Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
As an example, the market clearing price is set in this way in the Spanish (http://www.omel.es) and British (http://www.nationalgrid.com) electricity markets.
Less formally, Constraint 4 ensures that if two slots are equal to \(W^{S_A}\), then there is no slot in between that is equal to 0.
Charging consumers for energy consumption and power capacity is common in many countries, such as Spain and UK, although in some other countries (e.g., Germany) consumers are charged exclusively for their energy consumption.
In reality, this may not be the case. For example, usually the presence of a dryer is conditioned to the presence of the washing machine.
For more details we refer the reader to Vasirani and Ossowski (2012).
References
Conejo AJ, Morales JM, Baringo L (2010) Real-time demand response model. IEEE Trans Smart Grid 1(3):236–242
Fundación de la Energía de la Comunidad de Madrid (ed) (2009) Guía del Vehículo Eléctrico. FENERCOM
Gottwalt S, Ketter W, Block C, Collins J, Weinhardt C (2011) Demand side management—a simulation of household behavior under variable prices. Energy Policy 39(12):8163–8174
Hernandez Neto A, Sanzovo Fiorelli FA (2008) Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption. Energy Build 40:2169–2176
Hubert T, Grijalva S (2011) Realizing smart grid benefits requires energy optimization algorithms at residential level. In: Proceedings of the innovative smart grid technologies conference (ISGT 2011), pp 1–8
Instituto para la Diversificación y Ahorro de la Energía (ed) (2011) Análisis del consumo energéetico del sector residencial en España. IDAE
Kok K, Karnouskos S, Nestle D, Dimeas A, Weidlich A, Warmer C, Strauss P, Buchholz B, Drenkard S, Hatziargyriou N, Lioliou V (2009) Smart houses for a smart grid. In: The proceedings of the 20th international conference and exhibition on electricity, distribution, pp 1–4
Kota R, Chalkiadakis G, Robu V, Rogers A, Jennings NR (2012) Cooperatives for demand side management. In: Proceedings of the conference on prestigious applications of intelligent systems (PAIS @ ECAI 2012), pp 969–974
Matetic S, Babic J, Matijas M, Petric A, Podobnik V (2012) The crocodileagent 2012: negotiating agreements in smart grid tariff market, volume 918 of CEUR workshop proceedings, pp 203–204
Mohsenian-Rad A, Wong VWS, Jatskevich J, Schober R (2010) Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid. In: Proceedings of the innovative smart grid technologies conference (ISGT 2010), pp 1–6
Molderink A, Bakker V, Bosman MGC, Hurink JL, Smit GJM (2010) Management and control of domestic smart grid technology. IEEE Trans Smart Grid 1(2):109–119
Nemry F, Brons M (2010) Plug-in hybrid and battery electric vehicles. Market penetration scenarios of electric drive vehicles. Technical report, JRC Technical Notes
Office of Electric Transmission and Distribution (2003) Grid 2030: a national vision for electricity’s second 100 years. Technical report, US Department of Energy
Ossowski S (ed) (2013) Agreement technologies, volume 8 of law, governance and technology. Springer, Berlin
Palensky P, Dietrich D (2011) Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Ind Inform 7(3):381–388
Ramchurn SD, Vytelingum P, Rogers A, Jennings NR (2012) Putting the ‘smarts’ into the smart grid: a grand challenge for artificial intelligence. Commun ACM 55(4):86–97
Red Eléctrica de España (ed) (1999) Atlas de la demanda eléctrica española. REE
Schavemaker P, van der Sluis L (2008) Electrical power system essentials. Wiley, New York
Seebach D, Timpe C, Bauknecht D (2009) Costs and benets of smart appliances in Europe. Technical report, WP7 Report from Smart-A Project, Öko-Institut e.V
Strbac G (2008) Demand side management: benefits and challenges. Energy Policy 36(12):4419–4426
Su C, Kirschen D (2009) Quantifying the effect of demand response on electricity markets. IEEE Trans Power Syst 24(3):1199–1207
Tompros SL, Mouratidis NP, Draaijer M, Foglar A, Hrasnica H (2009) Enabling applicability of energy saving applications on the appliances of the home environment. IEEE Netw 23(6):8–16
Vasirani M, Ossowski S (2012) A collaborative model for participatory load management in the smart grid. In: Proceedings of the first international conference on agreement technologies (AT, 2012)
Vinyals M, Bistaffa F, Farinelli A, Rogers A (2012) Coalitional energy purchasing in the smart grid. In: Proceedings of the IEEE international energy conference & exhibition (ENERGYCON 2012)
Vytelingum P, Voice TD, Ramchurn SD, Rogers A, Jennings NR (2012) Agent-based micro-storage management for the smart grid. In: Proceedings of the 9th international conference on autonomous agents and multiagent systems (AAMAS 2010), pp 39–46
Zhao H, Magoulés F (2012) A review on the prediction of building energy consumption. Renew Sustain Energy Rev 16:3586–3592
Acknowledgments
Work partially supported by the Spanish Ministry of Science and Innovation through the projects “OVAMAH” (grant TIN2009-13839-C03-02; co-funded by Plan E) and “AT” (grant CSD2007-0022; CONSOLIDER-INGENIO 2010) and by the Spanish Ministry of Economy and Competitiveness through the project “iHAS” (grant TIN2012-36586-C03-02).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Vasirani, M., Ossowski, S. Smart consumer load balancing: state of the art and an empirical evaluation in the Spanish electricity market. Artif Intell Rev 39, 81–95 (2013). https://doi.org/10.1007/s10462-012-9391-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-012-9391-6