CARBON REDUCTION POTENTIAL WITH INTELLIGENT CONTROL OF POWER SYSTEMS

Abstract Climate change caused by anthropogenic greenhouse gas (GHG) emissions such as carbon dioxide (CO2) is now widely accepted as a real condition that has potentially serious consequences for human society and industries need to factor this into their strategic plans. One salient planning assumption is that energy - essential for every activity - will become more expensive relative to other inputs. Economic growth does not have to be linked to an increase of GHG emissions and can be attained in addition to the usage of renewable energy sources by using energy efficiency technologies for power system generation, transmission, and distribution. The development of intelligent energy-efficient control technologies will both soften negative effects of the climate change on the economy and enhance energy security. This paper outlines the significant carbon reduction potential with intelligent control and optimization techniques applied to power system generation and transmission systems with and without wind farms.

[1]  Michael Grubb,et al.  The Kyoto Protocol: A Guide and Assessment , 1999 .

[2]  G.K. Venayagamoorthy,et al.  Optimal design of power system stabilizers using a small population based PSO , 2006, 2006 IEEE Power Engineering Society General Meeting.

[3]  Vincent R. Gray.  Climate Change 2007: The Physical Science Basis Summary for Policymakers , 2007 .

[4]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[5]  Ronald G. Harley,et al.  Computational Intelligence Techniques for Control of FACTS Devices , 2005 .

[6]  Rodney R. White,et al.  Carbon Finance: The Financial Implications of Climate Change , 2007 .

[7]  Partha Dasgupta,et al.  Commentary: The Stern Review's Economics of Climate Change , 2007, National Institute Economic Review.

[8]  Kwang Y. Lee,et al.  Application of Multi Objective Evolutionary Programming to Combined Economic Emission Dispatch Problem , 2007, 2007 International Joint Conference on Neural Networks.

[9]  Joseph J. Romm,et al.  Cool Companies: How the Best Businesses Boost Profits and Productivity by Cutting Greenhouse Gas Emmissions , 1999 .

[10]  U.D. Annakkage,et al.  A platform for validation of FACTS models , 2006, IEEE Transactions on Power Delivery.

[11]  N. Stern.  The Economics of Climate Change: Implications of Climate Change for Development , 2007 .

[12]  H.C.S. Rughooputh,et al.  Elitist multiobjective evolutionary algorithm for environmental/economic dispatch , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[13]  S. Lindahl,et al.  Wide area protection and emergency control , 2004 .

[14]  Rodney R. White,et al.  Environmental Finance: A Guide to Environmental Risk Assessment and Financial Products , 2002 .

[15]  J. W. Lamont,et al.  Emission dispatch models and algorithms for the 1990s , 1995 .

[16]  Wolfgang Lutz,et al.  The Kyoto Protocol: A Guide and Assessment , 1999 .

[17]  Ganesh K. Venayagamoorthy.  Development of a Computational Intelligence Course for Undergraduate and Graduate Studies , 2005 .

[18]  Floyd Trefny,et al.  Economic Fuel Dispatch , 1981, IEEE Transactions on Power Apparatus and Systems.

[19]  Paul J. Werbos,et al.  Approximate dynamic programming for real-time control and neural modeling , 1992 .

[20]  Salman Mohagheghi,et al.  Trainin MLP Neural Networks for Identification of a Small Power System: Comparison of PSO and Backpropagation, Yamille Del Valle , 2005 .

[21]  Ronald G. Harley,et al.  Computational Intelligence for Control of FACTS Devices , 2004 .

[22]  P. Usher.  World conference on the changing atmosphere: implications for global security , 1989 .