Generation and Transmission Expansion Under Risk Using Stochastic Programming

By Juan Álvarez López, Kumaraswamy Ponnambalam, V. H. Quintana. IEEE Transactions on Power Systems, 2007

159 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/ca6b5b4cf6c4567664948a9dfaf00050015c7067

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In Electrical and Electronic Engineering

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Citations in the corpus

Transmission and Wind Power Investment (2012, background): “In [6], the investment problem is tackled modeling as random events the demands, the availability of the power units, and the transmission capacity factors of the lines.”
Multi-Stage Flexible Expansion Co-Planning Under Uncertainties in a Combined Electricity and Gas Market (2015, background): “Alvarez Lopez, et al, 2007 [65] Propose a new model for generation and transmission expansion planning, while considering random events such as demand, availability of generating plants and transmission capacity factor of transmission lines.”
Toward Fully Renewable Electric Energy Systems (2015, background): “Reference [4] proposes a mixed integer non-linear programming model that considers demand, generation and transmission capacity factors as random events.”
Designing effective and efficient incentive policies for renewable energy in generation expansion planning (2011, background): “This evaluation is conducted in the context of generation expansion planning (GEP) [9, 10, 11, 12, 13, 14, 15], because the current installed renewable generation capacity is far less than sufficient to meet the renewable portfolio standards set by many states in America [16].”
Co-optimization of electricity transmission and generation resources for planning and policy analysis: review of concepts and modeling approaches (2016, methodology): “Stochastic programming was applied in [66, 67] to simulate random outages of system components.”
Transmission expansion planning with re-design (2010, background): “Reported results are mostly for small power systems (6 to 30 buses) [35, 12, 25]; see also [10, 11, 17, 43, 9, 23, 24, 34, 15].”
Multistage Stochastic Investment Planning With Multiscale Representation of Uncertainties and Decisions (2018, background): “Stochastic optimization, as a framework for modeling optimization problems that involve uncertainty, constitutes a useful tool for capturing the impacts of such uncertainties [2], [5]–[8], [10].”