The aim of this Master"s Thesis is to develop stochastic programming models that can be used by the supplier of custom contracts to minimize the cost of supply in meeting contract obligations. In the first part, we develop a two-stage stochastic programming model that minimizes expected costs of supply in replicating a single custom electricity forward contract. In the second part, we use a similar framework to look at the replication of multiple custom forward contracts: the same set of replicating instruments to meet the contracts obligations. Instead of minimizing expected cost with a proxy of market risk, we minimize the expected loss under an exogenously determined worse condition with a risk measure: Conditional Value-at-Risk (CVaR). In the third part, we describe the numerical implementation. The procurement strategy using the expected cost approach and the CVaR approach are analyzed and compared.
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A Stochastic Programming Approach to Electric Energy Procurement for Large Consumers Abstract: 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. Cited by: 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 analyzedCited by: Stochastic dynamic programming is a natural approach to this valuation problem, but it does not seem to be widely used in practice because it is at . The electricity demands at nodes of the network are treated as given in the existing formulations. In this paper, a stochastic programming formulation of the optimal load flow problem is presented by treating the electricity demand as a random .
• Stochastic optimization models for electricity portfolio and air-line revenue management and their solution by Lagrangian re-laxation. • Modelling of scenario trees for the relevant stochastic pro-cesses. • Measuring and minimizing risk, in particular, in electricity port-folio management models. Stochastic programming models for replication of electricity forward contracts for industry considers the problem of selecting custom electricity contracts and finding the optimal procurement. stage stochastic programming is joint work with Camilla Schaumbug-Mu¨ller and is submitted to an operations research journal. Finally, the survey The development in stochastic programming models for power production and trading must be considered work in progress. ˚Arhus, April Trine Krogh Kristoﬀersen Acknowledgments. This paper develops stochastic programming models that can be used by the supplier of a custom contract to design a procurement strategy that minimizes its expected costs of supply in meeting.
Generally, stochastic programming refers to a problem class, and not to the choice of solution procedures. Many of the models in this class can be solved both with tools from mathematical programming and as stochastic dynamic programs (SDPs). This book is about stochastic programs solved with tools from mathematical programming. A stochastic constraint optimization problem (stochastic COP) is a stochastic CSP plus a cost function defined over the decision and stochastic variables. In [Walsh, ], the only goal considered was to find a solution that satisfies the stochastic CSP which minimizes or maximizes the expected value of the objective function. the development of linear programming, and most models within the market paradigm have not yet found their ﬁnal form. Key words: Stochastic programming, energy, regulated markets, deregulation, uncertainty, electricity, natural gas, oil. 1 Introduction The purpose of this chapter is to discuss the use of stochastic programming in energy models. This section of the book contains 21 stochastic programming applications presented in five sections: (1) production, supply chain, and scheduling; (2) gaming; (3) environment and pollution; (4) finance; and (5) telecom and electricity. The section on production, supply chain, and .