A Stochastic Programming Approach to Electric Energy Procurement for Large Consumers

By Miguel Carrión, Andy Philpott, Antonio J. Conejo, J. M. Arroyo. IEEE Transactions on Power Systems, 2007

231 citations recorded by the source corpus.

This paper provides a technique based on stochastic programming to optimally solve the electricity procurement problem faced by a large consumer. Supply sources include bilateral contracts, a limited amount of self-production and the pool. Risk aversion is explicitly modeled using the conditional value-at-risk methodology. Results from a realistic case study are provided and analyzed

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/b1037d73afe248bba5780f8a2f450d146503e717

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Citations in the corpus

Enterprise-wide optimization for industrial demand side management: Fundamentals, advances, and perspectives (2016, background): “Zhang & Sargent (1996), Schilling & Pantelides (1996), and Castro et al. (2004). Some researchers have combined batch and continuous scheduling, or have focused on the case with parallel processes (Karimi & McDonald, 1997; Kopanos et al.”
Combined Operations of Renewable Energy Systems and Responsive Demand in a Smart Grid (2011, background): “T ODAY, the integration of large amounts of renewable energy systems (RESs) with the grid [1]–[6] is widely studied by many researchers, but only few of them address these problems in connection with a consumers’ potential participation to the electricity market [7]–[9], or analyze the additional ba”
Optimal Involvement in Futures Markets of a Power Producer (2008, background): “A variety of contracts are available for a power producer, such as contracts for differences [2] and cap contracts.”
Real-Time Demand Response Model (2010, background): “sufficiently large to participate in the electricity markets to minimize its energy procurement costs (see, for instance, [17]–[19] and the references therein).”
Short-Term Trading for a Wind Power Producer (2010, background): “Within this market framework, we develop a multistage stochastic programming model (see [5] or [6]) that embodies risk control on profit variability and provides optimal offering curves for the day-ahead market.”
Strategic Bidding for a Large Consumer (2015, background): “The long-term procurement of electric energy using risk-constrained stochastic programming is considered in [9].”