Market Risks and Strategies in Power Systems Integrating Renewable Energy

Energy businesses are going through a series of swift and radical transformations to meet the growing demands for sustainable energy. The integration of wind and solar introduces more low marginal costs suppliers to power markets, as no fuels are needed to produce electricity. Most power produced by renewable energy sources is however variable and difficult to predict by nature, putting current power system operations under pressure and causing prices to fluctuate heavily. Increased competition, new production technologies and volatile prices completely changed operations in today’s power markets. In this dissertation, we assess the integration of intermittent renewable energy sources in relation to agents' risk preferen

Machine learning for identifying demand patterns of home energy management systems with dynamic electricity pricing

Energy management plays a crucial role in providing necessary system flexibility to deal with the ongoing integration of volatile and intermittent energy sources. Demand Response (DR) programs enhance demand flexibility by communicating energy market price volatility to the end-consumer. In such environments, home energy management systems assist the use of flexible end-appliances, based upon the individual consumer's personal preferences and beliefs. However, with the latter heterogeneously distributed, not all dynamic pricing schemes are equally adequate for the individual needs of households. We conduct one of the first large scale natural experiments, with multiple dynamic pricing schemes for end consumers, allowing us to analyze different demand behavior in relation with household attri

A closer look at adaptive regret

For the prediction with expert advice setting, we consider methods to construct algorithms that have low adaptive regret. The adaptive regret of an algorithm on a time interval [t1,t2] is the loss of the algorithm minus the loss of the best expert over that interval. Adaptive regret measures how well the algorithm approximates the best expert locally, and so is different from, although closely related to, both the classical regret, measured over an initial time interval [1,t], and the tracking regret, where the algorithm is compared to a good sequence of experts over [1,t]. We investigate two existing intuitive methods for deriving algorithms with low adaptive regret, one based on specialist experts and the other based on restarts. Quite surprisingly, we show that both methods lead to the same algorithm, namely Fixed Share, which is known for its tracking regret. We provide a thorough analysis of the adaptive regret of Fixed Share. We obtain the exact worst-case adaptive regret for Fixed Share, from which the classical tracking bounds follow. We prove that Fixed Share is optimal for adaptive regret: the worst-case adaptive regret of any algorithm is at least that of an instance of Fixed Share