Distributed electric system integrating wind and solar generation is reshaping the landscape of power industry. Distributed renewable generation allows the utility companies to lower the carbon footprints as well as deferring the expansion of grid infrastructure. However, power intermittency and equipment cost are the main hurdles confining the large adoption of wind- and solar-based energy solutions. This paper proposes a multistage, multicriteria approach maximizing the renewable energy throughput, while minimizing the levelised cost of energy. In particular, we optimize the sizing, siting, and maintenance of renewable sources under stringent reliability, power quality, and environmental constraints. A two-stage meta-heuristics, consisting of genetic algorithms and the gradient method, is developed, in order to search for the non-dominant solution set. A 13-node distribution network is used to demonstrate the performance of the proposed planning model. The results are compared with simulations and other meta-heuristics, and it is shown that the genetic algorithm excels in terms of computational time and quality of the results.
|Publication status||Published - Jul 2015|
- distributed wind integration
- distributed solar integration
- distributed power service
- wind power