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Found 9 papers, 3 papers with code
The energy return on investment of whole energy systems: application to Belgium
This paper is the first to develop a novel approach by adding the energy return on investment (EROI) to a whole energy system optimization model.
A deep generative model for probabilistic energy forecasting in power systems: normalizing flows
This paper presents to the power systems forecasting practitioners a recent deep learning technique, the normalizing flows, to produce accurate scenario-based probabilistic forecasts that are crucial to face the new challenges in power systems applications.
Probabilistic Forecasting of Imbalance Prices in the Belgian Context
This matrix is then used to infer the imbalance prices since the net regulation volume can be related to the level of reserves activated and the corresponding marginal prices for each activation level are published by the Belgian Transmission System Operator one day before electricity delivery.
Coordination of operational planning and real-time optimization in microgrids
Hierarchical microgrid control levels range from distributed device level controllers that run at a high frequency to centralized controllers optimizing market integration that run much less frequently.
Deep learning-based multi-output quantile forecasting of PV generation
The results indicate this architecture improves the forecast quality and is computationally efficient to be incorporated in an intraday decision-making tool for robust optimization.
A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market: extended version
This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to promote renewable power generation facilities in small non-interconnected grids.
Lifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning
We propose a novel model based reinforcement learning algorithm that is able to address both types of changes.
Model-based Reinforcement Learning
reinforcement-learning
+1
A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding
In this paper, we propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market where exchanges occur through a centralized order book.
Classification of load forecasting studies by forecasting problem to select load forecasting techniques and methodologies
This classification aims to provide a synthetic view of the relevant forecasting techniques and methodologies by forecasting problem.
