An affine adjustable robust model for generation and transmission network planning

• Published in: International journal of electrical power & energy systems 2014-09, Vol.60, p.141-152
• Main Authors: Ng, Tsan Sheng, Sy, Charlle
• Format: Article
• Language: English
• Subjects: Adjustable; Applied sciences; Budgeting; Electric power plants; Electrical engineering. Electrical power engineering; Electrical power engineering; Exact sciences and technology; Generation planning; Linear programming; Mathematical models; Miscellaneous; Network flow; Networks; Operation. Load control. Reliability; Partitioning; Power networks and lines; Programming; Robust optimization; Uncertainty
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2014.02.026

•Uncertainties in operational cost parameters in generation and transmission planning.•Two stage affine adjustable robust mixed integer model for generation planning.•Novel approach to achieve a safe and tractable approximation of the optimization model.•Effective planning solutions compared to stochastic programming models. This work studies an electricity generation and transmission network planning problem where loads and cost parameters are uncertain. The problem is to first determine the generation and transmission capacities to install in the supply network. When the uncertainties are revealed, a flow plan is developed to minimize the total costs and to balance loads. A two-stage mixed-integer programming model is proposed to maximize the robustness of the plan in achieving a total cost budget target. The modeling approach in this study synthesizes recent developments in affine adjustable robust optimization technology and decision-making behavior under uncertainty. A novel solution approach is also proposed to achieve a safe and tractable approximation of the model. This involves the partitioning of the total cost budget target in order to transform the original problem into a small collection of mixed integer programming models that can be solved efficiently using standard mixed integer programming solvers. Numerical studies using a power generation network are performed, which demonstrate that the proposed robust planning model performs favorably compared to a stochastic programming model across different performance measures. The computational results strongly suggest the ability of the robust planning model to effectively mitigate the effect of uncertainties.