Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

Nadeem Javaid, Sardar Mehboob Hussain, Ibrar Ullah, Muhammad Asim Noor, Wadood Abdul, Ahmad Almogren and Atif Alamri
Additional contact information
Nadeem Javaid: Department of Computer Science, COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Sardar Mehboob Hussain: Department of Computer Science, COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Ibrar Ullah: University of Engineering and Technology Peshawar, Bannu 28100, Pakistan
Muhammad Asim Noor: Department of Computer Science, COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Wadood Abdul: Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
Ahmad Almogren: Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
Atif Alamri: Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia

Energies, 2017, vol. 10, issue 8, 1-29

Abstract: Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.

Keywords: microgrid (MG); renewable energy sources (RESs); demand side management (DSM); heuristic techniques; planning and scheduling; storage system; zero energy buildings (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 (12)

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