[HTML][HTML] Dynamic grid stability in low carbon power systems with minimum inertia

F Ahmed, D Al Kez, S McLoone, RJ Best, C Cameron… - Renewable Energy, 2023‏ - Elsevier
The power system transition to large penetrations of renewable generation has become a
core target of decarbonisation roadmaps in many countries. However, switching from …

[HTML][HTML] A comprehensive review on demand side management and market design for renewable energy support and integration

S Panda, S Mohanty, PK Rout, BK Sahu, SM Parida… - Energy Reports, 2023‏ - Elsevier
The traditional power system is facing significant transformations due to the integration of
emerging technologies, renewable energy sources (RES), and storage devices. This review …

Two-phase operation for coordinated charging of electric vehicles in a market environment: From electric vehicle aggregators' perspective

Y Zheng, Y Wang, Q Yang - Renewable and Sustainable Energy Reviews, 2023‏ - Elsevier
The increasing penetration of electric vehicles (EVs) poses challenges to the operation of
existing power systems owing to the spatial and temporal randomness and dynamics of EV …

IoT-based optimal demand side management and control scheme for smart microgrid

BE Sedhom, MM El-Saadawi, MS El Moursi… - International Journal of …, 2021‏ - Elsevier
Renewable energy resources (RESs) are highly speared to cover colossal electricity
demand. Smart microgrids (SMGs) are engaged with demand-side management (DSM) to …

Explainable multi-agent deep reinforcement learning for real-time demand response towards sustainable manufacturing

L Yun, D Wang, L Li - Applied Energy, 2023‏ - Elsevier
The demand response (DR) plays a significant role in manufacturing system energy
management and sustainable industrial development. Current literature on DR management …

Refined peak shaving potential assessment and differentiated decision-making method for user load in virtual power plants

X Kong, Z Wang, C Liu, D Zhang, H Gao - Applied Energy, 2023‏ - Elsevier
There is a consensus regarding the need to realize the transformation of renewable energy
by enhancing demand-side regulating ability. This paper proposes a peak shaving potential …

DRL-HEMS: Deep reinforcement learning agent for demand response in home energy management systems considering customers and operators perspectives

AA Amer, K Shaban… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
With the smart grid and smart homes development, different data are made available,
providing a source for training algorithms, such as deep reinforcement learning (DRL), in …

Incentive-based integrated demand response for multiple energy carriers considering behavioral coupling effect of consumers

S Zheng, Y Sun, B Li, B Qi, K Shi… - IEEE Transactions on …, 2020‏ - ieeexplore.ieee.org
Incentive-based demand response (DR) has been recognized as a powerful tool to mitigate
supply-demand imbalance in electricity market. However, it cannot be directly applied into …

Online pricing of demand response based on long short-term memory and reinforcement learning

X Kong, D Kong, J Yao, L Bai, J **ao - Applied energy, 2020‏ - Elsevier
Incentive-based demand response is playing an increasingly important role in ensuring the
safe operation of the power grid and reducing system costs, and advances in information …

Robust day-ahead coordinated scheduling of multi-energy systems with integrated heat-electricity demand response and high penetration of renewable energy

J Lu, T Liu, C He, L Nan, X Hu - Renewable Energy, 2021‏ - Elsevier
As fossil fuels dry up and the proportion of renewable energy increases, multi-energy system
(MES) becomes an effective way to realize renewable energy accommodation. High …