A Multimarket Decision-Making Framework for GENCO Considering Emission Trading Scheme

In a multimarket environment, a GENCO produces electricity subject to a number of factors, including physical and environmental constraints, together with trading strategies in the electricity market (EM), fuel market (FM) and carbon market (CM). To assist a GENCO to maximize its profits from EM, FM and CM, this paper proposes a dynamic decision making model with two consecutive stages. Fuzzy differential evolution algorithm is used to solve this decision-making problem. Taking transactions in the three interactive markets into account, the proposed model has been tested for a GENCO consisting of seven thermal units and a wind farm. A rational tradeoff between the profit-making and emission reduction has been demonstrated by the GENCO using the proposed model, indicating a well alignment with the intended goal of the introducing emission trading scheme (ETS).

[1]  Fushuan Wen,et al.  Impacts of emission trading and renewable energy support schemes on electricity market operation , 2011 .

[2]  Whei-Min Lin,et al.  Bid-based dynamic economic dispatch with an efficient interior point algorithm , 2002 .

[3]  K. P. Wong,et al.  Optimal decision making model for GENCO under the Emission Trading Scheme , 2012, 2012 IEEE Power and Energy Society General Meeting.

[4]  Patrick James,et al.  Pole-mounted horizontal axis micro-wind turbines: UK field trial findings and market size assessment , 2011 .

[5]  Z. Dong,et al.  A Statistical Approach for Interval Forecasting of the Electricity Price , 2008, IEEE Transactions on Power Systems.

[6]  Goran Strbac,et al.  Multi-time period combined gas and electricity network optimisation , 2008 .

[7]  W. Marsden I and J , 2012 .

[8]  Witold Pedrycz,et al.  Fuzzy control and fuzzy systems , 1989 .

[9]  Nima Amjady,et al.  Mixed price and load forecasting of electricity markets by a new iterative prediction method , 2009 .

[10]  Neil Genzlinger A. and Q , 2006 .

[11]  Ke Meng,et al.  Day-ahead electricity price forecasting based on panel cointegration and particle filter , 2013 .

[12]  L. H. Wua,et al.  Environmental/economic power dispatch problem using multi-objective differential evolution algorithm , 2010 .

[13]  Jouni Lampinen,et al.  A Fuzzy Adaptive Differential Evolution Algorithm , 2005, Soft Comput..

[14]  Marc S. Paolella,et al.  An econometric analysis of emission allowance prices , 2008 .

[15]  José L. Bernal-Agustín,et al.  Forecast of Hourly Average Wind Speed Using ARMA Model with Discrete Probability Transformation , 2011 .

[16]  Nicholas A. Linacre,et al.  State and Trends of the Carbon Market 2011 , 2010 .

[17]  Kit Po Wong,et al.  Differential Evolution, an Alternative Approach to Evolutionary Algorithm , 2005 .

[18]  J. P. Zhan,et al.  Dynamic economic emission dispatch based on group search optimizer with multiple producers , 2012 .

[19]  Ivana Kockar,et al.  Combined pool/bilateral dispatch. I. Performance of trading strategies , 2002 .

[20]  Zhao Yang Dong,et al.  Partial Carbon Permits Allocation of Potential Emission Trading Scheme in Australian Electricity Market , 2010, IEEE Transactions on Power Systems.

[21]  David A. Bessler,et al.  Market integration among electricity markets and their major fuel source markets , 2009 .

[22]  C.W. Yu,et al.  Impacts of emission trading on Carbon, Electricity and Renewable Markets: A review , 2010, IEEE PES General Meeting.