Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid

Nadeem Javaid, Fahim Ahmed, Ibrar Ullah, Samia Abid, Wadood Abdul, Atif Alamri and Ahmad S. Almogren
Additional contact information
Nadeem Javaid: COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Fahim Ahmed: COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Ibrar Ullah: COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Samia Abid: COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Wadood Abdul: Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
Atif Alamri: Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
Ahmad S. Almogren: Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia

Energies, 2017, vol. 10, issue 10, 1-27

Abstract: In a smart grid, several optimization techniques have been developed to schedule load in the residential area. Most of these techniques aim at minimizing the energy consumption cost and the comfort of electricity consumer. Conversely, maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task. Therefore, in this paper, we aim to minimize the electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyse the performance of a traditional dynamic programming (DP) technique and two heuristic optimization techniques: genetic algorithm (GA) and binary particle swarm optimization (BPSO) for residential load management. Based on these techniques, we propose a hybrid scheme named GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi dimensional knapsack is used to ensure that the load of electricity consumer will not escalate during peak hours. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analysed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with GA, BPSO and DP, and validate that the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.

Keywords: demand side management; demand response; home energy management system; meta-heuristic techniques (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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