Managing energy in time and space in smart grids using TRIANA

Increasing electrification will push low-voltage networks to the limits of their capacity and imposes serious threats to the supplied power quality (PQ). Demand side management (DSM) is often seen as a promising solution. In this paper we exploit network models to improve the performance of DSM on a physical grid by managing energy in both time and space. The TRIANA methodology is used to integrate network topologies. Simulated results show that this approach is promising. With the integration of network models, no PQ violations of the EN-50160 regulations are reported and no network asset is overloaded. The distribution losses are reduced by 26.9% when compared to the approaches where no grid topology is considered. These improvements are achieved without significant load profile flattening performance reductions.

[1]  Johan Driesen,et al.  LV distribution network feeders in Belgium and power quality issues due to increasing PV penetration levels , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[2]  Han La Poutré,et al.  Towards a European Smart Energy System - ICT innovation goals and considerations , 2014 .

[3]  M.P.F. Hommelberg,et al.  A novel architecture for real-time operation of multi-agent based coordination of demand and supply , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[4]  Gerard J. M. Smit,et al.  Integration of heat pumps in distribution grids: Economic motivation for grid control , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[5]  Andrew Keane,et al.  Impact of high penetrations of micro-generation on low voltage distribution networks , 2009 .

[6]  J.K.A.H.P.J. Kok,et al.  The PowerMatcher: Smart Coordination for the Smart Electricity Grid , 2013 .

[7]  Gerard J. M. Smit,et al.  Comparing demand side management approaches , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[8]  Albert Molderink,et al.  A three-step methodology to improve domestic energy efficiency , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[9]  Ray D. Zimmerman,et al.  Comprehensive distribution power flow: modeling, formulation, solution algorithms and analysis , 1996 .

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

[11]  Mattijs Ghijsen,et al.  Market-based coordinated charging of electric vehicles on the low-voltage distribution grid , 2011, 2011 IEEE First International Workshop on Smart Grid Modeling and Simulation (SGMS).

[12]  Koen Vanthournout,et al.  Distributed voltage control mechanism in low-voltage distribution grid field test , 2013, IEEE PES ISGT Europe 2013.

[13]  Geert Deconinck,et al.  Reducing overvoltage problems with active power curtailment —Simulation results , 2013, IEEE PES ISGT Europe 2013.

[14]  Gerard J. M. Smit,et al.  On simulating the effect on the energy efficiency of smart grid technologies , 2010, Proceedings of the 2010 Winter Simulation Conference.

[15]  Gerard J. M. Smit,et al.  Integrating LV network models and load-flow calculations into smart grid planning , 2013, IEEE PES ISGT Europe 2013.

[16]  Albert Molderink,et al.  Comparative analysis of tertiary control systems for smart grids using the Flex Street model , 2014 .

[17]  M. Thomson,et al.  Network Power-Flow Analysis for a High Penetration of Distributed Generation , 2006, IEEE Transactions on Power Systems.