a b s t r a c tCarbon markets are a world-wide accepted market mechanism to promote emission reduction. Increasing stress on emission reduction from the power industry has led to a shift in the market mechanism, from free allocation to full auction. Consequent increase in volatility of emission market and its interdependency with electricity market is predominantly affecting the fossil-fuel generation companies (GenCos). For accurate realization of their optimal electricity trading portfolio selection, GenCos need to incorporate cost side uncertainties arising from fuel and emission market volatilities. This paper proposes a novel framework for electricity trading portfolio optimization of a GenCo, considering uncertainties of electricity, fuel and emission markets, to secure its future trading position. This optimization problem is modeled using mean variance portfolio theory, considering spot market, bilateral contracts as electricity trading options. Results show that considering correlation effects of electricity market with emission markets, the proposed framework is capable of improving profit risk trade-off for the portfolio. Positively correlated electricity, emission market prices lead to an increased trading in spot market. In such a situation, the model reflects that spot selling could offer higher risk protection vis-à-vis bilateral contracts, and can prominently help high emission GenCos to minimize their market risks.
An independent Generation Company (GenCo) secures its future trading position by managing its portfolio among multiple trading options. Future returns of these trading options are not known during decision making and are traditionally estimated using probabilistic or fuzzy methods. Quantifying such uncertainty of market returns by conventional methods does not reflect the information gap existing between estimated and actual market returns. Based on quantification of this information gap, the paper proposes GenCo's portfolio optimization using a non-probabilistic Information Gap Decision Theory (IGDT). This framework comprehensively models GenCo's behavior in deciding its trading strategy. Considering GenCo's risk-averse behavior, the framework provides decisions that are robust towards losses, while considering its risk-seeking behavior the framework offers opportunity to capture windfall gains. The proposed approach has been validated through practical case study of PJM market. Index Terms-GenCo, information gap decision theory, portfolio optimization, uncertainty. I. NOMENCLATURE A. Indices , ij Index of trading contract k Index of trading interval B. Parameters a No-load heat-rate coefficient in MW b Linear heat-rate coefficient in MW/MBtu c Quadratic heat-rate coefficient in MW/MBtu 2 , ik LMP LMP of trading area i in k th trading interval M Considered time horizon or planning period n Number of locations , Min ik p Minimum trading limit for contract i in k th trading interval , Max ik p Maximum trading limit for contract i in k th trading interval r Set of uncertain returns from contracts (1~)
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