Potential of residential demand flexibility - Italian scenario

Aggregator in a microgrid is responsible for its secure and economic operation. As far as system economics is concerned, there are many factors upon which energy cost is dependent, for example peak demand rates and penalties due to violations in energy purchase contracts. Extra charges due to high energy demand and contract violation penalties can be avoided using demand side flexibility. Demand side flexibility has many benefits in normal as well as emergency conditions like less cost and quick response. Residential loads are the major part to be supplied and have 7 days and 24 hour availability for flexible operation. This paper presents the potential for effective use of demand flexibility from residential customers for peak reduction. Demand flexibilities are calculated with Monte Carlo Simulation using probabilistic data of Italian households. Different scenarios are generated to demonstrate the effectiveness of flexibility in residential sector.

[1]  B. HASTINGS,et al.  Ten Years of Operating Experience with a Remote Controlled Water Heater Load Management System at Detroit Edison , 1980, IEEE Transactions on Power Apparatus and Systems.

[2]  N. Hatziargyriou,et al.  Microgrids: an overview of ongoing research, development, anddemonstration projects , 2007 .

[3]  D. Koester,et al.  Hot spot cooling using embedded thermoelectric coolers , 2006, Twenty-Second Annual IEEE Semiconductor Thermal Measurement And Management Symposium.

[4]  Yong Choi,et al.  Optimization of pinhole collimator for small animal SPECT using Monte Carlo simulation , 2002 .

[5]  Furong Li,et al.  Demand response in the UK's domestic sector , 2009 .

[6]  F. Schweppe.  Spot Pricing of Electricity , 1988 .

[7]  Chris Marnay,et al.  Integration of distributed energy resources. The CERTS Microgrid Concept , 2002 .

[8]  N. Kumarappan,et al.  Evaluation of composite reliability indices based on non-sequential Monte Carlo simulation and particle swarm optimization , 2010, IEEE Congress on Evolutionary Computation.

[9]  Katarina Kostkova,et al.  An introduction to load management , 2013 .

[10]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[11]  I. Mansouri,et al.  Energy consumption in UK households: Impact of domestic electrical appliances , 1996 .

[12]  L. Bertling,et al.  A Monte Carlo Simulation Model for Multi-Area Generation Reliability Evaluation , 2006, 2006 International Conference on Probabilistic Methods Applied to Power Systems.

[13]  W. Kling,et al.  Assessing the economic benefits of flexible residential load participation in the Dutch day-ahead auction and balancing market , 2012, 2012 9th International Conference on the European Energy Market.