Decision support for grid-connected renewable energy generators planning
Recent technological advances and the incremental demand for electrical energy are leading a growth in the prevalence of distributed generation. There are some off-the-shelf tools to support grid planners in locating and sizing a given number of Distributed Generators (DGs), but they approach the pr...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2016 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10256/12888 |
| Acceso en línea: | http://hdl.handle.net/10256/12888 |
| Access Level: | acceso abierto |
| Palabra clave: | Energia elèctrica -- Distribució Electric power distribution Energia elèctrica -- Producció Electric power production Sistemes d'ajuda a la decisió Decision support systems |
| Sumario: | Recent technological advances and the incremental demand for electrical energy are leading a growth in the prevalence of distributed generation. There are some off-the-shelf tools to support grid planners in locating and sizing a given number of Distributed Generators (DGs), but they approach the problem using a single set of the variables (either location, size or number of DGs). This paper reviews the problem and provides a new pathway for supporting grid planning with an integrated view; hence, a new planning problem is formulated to jointly determine how many new DGs are needed, of which type, their location and size, while attempting to maximise the profit of the generators, minimise the system losses and improve the voltage profile. Accompanying the new grid planning problem, solution approaches based on meta-heuristic methods are provided. A detailed performance analysis of the proposed approaches is carried out on 14- and 57-bus systems to illustrate what could be the outcomes of the new problem. In so doing, particle swarm optimisation-based approaches are able to find the best optimised solutions |
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