Improved peak load management control technique for nonlinear and dynamic residential energy consumption pattern

Advancements in computational technologies for residential energy management have become widespread due to increased energy usage and peak demand in households. Controlled switching of appliances during peak periods for energy reduction is one of such solutions. However, this technique may constrict the consumer’s flexibility to utilize desired appliances to fulfil their needs. This study proposes a peak load control technique based on an end-use appliance prioritization and event detection algorithm. A noteworthy feature of the technique is the users’ preferred appliance (UPA), which provides the occupants with the flexibility to use any electric load to fulfill their needs at any time whether peak demand control has been initiated or not. Furthermore, to address the challenging issue around the uncertain and varying nature of the users’ energy usage pattern, an empirical bottom-up approach is adopted to characterize the consumers’ diverse end-use behavior. The results obtained show good performance in terms of peak demand and energy consumption reduction, with 3% to 20% and at least 14.05% in demand reduction for time of use (ToU) periods and energy savings, respectively. These offer a further perspective on demand side management, affording load users a new cost-saving energy usage pattern.

[1]  Bin Chen,et al.  Factor Analysis of Residential Energy Consumption at the Provincial Level in China , 2014 .

[2]  Merih Aydinalp,et al.  Modeling of the appliance, lighting, and space-cooling energy consumptions in the residential sector using neural networks , 2002 .

[3]  Jiangfeng Zhang,et al.  Optimal scheduling of household appliances for demand response , 2014 .

[4]  Regina Lamedica,et al.  A bottom-up approach to residential load modeling , 1994 .

[5]  V. Lo Brano,et al.  Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area , 2008 .

[6]  Xiaohua Xia,et al.  Optimal scheduling of household appliances with a battery storage system and coordination , 2015 .

[7]  J. L. Munda,et al.  Peak demand control system using load prioritisation for domestic households , 2017, 2017 IEEE AFRICON.

[8]  O. M. Popoola,et al.  Computational intelligence modelling based on variables interlinked with behavioral tendencies for energy usage profile – A necessity , 2018 .

[9]  Ben Anderson,et al.  Electricity consumption and household characteristics: Implications for census-taking in a smart metered future , 2017, Comput. Environ. Urban Syst..

[10]  Vincent W. S. Wong,et al.  Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid , 2010, IEEE Transactions on Smart Grid.

[11]  Judith Gurney.  BP Statistical Review of World Energy , 1985 .

[12]  Sunil Kumar,et al.  An Intelligent Home Energy Management System to Improve Demand Response , 2013, IEEE Transactions on Smart Grid.

[13]  Onur Elma,et al.  A new home energy management algorithm with voltage control in a smart home environment , 2015 .

[14]  Hussain Shareef,et al.  Application of load monitoring in appliances’ energy management – A review , 2017 .

[15]  Yacine Rezgui,et al.  An investigation into factors influencing domestic energy consumption in an energy subsidized developing economy , 2015 .

[16]  Josiah L. Munda,et al.  Comparative analysis and assessment of ANFIS-based domestic lighting profile modelling , 2015 .

[17]  Gang Xiong,et al.  A Communication-Based Appliance Scheduling Scheme for Consumer-Premise Energy Management Systems , 2013, IEEE Transactions on Smart Grid.

[18]  Vijay Devabhaktuni,et al.  Solar energy: Trends and enabling technologies , 2013 .

[19]  David Martin,et al.  Estimating potential saving with energy consumption behaviour model in higher education institutions , 2016 .

[20]  Josiah L. Munda,et al.  Residential lighting load profile modelling , 2015 .

[21]  Takashi Ikegami,et al.  Development of the Optimum Operation Scheduling Model of Domestic Electric Appliances for the Supply-Demand Adjustment in a Power System , 2010 .

[22]  Rory V. Jones,et al.  The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings , 2015 .

[23]  V. Ismet Ugursal,et al.  Modeling of end-use energy consumption in the residential sector: A review of modeling techniques , 2009 .

[24]  Zhong Fan,et al.  Overview of demand management in smart grid and enabling wireless communication technologies , 2012, IEEE Wireless Communications.

[25]  Peter Wyatt.  A dwelling-level investigation into the physical and socio-economic drivers of domestic energy consumption in England , 2013 .

[26]  Kirsten Gram-Hanssen,et al.  Households' energy use - which is the more important: efficient technologies or user practices? , 2011 .

[27]  Darren Robinson,et al.  A bottom-up stochastic model to predict building occupants' time-dependent activities , 2013 .

[28]  Juan C. Vasquez,et al.  Load shifting control and management of domestic microgeneration systems for improved energy efficiency and comfort , 2015, IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society.

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

[30]  Hanife Apaydin Ozkan.  A new real time home power management system , 2015 .

[31]  C. Bartusch,et al.  Exploring variance in residential electricity consumption: Household features and building properties , 2012 .

[32]  Nadarajah Kannan,et al.  Solar energy for future world: - A review , 2016 .

[33]  Hamed Mohsenian Rad,et al.  Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments , 2010, IEEE Transactions on Smart Grid.

[34]  Tomohiro Hayashida,et al.  An intelligent Home Energy Management System with classifier system , 2015, 2015 IEEE 8th International Workshop on Computational Intelligence and Applications (IWCIA).