Review of Optimization Methods for Energy Hub Planning, Operation, Trading, and Control
Author:
Affiliation:
1. Department of Electrical Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China
2. ECE Department, Illinois Institute of Technology, Chicago, IL, USA
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shaanxi Province
Northwest Branch of State Grid Corporation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Renewable Energy, Sustainability and the Environment
Link
http://xplorestaging.ieee.org/ielx7/5165391/9802539/09767577.pdf?arnumber=9767577
Reference170 articles.
1. Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application
2. Multi-Agent Bargaining Learning for Distributed Energy Hub Economic Dispatch
3. A multi-agent deep reinforcement learning approach enabled distributed energy management schedule for the coordinate control of multi-energy hub with gas, electricity, and freshwater
4. Mixed-integer linear programming-based optimal configuration planning for energy hub: Starting from scratch
5. Optimal Design of Multicarrier Energy Systems Considering Reliability Constraints
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