Domestic load characterization for demand-responsive energy management systems

The final goal of our research is to design a methodology to be implemented in a demand-responsive energy management system for controlling domestic energy resources in a smart grid scenario, taking into account the existence of dynamic tariffs and quality of service constraints. This paper aims at fulfilling an essential step towards that goal: evaluating the manageable demand available at the residential sector and assess the impacts of automated demand response actions in the domestic aggregated load diagram. Domestic energy resources comprise typical manageable loads, energy storage systems, plugin electric vehicles and micro-generation systems. The management strategy must consist of real time local energy monitoring and load control assuring the quality of the energy service provided and the end-user's comfort requirements and preferences while optimizing the operation of power systems, contributing to maximize the integration of renewables and minimizing the electricity bill.

[1]  Enrico Carpaneto,et al.  Probabilistic characterisation of the aggregated residential load patterns , 2008 .

[2]  T Joseph Lui,et al.  Get Smart , 2010, IEEE Power and Energy Magazine.

[3]  Muhammad Shahbaz,et al.  Electricity consumption and economic growth nexus in Portugal using cointegration and causality approaches , 2011 .

[4]  Ulrich Denecke,et al.  A Tool for Modeling and Optimization of Residential Electricity Consumption , 2008, EnviroInfo.

[5]  R. Larson,et al.  The Energy Box: Locally Automated Optimal Control of Residential Electricity Usage , 2009 .

[6]  Farrokh Albuyeh,et al.  Grid of the future , 2009, IEEE Power and Energy Magazine.

[7]  Ning Lu,et al.  Appliance Commitment for Household Load Scheduling , 2011, IEEE Transactions on Smart Grid.

[8]  Filipe Joel Soares,et al.  Optimized Bidding of a EV Aggregation Agent in the Electricity Market , 2012, IEEE Transactions on Smart Grid.

[9]  Fabien Cromieres,et al.  Implementing peak load reduction algorithms for household electrical appliances , 2012 .

[10]  M. Lehtla,et al.  Residential electricity consumption and loads pattern analysis , 2010, Proceedings of the 2010 Electric Power Quality and Supply Reliability Conference.

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

[12]  S. Grijalva,et al.  Realizing smart grid benefits requires energy optimization algorithms at residential level , 2011, ISGT 2011.

[13]  A.G. Martins,et al.  A Multiple Objective Approach to Direct Load Control Using an Interactive Evolutionary Algorithm , 2007, IEEE Transactions on Power Systems.

[14]  Luis C. Dias,et al.  Structuring an MCDA model using SSM: A case study in energy efficiency , 2009, Eur. J. Oper. Res..

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

[16]  Eric Williams,et al.  Scoping the potential of monitoring and control technologies to reduce energy use in homes , 2007, ISEE 2007.

[17]  Yanpei Chen,et al.  An information-centric energy infrastructure: The Berkeley view , 2011, Sustain. Comput. Informatics Syst..

[18]  C. H. Antunes,et al.  A multiple objective evolutionary approach for the design and selection of load control strategies , 2004, IEEE Transactions on Power Systems.

[19]  Gerard J. M. Smit,et al.  Management and Control of Domestic Smart Grid Technology , 2010, IEEE Transactions on Smart Grid.