Abstract
The increasing penetration of distributed energy sources, mainly based on renewable generation, calls for an urgent emergence of novel advanced methods to deal with the associated problems. The consensus behind smart grids (SGs) as one of the most promising solutions for the massive integration of renewable energy sources in power systems has led to the development of several prototypes that aim at testing and validating SG methodologies. The urgent need to accommodate such resources require alternative solutions. This chapter presents a multi-agent based SG simulation platform connected to physical resources, so that realistic scenarios can be simulated. The SG simulator is also connected to the Multi-Agent Simulator of Competitive Electricity Markets, which provides a solid framework for the simulation of electricity markets. The cooperation between the two simulation platforms provides huge studying opportunities under different perspectives, resulting in an important contribution to the fields of transactive energy, electricity markets, and SGs. A case study is presented, showing the potentialities for interaction between players of the two ecosystems: a SG operator, which manages the internal resources of a SG, is able to participate in electricity market negotiations to trade the necessary amounts of power to fulfill the needs of SG consumers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
European Commission: The 2020 climate and energy package (2009)
European Commission: 2030 Framework for climate and energy policies (2014). https://ec.europa.eu/clima/policies/strategies/2030_en. Accessed 10 September 2017
Sioshansi, P.: Evolution of Global Electricity Markets. New paradigms, New Challenges, New Approaches. Academic Press, Oxford (2013)
PCR: EUPHEMIA public description: PCR market coupling algorithm. Price coupling of regions (2014). https://www.apxgroup.com/wp-content/uploads/Euphemia-public-description-Nov-20131.pdf. Accessed 15 September 2017
Shahidehpour, M., Yamin, H., Li, Z.: Market Operations in Electric Power Systems: Forecasting, Scheduling, and Risk Management. Wiley, New York (2002)
Saber, Y., Venayagamoorthy, K.: Resource scheduling under uncertainty in a smart grid with renewables and plug-in vehicles. IEEE Syst. J. 4, 103–109 (2012)
Gomes, L., Faria, P., Morais, H., Vale, Z., Ramos, C.: Distributed, agent-based intelligent system for demand response program simulation in smart grids. IEEE Intell. Syst. 29, 56–65 (2014)
Borlase, S.: Smart Grids: Infrastructure, Technology, and Solutions. CRC Press, New york (2013)
Covrig, C., Ardelean, M., Vasiljevska, J., Mengolini, J., Fuli, G., Amoiralis, E.: Smart Grid Projects Outlook 2014. Science and Policy Report by the Joint Research Centre of the European Commission, Luxembourg (2014)
European Commission: Incorporing Demand Side Flexibility, in Particular Demand Response, in Electricity Markets. Commission Staff Working Document (2013)
DOE: Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them. A Report to the United States Congress Pursuant to Section 1252 of the Energy Policy Act of 2005, US Department of Energy (2006)
Faria, P., Vale, Z., Baptista, J.: Constrained consumption shifting management in the distributed energy resources scheduling considering demand response. Energy Convers. Manag. 93, 309–320 (2015)
Walton, R.: 2014 for Demand Response: The Best of Times, the Worst of Times. Utility Dive (2014)
Oliveira, P., Pinto, T., Morais, H., Vale, Z.: MASGriP - a multi-agent smart grid simulation platform. In: IEEE Power and Energy Society General Meeting, pp. 1–5. IEEE Press (2012)
Fernandes, F., Silva, M., Faria, P., Vale, Z., Ramos, C., Morais, H.: Real-time simulation of energy management in a domestic consumer. In: IEEE/PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), pp. 1–5. IEEE Press (2013)
Praça, I., Ramos, C., Vale, Z., Cordeiro, M.: MASCEM: a multiagent system that simulates competitive electricity markets. IEEE Intell. Syst. 18(6), 54–60 (2003)
Pinto, T., Vale, Z., Sousa, T., Praça, I., Santos, G., Morais, H.: Adaptive learning in agents behaviour: a framework for electricity markets simulation. Integr. Comput. Aided Eng. 21(4), 399–415 (2014)
Pinto, T., Morais, H., Oliveira, P., Vale, Z., Praça, I., Ramos, C.: A new approach for multi-agent coalition formation and management in the scope of electricity markets. Energy 36(8), 5004–5015 (2011)
Teixeira, B., Silva, F., Pinto, T., Praça, I., Santos, G., Vale, Z.: Data mining approach to support the generation of realistic scenarios for multi-agent simulation of electricity markets. In: IEEE Symposium on Intelligent Agents (IA), pp. 1–5. IEEE Press (2014)
FIPA: Agent management specification. Foundation for intelligent physical agents, Document number SC00023K (2004). http://www.fipa.org/specs/fipa00023/SC00023K.pdf. Accessed 15 September 2017
FIPA: FIPA ACL message structure specification. Foundation for intelligent physical agents, Document number SC00061G (2002). http://www.fipa.org/specs/fipa00061/SC00061G.pdf. Accessed 15 September 2017
Santos, G., Pinto, T., Morais, H., Sousa, T., Pereira, I., Fernandes, R., Praça, I., Vale, Z.: Multi-agent simulation of competitive electricity markets: autonomous systems cooperation for European market modelling. Energy Convers. Manag. 99, 387–399 (2015)
Moran, D., Suzuki, J.: Curtailment service providers: they bring the horse to water\(\dots \) do we care if it drinks? In: ACEEE Summer Study on Energy Efficiency in Buildings, pp. 287-298. ACEEE Publications (2010)
Fernandes, F., Morais, H., Vale, Z., Ramos, C.: Dynamic load management in a smart home to participate in demand response events. Energy Build. 82, 59–606 (2014)
OPAL-RT: OP5600 Off-the-shelf Hardware-in-the-Loop (HIL) simulator. OPAL-RT Technologies, Inc., Québec, Canada. www.opal-rt.com. Accessed 15 September 2017
Pinto, T., Sousa, T., Vale, Z.: Dynamic artificial neural network for electricity market prices forecast. In: IEEE International Conference on Intelligent Engineering Systems (INES), pp. 1–5. IEEE Press (2012)
Pinto, T., Ramos, S., Sousa, T., Vale, Z.: Short-term wind speed forecasting using support vector machines. In: IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), pp. 1–4. IEEE Press (2014)
Marques, L., Pinto, T., Sousa, T., Praça, I., Vale, Z., Abreu, S.: Solar intensity forecasting using artificial neural networks and support vector machines. In: 2nd ELECON Workshop – Consumer control in Smart Grids, pp. 83–93. ELECON Press(2014)
Ramos, S., Soares, J., Vale, Z., Ramos, S.: Short-term load forecasting based on load profiling. In: IEEE Power and Energy Society General Meeting (PES), pp. 1–5. IEEE Press (2013)
Mitra, J., Suryanarayanan, S.: System Analytics for Smart Microgrids. In: IEEE Power and Energy Society General Meeting (PES), pp. 1–4. IEEE Press (2010)
Acknowledgements
This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N. 641794 (project DREAM-GO). It has also received FEDER Funds through the COMPETE program and National Funds through FCT under the project UID/EEA/00760/2013. The authors gratefully acknowledge the valuable contribution of Bruno Canizes, Daniel Paiva, Gabriel Santos and Marco Silva to the work presented in the chapter.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Pinto, T., Vale, Z., Praça, I., Gomes, L., Faria, P. (2018). Multi-agent Electricity Markets and Smart Grids Simulation with Connection to Real Physical Resources. In: Lopes, F., Coelho, H. (eds) Electricity Markets with Increasing Levels of Renewable Generation: Structure, Operation, Agent-based Simulation, and Emerging Designs. Studies in Systems, Decision and Control, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-319-74263-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-74263-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74261-8
Online ISBN: 978-3-319-74263-2
eBook Packages: EngineeringEngineering (R0)