Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject

BOID*: Autonomous Goal Deliberation through Abduction

The original BOID [5] is a cognitive architecture that unifies Belief, Obligation, Intention and Desire rules to calculate which actions should an agent undertake next. In the current paper, we adapt the original BOID with an aim to model autonomous agency. The new BOID* architecture is able to capture anticipation that we believe to be one of the hallmarks of autonomous agency. We focus on developing algorithms for anticipatory reasoning through a new BOID* goal deliberation component. The key method that BOID* introduces is abductive reasoning as a way to represent motivational attitudes, such as desires and obligations. As a result of deliberation via abduction, BOID* specifies intention revision procedures that connect motivational and informational attitudes. The BOID* is a part of the project to build autonomous AI models that make explicit the reasoning behind adopting future goals, prioritizing selected goals and forming intentions

Operational model of a RES plant coupled with battery storage considering the imbalance settlement

peer reviewedIn the European energy market, every market participant has a balance responsibility. With the expiration of feed-in tariffs, renewables are also becoming balance responsible. Since renewables, such as wind and solar, have very variable and not entirely predictable output, their imbalance management is highly challenging. This paper investigates the possibility of their imbalance management by installing battery energy storage within the renewable facility. The proposed bidding and balancing model, applicable to any type of distributed energy resource, is simulated for deterministic prices from the year 2020 and for the stochastic solar production scenarios.9. Industry, innovation and infrastructur

BOID*: Autonomous Goal Deliberation through Abduction

The original BOID [5] is a cognitive architecture that unifies Belief, Obligation, Intention and Desire rules to calculate which actions should an agent undertake next. In the current paper, we adapt the original BOID with an aim to model autonomous agency. The new BOID* architecture is able to capture anticipation that we believe to be one of the hallmarks of autonomous agency. We focus on developing algorithms for anticipatory reasoning through a new BOID* goal deliberation component. The key method that BOID* introduces is abductive reasoning as a way to represent motivational attitudes, such as desires and obligations. As a result of deliberation via abduction, BOID* specifies intention revision procedures that connect motivational and informational attitudes. The BOID* is a part of the project to build autonomous AI models that make explicit the reasoning behind adopting future goals, prioritizing selected goals and forming intentions