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Jai Govind Singh

Researcher at Asian Institute of Technology

Publications -  118
Citations -  1316

Jai Govind Singh is an academic researcher from Asian Institute of Technology. The author has contributed to research in topics: Electric power system & Renewable energy. The author has an hindex of 16, co-authored 104 publications receiving 1064 citations. Previous affiliations of Jai Govind Singh include Indian Institute of Technology Kanpur.

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An efficient two stage stochastic optimal energy and reserve management in a microgrid

TL;DR: In this paper, an efficient two-stage stochastic optimal energy and reserve management approach is proposed for a microgrid, where in the first stage, the optimal power schedule is determined based on the load, wind and solar power forecasts.
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An Approach for Optimal Placement of Static VAr Compensators Based on Reactive Power Spot Price

TL;DR: In this paper, a reactive power spot price index (QSPI) has been suggested to determine the optimal location of static VAr compensator (SVC) in the power system.
Journal ArticleDOI

Sortino Ratio Based Portfolio Optimization Considering EVs and Renewable Energy in Microgrid Power Market

TL;DR: In this paper, the authors adapted Sortino ratio for power portfolio optimization in microgrid where total load demand (including losses) is optimally distributed to different microsources so that profit per unit risk of aggregator is maximized.
Proceedings ArticleDOI

Placement of FACTS controllers for enhancing power system loadability

TL;DR: In this paper, a sensitivity based approach is proposed to locate thyristor controlled series compensator (TCSC) and unified power flow controller (UPFC) for enhancing the power system loadability.
Proceedings ArticleDOI

An approach for optimal placement of static VAr compensators based on reactive power spot price

TL;DR: The results show that the suggested method of SVC placement is effective in reducing the real and reactive power spot prices, generation cost, system real power loss, total wheeling charges, and enhancing the system loading margin during normal as well as critical contingency cases.