Quantifying economic benefits in the ancillary electricity market for smart appliances in Singapore households

The adoption of smart grid systems has been accelerating around the world, with many pilot projects initiated throughout the developed world. However, there has been a lack of studies quantifying appliance level economic benefits that are available to the end consumers. Through coupling historical market information with an agent based residential load model, this study has quantified the economic benefits of residential smart grid participation in ancillary electricity markets. In this study, we looked at the energy market of Singapore. The regulation and reserve ancillary electricity markets were examined and the associated economic benefits from market participation were assumed to be fully passed on to the consumers. A lesson is that the potential returns for consumer investment in smart appliance technology could be very low. No matter which market that the aggregator participates in, the corresponding credits that a single appliance can earn for the consumer through ancillary markets may not be attractive enough for the consumer. Although there could be other cost saving options such as dynamic electricity prices which could be complementary to such schemes; this result highlights the fact that economic benefits alone may not be attractive enough for smart appliance adoption in the current local policy climate.

[1]  Gintaras V. Reklaitis,et al.  The effects of vehicle-to-grid systems on wind power integration , 2012 .

[2]  Ahmad Faruqui,et al.  Unlocking the €53 Billion Savings from Smart Meters in the EU - How Increasing the Adoption of Dynamic Tariffs Could Make or Break the EU’s Smart Grid Investment , 2009 .

[3]  J. Torriti,et al.  A review of the costs and benefits of demand response for electricity in the UK , 2013 .

[4]  Gintaras V. Reklaitis,et al.  Quantifying System-Level Benefits from Distributed Solar and Energy Storage , 2012 .

[5]  Bala Venkatesh,et al.  A probabilistic reserve market incorporating interruptible load , 2006 .

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

[7]  A. Faruqui,et al.  Quantifying Customer Response to Dynamic Pricing , 2005 .

[8]  Chau Yuen,et al.  Electricity cost minimization for a residential smart Grid with distributed generation and bidirectional power transactions , 2013, 2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT).

[9]  P. Cappers,et al.  Demand Response in U.S. Electricity Markets: Empirical Evidence , 2010 .

[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]  Alec Brooks,et al.  Demand Dispatch , 2010, IEEE Power and Energy Magazine.

[12]  Yongliang Wang,et al.  Fisher information matrix, Rao test, and Wald test for complex-valued signals and their applications , 2014, Signal Process..

[13]  M. Roth,et al.  Diurnal and weekly variation of anthropogenic heat emissions in a tropical city, Singapore , 2012 .

[14]  Jukka Paatero,et al.  A model for generating household electricity load profiles , 2006 .

[15]  M. Loock,et al.  Customer value of smart metering: Explorative evidence from a choice-based conjoint study in Switzerland , 2013 .

[16]  Karen Herter Residential implementation of critical-peak pricing of electricity , 2007 .

[17]  Chi-Keung Woo,et al.  Residential winter kWh responsiveness under optional time-varying pricing in British Columbia , 2013 .

[18]  Lazaros Gkatzikis,et al.  The Role of Aggregators in Smart Grid Demand Response Markets , 2013, IEEE Journal on Selected Areas in Communications.

[19]  Mariesa Crow,et al.  The New Centurions , 2010, IEEE Power and Energy Magazine.

[20]  Gintaras V. Reklaitis,et al.  A multi-paradigm modeling framework for energy systems simulation and analysis , 2011, Comput. Chem. Eng..

[21]  Bri-Mathias Hodge,et al.  The effects of electric vehicles on residential households in the city of Indianapolis , 2012 .

[22]  W. Fichtner,et al.  Smart Homes as a Means to Sustainable Energy Consumption: A Study of Consumer Perceptions , 2012 .

[23]  Chau Yuen,et al.  Demand response management for power throttling air conditioning loads in residential Smart Grids , 2014, 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[24]  Hanne Sæle,et al.  Demand Response From Household Customers: Experiences From a Pilot Study in Norway , 2011, IEEE Transactions on Smart Grid.

[25]  D. Mah,et al.  Consumer perceptions of smart grid development: Results of a Hong Kong survey and policy implications , 2012 .

[26]  K. Schisler,et al.  The role of demand response in ancillary services markets , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[27]  Lingfeng Wang,et al.  Smart meters for power grid — Challenges, issues, advantages and status , 2011, 2011 IEEE/PES Power Systems Conference and Exposition.

[28]  Gintaras V. Reklaitis,et al.  The effects of electricity pricing on PHEV competitiveness , 2011 .

[29]  Madeleine Gibescu,et al.  Scenario-based modelling of future residential electricity demands and assessing their impact on distribution grids , 2013 .

[30]  Liang He,et al.  Design of a Mobile Charging Service for Electric Vehicles in an Urban Environment , 2015, IEEE Transactions on Intelligent Transportation Systems.

[31]  Chau Yuen,et al.  Peak-to-Average Ratio Constrained Demand-Side Management With Consumer's Preference in Residential Smart Grid , 2014, IEEE Journal of Selected Topics in Signal Processing.

[32]  Saifur Rahman,et al.  Grid Integration of Electric Vehicles and Demand Response With Customer Choice , 2012, IEEE Transactions on Smart Grid.