Perspectives and Intensification of Energy Efficiency in Commercial and Residential Buildings Using Strategic Auditing and Demand-Side Management

With the ever-growing power demand, the energy efficiency in commercial and residential buildings is a matter of great concern. Also, strategic energy auditing (SEA) and demand-side management (DSM) are cost-effective means to identify the requirements of power components and their operation in the energy management system. In a commercial or residential building, the major components are light sources and heating, ventilation, and air conditioning. The number of these components to be installed depends upon the technical and environmental standards. In this scenario, energy auditing (EA) allows identifying the methods, scope, and time for energy management, and it helps the costumers to manage their energy consumption wisely to reduce electricity bills. In the literature, most of the traditional strategies employed specific system techniques and algorithms, whereas, in recent years, load shifting-based DSM techniques were used under different operating scenarios. Considering these facts, the energy data in a year were collected under three different seasonal changes, i.e., severe cold, moderate, and severe heat for the variation in load demand under different environmental conditions. In this work, the energy data under three conditions were averaged, and the DSM schemes were developed for the operation of power components before energy auditing and after energy auditing. Moreover, the performance of the proposed DSM techniques was compared with the practical results in both scenarios, and, from the results, it was observed that the energy consumption reduced significantly in the proposed DSM approach.

[1]  H. Salgo PURPA (Public Utility Regulatory Practices Act) implementation: Policy issues and choices: The Northeast Regional Biomass Program , 1986 .

[2]  Sila Kiliccote,et al.  Design and Operation of an Open, Interoperable Automated Demand Response Infrastructure for Commercial Buildings , 2009, J. Comput. Inf. Sci. Eng..

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

[4]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[5]  R. M. Shereef,et al.  Review of demand response under smart grid paradigm , 2011, ISGT2011-India.

[6]  Giuseppe Tommaso Costanzo,et al.  A System Architecture for Autonomous Demand Side Load Management in Smart Buildings , 2012, IEEE Transactions on Smart Grid.

[7]  Thillainathan Logenthiran,et al.  Demand Side Management in Smart Grid Using Heuristic Optimization , 2012, IEEE Transactions on Smart Grid.

[8]  H. Vincent Poor,et al.  Demand-side energy storage system management in smart grid , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[9]  H. Vincent Poor,et al.  Energy Imbalance Management Using a Robust Pricing Scheme , 2013, IEEE Transactions on Smart Grid.

[10]  Alessandro Agnetis,et al.  Load Scheduling for Household Energy Consumption Optimization , 2013, IEEE Transactions on Smart Grid.

[11]  Zhao Yang Dong,et al.  Demand response: a strategy to address residential air-conditioning peak load in Australia , 2013 .

[12]  César Martín-Gómez,et al.  Simulation and evaluation of Building Information Modeling in a real pilot site , 2014 .

[13]  Ikbal Ali,et al.  Energy efficient reconfiguration for practical load combinations in distribution systems , 2015 .

[14]  Raj Jain,et al.  An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments , 2015, IEEE Internet of Things Journal.

[15]  Peter Lund,et al.  Review of energy system flexibility measures to enable high levels of variable renewable electricity , 2015 .

[16]  Murat Kuzlu Score-based intelligent home energy management (HEM) algorithm for demand response applications and impact of HEM operation on customer comfort , 2015 .

[17]  Jiming Chen,et al.  A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches , 2015, IEEE Transactions on Industrial Informatics.

[18]  Ikbal Ali,et al.  Energy efficiency analysis of reconfigured distribution system for practical loads , 2016 .

[19]  Adam J. Collin,et al.  Assessment of the Cost and Environmental Impact of Residential Demand-Side Management , 2016, IEEE Transactions on Industry Applications.

[20]  C. Rottondi,et al.  Evaluating the effects of social interactions on a distributed demand side management system for domestic appliances , 2017, Energy Efficiency.

[21]  Robert G. Pratt,et al.  Transactive Control of Commercial Buildings for Demand Response , 2017, IEEE Transactions on Power Systems.

[22]  Ikbal Ali,et al.  Imposing voltage security and network radiality for reconfiguration of distribution systems using efficient heuristic and meta-heuristic approach , 2017 .

[23]  Mohammad S. Obaidat,et al.  Distributed Home Energy Management System With Storage in Smart Grid Using Game Theory , 2017, IEEE Systems Journal.

[24]  Zhao Yang Dong,et al.  A Practical Pricing Approach to Smart Grid Demand Response Based on Load Classification , 2018, IEEE Transactions on Smart Grid.

[25]  E. Caamaño-Martín,et al.  SwarmGrid: Demand-Side Management with Distributed Energy Resources Based on Multifrequency Agent Coordination , 2018 .

[26]  Srete Nikolovski,et al.  ANFIS-Based Peak Power Shaving/Curtailment in Microgrids Including PV Units and BESSs , 2018, Energies.

[27]  Frede Blaabjerg,et al.  A Review of Internet of Energy Based Building Energy Management Systems: Issues and Recommendations , 2018, IEEE Access.

[28]  Daniele D. Giusto,et al.  Sensor-Based Early Activity Recognition Inside Buildings to Support Energy and Comfort Management Systems , 2019, Energies.

[29]  Rakesh Chandra Jha,et al.  Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization , 2019 .

[30]  Jianwei Huang,et al.  Data Center Demand Response in Deregulated Electricity Markets , 2019, IEEE Transactions on Smart Grid.