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Risk Management for Decision-making Under Uncertainty in Electricity Markets
The deregulation of the electricity sector and the increasing deployment of various emerging technologies, though coming with benefits, have been introducing various challenges that impact power system operations and economics. The introduction of new uncertainties and associated risks along with the increasing variability of the preexisting uncertainties and risks are among those challenges that influence the objectives of entities in the electricity markets. Accordingly, risk management is becoming more and more crucial in the decision-making processes of these entities. This dissertation focuses on the risk management implementation in the stochastic programming and game-theoretic models for decision-making under uncertainty applied to the electricity markets. Its objectives are threefold: study the performance of the commonly used risk management approaches in the electricity sector; investigate the potential and benefits of using the stochastic dominance (SD) theory for risk management in decision-making under uncertainty; and propose novel risk management approaches for decision-making models of electricity market entities. After reviewing the literature, a stochastic program for generating a wind power producer’s bidding strategy in the electricity market is used to compare the performance of different risk management approaches, including the Markowitz mean-risk approach with different risk measures and the SD constraints (SDCs). The SDCs demonstrate superior risk management performance. Nonetheless, the implementation of SDCs in risk management is constrained by the difficulty in defining a benchmark distribution that best represents the decision-maker’s risk preference while maintaining the model’s feasibility. To overcome this obstacle, three rigorous benchmark selection approaches applicable to any stochastic program with second-order SDCs are proposed and validated. Moreover, two hybrid risk management approaches are proposed and demonstrated using the wind bidding problem. The first uses multiple risk measures in the mean-risk model to manage multiple parameters of the objective function’s distribution simultaneously. The second consolidates the mean-risk model and the SDCs. Finally, a novel implementation of the SD theory for risk management in game-theoretic models is proposed. Based on that, two risk-constrained decision-making models for nonstrategic players in oligopoly markets are developed.
AlAshery, Mohamed Kareem, "Risk Management for Decision-making Under Uncertainty in Electricity Markets" (2020). ETD collection for University of Nebraska - Lincoln. AAI28031204.