Evaluating the impact of FIT financial burden on social welfare in renewable expansion planning

Generation expansion planning (GEP) is the problem of determining the optimal strategy to plan the construction of new generation plants while satisfying technical and economic constraints. Over the past few years, the environmental issues have become a society concern and the Clean Air Act Amendments passage the laws indicating the need for renewable resources promotion. Multifarious incentive-based support schemes have been designed to increase the penetration rate of the renewables in power generation. In this context, open questions remain regarding the financial resources of the support schemes. This paper addresses the impacts of Feed-In-Tariff (FIT) mechanism on the social welfare in an integrated renewable-conventional GEP framework, while consumers are considered for patronizing the financial burden of FIT (БFIT). Hence, after applying the gravitational search algorithm to a multistage GEP model, the benefit of generation company (GENCO) and consumer surplus are both determined as the social welfare terms. The virtual price criterion is also introduced to evaluate the effect of FIT expenditure on consumers' surplus. The numerical studies emphasize that implementation of FIT regime in the GEP results in social welfare improvement even if the БFIT is imposed on the demand-side consumers.

[1]  S. A. Al-Baiyat,et al.  Economic load dispatch multiobjective optimization procedures using linear programming techniques , 1995 .

[2]  M. Thring World Energy Outlook , 1977 .

[3]  Jay Zarnikau,et al.  Successful renewable energy development in a competitive electricity market: A Texas case study , 2011 .

[4]  John Foster,et al.  Australian renewable energy policy: Barriers and challenges , 2013 .

[5]  Jayanta Deb Mondol,et al.  Overview of challenges, prospects, environmental impacts and policies for renewable energy and sustainable development in Greece , 2013 .

[6]  Frederic H. Murphy,et al.  Generation Capacity Expansion in Imperfectly Competitive Restructured Electricity Markets , 2005, Oper. Res..

[7]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[8]  O. Edenhofer,et al.  Renewable Energy Sources and Climate Change Mitigation , 2011 .

[9]  Carmen L. T. Borges,et al.  An overview of reliability models and methods for distribution systems with renewable energy distributed generation , 2012 .

[10]  A. Gomes Martins,et al.  A multiple objective mixed integer linear programming model for power generation expansion planning , 2004 .

[11]  Zeng Ming,et al.  Overall review of renewable energy tariff policy in China: Evolution, implementation, problems and countermeasures , 2013 .

[12]  C. Genesi,et al.  Generation Expansion Planning in the Age of Green Economy , 2011 .

[13]  Jose L. Ceciliano Meza,et al.  A Model for the Multiperiod Multiobjective Power Generation Expansion Problem , 2007, IEEE Transactions on Power Systems.

[14]  John P. Rice,et al.  Developing renewable energy supply in Queensland, Australia: A study of the barriers, targets, policies and actions , 2012 .

[15]  Thomas J Overbye,et al.  Key Technical Challenges for the Electric Power Industry and Climate Change , 2010, IEEE Transactions on Energy Conversion.

[16]  V. Kachitvichyanukul,et al.  A New Efficient GA-Benders' Decomposition Method: For Power Generation Expansion Planning With Emission Controls , 2007, IEEE Transactions on Power Systems.

[17]  L. Barroso,et al.  The Green Effect , 2010, IEEE Power and Energy Magazine.

[18]  Zainuddin Abdul Manan,et al.  Optimal planning of renewable energy-integrated electricity generation schemes with CO2 reduction target , 2010 .

[19]  Mohsen Parsa Moghaddam,et al.  An investigation on the impacts of regulatory interventions on wind power expansion in generation planning , 2011 .

[20]  Adelino J. C. Pereira,et al.  A decision support system for generation expansion planning in competitive electricity markets , 2010 .

[21]  Arun Somani,et al.  A Long-Term Investment Planning Model for Mixed Energy Infrastructure Integrated with Renewable Energy , 2010, 2010 IEEE Green Technologies Conference.

[22]  A. Izadian,et al.  Renewable Energy Policies: A Brief Review of the Latest U.S. and E.U. Policies , 2013, IEEE Industrial Electronics Magazine.

[23]  Ming-Tong Tsay,et al.  Generation expansion planning of the utility with refined immune algorithm , 2006 .

[24]  Kristin Seyboth,et al.  Renewable Energy Sources and Climate Change Mitigation: Reviewers of the IPCC Special Report , 2011 .

[25]  Qixin Chen,et al.  Power Generation Expansion Planning Model Towards Low-Carbon Economy and Its Application in China , 2010, IEEE Transactions on Power Systems.

[26]  A. Botterud,et al.  Optimal investments in power generation under centralized and decentralized decision making , 2005, IEEE Transactions on Power Systems.

[27]  Aniruddha Bhattacharya,et al.  Solution of multi-objective optimal power flow using gravitational search algorithm , 2012 .

[28]  M. Parsa Moghaddam,et al.  An investigation on the impacts of regulatory support schemes on distributed energy resource expansion planning , 2013 .

[29]  Bassam Abu-Hijleh,et al.  Strategies and policies from promoting the use of renewable energy resource in the UAE , 2013 .

[30]  N. Meyer European schemes for promoting renewables in liberalised markets , 2003 .