Hedging electricity portfolios via stochastic programming

By Stein-Erik Fleten, Stein W. Wallace, William T. Ziemba. 2002

150 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Finance, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/5826320209b402472d55c565d36bd9f8af8e5a95

How researchers see this paper

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

In Electrical and Electronic Engineering

1. Antonio J. Conejo
2. J. M. Arroyo
3. D. W. Bunn
4. Rafal Weron
5. Werner Römisch
6. Holger Heitsch
7. Francisco Javier Nogales
8. A. Saló

In Finance

1. M. Ludkovski
2. René A. Carmona
3. Erling Pettersen
4. D. W. Bunn
5. A. Saló
6. Janne Kettunen
7. J. M. Arroyo
8. Stein-Erik Fleten

In Ocean Engineering

1. Antonio J. Conejo
2. Werner Römisch
3. Holger Heitsch
4. J. M. Arroyo
5. A. Saló
6. D. W. Bunn
7. Janne Kettunen
8. Erling Pettersen

Citations in the corpus

• Constructing bidding curves for a price-taking retailer in the norwegian electricity market (2005, methodology): “An alternative way of modeling risk is by using shortfall costs, as shown, for example, in [7].”
• Prosumer bidding and scheduling in electricity markets (2016, background): “Reducing risk through changing operating decisions can be costly compared to financial operations [24].”
• Risk-constrained self-scheduling of a thermal power producer (2004, background): “The long/medium-term decision of which contracts to sign is better taken within a stochastic programming framework, similar to the one stated, for instance, in [13]–[16] or [17].”
• Scenario reduction and scenario tree construction for power management problems (2003, unknown): “[1], [6], [7], [8], [10], [15], [18], [21], [22] and the state-of-the-art survey [24].”
• Valuation of energy storage: an optimal switching approach (2010, background): “In contrast, in say silver, the volatility in prices is almost entirely speculative, but nevertheless can sometimes lead to price swings of 100% within one year*Corresponding author.”
• Generation of multivariate scenario trees to model stochasticity in power management (2005, background): “The corresponding stochastic optimization models rquire decisions on the basis of given probabilistic information on random data.”
• Optimization of Electricity Retailer's Contract Portfolio Subject to Risk Preferences (2010, methodology): “Carlo simulation and a scenario reduction technique [6], [16]”