@article { ,
	title = {Stochastic optimal generation bid to electricity markets with emissions risk constraints.},
	journal = {Journal of Environmental Management},
	volume = {207},
	year = {2018},
	month = {February 2018},
	pages = {12},
	publisher = {Elsevier},
	chapter = {432},
	abstract = {There are many factors that influence the day-ahead market bidding strategies of a generation company (GenCo) within the framework of the current energy market. Environmental policy issues are giving rise to emission limitation that are becoming more and more important for fossil-fueled power plants, and these must be considered in their management. This work investigates the influence of the emissions reduction plan and the incorporation of the medium-term derivative commitments in the optimal generation bidding strategy for the day-ahead electricity market. Two different technologies have been considered: the high-emission technology of thermal coal units and the low-emission technology of combined cycle gas turbine units. The Iberian Electricity Market (MIBEL) and the Spanish National Emissions Reduction Plan (NERP) defines the environmental framework for dealing with the day-ahead market bidding strategies. To address emission limitations, we have extended some of the standard risk management methodologies developed for financial markets, such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), thus leading to the new concept of Conditional Emission at Risk (CEaR). This study offers electricity generation utilities a mathematical model for determining the unit?s optimal generation bid to the wholesale electricity market such that it maximizes the long-term profits of the utility while allowing it to abide by the Iberian Electricity Market rules as well as the environmental restrictions set by the Spanish National Emissions Reduction Plan. We analyze the economic implications for a GenCo that includes the environmental restrictions of this National Plan as well as the NERP?s effects on the expected profits and the optimal generation bid.},
	keywords = {research; OR in Energy; Stochastic Programming; Risk Management; Electricity market; Emissions reduction; paper},
	URL = {https://doi.org/10.1016/j.jenvman.2017.11.010},
	author = {F.-Javier Heredia;Julián Cifuentes-Rubiano and Cristina Corchero}
}

