Seasonal Effect on the Flexibility Assessment of Electrical Demand

With the modernization of the electricity grid, the role of the demand side becomes more vital to balance the mismatch between supply and demand. Demand flexibility assessment is a key aspect to design and initiate demand response (DR) programs. In particular, flexibility assessment of the residential electrical demand is important due to the diversity of energy usage of the residents during different time slots of a day. In this paper, results of the flexibility assessment of aggregate electrical demand are provided for different seasons by using a flexibility indicator based on the binomial probability distribution model. The information extracted from this analysis gives a comparison of demand flexibility for different seasons of the year, which is beneficial for a system operator or an aggregator to design and carry out DR programs.

[1]  O. K. Larsen,et al.  Household electricity demand profiles – A high-resolution load model to facilitate modelling of energy flexible buildings , 2016 .

[2]  Johanna L. Mathieu,et al.  Uncertainty in the flexibility of aggregations of demand response resources , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[3]  Brian Vad Mathiesen,et al.  Comparative analyses of seven technologies to facilitate the integration of fluctuating renewable energy sources , 2009 .

[4]  David E. Culler,et al.  Flexible loads in future energy networks , 2013, e-Energy '13.

[5]  Lieve Helsen,et al.  Bottom-up Quantification Of The Flexibility Potential Of Buildings , 2013, Building Simulation Conference Proceedings.

[6]  N. Menemenlis,et al.  Thoughts on power system flexibility quantification for the short-term horizon , 2011, 2011 IEEE Power and Energy Society General Meeting.

[7]  G. Chicco,et al.  Characterisation of the aggregated load patterns for extraurban residential customer groups , 2004, Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference (IEEE Cat. No.04CH37521).

[8]  R. Newcombe Two-sided confidence intervals for the single proportion: comparison of seven methods. , 1998, Statistics in medicine.

[9]  Gianfranco Chicco,et al.  Effect of aggregation level and sampling time on load variation profile — A statistical analysis , 2014, MELECON 2014 - 2014 17th IEEE Mediterranean Electrotechnical Conference.

[10]  Saifur Rahman,et al.  Development of physical-based demand response-enabled residential load models , 2013, IEEE Transactions on Power Systems.

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

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

[13]  Tyrone L. Vincent,et al.  Potentials and Economics of Residential Thermal Loads Providing Regulation Reserve , 2014, ArXiv.

[14]  Gianfranco Chicco,et al.  A probabilistic approach to study the load variations in aggregated residential load patterns , 2014, 2014 Power Systems Computation Conference.

[15]  Thomas Demeester,et al.  Modeling and analysis of residential flexibility: Timing of white good usage , 2016 .

[16]  Birgitte Bak-Jensen,et al.  Probabilistic quantification of potentially flexible residential demand , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[17]  Luigi Martirano,et al.  Flexibility assessment indicator for aggregate residential demand , 2017, 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe).

[18]  L. Brown,et al.  Interval Estimation for a Binomial Proportion , 2001 .

[19]  Gianfranco Chicco,et al.  Definitions of Demand Flexibility for Aggregate Residential Loads , 2016, IEEE Transactions on Smart Grid.

[20]  Sean P. Meyn,et al.  Ancillary Service to the Grid Through Control of Fans in Commercial Building HVAC Systems , 2014, IEEE Transactions on Smart Grid.

[21]  Thomas E. Carroll,et al.  Generalized aggregation and coordination of residential loads in a smart community , 2015, 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[22]  Jianzhong Wu,et al.  Flexible Demand in the GB Domestic Electricity Sector in 2030 , 2015 .

[23]  R. Belhomme,et al.  Evaluating and planning flexibility in sustainable power systems , 2013, 2013 IEEE Power & Energy Society General Meeting.