Probabilistic perspective of the optimal distributed generation integration on a distribution system

• Published in: Electric power systems research 2019-02, Vol.167, p.9-20
• Main Authors: Lujano-Rojas, Juan M., Dufo-López, Rodolfo, Bernal-Agustín, José L., Domínguez-Navarro, José A., Catalão, João P.S.
• Format: Article
• Language: English
• Subjects: Distributed generation; Distribution system; Electric power distribution; Electricity; Energy conversion efficiency; Exhausting; Genetic algorithm; Genetic algorithms; Mathematical models; Optimization; Panels; Photovoltaic cells; Power converters; Power flow; Probability; Renewable energy; Residential energy; Solar cells; Solar photovoltaic energy; Statistical analysis; Substations; Temperature; Variables; Wind energy; Wind power; Wind turbines
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2018.10.015

•The effects of optimal dimensioning and integration of DG on a DS from a probabilistic viewpoint is presented.•The proposed method is illustrated by studying a DS supposed to be located in Zaragoza, Spain.•The proposed technique was applied resulting in a system mainly based on wind energy of at least 40% of substation capacity.•This model could be used to perform the renewable energy integration analysis on DS. The effects of optimal dimensioning and integration of distributed generation (DG) on an electricity distribution system (DS) from a probabilistic viewpoint is presented in this paper, as a new contribution to earlier studies. The proposed methodology pays special attention to preventing reverse power flow at substation as a consequence of excessive integration of renewable energy based DG. As the analysis of large amounts of data typically measured on an annual basis could be exhausting from a computational perspective, a methodology based on estimating the potential of wind and solar resources is applied; from this procedure, those months of highest renewable potential are selected so that indirectly those situations with probability of reverse power flow at substation are considered. After this, time series of load demand per node and phase are generated using typical profiles and the corresponding peak-load expected. Finally, all this information is introduced on an optimization algorithm based on a genetic algorithm in order to minimize the net present cost over the project lifetime, obtaining the type and number of photovoltaic (PV) panels and wind turbines (WTs) to be installed. This approach allows integrating detailed mathematical models of DG related to PV and wind generation, including specific factors frequently reported by the manufacturers such as temperature coefficients, nominal operating cell temperature, particular WT power curves, and variable efficiency of power converter, among other characteristics. The proposed method is illustrated by studying a DS supposed to be located in Zaragoza, Spain, with 35 nodes under unbalanced conditions, with residential as well as small, medium, and large commercial electricity demands. Focusing our attention on the month of February, due to its high renewable potential, the proposed technique was applied resulting in a system mainly based on wind energy of at least 40% of the substation capacity. This model could be used to perform the renewable energy integration analysis on DS, starting from