Application of precautionary and corrective energy management strategies in improving networked microgrids resilience: A two-stage linear programming

2022 ◽  
Vol 204 ◽  
pp. 107704
Author(s):  
Kamran Jalilpoor ◽  
Saman Nikkhah ◽  
Mohammad Sadegh Sepasian ◽  
Mahtab Ghobadi Aliabadi
Keyword(s):  
Linear Programming ◽  
Energy Management ◽  
Management Strategies ◽  
Two Stage ◽  
Networked Microgrids

Two-stage energy management for networked microgrids with high renewable penetration

2018 ◽  
Vol 226 ◽  
pp. 39-48 ◽  
Author(s):  
Dongxiao Wang ◽  
Jing Qiu ◽  
Luke Reedman ◽  
Ke Meng ◽  
Loi Lei Lai
Keyword(s):  
Energy Management ◽  
Two Stage ◽  
High Renewable Penetration ◽  
Networked Microgrids

Microgrid energy management strategies assessment through coupled thermal-electric considerations

2021 ◽  
Vol 228 ◽  
pp. 113711
Author(s):  
Spyridon Chapaloglou ◽  
Athanasios Nesiadis ◽  
Konstantinos Atsonios ◽  
Nikos Nikolopoulos ◽  
Panagiotis Grammelis ◽  
...  
Keyword(s):  
Energy Management ◽  
Management Strategies ◽  
Microgrid Energy Management

A Novel Energy Management Approach to Networked Microgrids for Disaster Resilience

2021 ◽  
Author(s):  
Mumbere Kihembo Samuel ◽  
Atsushi Fukuhara ◽  
Yutaka Sasaki ◽  
Yoshifumi Zoka ◽  
Naoto Yorino ◽  
...  
Keyword(s):  
Energy Management ◽  
Management Approach ◽  
Disaster Resilience ◽  
Networked Microgrids

scholarly journals Leveraging a Genetic Algorithm for the Optimal Placement of Distributed Generation and the Need for Energy Management Strategies Using a Fuzzy Inference System

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 172
Author(s):  
Sunny Katyara ◽  
Muhammad Fawad Shaikh ◽  
Shoaib Shaikh ◽  
Zahid Hussain Khand ◽  
Lukasz Staszewski ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Fuzzy Inference System ◽  
Distribution System ◽  
Fuzzy Inference ◽  
Management Strategies ◽  
Test System ◽  
Optimal Placement ◽  
Inference System ◽  
Simulated Environment

With the rising load demand and power losses, the equipment in the utility network often operates close to its marginal limits, creating a dire need for the installation of new Distributed Generators (DGs). Their proper placement is one of the prerequisites for fully achieving the benefits; otherwise, this may result in the worsening of their performance. This could even lead to further deterioration if an effective Energy Management System (EMS) is not installed. Firstly, addressing these issues, this research exploits a Genetic Algorithm (GA) for the proper placement of new DGs in a distribution system. This approach is based on the system losses, voltage profiles, and phase angle jump variations. Secondly, the energy management models are designed using a fuzzy inference system. The models are then analyzed under heavy loading and fault conditions. This research is conducted on a six bus radial test system in a simulated environment together with a real-time Power Hardware-In-the-Loop (PHIL) setup. It is concluded that the optimal placement of a 3.33 MVA synchronous DG is near the load center, and the robustness of the proposed EMS is proven by mitigating the distinct contingencies within the approximately 2.5 cycles of the operating period.

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