over time. A new PSO based uncertainty modelling technique is used to abolish the restrictions imposed by the conventional modelling algorithms. The stochastic models ar |
Table des matières
| 1 | |
Constrained Optimization | 31 |
Unit Commitment Problem | 63 |
Optimization under Uncertainty | 77 |
Selected Applications of PSO in Power Systems | 89 |
Conclusions | 135 |
Resume | 147 |
Expressions et termes fréquents
able adaptive algorithm applications approach APSO better binary chapter compared complex Computation considered constraint violations constraints convergence corresponding cost decisions demand dependent described distance distribution electrical error evaluations evolutionary exploration feasible fitness forecast formulation function groups hour huge increase indicates infeasible particles initial integer iteration load mean measured methods objective value operation optimization optimization problems parameters particle swarm optimization penalty penalty function penalty technique performance period planning position power system prediction probability problem programming proposed random reactive power represents reserve rules scenario scenario tree schedule search process selection shown in Fig simulation solve stage standard Step stochastic programming strategy Table term test functions tion TRIBE uncertainties unit unit commitment update variables wind wind farm wind power xx xx xxxxxxx xxxxxxx xxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx