Priority-based scheduling in residential energy management systems integrated with renewable sources using adaptive Salp swarm algorithm

Abstract

With the remarkable growth and implementation of communication technology, sensors, and measurement equipment in the Smart Grid (SG) environment, demand side management (DSM) and demand response (DRs) can be easily implementable in residential energy systems integrated with renewable energy sources (RES). Looking at this perspective, this paper suggests an intelligent and dynamic load-priority-based scheduling optimal smart residential energy management system (REMS). The objectives to achieve through priority-based scheduling in the case of a residential energy management system are multi-focussed in terms of peak load reduction, consumer choice of consumption according to priority basis, and cost-effectiveness towards electricity price savings. The issues related to uncertainties with RES due to environmental dependency must be incorporated into the DSM. A single objective discrete formulation based on the Adaptive Salp Swarm Algorithm (ASSA) has been done on modelling and optimizing the crucial system parameters for scheduling, ideally the operation of residential appliances, along with the sources and prioritized-based loads available. System constraints, consumer priorities, energy source availability, uncertainties, and objectives are considered in the formulation to justify the approach that is feasible in real-time conditions. To enhance the search capabilities of SSA, the control parameters vary optimally in both the exploration and exploitation stages of searching. Comparative results with genetic algorithms (GA), particle swarm optimization (PSO), and conventional SSA are presented in different cases, such as (1) traditional homes without REMS, (ii) smart homes with REMS (iii) smart homes using REMS with RES.