Optimal investments in power generation under centralized and decentralized decision making

By Audun Botterud, Marija D. Ilic, Ivar Wangensteen. IEEE Transactions on Power Systems, 2005

203 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Kalman filtering with intermittent observations (2004, unknown): “For example, a sensor node can be flooded with TCP requests which results in power exhaustion.”
Market-Based Coordination of Transmission and Generation Capacity Planning (2007, methodology): “Reference [12] used stochastic dynamic programming to identify an optimal investment strategy for one investor when the objective was to maximize the investor’s expected profit.”
Two-Stage Robust Generation Expansion Planning: A Mixed Integer Linear Programming Model (2014, background): “Most of the on-hand nondeterministic GEP approaches cope with the enforced planning uncertainties using: • scenario analysis (SA-GEP) [7], [8]; • stochastic dynamic programming (SDP-GEP) [9], [10]; • stochastic programming (SP-GEP) [11]–[15]; • chance-constrained programming (CCP-GEP) [11], [16], [1”
Risk-Constrained Multi-Stage Wind Power Investment (2013, background): “, [7]–[9], allow representing a multi-stage framework.”
Strategic Generation Investment Using a Complementarity Approach (2011, unknown): “In [ 7 ], a stochastic dynamic optimization model is used to evaluate generation investments under both centralized and decentralized frameworks, but not modeling the network.”