Multi-Objective Energy Management in Microgrids with Hybrid
This study introduces a novel multi-objective optimization framework for microgrids, integrating hybrid renewable energy sources (PV, WT, FC, MT, DG) and ESS to minimize costs,
Optimizing microgrid performance a multi-objective strategy for
It explores the integration of hybrid renewable energy sources into a microgrid (MG) and proposes an energy dispatch strategy for MGs operating in both grid-connected and standalone modes.
Data‐driven energy sharing for multi‐microgrids with building
To cope with the above issues, an XGBoost-embedded multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed in this paper to address the real-time energy
A Novel Multimode Coordination Strategy for Hybrid AC/DC Microgrids
This paper proposes a novel hybrid transformer-interconnected HMG (HT-HMG) and a multimode coordination strategy based on the multiplexing design of a multifunctional converter (MFC). The
HIERARCHICAL DISTRIBUTED MODEL PREDICTIVE
In this paper, an effective hierarchical distributed model predictive control (HDMPC) method is proposed for a DC microgrid with multiple hybrid energy storage systems.
Autonomous Cooperative Control for Hybrid AC/DC Microgrids
Matching power transfer between microgrids enables maximum utilization of distributed energy resources, a cluster-oriented cooperative control strategy for multiple AC micro-grid clusters
Distributed Hybrid-Triggered Observer-Based Secondary Control of Multi
Abstract: The secondary control (SC) of DC Microgrids (MGs) exhibits fast control dynamics as a consequence of low system inertia, leading to substantial communication, networked sensor
Multi-objective distributed event-triggered control for hybrid energy
The proposed strategy aims to achieve multiple control objectives, including power allocation, precise bus voltage regulation, energy balancing of batteries, and proportional power
Multiple microgrids intelligent energy management with capacity
This paper presents a hybrid intelligent energy management framework for interconnected microgrids (MGs), integrating deep neural networks (DNNs), model-free reinforcement learning (RL),
Enhanced Distributed Coordinated Control Strategy for DC Microgrid
A novel enhanced distributed coordinated control framework, based on adaptive event-triggered mechanisms, is developed for the efficient management of multiple hybrid energy storage
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