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Stochastic optimization models for power generation capacity expansion with risk management

机译:带有风险管理的发电容量扩展的随机优化模型

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We propose a two-stage stochastic optimization model for maximizing the profit of a price-taker power producer who has to decide his own power generation capacity expansion plan in a long time horizon, taking into account the uncertainty of the following parameters: fuel costs; market electricity prices, as well as prices of green certificates and CO2 emission allowances; market share. The parameter uncertainty is represented by scenarios on their values along the planning horizon and the associated probability of occurrence. We first discuss the risk neutral stochastic model, that maximizes over all scenarios the net present value of the expected profit along the planning horizon. The risk neutral model does not take into account the variability of the objective function value over the scenarios and, then, the possibility of realizing in some scenarios a very low profit. Several approches have been introduced in the literature for measuring the profit risk. In this work we consider the Conditional Value at Risk, that requires a confidence level to be defined, and the First-order Stochastic Dominance constraints, for which a benchmark need to be assigned. By using a realistic case study, we report the main results of considering risk averse strategies under different hypotheses of the available budget, analysing the impact on the expected profit.
机译:考虑到以下参数的不确定性,我们提出了一个两阶段的随机优化模型,以使需要长期决定自己的发电能力扩展计划的价格接受电力生产商的利润最大化。市场电价,以及绿色证书和CO 2 排放配额的价格;市场份额。参数不确定性由沿计划范围的情景值及其关联的发生概率来表示。我们首先讨论风险中性随机模型,该模型在所有情况下都沿计划范围最大化了预期利润的净现值。风险中性模型没有考虑目标函数值在各种情况下的可变性,因此没有考虑在某些情况下实现非常低的利润的可能性。文献中已经引入了几种方法来测量利润风险。在这项工作中,我们考虑了条件风险值(需要定义一个置信度)和一阶随机优势约束(需要为其指定基准)。通过使用现实的案例研究,我们报告了在可用预算的不同假设下考虑风险规避策略的主要结果,分析了对预期利润的影响。

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