This paper reviews renewable energy integration with the electrical power grid through the
use of advanced solutions at the device and system level, using smart operation with better …

The risk of transmission overload (TO) in power grids is increasing with the large-scale
integration of intermittent renewable energy sources. An effective online preventive control …

The growing complexity of power system management has led to an increased interest in
the use of reinforcement learning (RL). However, no tool for comprehensive and realistic …

In recent research addressing energy arbitrage with energy storage systems (ESS s),
discrete reinforcement learning (RL) has often been employed, while the underlying reasons …

The application of deep reinforcement learning algorithms to economic battery dispatch
problems has significantly increased recently. However, optimizing battery dispatch over …

In this paper, active power curtailment (APC) is studied as a method to prevent safety limit
violations in residen-tial low-voltage grids with high electric vehicle and solar panel …

Energy arbitrage is one of the most profitable sources of income for battery operators,
generating revenues by buying and selling electricity at different prices. Forecasting these …

The growing share of distributed and intermittent energy resources, as well as the
electrification of transportation and heating, challenge traditional supply-side approaches to …

Deep reinforcement learning (DRL) is an increasingly popular optimization tool for the
economic dispatch of battery energy systems. However, it remains largely unclear which …

The fast-paced installation of electric vehicles (EV) and photovoltaic units (PV) in low voltage
distribution grids calls for sophisticated control strategies that ensure a safe grid operation …