Abstract
Smart grid is a recently growing area of research including optimum and reliable operation of bulk power grid from production to end-user premises. Demand side activities like demand response (DR) for enabling consumer participation are also vital points for a smarter operation of the electric power grid. For DR activities in end-user level regulated by energy management systems, a dynamic price variation determined by optimum operating strategies should be provided aiming to shift peak demand periods to off-peak periods of energy usage. In this regard, an optimum generation scheduling based price making strategy is evaluated in this paper together with the analysis of the impacts of dynamic pricing on demand patterns with case studies. Thus, the importance of considering DR based demand pattern changes on price making strategy is presented for day-ahead energy market structure.
Chapter PDF
Similar content being viewed by others
References
Gellings, C.W.: The Smart Grid: Enabling Energy Efficiency and Demand Response. CRC Press (2009)
Borlease, S.: Smart Grids: Infrastructure, Technology and Solutions. CRC Press (2013)
Simoglou, C.K., Biskas, P.N., Bakirtzis, A.G.: A MILP approach to the short term hydrothermal self-scheduling problem. In: IEEE Bucharest Power Tech Conference (2009)
Morales, J.M., Conejo, A.J., Ruiz, J.P.: Economic valuation of reserves in power systems with high penetration of wind power. IEEE Trans. Power Systems 24, 900–910 (2009)
Chen, Z., Wu, L., Fu, Y.: Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Trans. Smart Grid 3, 1822–1831 (2012)
Tsui, K.M., Chan, S.C.: Demand response optimization for smart home scheduling under real-time pricing. IEEE Trans. Smart Grid 3, 1812–1821 (2012)
Pipattanasomporn, M., Kuzlu, M., Rahman, S.: An algorithm for intelligent home energy management and demand response analysis. IEEE Trans. Smart Grid 3, 2166–2173 (2012)
Kuzlu, M., Pipattanasomporn, M., Rahman, S.: Hardware demonstration of a home energy management system for demand response applications. IEEE Trans. SmartGrid 3, 1704–1711 (2012)
Shao, S., Pipattanasomporn, M., Rahman, S.: Demand response as a load shaping tool in an intelligent grid with electric vehicles. IEEE Trans. Smart Grid 2, 624–631 (2011)
De Angelis, F., Boaro, M., Squartini, S., Piazza, F., Wei, Q.: Optimal home energy management under dynamic electrical and thermal constraints. IEEE Trans. Industrial Informatics 9, 1518–1527 (2013)
Chen, X., Wei, T., Hu, S.: Uncertainty-aware household appliance scheduling considering dynamic electricity pricing in smart home. IEEE Trans. Smart Grid 4, 932–941 (2013)
GM Chevy Volt specifications, http://gm-volt.com/full-specifications/
Hellenic Statistical Authority, http://www.statistics.gr
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 IFIP International Federation for Information Processing
About this paper
Cite this paper
Paterakis, N.G., Erdinc, O., Catalão, J.P.S., Bakirtzis, A.G. (2014). Optimum Generation Scheduling Based Dynamic Price Making for Demand Response in a Smart Power Grid. In: Camarinha-Matos, L.M., Barrento, N.S., Mendonça, R. (eds) Technological Innovation for Collective Awareness Systems. DoCEIS 2014. IFIP Advances in Information and Communication Technology, vol 423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54734-8_41
Download citation
DOI: https://doi.org/10.1007/978-3-642-54734-8_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54733-1
Online ISBN: 978-3-642-54734-8
eBook Packages: Computer ScienceComputer Science (R0)