Advances in Distributed Computing and Artificial Intelligence Journal
ENABLING COMMUNICATIONS IN HETEROGENEOUS MULTI-AGENT SYSTEMS: ELECTRICITY MARKETS ONTOLOGY
Gabriel SANTOS, Tiago PINTO, Zita VALE, Isabel PRAÇA, Hugo MORAIS
Regular Issue 5 (2), 2016
Electricity markets worldwide are complex and dynamic environments with very particular characteristics, resulting from their restructuring and evolution into regional and continental scales, along with the constant changes brought by the increasing necessity for an adequate integration of renewable energy sources. The rising complexity and unpredictability in electricity markets has increased the need for the intervenient entities in foreseeing market behaviour. Several modelling tools directed to the study of restructured wholesale electricity markets have emerged. However, they have a common limitation: the lack of interoperability between the various systems to allow the exchange of information and knowledge, to test different market models and to allow market players from different systems to interact in common market environments. This paper proposes the Electricity Markets Ontology, which integrates the essential necessary concepts related with electricity markets, while enabling an easier cooperation and adequate communication between related systems. Additionally, it can be extended and complemented according to the needs of other simulators and real systems in this area.
ACER – Agency for the Cooperation of Energy Regulators, Electricity > Regional initiatives > Cross Regional Roadmaps > Cross-Border Intraday, http://www.acer.europa.eu/en/Electricity/Regional_initiatives/Cross_Regi... [accessed on July 2016].
ACER – Agency for the Cooperation of Energy Regulators, Electricity > Regional initiatives > Cross Regional Roadmaps > Market Coupling, http://www.acer.europa.eu/en/Electricity/Regional_initiatives/Cross_Regi... [accessed on July 2016].
Alexopoulos P., Kafentzis K., and Zoumas C. 2009. ELMO: An Interoperability Ontology for the Electricity Market. Proceedings of the International Conference on e-Business, Milan, Italy.
Alvarado-Pérez J. C., Peluffo-Ordó-ez D. H., and Therón R., 2015. Bridging The Gap Between Human Knowledge And Machine Learning, Advances In Distributed Computing And Artificial Intelligence Journal, Salamanca University Press Journal, vol. 4, no.1.
CAISO – California Independent System Operator, homepage, http://www.caiso.com [accessed on July 2016].
Catterson V. et al. An upper ontology for power engineering applications. April 2010. [Online]. Available: http://sites.ieee.org/pes-mas/, [accessed on July 2016].
Catterson V., 2006. Engineering Robustness, Flexibility, and Accuracy into a Multi-Agent System for Transformer Condition Monitoring. Doctoral thesis, University of Strathclyde.
Cincotti S., and Gallo G., 2013. Genoa Artificial Power-Exchange. Agents Artificial Intelligence. Springer Berlin Heidelberg 2013:348-63. https://doi.org/10.1007/978-3-642-36907-0_23
Dai W., Dubinin V., and Vyatkin V. Automatically Generated Layered Ontological Models for Semantic Analysis of Component-Based Control Systems, 2013. IEEE Transactions on Industrial Informatics, vol.9, issue 4, pp. 2124-2136. https://doi.org/10.1109/TII.2012.2235450
Dam K., and Chapping E., 11 May 2010. Coupling agent-based models of natural gas and electricity markets. In Proceedings of the First International Workshop on Agent Technologies for Energy Systems (ATES 2010), pages 45-52.
Dam K., and Keirstead J, 11-13 November 2010. Re-use of an ontology for modelling urban energy systems. In Proceedings of the 3rd International Conference on Infrastructure Systems and Services: Next Generation Infrastructure Systems for Eco-Cities (INFRA), Shenzhen, China.
EPEXSPOT - European Power Exchange, 2016. [Online]. Homepage: https://www.epexspot.com/, [accessed on July 2016].
EPEXSPOT - European Power Exchange, Products, Day-Ahead Auction, 2015. [Online]. Available: https://www.epexspot.com/en/product-info/auction, [accessed on July 2016].
EUPHEMIA Public Description, PCR Market Coupling Algorithm, January 2016, available: https://www.epexspot.com/document/34460/Euphemia%3A%20Public%20documenta... [accessed on July, 2016].
Ferber J., 1999. Multi-Agent system: an introduction to distributed artificial intelligence. Harlow: Addison Wesley Longman.
Foundation for Intelligent Physical Agents (FIPA), ACL Message Structure Specification, 2002. [Online]. Available: http://www.fipa.org/specs/fipa00061/, [accessed on July 2016].
Foundation for Intelligent Physical Agents (FIPA), FIPA Agent Management Specification, 2004. [Online]. Available: http://fipa.org/specs/fipa00023/, [accessed on July 2016].
Foundation for Intelligent Physical Agents (FIPA), FIPA Ontology Service Specification, 2001. [Online]. Available: http://www.fipa.org/specs/fipa00086/XC00086D.html, [accessed on September 2015].
Frikha M., Mhiri M., and Gargour F., 2015. A Semantic Social Recommender System Using Ontologies Based Approach For Tunisian Tourism. Advances, in Distributed Computing And Artificial Intelligence Journal, Salamanca University Press Journal, vol. 4, no.1.
Gestori Mercati Energetici, homepage, 2016. [Online]. Available: http://www.mercatoelettrico.org/En/Default.aspx, [accessed on July 2016].
Koritarov V., 2004. Real-World Market Representation with Agents: Modeling the Electricity Market as a Complex Adaptive System with an Agent-Based Approach. IEEE Power & Energy magazine, pp. 39-46. https://doi.org/10.1109/MPAE.2004.1310872
Li H., and Tesfatsion L., 2009. Development of Open Source Software for Power Market Research: The AMES Test Bed. Journal of Energy Markets, vol. 2, no. 2, pp. 111-128. https://doi.org/10.21314/JEM.2009.020
McArthur S., et al., 2007. Multi-agent systems for power engineering applications - part ii: Technologies, standards, and tools for building multi-agent systems. Power Systems, IEEE Transactions on.
Meeus L, Purchalaa K, and Belmans R., 2005. Development of the internal electricity market in Europe. Electr J 18(6):25-35. https://doi.org/10.1016/j.tej.2005.06.008
MIBEL - Mercado Ibérico de Electricidade, 2016. [Online]. Homepage: http://www.mibel.com/, [accessed on July 2016].
MISO Energy, homepage, http://www.misoenergy.org [accessed on July 2016].
Morais H., et al., 2014. SOSPO-SP: secure operation of sustainable power systems simulation platform for real-time system state evaluation and control. IEEE Trans Ind Inf: 2318-29.
Pinto T., Morais H., Sousa T.M., Sousa T., Vale Z., Praça I., Faia R., and Pires E.J.S., August 2016. Adaptive Portfolio Optimization for Multiple Electricity Markets Participation. IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no.8, pp. 1720-1733. https://doi.org/10.1109/TNNLS.2015.2461491
Pinto T., Vale Z., Rodrigues F., Morais H., and Praça I., 2011. Bid definition method for electricity markets based on an adaptive multiagent system. Advances on Practical Applications of Agents and Multiagent Systems, 309-316.
Pinto T., Vale Z., Sousa T.M., and Praça, I., June 2015. Negotiation Context Analysis in Electricity Markets. Energy, Elsevier, vol. 85, pp. 78-93. https://doi.org/10.1016/j.energy.2015.03.017
Nord Pool Spot - Trading, Day-ahead market Elspot, 2016 [Online]. Available: http://www.nordpoolspot.com/TAS/Day-ahead-market-Elspot/, [accessed on July 2016].
Nord Pool Spot, 2016 [Online]. Homepage: http://www.nordpoolspot.com/, [accessed on July 2016].
OMIE, Markets and Products, Electricity Market, About our Market, available: http://www.omie.es/en/home/markets-and-products/about-our-market, [accessed on July 2016].
ONS – Operador Nacional do Sistema Elétrico (Electrical System Nacional Operator), homepage, http://www.ons.org.br [accessed on July 2016].
Santos G., Pinto T., Morais H., Praça I., and Vale Z., 2011. Complex Market integration in MASCEM electricity market simulator. International Conference on the European Energy Market 11 – EEM. https://doi.org/10.1109/eem.2011.5953019
Santos G., et al., July 2015. Multi-Agent Simulation of Competitive Electricity Markets: Autonomous systems cooperation for European Market modelling. Energy Conversion and Management, 99, 387-399. https://doi.org/10.1016/j.enconman.2015.04.042
Santos G., Pinto T., Praça I., and Vale Z., September 2016. MASCEM: Optimizing the performance of a multi-agent system. Energy, vol. 111, pp. 513-524 https://doi.org/10.1016/j.energy.2016.05.127
Shahidehpour M, Yamin H, and Li Z, 2002. Market operations in electric power systems: forecasting, scheduling, and risk management". Wiley-IEEE Press. p. 233-74. https://doi.org/10.1002/047122412X.ch7
Sharma K.C., Bhakar R., and Tiwari, H.P., 2014. Strategic bidding for wind power producers in electricity markets. Energy Conversion and Management 86, 259–267, DOI: 10.1016/j.enconman.2014.05.002. Available online: http://www.sciencedirect.com/science/article/pii/S0196890414004129 [accessed on July 2016]. https://doi.org/10.1016/j.enconman.2014.05.002
Sioshansi F.P., 2013. "Evolution of Global Electricity Markets – New paradigms, new challenges, new approaches", Academic Press.
Vrba P., et al., 2014. A review of agent and service-oriented concepts applied to intelligent energy systems. IEEE Trans Ind Inf : 10(3):1890-903. https://doi.org/10.1109/TII.2014.2326411
Wu J., Guan X., Gao F., and Sun G., June 2008. Social welfare maximization auction for electricity markets with elastic demand. 7th World Congress on Intelligent Control and Automation, pp. 7157-7162, 25-27.