Analysis of techno-economic viability with demand response strategy of a grid-connected microgrid model for enhanced rural electrification in Uttar Pradesh state, India

This paper presents an economic and enhanced technological solution for rural electrification of a village Jalalabad, in Ghaziabad District, Uttar Pradesh state, India. The considered system concentrates on optimal sizing of an integrated renewable energy system (IRES) with grid provide a more continuous power supply with respect to the existing system. The different available sources Solar Photo Voltaic (SPV), Battery Energy Storage System (BESS), and a time constrained grid are considered in designing of the system. The system size optimization, sensitivity analysis and techno-economic viability has been done by using a standard software tool HOMER to fulfil the demand of the study area under consideration. A demand response strategy has been applied for the different load profiles. The configuration with the lowest Levelized Cost of Electricity (LCOE) and Total Net Present Cost (TNPC) with maximum value of renewable fraction (RF) has been considered as optimum. This ensures the minimization of the Carbon dioxide (CO2) emission. A comparative analysis has been presented showing the impact of the demand response on the LCOE and NPC as well as the system size. The economic analysis has been done according to four different cases obtained by varying the fraction (beta(NELS)) of non-essential load shifting. It has been observed that the value of LCOE with the strategy of demand response and optimal resource management under which beta(NELS) = 0.25 is reduced Rs. 11.23/kWh and Rs. 8.36/kWh with the grid tariff Rs.10/kWh and Rs.6/kWh respectively while the value of TNPC Rs. 1.99 x 10(7) and Rs. 1.48 x 10(7) respectively with the maximum renewable fraction of 31.06. An exhaustive performance analysis has been done and the obtained results show the effectiveness of proposed resource and demand management to provide 24 x 7 supply with limited, time constrained grid availability. (C) 2019 Elsevier Ltd. All rights reserved.