In line with the three main objectives of FlexAnalytics, our work has been structured around the following three lines of action:
1. The design of novel schemes for data-driven decision-making under uncertainty. The key idea here is that the decisions we make are typically influenced by uncertain (random) phenomena. Our goal as decision-makers is thus to minimize the regret or cost that we foresee our decisions will entail. To this aim, besides, we usually gather information on all those factors that, we believe, can help us reduce the level of uncertainty we are faced with. This information is often known as “the context”. In this line, we have developed alternative schemes for decision-making under uncertainty with contextual information. More specifically, we have investigated both parametric and non-parametric approaches. In the former, we assume that the relation between the decisions we make and the context can be mathematically described by a member of a prespecified parametric family of functions, which is to be determined (by way of an optimization problem). In the latter, in contrast, the decisions are directly inferred from the data with no a-priori restriction on their relationship (which can be, therefore, of any nature). Logically, each of these two approaches has its pros and cons. Whereas the parametric scheme is easier to understand and interpret, and often leads to optimization problems that are computationally more tractable, it is often quite limited in the type of relations “context-decisions” that it can capture. On the contrary, the nonparametric scheme offers a superior modeling power, but is “data hungry”, more prone to “overfitting” (which can be mitigated via robustification), more difficult to interpret and more computationally demanding.
2. The development of a system for the participation of a pool of flexible loads in the wholesale electricity markets. We have primarily focused on two types of flexible loads of strategic importance to the future power sector, namely, a fleet of electric vehicles (EVs), with vehicle-to-grid capabilities, and a cluster of smart buildings. We have first built tools to mimic, as realistically as possible, the behavior of these two types of flexible loads and aggregations thereof, in order to assess the extent to which they can respond to the electricity price. Subsequently, we have developed technologies, based on data-driven inverse optimization, whereby we can encode the price-response of the loads in the form of a complex bid that can be directly submitted to wholesale electricity markets. Very importantly, the ability of this bid to predict the reaction of the flexible loads to the electricity price is equal to or superior than that of state-of-the-art forecasting techniques. However, the bid our system produces has the great advantage that it can be directly interpreted, used, and processed by the market, precisely because it is a bid.
3. New methods for data-driven power system operations. We have successfully applied the novel schemes for data-driven decision-making under uncertainty that we have developed to address a number of paradigmatic problems in power system operations, such as the participation of weather-driven renewable power producers in electricity markets, the optimal power flow problem, the networked-constrained unit commitment problem, etc. In parallel, we have also devised data-driven and computational algorithms to efficiently solve these problems by means, for example, of screening out superfluous constraints or the tightening of large constants, achieving substantial computational savings.
