Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations

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.

Smart energy systems for sustainable smart cities: Current developments, trends and future directions

“…From an operational perspective, advanced control approaches and optimised scheduling play a significant role [98]. In [99] for example, a model is developed to handle the stochastic nature of renewable generation while minimising costs through optimised scheduling based on Genetic Algorithms (GAs), teaching learning-based optimisation (TLBO) and enhanced differential evolution (EDE). In [100] game theoretic approaches and adaptive fuzzy control strategies are used to manage the demand and supply respectively.…”

Methodology for the Analysis of Electrical Consumption

“…Javaid et al in [5] focused over to control electric consumption and maintenance the comfort taking into consideration the user preferences. Was implemented four algorithms: genetic algorithms (GA), teaching-learning base on optimization (TLBO), enhanced differential evolution (EDE) and enhanced differential teaching-learning algorithm (EDTL).…”

“…An Interruptible system refers to a system that can be turned on and off without any particular constraint. Interruptible devices are defined like devices that could be used in anytime, and the time of use varies according to the user needs [5,7,10,13,17]. Not-interruptible devices stop their function once they are finished, their consume could be variable or constant [7,10,13,16,17].…”

“…The works focused in the search of the improvement of the electric consumption are about devices or control variables [2][3][4][5] buildings consumption [3], houses [6], costs [2,[7][8][9][10] sensor infrastructure [3] and control algorithms [4,10]. Few works focus on the current challenge of developing solutions supported by IoT.…”

Informatics Solution for Energy Efficiency Improvement and Consumption Management of Householders

“…The results point out the necessity of improving energy literacy in order to promote and encourage energy efficient measures and new smart meters with the potential to increase savings and impact climate change strategies. Paper [26] proposes various DSM strategies using the genetic algorithm, teaching learning-based optimization, the enhanced differential evolution algorithm and the proposed enhanced differential teaching learning algorithm to manage energy and comfort, while taking the human preferences into consideration. The operation of programmable home appliances is changed in response to the real-time tariff signal in order to get monetary savings.…”