Abstract
Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.
Highlights
In recent decades, energy demand around the globe has shown the increasing trend
Results illustrate that the hybridization of realtime pricing (RTP) and Time of Use pricing (ToUP) schemes is effective for reduction of bill and peak-to-average ratio (PAR)
After the integration of renewable energy source (RES) and energy storage system (ESS), the constrained optimization problem is mathematically formulated by using multiple knapsack problems (MKP), and solved by heuristic algorithms: genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-particle swarm optimization (PSO) (HGPO) algorithms
Summary
Energy demand around the globe has shown the increasing trend. In past, most of the power generation was being done from fossil fuels. The penetration of renewable energy sources (RESs) significantly increased power system complexity and dynamics [1], and the existing power system is not capable of maintaining its stability if the integration of RESs and distributed generation (DG) is done at a large scale. In this context, Energies 2017, 10, 549; doi:10.3390/en10040549 www.mdpi.com/journal/energies. With cutting edge information and communication technologies (ICTs) [2] These advanced ICTs enable SG to incorporate the DG and RESs and enhance the stability and reliability of power system. The vital aspect of SG is the control of power production, transmission and distribution through advanced
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