Sustainability and Capacity Estimation of Photovoltaic Based Distribution Network Integrated with Demand Response Using Arithmetic Optimizer Algorithm

Distribution networks (DNs) are becoming congested due to the rapid rise in the number of consumers, resulting in an energy crisis, voltage stability issues, and unforeseen power outages. Also, the harmful effects of emissions from fossil fuel-based power plants, limited fossil fuel reserves, etc. are making network operators integrate distributed generators (DGs) based on renewable energy sources into the DNs. This paper proposes an incentive-based demand response (DR) program for PV-integrated DNs in which a price-demand elasticity matrix relates the load demand and the electricity prices. The hourly solar irradiance for different seasons is modeled using geographical equations for the location in Jammu and Kashmir, India. An objective function based on technical, economic, and environmental benefits, averaged over the entire day, and subjected to operational constraints, has been developed, which is maximized using the Arithmetic Optimizer Algorithm. Nine different scenarios considering different combinations of incentives and demand-electricity price elasticities are tested on the IEEE 69 bus test DN. This work also estimates the optimal number of PV modules required to be installed based on solar irradiance modeling using geographical data for the location, uncertainties, and seasonal changes. The maximum percentage improvement in the overall day-averaged objective of IEEE 69 bus DN is 30.82%. The percentage reduction in the day-averaged active power loss corresponding to the best test case is 5.54%, considering the combined effects of PV and DR.