Contents lists available at ScienceDirect Sustainable Cities and Society journal homepage: www.elsevier.com/locate/scs A new heuristically optimized Home Energy Management controller for smart grid Nadeem Javaid a, ⁎ , Mudassar Naseem a , Muhammad Babar Rasheed a , Danish Mahmood a , Shahid Ahmed Khan a , Nabil Alrajeh b , Zafar Iqbal c a COMSATS Institute of Information Technology, Islamabad 44000, Pakistan b CAMS, Department of Biomedical Technology, KSU, Riyadh 11633, Saudi Arabia c PMAS Arid Agriculture University, Rawalpindi 4600, Pakistan ARTICLE INFO Keywords: Real time pricing Home Energy Management Scheduling Heuristic algorithms Peak to average ratio ABSTRACT Recently, Home Energy Management (HEM) controllers have been widely used for residential load management in a smart grid. Generally, residential load management aims to reduce the electricity bills and also curtail the Peak-to-Average Ratio (PAR). In this paper, we design a HEM controller on the basis of four heuristic algorithms: Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO). Moreover, we proposed a hybrid algorithm which is Genetic BPSO (GBPSO). All the selected algorithms are tested with the consideration of essential home ap- pliances in Real Time Pricing (RTP) environment. Simulation results show that each algorithm in the HEM controller reduces the electricity cost and curtails the PAR. GA based HEM controller performs relatively better in term of PAR reduction; it curtails approximately 34% PAR. Similarly, BPSO based HEM controller performs relatively better in term of cost reduction, as it reduces approximately 36% cost. Moreover, GBPSO based HEM controller performs better than the other algorithms based HEM controllers in terms of both cost reduction and PAR curtailment. 1. Introduction The ever increasing energy demand has created problems like blackout, load shedding, voltage instability, frequency drop, etc. As a solution, two approaches are nowadays in practice: (i) increasing the generation capacity, and (ii) managing the load according to existing power generation capacity through Home Energy Management (HEM) system (Saha et al., 2014). The earlier approach majorly depends on the installation of new power generation substations. In the later approach, Demand Side Management (DSM) programs are utilized which aim to manage the load according to existing generation capacity through scheduling techniques. In fact, the scheduling techniques are optimi- zation algorithms for managing the load between on-peak hours and off-peak hours while taking into account user and utility requirements. Substantial research efforts have been made to investigate the sche- duling problem in the residential sector (refer to Fig. 1 for a pictorial view of the residential area based smart grid). For example, Bozchalui, Hashmi, Hassen, Canizares, and Bhattacharya (2012) used Mixed In- teger Linear Programming (MILP) to schedule residential appliances. They integrate Photovoltaic (PV), storage, lighting, heating and air conditioning systems. Case study results show a reduction in cost and Peak-to-Average Ratio (PAR), however, system complexity is increased. In Roh and Lee (2016), Mixed Integer Non-Linear Programming (MINLP) is used to schedule appliances belonging to multiple classes. Similarly, in Gholian, Mohsenian-Rad, and Hua (2016) and Tsui and Chan (2012) MILP and MINLP are used for appliance scheduling to reduce the electricity cost. In Chana (2013), Bacterial Foraging Opti- mization Algorithm (BFOA) is implemented for resource scheduling problem in grid computing aiming at electricity cost minimization. MINLP and Genetic Algorithm (GA) are used in Fernandes et al. (2011) for controlling home appliances. Zhao, Lee, Shin, and Song (2013) use GA for scheduling in residential appliances subject to electricity cost reduction. In Pedrasa, Spooner, and MacGill (2009), Binary Particle Swarm Optimization (BPSO) is used for scheduling interruptible load. Their simulation results verify the effectiveness of BPSO in terms of electricity bill reduction and energy profile stability. Similarly, Zhou, Chen, Xu, and Zhang (2014) studied load shifting techniques in HEM system by using Particle Swarm Optimization (PSO). Cost and energy http://dx.doi.org/10.1016/j.scs.2017.06.009 Received 12 March 2017; Received in revised form 4 June 2017; Accepted 16 June 2017 ⁎ Corresponding author. E-mail address: nadeemjavaid@comsats.edu.pk (N. Javaid). URL: http://www.njavaid.com (N. Javaid). Sustainable Cities and Society 34 (2017) 211–227 Available online 01 July 2017 2210-6707/ © 2017 Elsevier Ltd. All rights reserved. MARK