We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be used as a proxy for quickly approximating outcomes of short-term decisions, to make tractable hierarchical long-term assessment and planning for large power systems. Experimental results on updated versions of IEEE-RTS79 and IEEE-RTS96 show high accuracy measured on operational cost, achieved in runtimes that are lower in several orders of magnitude than the traditional approach.
@cite_18: The aim of this work is to design intra-daily recourse strategies which may be used by operators to decide in real-time the modifications to bring to planned generation schedules of a set of units in order to respond to deviations from the forecasted operating scenario. Our aim is to design strategies that are interpretable by human operators, that comply with real-time constraints and that cover the major disturbances that may appear during the next day. To this end we propose a new framework using supervised learning to infer such recourse strategies from simulations of the system under a sample of conditions representing possible deviations from the forecast. This framework is validated on a realistic generation system of medium size.
@cite_11: The power grid is a complex and vital system that necessitates careful reliability management. Managing the grid is a difficult problem with multiple time scales of decision making and stochastic behavior due to renewable energy generations, variable demand and unplanned outages. Solving this problem in the face of uncertainty requires a new methodology with tractable algorithms. In this work, we introduce a new model for hierarchical decision making in complex systems. We apply reinforcement learning (RL) methods to learn a proxy, i.e., a level of abstraction, for real-time power grid reliability. We devise an algorithm that alternates between slow time-scale policy improvement, and fast time-scale value function approximation. We compare our results to prevailing heuristics, and show the strength of our method.
@cite_12: We introduce a new plan repair method for problems cast as Mixed Integer Programs. In order to tackle the inherent complexity of these NP-hard problems, our approach relies on the use of Supervised Learning method for the offline construction of a predictor which takes the problem's parameters as input and infers values for the discrete optimization variables. This way, the online resolution time of the plan repair problem can be greatly decreased by avoiding a large part of the combinatorial search among discrete variables. This contribution was motivated by the large-scale problem of intra-daily recourse strategy computation in electrical power systems. We report and discuss results on this benchmark, illustrating the different aspects and mechanisms of this new approach which provided close-to-optimal solutions in only a fraction of the computational time necessary for existing solvers.
@cite_17: In this paper we propose a methodology based on supervised automatic learning in order to classify the behaviour of generators in terms of their performance in providing primary frequency control ancillary services. The problem is posed as a time-series classification problem, and handled by using state-of- the-art supervised learning methods such as ensembles of decision trees and support-vector machines combined with several preprocessing techniques. The method was designed in the context of the Belgian system and is validated on real-life data composed of more than 600 time-series recorded on this system.
The literature contains several works that use machine learning for predicting outcomes of decision processes in power networks based on pre-solved various input conditions; such methods are referred to as supervised learning algorithms. We limit our analysis to the problem of generation (re)scheduling and reserve activation. In @cite_17 , frequency and active power time series are used for determining whether generator reserve activation is satisfactory or not. In this application, manual labeling of the data is required by experts, and there are only four possible classes. Reliability is maximized in @cite_11 by learning a function that assesses the implications of rescheduling. The sought output is a policy, that dynamically maps system states to rescheduling actions. Reference @cite_18 uses supervised learning for finding recourse strategies in generation management, by generating a training set via Monte-Carlo simulation of load and generation disturbances and then learning a near-optimal recourse strategy to handle similar disturbances observed in real-time. Recourse strategy learning is also investigated in @cite_12 , where boosting is used to create binary classifiers for boolean variables of the mixed integer programs resulting from daily generation re-planning problems.