M
M. Ghofrani
Researcher at University of Washington
Publications - 44
Citations - 2017
M. Ghofrani is an academic researcher from University of Washington. The author has contributed to research in topics: Wind power & Cluster analysis. The author has an hindex of 18, co-authored 43 publications receiving 1688 citations. Previous affiliations of M. Ghofrani include University of Tehran & Sharif University of Technology.
Papers
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Journal ArticleDOI
Genetic-Algorithm-Based Optimization Approach for Energy Management
TL;DR: In this paper, the authors proposed a new strategy to meet the controllable heating, ventilation, and air conditioning (HVAC) load with a hybrid-renewable generation and energy storage system.
Journal ArticleDOI
A Framework for Optimal Placement of Energy Storage Units Within a Power System With High Wind Penetration
TL;DR: This paper deals with optimal placement of the energy storage units within a deregulated power system to minimize its hourly social cost using probabilistic optimal power flow (POPF) and uses a genetic algorithm to maximize wind power utilization over a scheduling period.
Journal ArticleDOI
Stochastic Performance Assessment and Sizing for a Hybrid Power System of Solar/Wind/Energy Storage
TL;DR: In this article, a stochastic framework for optimal sizing and reliability analysis of a hybrid power system including the renewable resources and energy storage system is proposed, where a pattern search-based optimization method is used in conjunction with a sequential Monte Carlo simulation (SMCS) to minimize the system cost and satisfy the reliability requirements.
Proceedings ArticleDOI
Smart meter based short-term load forecasting for residential customers
TL;DR: The results qualitatively demonstrate that achieving the desired prediction accuracy while avoiding a high computational load requires limiting the volume of data used for prediction, and the measurement sampling rate must be carefully selected as a compromise between these two conflicting requirements.
Journal ArticleDOI
A Multi-Objective Transmission Expansion Planning Framework in Deregulated Power Systems With Wind Generation
TL;DR: In this article, a stochastic framework for transmission grid reinforcement studies in a power system with wind generation is proposed, which considers the investment cost, absorption of private investment and reliability of the system as the objective functions.