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This paper evaluates how the effect of introducing a carbon emission tax and/or feed-in tariffs on capacity expansion decisions of generating companies varies depending on the number and size of competing firms and technical conditions of the network. To do so, it uses a Nash–Cournot model of the electricity market. This model is then applied to the IEEE 6-bus network. We study three cases: one with only a carbon tax consistent with current carbon prices; one with only a feed-in tariff consistent with current US levels, and one with simultaneous carbon taxation and feed-in tariff. We show that, at least in our case, the quantity of renewable capacity expansion and the electricity prices depend more significantly on the technical conditions of the network and the number of competitors in the market than it depends on the presence of economic penalties or incentives. We also show how interactions between imperfectly competitive markets and physical networks can produce counterintuitive results, such as an increase in consumer prices as a result of a reduction in network congestion. Our results imply that no two countries would experience the same effects from a policy on carbon tax and feed-in tariff if their electricity market does not have similarities in technical and competitive conditions

Previous work has analyzed the role of energy storage (ES) on generation investment planning through centralised cost-minimization models which are inherited from the era of regulated electricity utilities. This paper investigates this issue in the context of the deregulated market environment by proposing a new strategic generation investment planning model. The decision making of a strategic generation company is modeled through a multi-period bi-level optimization problem, where the upper level determines the profit-maximizing investment decisions of the generation company and the lower level represents the market clearing process, accounting for the timecoupling operational characteristics of ES. This bi-level problem is solved after converting it to a single-level mixed-integer linear problem (MILP). Case studies demonstrate that the introduction of ES reduces the total generation capacity investment and enhances investments in "must-run" baseload generation over flexible peaking generation, yielding significant system cost savings.

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