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Otimização Multiobjetivo na Análise da Integração de Geração Distribuída às Redes de Distribuição
| Content Provider | Semantic Scholar |
|---|---|
| Author | Maciel, Renan Silva |
| Copyright Year | 2012 |
| Abstract | The Distributed Generation (DG) plays an important role in the profound changes that distribution power systems are facing in the last decades. The impacts caused by high DG penetration are still a challenge for traditional distribution networks. Then, there is a need for innovation and development of computational tools for system analysis considering the new trends for designing, planning and operating distribution networks. This work investigates the potential of meta-heuristics for multi-objective optimization (MO) to evaluate the impact of the penetration of DG in medium voltage distribution networks. The research may be divided in two parts: firstly, the study is focused on the techniques for solving MO problems and the second part aimed to evaluate the impact of DG on technical aspects of the distribution network, such as voltage levels, short-circuit and current capacity, considering the problem of expansion planning of the distribution network. Regarding the study of the techniques of MO, the concepts of multi-objective optimization was investigated as well as the main metaheuristic based methods and the application of the MO to power systems problems, especially those related to the integration of DG in distribution. Two methods were implemented: the Nondominated Sorting Genetic Algorithm II (NSGA-II), based on Genetic Algorithms and a multi-objective Tabu Search (MOTS). Finally, the algorithm of a Multi-objective Evolutionary Particle Swarm Optimization (MEPSO) was developed within this thesis in order to exploit the performance gains observed with the hybrid meta-heuristic Evolutionary Particle Swarm Optimization (EPSO) in single-objective optimization problems. Concerning the study of the impact of DG, a methodology for analysis of the Pareto front was proposed which allows, in addition to obtaining the best trade-off solutions, the identification of patterns of DG impact related to elements such as the position and the size of the generation units. Two models of MO were defined: a simplified model for optimal allocation and sizing of DG and a model used for investigating the possibility of gains in network capacity and deferment of investments in network infrastructure. Using the first of these models, the parameters and structures of the MEPSO method were tested and the NSGA-II, MOTS and MEPSO methods were compared. The MEPSO methods presented a generally better performance than NSGA-II and MOTS methods. Then, the proposed methodology for impact analysis was applied to both models showing the relation between the DG impact on the technical indices and the location and size of the generation units. The analysis performed with the first model presented conclusions limited to cases where the penetration of DG is controlled by the distribution companies. The results obtained with the second model showed the extremes of positive and negative impacts that may be caused by DG on the capacity of the network. Conditions for connecting the DG were also identified to ensure the investment deferral with the integration of the generators. The information this analysis can be used for an impact study of DG, to evaluate the investment in generation by the utilities for the adequacy of the system or even be used in the expansion planning where the generators have free access to the distribution network. About MO techniques, MEPSO method presented remarkable performance compared to NSGA-II and BTMO methods, considering the problem of MO resolved in this thesis. However, a broader set of experiments should be conducted to determine whether this performance is maintained for problems with different characteristics. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.feis.unesp.br/Home/departamentos/engenhariaeletrica/lapsee/2012_tese_renan_maciel.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
