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.
@cite_4: This paper presents two different approaches to model expansion planning in electricity markets under imperfect competitive conditions. Both approaches consider a market in which firms compete in quantity as in the Nash-Cournot game. However, the two models differ from each other in how each firm makes its expansion-planning decisions. In the first model, the key assumption that permits its formulation as a Mixed Linear Complementarity Problem (LCP) is that firms not only decide their output in a Cournot manner but also their new generating capacity. In contrast, in the second model, there is a leader firm that anticipates the reaction of the follower firms as in the Stackelberg game. The latter model is formulated as a Mathematical Program with Equilibrium Constraints (MPEC), wherein the leader firm decides its optimal new capacity subject to a set of market equilibrium constraints. Both models have been implemented in GAMS. A simple example is also presented to illustrate their comparison and application.
@cite_32: We study the strategic utilization of storage in imperfect electricity markets. We apply a game-theoretic Cournot model to the German power market and analyze different counterfactual and realistic cases of pumped hydro storage. Our main finding is that both storage utilization and storage-related welfare effects depend on storage ownership and the operator's involvement in conventional generation. Strategic operators generally under-utilize owned storage capacity. Strategic storage operation may also lead to welfare losses, in particular if the total storage capacity is controlled by an oligopolistic generator that also owns conventional generation capacity. Yet in the current German situation, pumped hydro storage is not a relevant source of market power.
In @cite_4 @cite_32 , the storage firms are modeled as strategic players in Cournot-based electricity markets. However, they do not study storage sizing problem and the effect of intermittent renewables on the market. Therefore, to the best of our knowledge, the problem of finding optimal storage capacity subject to a price volatility management target in electricity markets has not been addressed before.