C.M. Colson
Montana State University
17 Papers
182 Citations
C.M. Colson is an academic researcher from Montana State University. The author has contributed to research in topics: Distributed generation & Power management. The author has an hindex of 12, co-authored 17 publications.
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Papers
Real-Time Energy Management of a Stand-Alone Hybrid Wind-Microturbine Energy System Using Particle Swarm Optimization
TL;DR: In this article, the application of particle swarm optimization (PSO) to find real-time optimal energy management solutions for a stand-alone hybrid wind-microturbine (MT) energy system is presented.
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A review of challenges to real-time power management of microgrids
C.M. Colson,M.H. Nehrir +1 more
- 26 Jul 2009
TL;DR: In this paper, a case is made for a real-time power management and control system that attempts to optimize microgrid systems based on multiple objectives, such as power demands, fuel consumption, environmental emissions, costs, dispatchable loads, etc.
200
Real-time energy management of a stand-alone hybrid wind-microturbine energy system using particle swarm optimization
Seyyed Ali Pourmousavi Kani,Hashem Nehrir,C.M. Colson,Caisheng Wang +3 more
- 24 Jul 2011
TL;DR: In this article, the application of particle swarm optimization (PSO), which is a biologically-inspired direct search method, to find real-time optimal energy management solutions for a stand-alone hybrid wind energy system is presented.
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Algorithms for distributed decision-making for multi-agent microgrid power management
C.M. Colson,M.H. Nehrir +1 more
- 24 Jul 2011
TL;DR: In this paper, a multi-agent based control architecture for micro-grids, capable of coordinating and cooperatively achieving user-defined objectives is presented, which facilitates the fundamental self-organizing and cooperative behavior amongst the micro-grid agents.
106
Ant colony optimization for microgrid multi-objective power management
C.M. Colson,M.H. Nehrir,Caisheng Wang +2 more
- 15 Mar 2009
TL;DR: In this paper, an intelligent supervisory controller that utilizes ant colony optimization (ACO) methods for AEDG microgrid dispatch control is presented, where the ACO is applied to the rapid microgrid power management problem given complex constraints and objectives including: environmental, fuel/resource availability, and economic considerations.
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