An Innovative Heuristic Algorithm for IoT-Enabled Smart Homes for Developing Countries

Over the past few years, active research on algorithm development for the optimal operations of home energy management systems (HEMSs) has been performed. The objective is to compute optimized schedules for shiftable home appliances. This is based on the demand response (DR) synergized with renewable energy sources and energy storage system optimal dispatch (DRSREOD). An improved algorithm for a DRSREOD-based HEMS is proposed in this paper. This heuristic-based algorithm considers DR, photovoltaic availability, the state of charge and charge/discharge rates of the storage battery and the sharing-based parallel operation of more than one power source to supply the required load. The HEMS problem has been solved to minimize the cost of energy (<inline-formula> <tex-math notation="LaTeX">$CE$ </tex-math></inline-formula>) and time-based discomfort (<inline-formula> <tex-math notation="LaTeX">$TBD$ </tex-math></inline-formula>) with conflicting tradeoffs. The mixed scheduling of appliances (delayed scheduling for some appliances and advanced scheduling for others) is introduced to improve the <inline-formula> <tex-math notation="LaTeX">$CE$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$TBD$ </tex-math></inline-formula> performance parameters. An inclining block rate scheme is also incorporated to reduce the peak load. A set of optimized tradeoffs between <inline-formula> <tex-math notation="LaTeX">$CE$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$TBD$ </tex-math></inline-formula> has been computed to address multi-objectivity using a multi-objective genetic algorithm (MOGA) with Pareto optimization (PO) to perform the tradeoff analysis and to enable consumers to select the most feasible solution. Due to the rapid increase in demand for electricity, developing countries are facing large-scale load shedding (LS). An innovative algorithm is also proposed for the optimal sizing of a dispatchable generator (DG) that can supply the DRSREOD-based HEMS during LS hours to ensure an uninterrupted supply of power. The proposed MOGA/PO-based algorithm enables consumers to select a DG of the optimal size from among a number of optimal choices based on tradeoffs between the DG size, <inline-formula> <tex-math notation="LaTeX">$CE$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$TBD$ </tex-math></inline-formula>.

[1]  Tarek Y. ElMekkawy,et al.  A dynamic MOPSO algorithm for multiobjective optimal design of hybrid renewable energy systems , 2014 .

[2]  Miao Pan,et al.  Decentralized Coordination of Energy Utilization for Residential Households in the Smart Grid , 2013, IEEE Transactions on Smart Grid.

[3]  Mahmud Fotuhi-Firuzabad,et al.  Outage Management in Residential Demand Response Programs , 2015, IEEE Transactions on Smart Grid.

[4]  Karl Henrik Johansson,et al.  Scheduling smart home appliances using mixed integer linear programming , 2011, IEEE Conference on Decision and Control and European Control Conference.

[5]  Kyung-Bin Song,et al.  An Optimal Power Scheduling Method for Demand Response in Home Energy Management System , 2013, IEEE Transactions on Smart Grid.

[6]  Phani Chavali,et al.  A Distributed Algorithm of Appliance Scheduling for Home Energy Management System , 2014, IEEE Transactions on Smart Grid.

[7]  Veluchamy Malathi,et al.  HEM algorithm based smart controller for home power management system , 2016 .

[8]  Mustafa Baysal,et al.  Energy management algorithm for smart home with renewable energy sources , 2013, 4th International Conference on Power Engineering, Energy and Electrical Drives.

[9]  Lingfeng Wang,et al.  Demand response simulation implementing heuristic optimization for home energy management , 2010, North American Power Symposium 2010.

[10]  Iain MacGill,et al.  Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services , 2010, IEEE Transactions on Smart Grid.

[11]  João P. S. Catalão,et al.  Coordinated Operation of a Neighborhood of Smart Households Comprising Electric Vehicles, Energy Storage and Distributed Generation , 2016, IEEE Transactions on Smart Grid.

[12]  João P. S. Catalão,et al.  Smart Household Operation Considering Bi-Directional EV and ESS Utilization by Real-Time Pricing-Based DR , 2015, IEEE Transactions on Smart Grid.

[13]  Hamidreza Zareipour,et al.  Home energy management systems: A review of modelling and complexity , 2015 .

[14]  Azah Mohamed,et al.  Real time optimal schedule controller for home energy management system using new binary backtracking search algorithm , 2017 .

[15]  Clark W Gellings,et al.  The Smart Grid: Enabling Energy Efficiency and Demand Response , 2020 .

[16]  Gwo-Ching Liao,et al.  The optimal economic dispatch of smart Microgrid including Distributed Generation , 2013, 2013 International Symposium on Next-Generation Electronics.

[17]  Hamidreza Zareipour,et al.  Residential Energy Management Using a Two-Horizon Algorithm , 2014, IEEE Transactions on Smart Grid.

[18]  M. L. Crow,et al.  Optimization in energy and power management for renewable-diesel microgrids using Dynamic Programming algorithm , 2012, 2012 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER).

[19]  Lingfeng Wang,et al.  Autonomous Appliance Scheduling for Household Energy Management , 2014, IEEE Transactions on Smart Grid.

[20]  João P. S. Catalão,et al.  Smart Households and Home Energy Management Systems with Innovative Sizing of Distributed Generation and Storage for Customers , 2015, 2015 48th Hawaii International Conference on System Sciences.

[21]  Alireza Askarzadeh,et al.  Distribution generation by photovoltaic and diesel generator systems: Energy management and size optimization by a new approach for a stand-alone application , 2017 .

[22]  C. Monteiro,et al.  Optimum residential load management strategy for real time pricing (RTP) demand response programs , 2012 .

[23]  Luigi Atzori,et al.  A novel Smart Home Energy Management system: Cooperative neighbourhood and adaptive renewable energy usage , 2015, 2015 IEEE International Conference on Communications (ICC).

[24]  Francesco Piazza,et al.  Optimization algorithms for home energy resource scheduling in presence of data uncertainty , 2013, 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP).

[25]  Nadeem Javaid,et al.  Exploiting heuristic algorithms to efficiently utilize energy management controllers with renewable energy sources , 2016 .

[26]  Lang Tong,et al.  Modeling and Stochastic Control for Home Energy Management , 2013, IEEE Transactions on Smart Grid.

[27]  Meng Liu,et al.  A Collaborative Design of Aggregated Residential Appliances and Renewable Energy for Demand Response Participation , 2015 .

[28]  Mohamed A. El-Sharkawi,et al.  Modern heuristic optimization techniques :: theory and applications to power systems , 2008 .

[29]  Antonio Capone,et al.  A framework for home energy management and its experimental validation , 2014 .

[30]  Nadeem Javaid,et al.  Realistic Scheduling Mechanism for Smart Homes , 2016 .

[31]  Muhammad Awais,et al.  Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes , 2016 .