Dynamic stochastic fractional programming for sustainable management of electric power systems
Dynamic stochastic fractional programming for sustainable management of electric power systems
复制标题
DOI：
10.1016/j.ijepes.2013.05.022
复制
发表时间：
2013-12
影响因子：
5.2
通讯作者：
H. Zhu;G. Huang
中科院分区：
文献类型：
--
作者：
H. Zhu;G. Huang
提出了一种求解不确定性电力系统容量规划问题的动态随机分式规划方法。传统的发电扩展规划侧重于以最低成本提供足够的能源供应。与使用最小成本模型不同，更可持续的管理方法是最大化可再生能源发电与系统成本之间的比率。所提出的DSFP方法可以解决这类涉及容量扩展和随机信息的比率优化问题。它在平衡冲突的目标，处理概率分布表示的不确定性，并在不同的风险水平下产生灵活的能力扩张策略。该方法被应用到城市发电系统的扩展案例研究。所得到的解决方案是有用的，在产生可持续的发电方案和容量扩展计划。结果表明，DSFP可以支持系统效率，经济成本和约束违反风险之间的相互作用的深入分析。
A dynamic stochastic fractional programming (DSFP) approach is developed for capacity-expansion planning of electric power systems under uncertainty. The traditional generation expansion planning focused on providing a sufficient energy supply at minimum cost. Different from using least-cost models, a more sustainable management approach is to maximize the ratio between renewable energy generation and system cost. The proposed DSFP method can solve such ratio optimization problems involving issues of capacity expansion and random information. It has advantages in balancing conflicting objectives, handling uncertainty expressed as probability distributions, and generating flexible capacity-expansion strategies under different risk levels. The method is applied to an expansion case study of municipal electric power generation system. The obtained solutions are useful in generating sustainable power generation schemes and capacity-expansion plans. The results indicate that DSFP can support in-depth analysis of the interactions among system efficiency, economic cost and constraint-violation risk.