Unified System-Level Modeling of Intermittent Renewable Energy Sources and Energy Storage for Power System Operation

The system-level consideration of intermittent renewable energy sources (RES) and small-scale energy storage in power systems remains a challenge as either type is incompatible with traditional operation concepts. Noncontrollability and energy constraints are still considered contingent cases in market-based operation. The design of operation strategies for up to 100% RES power systems requires an explicit consideration of nondispatchable generation and storage capacities, as well as the evaluation of operational performance in terms of energy efficiency, reliability, environmental impact, and cost. By abstracting from technology-dependent and physical unit properties, the power nodes modeling framework presented here allows the representation of a technologically diverse unit portfolio with a unified approach, while establishing the feasibility of energy-storage consideration in power system operation. After introducing the modeling approach, a case study is presented for illustration.

[1]  Madeleine Gibescu,et al.  A dynamic aggregate model for the simulation of short term power fluctuations , 2010, ICCS.

[2]  Morten Lind,et al.  Control architecture of power systems: Modeling of purpose and function , 2009, 2009 IEEE Power & Energy Society General Meeting.

[3]  Bart De Schutter,et al.  Multi-area predictive control for combined electricity and natural gas systems , 2009, 2009 European Control Conference (ECC).

[4]  Eric Hirst,et al.  Separating and measuring the regulation and load-following ancillary services , 1999 .

[5]  Dorota Kurowicka,et al.  Integration of stochastic generation in power systems , 2006 .

[6]  Ehud I. Ronn.  Real Options and Energy Management , 2002 .

[7]  Magnus Korpås,et al.  Improving the network infeed accuracy of non-dispatchable generators with energy storage devices ! , 2008 .

[8]  G. Strbac,et al.  Value of Bulk Energy Storage for Managing Wind Power Fluctuations , 2007, IEEE Transactions on Energy Conversion.

[9]  Danny Pudjianto,et al.  Virtual power plant and system integration of distributed energy resources , 2007 .

[10]  C N Rasmussen.  Improving wind power quality with energy storage , 2009, 2009 IEEE PES/IAS Conference on Sustainable Alternative Energy (SAE).

[11]  Julia Hertin,et al.  The Future of the European Electricity Supply: Moving from Energy-Mix Projections to Renewables-Based Scenarios , 2010 .

[12]  Goran Andersson,et al.  General frequency control with aggregated control reserve capacity from time-varying sources: The case of PHEVs , 2010, 2010 IREP Symposium Bulk Power System Dynamics and Control - VIII (IREP).

[13]  N. S. Rau,et al.  Optimum location of resources in distributed planning , 1994 .

[14]  Goran Andersson,et al.  Framework for Multiple Time-Scale Cascaded MPC Application in Power Systems , 2011 .

[15]  Florian Kienzle,et al.  Evaluation of Investments in Multi-Carrier Energy Systems under Uncertainty , 2010 .

[16]  Heike Brand,et al.  WILMAR: A Stochastic Programming Tool to Analyze the Large-Scale Integration of Wind Energy , 2009 .

[17]  Nadejda Komendantova,et al.  Renewables 2012 Global Status Report , 2012 .

[18]  M. Nicolosi.  Wind power integration and power system flexibility–An empirical analysis of extreme events in Germany under the new negative price regime , 2010 .

[19]  S.R. Sanders,et al.  An Architecture for Local Energy Generation, Distribution, and Sharing , 2008, 2008 IEEE Energy 2030 Conference.

[20]  Goran Andersson,et al.  Reliability modeling of multi-carrier energy systems , 2009 .

[21]  Eduardo Camponogara,et al.  Distributed model predictive control , 2002 .

[22]  Stephan Koch,et al.  Active Coordination of Thermal Household Appliances for Load Management Purposes , 2009 .

[23]  G. Andersson,et al.  Energy hubs for the future , 2007, IEEE Power and Energy Magazine.

[24]  G.W. Ault,et al.  Ubiquitous energy storage , 2005, 2005 International Conference on Future Power Systems.

[25]  Brian Vad Mathiesen,et al.  A review of computer tools for analysing the integration of renewable energy into various energy systems , 2010 .

[26]  Kai Heussen,et al.  Energy storage in power system operation: The power nodes modeling framework , 2010, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe).

[27]  G. Andersson,et al.  Demand Management of Grid Connected Plug-In Hybrid Electric Vehicles (PHEV) , 2008, 2008 IEEE Energy 2030 Conference.

[28]  D. Kirschen,et al.  Fundamentals of power system economics , 1991 .

[29]  Sioe T. Mak.  Knowledge based architecture serving as a rigid framework for Smart Grid applications , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[30]  Morten Lind,et al.  Requirement analysis for autonomous systems and intelligent agents in future Danish electric power systems , 2010 .

[31]  Boon-Teck Ooi,et al.  Macromodel of Spatial Smoothing in Wind Farms , 2007, IEEE Transactions on Energy Conversion.

[32]  Paul Hines,et al.  Controlling cascading failures with cooperative autonomous agents , 2007, Int. J. Crit. Infrastructures.

[33]  B. C. Ummels,et al.  Power system operation with large-scale wind power in liberalised environments , 2009 .