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Generation expansion planning under uncertainty with emissions quotas

机译：排放配额不确定的情况下的发电扩张计划

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Generation expansion planning for hydro-thermal power systems aims to find optimal investment decisions among a set of possible power plant projects. For any given investment plan, expansion planning models must account for investment costs as well as expected operational costs. Additionally, when modeling hydro-thermal power systems, one must consider the inherent uncertainty in hydro inflows because variations in inflows can significantly impact operational decisions. To model the expansion planning problem for a hydro-thermal power producer, we propose a novel decomposition algorithm, based on Benders decomposition. The Benders master problem computes the investment decisions while the separation problems are emissions-constrained, least-cost, and stochastic hydro-thermal scheduling problems. The separation problems are solved using a standard stochastic dual dynamic programming (SDDP) algorithm. To demonstrate the effectiveness of the approach, we present a case study for Panama's power system. The computational results allow us to price carbon dioxide emissions reductions, aiding the evaluations of environmental policies.
机译：水热发电系统的发电扩展计划旨在在一组可能的电厂项目中找到最佳投资决策。对于任何给定的投资计划，扩展计划模型都必须考虑投资成本以及预期的运营成本。此外，在对水热发电系统进行建模时，必须考虑水流的内在不确定性，因为水流的变化会显着影响运营决策。为了对水火力发电厂的扩展计划问题建模，我们提出了一种基于Benders分解的新颖分解算法。 Benders主问题计算投资决策，而分离问题则是排放受限，成本最低且随机的水热调度问题。使用标准随机双动态规划（SDDP）算法解决了分离问题。为了证明这种方法的有效性，我们提出了一个有关巴拿马电力系统的案例研究。计算结果使我们能够对二氧化碳减排量进行定价，以帮助评估环境政策。

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