Adaptive User-Oriented Direct Load-Control of Residential Flexible Devices

Demand Response (DR) schemes are effective tools to maintain a dynamic balance in energy markets with higher integration of fluctuating renewable energy sources. DR schemes can be used to harness residential devices' flexibility and to utilize it to achieve social and financial objectives. However, existing DR schemes suffer from low user participation as they fail at taking into account the users' requirements. First, DR schemes are highly demanding for the users, as users need to provide direct information, e.g. via surveys, on their energy consumption preferences. Second, the user utility models based on these surveys are hard-coded and do not adapt over time. Third, the existing scheduling techniques require the users to input their energy requirements on a daily basis. As an alternative, this paper proposes a DR scheme for user-oriented direct load-control of residential appliances operations. Instead of relying on user surveys to evaluate the user utility, we propose an online data-driven approach for estimating user utility functions, purely based on available load consumption data, that adaptively models the users' preference over time. Our scheme is based on a day-ahead scheduling technique that transparently prescribes the users with optimal device operation schedules that take into account both financial benefits and user-perceived quality of service. To model day-ahead user energy demand and flexibility, we propose a probabilistic approach for generating flexibility models under uncertainty. Results on both real-world and simulated datasets show that our DR scheme can provide significant financial benefits while preserving the user-perceived quality of service.

[1]  Christos V. Verikoukis,et al.  A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms , 2015, IEEE Communications Surveys & Tutorials.

[2]  Jin-ho Kim,et al.  Common failures of demand response , 2011 .

[3]  Hartmut Schmeck,et al.  Modeling and Valuation of Residential Demand Flexibility for Renewable Energy Integration , 2017, IEEE Transactions on Smart Grid.

[4]  Lingfeng Wang,et al.  Autonomous Appliance Scheduling for Household Energy Management , 2014, IEEE Transactions on Smart Grid.

[5]  Victor O. K. Li,et al.  Maximizing aggregator profit through energy trading by coordinated electric vehicle charging , 2016, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[6]  Torben Bach Pedersen,et al.  Dependency-based FlexOffers: scalable management of flexible loads with dependencies , 2016, e-Energy.

[7]  Jesper Kjeldskov,et al.  Aesthetic, Functional and Conceptual Provocation in Research Through Design , 2017, Conference on Designing Interactive Systems.

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

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

[10]  Sousso Kelouwani,et al.  Non-intrusive load monitoring through home energy management systems: A comprehensive review , 2017 .

[11]  João P. S. Catalão,et al.  Optimal Household Appliances Scheduling Under Day-Ahead Pricing and Load-Shaping Demand Response Strategies , 2015, IEEE Transactions on Industrial Informatics.

[12]  Torben Bach Pedersen,et al.  Aggregating and Disaggregating Flexibility Objects , 2012, IEEE Transactions on Knowledge and Data Engineering.

[13]  Torben Bach Pedersen,et al.  Generation and Evaluation of Flex-Offers from Flexible Electrical Devices , 2017, e-Energy.

[14]  Chris Develder,et al.  Quantifying flexibility in EV charging as DR potential: Analysis of two real-world data sets , 2016, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[15]  Geert Deconinck,et al.  Potential of Active Demand Reduction With Residential Wet Appliances: A Case Study for Belgium , 2015, IEEE Transactions on Smart Grid.

[16]  Torben Bach Pedersen,et al.  Data management in the MIRABEL smart grid system , 2012, EDBT-ICDT '12.

[17]  Fernando L. Alvarado,et al.  Using utility information to calibrate customer demand management behavior models , 2002, 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309).

[18]  Jan Schoormans,et al.  A real-life assessment on the effect of smart appliances for shifting households’ electricity demand , 2015 .

[19]  Bo Thiesson,et al.  Towards Flexibility Detection in Device-Level Energy Consumption , 2014, DARE.

[20]  Jasper Frunt,et al.  Load shifting potential of the washing machine and tumble dryer , 2016, 2016 IEEE International Energy Conference (ENERGYCON).

[21]  Torben Bach Pedersen,et al.  Aggregating energy flexibilities under constraints , 2016, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[22]  Jesper Kjeldskov,et al.  Washing with the Wind: A Study of Scripting towards Sustainability , 2018, Conference on Designing Interactive Systems.

[23]  Torben Bach Pedersen,et al.  Measuring and Comparing Energy Flexibilities , 2015, EDBT/ICDT Workshops.

[24]  Na Li,et al.  Optimal demand response based on utility maximization in power networks , 2011, 2011 IEEE Power and Energy Society General Meeting.

[25]  Zhe Chen,et al.  A novel technique to enhance demand responsiveness: An EV based test case , 2015, 2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[26]  Karl Aberer,et al.  Electricity load forecasting for residential customers: Exploiting aggregation and correlation between households , 2013, 2013 Sustainable Internet and ICT for Sustainability (SustainIT).

[27]  Vincent W. S. Wong,et al.  Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design , 2012, IEEE Transactions on Smart Grid.

[28]  Salil S. Kanhere,et al.  Can smart plugs predict electric power consumption?: a case study , 2014, MobiQuitous.

[29]  Claire J. Tomlin,et al.  Residential demand response targeting using machine learning with observational data , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[30]  Lingyang Song,et al.  Residential Load Scheduling in Smart Grid: A Cost Efficiency Perspective , 2016, IEEE Transactions on Smart Grid.

[31]  Wolf Fichtner,et al.  Load-shifting potentials in households including electric mobility - A comparison of user behaviour with modelling results , 2013, 2013 10th International Conference on the European Energy Market (EEM).

[32]  Mark W. Newman,et al.  Making sustainability sustainable: challenges in the design of eco-interaction technologies , 2014, CHI.

[33]  Bo Thiesson,et al.  Evaluating the Value of Flexibility in Energy Regulation Markets , 2015, e-Energy.

[34]  Jesper Kjeldskov,et al.  HeatDial: Beyond User Scheduling in Eco-Interaction , 2016, NordiCHI.

[35]  Shing-Chow Chan,et al.  Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing , 2012, IEEE Transactions on Smart Grid.

[36]  Gerard J. M. Smit,et al.  Generation of flexible domestic load profiles to evaluate Demand Side Management approaches , 2016, 2016 IEEE International Energy Conference (ENERGYCON).