Information Gap Decision Theory based congestion and voltage management in the presence of uncertain wind power

Murphy, Conor; Soroudi, Alireza; Keane, Andrew

doi:10.1109/pesgm.2016.7741221

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…using information gap decision theory, uncertainty of wind power ramp prediction has been modeled in [73]. Optimal power flow problem in the presence of wind generation uncertainty has been evaluated in [74].…”

Section: • Transmission and Distribution Sectormentioning

confidence: 99%

Application of information gap decision theory in practical energy problems: A comprehensive review

2019

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The uncertainty and risk modeling is a hot topic in operation and planning of energy systems. The system operators and planners are decision makers that need to handle the uncertainty of input data of their models. As an example, energy consumption has always been a critical problem for operators since the forecasted values, and the actual consumption is never expected to be the same. The penetration of renewable energy resources is continuously increasing in recent and upcoming years.These technologies are not dispatch-able and are highly dependent on natural resources. This would make the real-time energy balancing more complicated. Another source of uncertainty is related to energy market prices which are determined by the market participants' behaviors. To consider these issues, the uncertainty modeling should be performed. Various approaches have been previously utilized to model the uncertainty of these parameters such as probabilistic approaches, possibilistic approaches, hybrid possibilisticprobabilistic approach, information gap decision theory, robust and interval optimization techniques. This paper reviews the research works that used information gap decision theory for uncertainty modeling in energy and power systems.

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Section: • Transmission and Distribution Sectormentioning

confidence: 99%

Application of information gap decision theory in practical energy problems: A comprehensive review

2019

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Optimal integration of distributed generation (DG) resources in unbalanced distribution system considering uncertainty modelling

Maya

Jasmin

2016

Int. Trans. Electr. Energ. Syst.

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Summary The advancements in distributed generation (DG) technologies and growing concern for environmental friendly sources of energy necessitate an accurate analysis of distribution system with DG sources. The photovoltaic (PV) source is one of the most promising DG types that can be used for power generation at the distribution level because of its abundance. The efficient operation of the distribution system after the interconnection of DG sources is highly dependent on its integration to the distribution network. The uncertainty associated with the generation from the DG sources should also be considered while planning the integration. This paper presents the optimal sizing of the PV sources in the unbalanced distribution network by Reinforcement Learning, which is an efficient strategy for handling the stochastic data in practical situations. The uncertainty associated with the power output from the PV source is included in the power flow as a variable with multiple states. Here, the beta probability density function is used to model the randomness of the PV source. The seasonal variation in the power loss reduction obtained shows the effectiveness of the uncertainty model. The proposed algorithm is validated and tested for the Institute of Electrical and Electronics Engineers (IEEE) 13‐bus and 37‐bus distribution feeders, which shows its suitability for implementation in a real system. Copyright © 2016 John Wiley & Sons, Ltd.

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Information Gap Decision Theory based congestion and voltage management in the presence of uncertain wind power

Cited by 2 publications

References 14 publications

Application of information gap decision theory in practical energy problems: A comprehensive review

Application of information gap decision theory in practical energy problems: A comprehensive review

Optimal integration of distributed generation (DG) resources in unbalanced distribution system considering uncertainty modelling

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