Forecasting for wind and solar renewable energy is becoming more important as the amount
of energy generated from these sources increases. Forecast skill is improving, but so too is …

As one of the fastest growing renewable energy sources, the integration of solar power
poses great challenges to power systems due to its variable and uncertain nature. As an …

This paper proposes attention enabled multi-agent deep reinforcement learning (MADRL)
framework for active distribution network decentralized Volt-VAR control. Using the …

Growing demand for energy carriers has led to an increased interest in developing and
managing multiple energy carrier microgrids. Furthermore, the volatile nature of renewable …

This study proposes a deep reinforcement learning (DRL) based approach to analyze the
optimal power flow (OPF) of distribution networks (DNs) embedded with renewable energy …

To realize the potential of active distribution networks (ADNs) for improving power system
flexibility and to cope with multiple uncertainties, a coordinated robust reserve scheduling …

Around the world wind energy is starting to become a major energy provider in electricity
markets, as well as participating in ancillary services markets to help maintain grid stability …

The conventional single objective optimization cannot meet the requirements for the optimal
operation of active distribution networks (ADNs) with multiple uncertainties brought by the …

The decarbonization of the economy, for which the contribution of power systems is
significant, is a growing trend in Europe and in the world. In order to achieve the Paris …

The significant penetration of rooftop solar photovoltaics (PV) is pushing low-voltage
distribution networks to the brink of their technical limits, which increasingly requires …