A Priori Multiobjective Self-Adaptive Multi-Population Based Jaya Algorithm to Optimize DERs Operations and Electrical Tasks

A smart grid (SG) is an emerging technology that provides electricity in a cost-efficient and eco-friendly way. SG combined with distributed energy resources (DERs) plays a crucial role in extending the existing grid's capacity while mitigating carbon emissions. The potential sources of DERs include solar, wind, and tidal energy. Usually, these DERs are located far away from the grid and not necessarily tied to the grid system. However, the energy trading capabilities of a grid-tied DERs are getting attention, both from academia and industry. This bonding of grid-tied DERs helps to decrease the loss of surplus energy, build an energy storage capacity, and other operational charges. Energy-consuming flexible home tasks can be optimized coordinately with the operations of DERs to minimize the economic cost and CO_{2} emissions. In this work, our problem is multi-objective and we aim to reduce both electricity price and CO_{2} emission. We proposed a multi-objective self-adaptive multi-population based Jaya algorithm (PMO-SAMP-Jaya) to schedule the operations of flexible home tasks. Different pricing schemes have been applied to uncover the correlation between CO_{2} emission, economic cost, and pricing schemes. We assume a smart building, including 30 smart homes with PV and energy storage system (ESS) as DERs. Promising results have shown the effectiveness of our proposed scheme.