Long-term microgrid expansion planning with resilience and environmental benefits using deep reinforcement learning
Microgrid plays an increasingly important role to enhance power resilience and
environmental protection regarding greenhouse gas emission reduction through the …
environmental protection regarding greenhouse gas emission reduction through the …
Resilience-driven optimal sizing and pre-positioning of mobile energy storage systems in decentralized networked microgrids
Networked microgrids are considered an effective way to enhance resilience of localized
energy systems. Recently, research efforts across the world have been focusing on the …
energy systems. Recently, research efforts across the world have been focusing on the …
Exploiting battery storages with reinforcement learning: a review for energy professionals
The transition to renewable production and smart grids is driving a massive investment to
battery storages, and reinforcement learning (RL) has recently emerged as a potentially …
battery storages, and reinforcement learning (RL) has recently emerged as a potentially …
Configuration optimization of an off-grid multi-energy microgrid based on modified NSGA-II and order relation-TODIM considering uncertainties of renewable energy …
Z Lu, Y Gao, C Xu, Y Li - Journal of Cleaner Production, 2023 - Elsevier
This study develops a two-stage hybrid decision framework to configure an off-grid multi-
energy microgrid (MEMG) while considering uncertainties in renewable energy resources …
energy microgrid (MEMG) while considering uncertainties in renewable energy resources …
Bi-level adaptive storage expansion strategy for microgrids using deep reinforcement learning
Battery energy storage (BES) is a versatile resource for the secure and economic operation
of microgrids (MGs). Prevailing stochastic optimization-based approaches for BES …
of microgrids (MGs). Prevailing stochastic optimization-based approaches for BES …
Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy
L Yin, Y Wu - Applied Energy, 2022 - Elsevier
The large-scale application of renewable energy can promote the global goal of carbon
neutrality. However, the stochastic nature of wind and solar energy aggravates the active …
neutrality. However, the stochastic nature of wind and solar energy aggravates the active …
Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies
The efficient planning of electric power systems is essential to meet both the current and
future energy demands. In this context, reinforcement learning (RL) has emerged as a …
future energy demands. In this context, reinforcement learning (RL) has emerged as a …
Deep reinforcement learning for resilient microgrid expansion planning with multiple energy resource
K Pang, J Zhou, S Tsianikas… - Quality and Reliability …, 2024 - Wiley Online Library
Microgrid has attracted more and more attention to provide backup power for customers in
the case of power grid outages. Microgrid expansion planning is significant to handle the …
the case of power grid outages. Microgrid expansion planning is significant to handle the …
An expansion planning method for extending distributed energy system lifespan with energy storage systems
The recent advances in the modern power grids, such as growing energy demand and
penetration of higher amounts of distributed energy generators like renewable energy …
penetration of higher amounts of distributed energy generators like renewable energy …
A simulation environment for training a reinforcement learning agent trading a battery storage
Battery storages are an essential element of the emerging smart grid. Compared to other
distributed intelligent energy resources, batteries have the advantage of being able to …
distributed intelligent energy resources, batteries have the advantage of being able to …