A benefits analysis for wind turbine allocation in a power distribution system

Abstract This paper proposes an algorithm to analyze the long-term benefits of Wind Turbine (WT) allocation at the demand side of a power distribution system. The benefits are evaluated based on the wind electricity generation and the avoidance of CO 2 emissions. The objective function includes the investment cost, maintenance cost, and the cost of loss reduction, subjective to operating limits and line flow constraints. Taking load growth into account, Particle Swarm Optimization (PSO) with a power flow algorithm is proposed to solve this problem. To enhance the performance of the optimization approach, a load flow model with Equivalent Current Injection (ECI) is used to analyze the power flow of distribution systems. By considering the power generation of WTs, electricity prices, and carbon trading prices, the long-term benefits of the installation of wind turbines in different scenarios are evaluated. Examples of IEEE 69-bus systems are presented to illustrate the efficiency and feasibility of the proposed algorithm. Simulation results can help decision makers in selecting the proper installation sites for WTs, as well as in determining the tradeoff between optimal investment and environmental policy for future electricity and carbon markets.

[1]  G. Amaratunga,et al.  Evaluation of small wind turbines in distributed arrangement as sustainable wind energy option for Barbados , 2008 .

[2]  Tian Pau Chang,et al.  Wind energy assessment incorporating particle swarm optimization method , 2011 .

[3]  Wen Lea Pearn,et al.  A wind turbine evaluation model under a multi-criteria decision making environment , 2012 .

[4]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[5]  Scott Kennedy,et al.  Wind power planning: assessing long-term costs and benefits , 2005 .

[6]  Swapan Kumar Goswami,et al.  A decoupled power flow algorithm using particle swarm optimization technique , 2009 .

[7]  Karsten Neuhoff,et al.  Wind power and market power in competitive markets , 2010 .

[8]  Cristian Carraretto,et al.  Power plant operation and management in a deregulated market , 2006 .

[9]  Francesco Gullí,et al.  Electricity pricing under "carbon emissions trading": A dominant firm with competitive fringe model , 2007 .

[10]  Cheng-Chien Kuo.  Generation dispatch under large penetration of wind energy considering emission and economy , 2010 .

[11]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[12]  J. N. Sheen,et al.  Fuzzy Financial Profitability Analyses of Demand Side Management Alternatives , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

[13]  Mhairi Aitken,et al.  Wind power and community benefits: Challenges and opportunities , 2010 .

[14]  Ming-Tang Tsai,et al.  The influence of carbon dioxide trading scheme on economic dispatch of generators , 2011 .

[15]  Caisheng Wang,et al.  Analytical approaches for optimal placement of distributed generation sources in power systems , 2004 .

[16]  Swapan Kumar Goswami,et al.  Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction, and voltage improvement including voltage rise issue , 2010 .

[17]  Roger K. Raufer,et al.  Emissions trading in China: A conceptual ‘leapfrog’ approach? , 2009 .

[18]  M. E. Baran,et al.  Optimal capacitor placement on radial distribution systems , 1989 .

[19]  Ramazan Köse,et al.  An evaluation of wind energy potential as a power generation source in Kütahya, Turkey , 2004 .

[20]  Jen-Nan Sheen,et al.  Economic profitability analysis of demand side management program , 2005 .

[21]  Carmen L. T. Borges,et al.  Optimal distributed generation allocation for reliability, losses, and voltage improvement , 2006 .

[22]  Frede Hvelplund.  Renewable energy and the need for local energy markets , 2006 .

[23]  U. K. Rout,et al.  Energy and emissions forecast of China over a long-time horizon , 2011 .

[24]  María Isabel Blanco.  The economics of wind energy , 2009 .

[25]  Hwa Meei Liou,et al.  Wind power in Taiwan: Policy and development challenges , 2010 .

[26]  Yim Ling Siu,et al.  Wind power in China – Dream or reality? , 2012 .

[27]  A. Bakhshai,et al.  A new adaptive control algorithm for maximum power point tracking for wind energy conversion systems , 2008, 2008 IEEE Power Electronics Specialists Conference.

[28]  J. Munksgaard,et al.  Energy and CO2 life-cycle analyses of wind turbines—review and applications , 2002 .

[29]  Caisheng Wang,et al.  Analytical approaches for optimal placement of distributed generation sources in power systems , 2004, IEEE Transactions on Power Systems.

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

[31]  J. Teng,et al.  Three-phase distribution network fast-decoupled power flow solutions , 2000 .

[32]  Jen-Hao Teng,et al.  Value-based distributed generator placements for service quality improvements , 2007 .

[33]  Ehab F. El-Saadany,et al.  Impact of wind generation control strategies, penetration level and installation location on electricity market prices , 2008 .

[34]  Tsai-Hsiang Chen,et al.  Distribution system power flow analysis-a rigid approach , 1991 .

[35]  Songfeng Lu,et al.  An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling , 2010 .