Smart consumer load balancing: state of the art and an empirical evaluation in the Spanish electricity market

The basis of an efficient functioning of a power grid is an accurate balancing of the electricity demand of all the consumers at any instant with supply. Nowadays, this task involves only the grid operator and retail electricity providers. One of the facets of the Smart Grid vision is that consumers may have a more active role in the problem of balancing demand with supply. With the deployment of intelligent information and communication technologies in domestic environments, homes are becoming smarter and able to play a more active role in the management of energy. We use the term Smart Consumer Load Balancing to refer to algorithms that are run by energy management systems of homes in order to optimise the electricity consumption, to minimise costs and/or meet supply constraints. In this work, we analyse different approaches to Smart Consumer Load Balancing based on (distributed) artificial intelligence. We also put forward a new model of Smart Consumer Load Balancing, where consumers actively participate in the balancing of demand with supply by forming groups that agree on a joint demand profile to be contracted in the market with the mediation of an aggregator. We specify the business model as well as the optimisation model for load balancing, showing the economic benefits for the consumers in a realistic scenario based on the Spanish electricity market.

[1]  S. Karnouskos,et al.  Smart Houses for a Smart Grid , 2009 .

[2]  Martijn Brons,et al.  MARKET PENETRATION SCENARIOS OF ELECTRIC-DRIVE VEHICLES , 2011 .

[3]  A. Farinelli,et al.  Coalitional energy purchasing in the smart grid , 2012, 2012 IEEE International Energy Conference and Exhibition (ENERGYCON).

[4]  Wolfgang Ketter,et al.  Demand side management—A simulation of household behavior under variable prices , 2011 .

[5]  D. Kirschen,et al.  Quantifying the Effect of Demand Response on Electricity Markets , 2007, IEEE Transactions on Power Systems.

[6]  Ramachandra Kota,et al.  Cooperatives for Demand Side Management , 2012, ECAI.

[7]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[8]  Sarvapali D. Ramchurn,et al.  Putting the 'smarts' into the smart grid , 2012, Commun. ACM.

[9]  Alberto Hernandez Neto,et al.  Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption , 2008 .

[10]  Juan M. Morales,et al.  Real-Time Demand Response Model , 2010, IEEE Transactions on Smart Grid.

[11]  P. H. Schavemaker,et al.  Electrical Power System Essentials , 2008 .

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

[13]  Vedran Podobnik,et al.  The CrocodileAgent 2012: Negotiating Agreements in Smart Grid Tariff Market , 2012, AT.

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

[15]  Robert Schober,et al.  Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[16]  Martijn Brons,et al.  Plug-in Hybrid and Battery Electric Vehicles. Market penetration scenarios of electric drive vehicles , 2010 .

[17]  Spyridon L. Tompros,et al.  Enabling applicability of energy saving applications on the appliances of the home environment , 2009, IEEE Network.

[18]  Goran Strbac,et al.  Demand side management: Benefits and challenges ☆ , 2008 .

[19]  Sarvapali D. Ramchurn,et al.  Agent-based micro-storage management for the Smart Grid , 2010, AAMAS.

[20]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[21]  Sascha Ossowski,et al.  A Collaborative Model for Participatory Load Management in the Smart Grid , 2012, AT.