Long-term Co-Planning of Distributed Chemical and Electrical Systems under Uncertainty

Interdisciplinary Areas: Data and Engineering Applications, Smart City, Infrastructure, Transportation, Power, Energy, and the Environment

Project Description

The chemical industry is a major source of carbon emissions. To achieve the goal of net-zero emissions, significant effort has to be made to electrify and decarbonize the chemical industry. A promising route is to produce chemical products, such as green hydrogen and ammonia, through electrochemical reactions in modular plants. The electricity used by the chemical plant has to come from renewable-based electricity in the future to achieve net-zero emissions.

On the other hand, as energy consumption shifts from fossil fuels to renewable-based electricity, we also plan to study revolutionary change in the electricity market. The FERC order No. 2222, approved in 2020, opens the wholesale electricity markets to distributed energy resources (DERs), which are broadly defined to include distributed generation (such as solar PV), energy storage, and demand response (DR). The distributed chemical plants can serve as energy storage and demand responses in the sense that they can ramp up/down their production given the price signals. We plan to integrate the planning of distributed chemical processes and power systems to improve grid resilience and reliability through mathematical programming. The research will also inform the future design of chemical and power market mechanisms. The future chemical and electrical systems will certainly be more decentralized and subject to more uncertainties. To aid grid operators in long-term planning to ensure system reliability and optimal generation capacity mix, while subject to autonomous DERs over a shared transmission network, we seek candidates to work with us to advance on several fronts of large-scale stochastic programming: including modeling, theories, and algorithms of distributed computation, and robustness against unknown uncertainty distributions.

Start Date

After February, 2024

Postdoc Qualifications

PhD in industrial engineering, operations research, chemical engineering, electrical engineering, applied math, computer science, or related fields.

Research expertise in one or more of the followings areas: linear/nonlinear/mixed-integer programming/machine learning/optimization under uncertainty.

Fluent programming in one of the following programming languages: Python/Julia/C++.


Name: Can Li
email: canli@purdue.edu
Affiliation: Davidson School of Chemical Engineering
Website: https://canli1.github.io/

Name: Andrew Liu
email: liu334@purdue.edu
Affiliation: School of Industrial Engineering
Website: https://web.ics.purdue.edu/~liu334/

Short Bibliography

1. Z. Zhao, F. Chen and A. L. Liu. Auction design through multi-agent learning in peer-to-peer energy trading. Accepted to IEEE Transactions on Smart Grid, 2022.

2. Z. Huang, Q Zheng and A. L. Liu. A nested cross decomposition algorithm for power system capacity expansion with multiscale uncertainties. INFORMS Journal on Computing, online first, https://doi.org/10.1287/ijoc.2022.1177, Mar 2022

3. R. Chen and A. L. Liu. A distributed algorithm for large-scale convex quadratically constrained quadratic programs. Computational Optimization and Applications. Vol. 80, pp 781 – 830, 2021.

4. S. Ramyar, A. L. Liu and Y. Chen. A power market model in presence of strategic prosumers. IEEE Transactions on Power Systems. Vol. 35, pp 898 – 908, 2019.

5. V. Krishan, J. Ho, B. H. Hobbs, A. L. Liu, J. D. McCalley, M. Shahidehpour, and Q. Zheng (2015). Co-optimization of electricity transmission and generation resources for planning and policy analysis: Review of concepts and modeling approaches. Energy Systems, Vol. 7, No. 2, pp 297 – 332.

6. Li, C., Conejo, A. J., Liu, P., Omell, B. P., Siirola, J. D., & Grossmann, I. E. (2022). Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems. European Journal of Operational Research, 297(3), 1071-1082.

7. Li, C., Conejo, A. J., Siirola, J. D., & Grossmann, I. E. (2022). On representative day selection for capacity expansion planning of power systems under extreme operating conditions. International Journal of Electrical Power & Energy Systems, 137, 107697.