Search

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]

Strategic users in a wireless network cannot be assumed to follow the network algorithms blindly. Moreover, some of these users aim to use their knowledge about network algorithms to maliciously gain more resources and also to create interference to other users. We consider a scenario, in which the network and legitimate users gather probabilistic information about the presence of malicious users by observing the network over a long time period. The network (mechanism designer) and legitimate users modify their actions according to this Bayesian information. We consider Bayesian mechanisms, both pricing schemes and auctions, and obtain the Bayesian Nash Equilibrium (BNE) points. The BNE points provide conditions under which, the uncertainty about user's nature (type) is better for regular (legitimate) users. To derive these conditions, we compare the Bayesian case to the complete information case. We obtain the optimal prices and allocations, which counter the malicious users. We also provide detection methods based on machine learning algorithms for the detection of malicious users, by observing the prices and rate allocations. In addition, we provide detection using regression learning by observing the anomalies in the utility functions of malicious users from prices, which is implemented along with the pricing mechanism itself. For the designer and the regular users, in a complementary fashion, the results of the detections provide a better estimate of the statistics of malicious users to implement the pricing mechanisms. We have also proposed a truthful Bayesian mechanism in the presence of malicious users. The numerical studies for malicious user detection are carried out with the model proposed in the paper as well as using real Botnet dataset. [ABSTRACT FROM PUBLISHER]