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A Game Theoretic Model for Generation Capacity Adequacy: Comparison Between Investment Incentive Mechanisms in Electricity Markets

Mohamed Haikel Khalfallah

Year: 2011
Volume: Volume 32
Number: Number 4
DOI: 10.5547/ISSN0195-6574-EJ-Vol32-No4-7
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Abstract:
In this paper we study the problem of long-term capacity adequacy in electricity markets. We implement a dynamic model in which firms compete for investment and electricity production under imperfect Cournot competition. The main aim of this work is to compare three investment incentive mechanisms: reliability options, forward capacity market and capacity payments. Apart from the oligopoly case, we also analyze collusion and monopoly cases. Dynamic programming is used to deal with the stochastic environment of the market and mixed complementarity problem and variational inequality formulations are employed to find a solution to the game. The main finding of this study is that market-based mechanisms would be the most cost-efficient mechanism for assuring long-term system capacity adequacy. Moreover, generators would exert market power when introducing capacity payments. Finally, compared with a Cournot oligopoly, collusion and monopolistic situations lead to more installed capacities with market-based mechanisms and increase consumers' payments.



Hedging Strategies: Electricity Investment Decisions under Policy Uncertainty

Jennifer Morris, Vivek Srikrishnan, Mort Webster, and John Reilly

Year: 2018
Volume: Volume 39
Number: Number 1
DOI: 10.5547/01956574.39.1.jmor
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Abstract:
Given uncertainty in long-term carbon reduction goals, how much non-carbon generation should be developed in the near-term? This research investigates the optimal balance between the risk of overinvesting in non-carbon sources that are ultimately not needed and the risk of underinvesting in non-carbon sources and subsequently needing to reduce carbon emissions dramatically. We employ a novel framework that incorporates a computable general equilibrium (CGE) model of the U.S. into a two-stage stochastic approximate dynamic program (ADP) focused on decisions in the electric power sector. We solve the model using an ADP algorithm that is computationally tractable while exploring the decisions and sampling the uncertain carbon limits from continuous distributions. The results of the model demonstrate that an optimal hedge is in the direction of more non-carbon investment in the near-term, in the range of 20-30% of new generation. We also demonstrate that the optimal share of non-carbon generation is increasing in the variance of the uncertainty about the long-term carbon targets, and that with greater uncertainty in the future policy regime, a balanced portfolio of non-carbon, natural gas, and coal generation is desirable.





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