A multistage decision-dependent stochastic bilevel programming approach for power generation investment expansion planning

ABSTRACT

In this article, we study the long-term power generation investment expansion planning problem under uncertainty. We propose a bilevel optimization model that includes an upper-level multistage stochastic expansion planning problem and a collection of lower-level economic dispatch problems. This model seeks for the optimal sizing and siting for both thermal and wind power units to be built to maximize the expected profit for a profit-oriented power generation investor. To address the future uncertainties in the decision-making process, this article employs a decision-dependent stochastic programming approach. In the scenario tree, we calculate the non-stationary transition probabilities based on discrete choice theory and the economies of scale theory in electricity systems. The model is further reformulated as a single-level optimization problem and solved by decomposition algorithms. The investment decisions, computation times, and optimality of the decision-dependent model are evaluated by case studies on IEEE reliability test systems. The results show that the proposed decision-dependent model provides effective investment plans for long-term power generation expansion planning.

Keywords:

• Multistage stochastic programming
• bilevel optimization
• generation expansion
• decision-dependent probability
• decomposition algorithms

Acknowledgments

The authors thank the reviewers and editors for their helpful suggestions and comments.

Additional information

Funding

This work is in part supported by National Science Foundation through Grant CMMI-1355939 and the AFRL Mathematical Modeling and Optimization Institute.

Notes on contributors

Yiduo Zhan

Yiduo Zhan is a Senior Operations Research Analyst for Monsanto Company. He received a B.S. degree in physics from the University of Science and Technology of China, Hefei, China, in 2010, and an M.S. degree in modeling and simulation in 2013 and Ph.D. degree in operations research and industrial engineering and management systems in 2017 from the University of Central Florida, Orlando. His research interests lie within the area of multistage optimization in energy systems.

Qipeng P. Zheng

Qipeng P. Zheng is an Assistant Professor with the Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando. He received a bachelor’s degree in automation from North China University of Technology, Beijing, China, in 2001; a master’s degree in automation from Tsinghua University, Beijing, in 2005; and a Ph.D. degree in industrial and systems engineering from the University of Florida, Gainesville, FL, in 2010. His research areas include optimization; network science; machine learning and applications, especially in management systems; energy and power systems; sustainability; and transportation planning under uncertainty. He is the co-editor-in-chief of the Springer journal Energy Systems.