A novel multi-period double frontier network DEA to sustainable location optimization of hybrid wind-photovoltaic power plant with real application

Due to the harmful pollution releasing from fossil fuels burning, a growing trend to use renewable energies has been emerged. Nevertheless, using only one renewable energy resource fails to meet electrical power demands on account of its intermittent nature. To overcome this problem, hybrid power plants are developed. The present paper makes an attempt to determine the suitable locations to establish hybrid power plants which can guarantee the feasibility of these systems. Thanks to the availability in the most regions and complementary characteristics, hybrid wind and photovoltaic (PV) systems are taken into account. Geographical and sustainability criteria including economic, social, and environmental aspects are defined to assess the efficiency of candidate locations for hybrid power plant establishment. A novel algorithm utilizing double frontier network data envelopment analysis (NDEA) has been proposed in which both single-period and multi-period programming models are considered. This algorithm sets out to determine the suitable location for hybrid power plant establishment by calculating the efficiency of each candidate locations. Subsequently, the presented approach can rank the candidate locations' priority to establish hybrid power plant. To evaluate the performance of the presented approach, a real case study in Iran is investigated. The results reveal that selected locations are closely nearby cardinal points. On the other hand, selected locations are in neighborhood by the provinces with highest electricity consumptions. Based on these findings, the proposed approach manages to meet decision-makers' expectations.