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首页> 外文期刊>International journal of electrical power and energy systems >Influence of the number of decision stages on multi-stage renewable generation expansion models
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Influence of the number of decision stages on multi-stage renewable generation expansion models

机译：决策阶段数量对多阶段可再生生成扩展模型的影响

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摘要

The capacity expansion problem of renewable sources faced by a central planner is essentially a long-term multistage decision-making problem under uncertainty. However, the size of the optimization problems describing multi-stage decision-making processes may lead to computational intractability even if a small number of stages is considered. We tackle this problem considering an explicit characterization of the long-term uncertainty and compare the outcomes of four different approaches for such problem, namely: (i) multi-stage stochastic -programming; (ii) linear decision rule (LDR); (iii) two-stage stochastic-programming under a rolling window procedure; (iv) and deterministic. The impact of considering an increasingly accurate representation of the evolution over time of the uncertain parameters is studied by solving the previous models for different planning schemes. The pros and cons of each approach are analyzed quantitatively and qualitatively using a case study based on the IEEE 24-node reliability test system (RTS). Finally, the performance of the stochastic programming and the LDR approaches is assessed by performing out-of-sample analyses.
机译：中央规划师面临的可再生能源的能力扩展问题基本上是在不确定性下的长期多级决策问题。然而，描述了描述多阶段决策过程的优化问题的大小，即使考虑少量阶段，也可以导致计算难害性。考虑到这一问题，考虑到了对长期不确定性的明确表现，并比较了四种不同方法的结果，即：（i）多级随机 - 预编程; （ii）线性决策规则（LDR）; （iii）在滚动窗口过程下的两级随机编程; （iv）和确定性。通过解决以前的不同规划方案，研究了考虑越来越准确地表达了不确定参数随着时间的推移的演变。使用基于IEEE 24节点可靠性测试系统（RTS）的案例研究，定量和定性地分析了各种方法的优缺点。最后，通过进行样品外分析来评估随机编程的性能和LDR方法。

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