Multi-Objective Energy Optimization with Load and Distributed Energy Source Scheduling in the Smart Power Grid

Abstract

Multi-objective energy optimization is indispensable for energy balancing and reliable operation of smart power grid (SPG). Nonetheless, multi-objective optimization is challenging due to uncertainty and multi-conflicting parameters at both the generation and demand sides. Thus, opting for a model that can solve load and distributed energy source scheduling problems is necessary. This work presents a model for operation cost and pollution emission optimization with renewable generation in the SPG. Solar photovoltaic and wind are renewable energy which have a fluctuating and uncertain nature. The proposed system uses the probability density function (PDF) to address uncertainty of renewable generation. The developed model is based on a multi-objective wind-driven optimization (MOWDO) algorithm to solve a multi-objective energy optimization problem. To validate the performance of the proposed model a multi-objective particle swarm optimization (MOPSO) algorithm is used as a benchmark model. Findings reveal that MOWDO minimizes the operational cost and pollution emission by 11.91% and 6.12%, respectively. The findings demonstrate that the developed model outperforms the comparative models in accomplishing the desired goals.

Effective Hybrid Search Technique Based Constraint Mixed-Integer Programming for Smart Home Residential Load Scheduling

“…4 Predicted solar energy time, besides, a compromise between electricity bill cost and user comfort has been tried. As the multi-objective optimization is a challenging programming because of existence of uncertainties and trade-offs between the problem's objectives, many researches were conducted in the literature on developing multi-objective optimization techniques [20] and [31], such as multi-objective particle swarm optimization and multi-objective wind-driven optimization techniques. Searching approaches of those techniques were applied for problems with non-integer decision variables but not for that combine integer and non-integer decision variables i.e.…”

Effective hybrid search technique based constraint mixed-integer programming for smart home residential load scheduling

“…In this study, the main objectives of the scheduling problem are simultaneously increasing the saving through reducing the utility bill and keeping the user comfort through reducing the waiting time, besides, a compromise between electricity bill cost and user comfort has been tried. As the multi-objective optimization is challenging programming because of the existence of uncertainties and trade-offs between the problem's objectives, many researches were conducted in the literature on developing multi-objective optimization techniques 23 and 34 , such as multi-objective particle swarm optimization and multi-objective wind-driven optimization techniques. Searching approaches of those techniques were applied for problems with non-integer decision variables but not for that combine integer and non-integer decision variables i.e., they were designed to find the optimal solutions without considering the integral constraints of the decision variables.…”