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A stochastic programming model for an energy planning problem: formulation, solution method and application
Annals of Operations Research ( IF 4.8 ) Pub Date : 2021-01-04 , DOI: 10.1007/s10479-020-03904-1
Chandra Ade Irawan , Peter S. Hofman , Hing Kai Chan , Antony Paulraj

The paper investigates national/regional power generation expansion planning for medium/long-term analysis in the presence of electricity demand uncertainty. A two-stage stochastic programming is designed to determine the optimal mix of energy supply sources with the aim to minimise the expected total cost of electricity generation considering the total carbon dioxide emissions produced by the power plants. Compared to models available in the extant literature, the proposed stochastic generation expansion model is constructed based on sets of feasible slots (schedules) of existing and potential power plants. To reduce the total emissions produced, two approaches are applied where the first one is performed by introducing emission costs to penalise the total emissions produced. The second approach transforms the stochastic model into a multi-objective problem using the $$\epsilon $$ ϵ -constraint method for producing the Pareto optimal solutions. As the proposed stochastic energy problem is challenging to solve, a technique that decomposes the problem into a set of smaller problems is designed to obtain good solutions within an acceptable computational time. The practical use of the proposed model has been assessed through application to the regional power system in Indonesia. The computational experiments show that the proposed methodology runs well and the results of the model may also be used to provide directions/guidance for Indonesian government on which power plants/technologies are most feasible to be built in the future.

中文翻译:

能源规划问题的随机规划模型:公式、求解方法和应用

本文研究了在电力需求不确定的情况下,国家/地区发电扩张规划的中长期分析。考虑到发电厂产生的总二氧化碳排放量,设计了一个两阶段随机规划来确定能源供应源的最佳组合,目的是最小化发电的预期总成本。与现有文献中可用的模型相比,所提出的随机发电扩展模型是基于现有和潜在发电厂的可行时隙(时间表)集构建的。为了减少产生的总排放量,采用了两种方法,第一种方法是通过引入排放成本来惩罚产生的总排放量。第二种方法使用 $$\epsilon $$ ϵ -constraint 方法将随机模型转换为多目标问题,以产生帕累托最优解。由于提出的随机能量问题难以解决,因此设计了一种将问题分解为一组较小问题的技术,以在可接受的计算时间内获得良好的解决方案。已通过在印度尼西亚区域电力系统中的应用评估了所提出模型的实际使用情况。计算实验表明,所提出的方法运行良好,模型的结果也可用于为印度尼西亚政府提供方向/指导,以指导未来最可行的电厂/技术建设。
更新日期:2021-01-04
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