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This paper presents a multi-nodal intertemporal Cournot gaming model with transmission constraints and uses it to simulate energy-only and capacity-energy market designs in the presence of uncertainties stemming from intermittent renewable power generation. Both market paradigms are compared to a competitive benchmark in order to determine which one performs better in a concentrated market with significant penetration of wind and solar generation. As a specific example, the model is applied to the South Australian zone of the Australian National Electricity Market (NEM). The simulation results for the time interval 2013–2030 show that the capacity-energy market has the potential to induce significant new capacity and push prices much closer to the competitive level in contrast to the current energy-only market design. [ABSTRACT FROM AUTHOR]

Wind generation variability in an energy-only market such Australian National Electricity Market (NEM) can create significant revenue uncertainties for incumbent generators and substantially increase price risks faced by retailers. This paper presents a Cournot game model to formally analyze how high volatility of wind generation in a concentrated energy-only market can raise the peak/shoulder period (of typically low wind generation) prices to offset the foregone revenue during off-peak periods (of high wind generation). A Monte Carlo simulation around a Cournot game is formulated as an inter-temporal nonlinear optimization problem to assess these issues. The model is implemented for the South Australian zone of the Australian NEM that has experienced high growth in wind generation in recent years. The model results support some of the observed spot pricing behavior in the region in recent years. These findings have significant ramifications for the efficacy of the energy-only market in scenarios with high penetration of intermittent generation. [ABSTRACT FROM PUBLISHER]

Recent studies show that the fast growing expansion of wind power generation may lead to extremely high levels of price volatility in wholesale electricity markets. Storage technologies, regardless of their specific forms, e.g., pump-storage hydro, large-scale, or distributed batteries, are capable of alleviating the extreme price volatility levels due to their energy usage time shifting, fast-ramping, and price arbitrage capabilities. In this paper, we propose a stochastic bilevel optimization model to find the optimal nodal storage capacities required to achieve a certain price volatility level in a highly volatile energy-only electricity market. The decision on storage capacities is made in the upper level problem and the operation of strategic/regulated generation, storage, and transmission players is modeled in the lower level problem using an extended stochastic (Bayesian) Cournot-based game. The South Australia (SA) electricity market, which has recently experienced high levels of price volatility, and a 30-bus IEEE system are considered as the case studies. Our numerical results indicate that 50% price volatility reduction in the SA electricity market can be achieved by installing either 430-MWh regulated storage or 530-MWh strategic storage. In other words, regulated storage firms are more efficient in reducing the price volatility than strategic storage firms. [ABSTRACT FROM PUBLISHER]