Optimization of electric power systems has traditionally been centralized with data collected and processed at a control center. For instance, system operators collect all necessary data and then centrally solve optimal power flow problems to schedule generator setpoints. The rapid adoption of distributed energy resources increasingly challenges the scalability of the communicating, modeling, and computing tasks associated with centralized optimization. Distributed optimization addresses these challenges by allowing many local controllers to cooperatively solve large optimization problems using local computations informed by communication with neighboring controllers. Distributed optimization has several potential advantages over centralized approaches, including enhanced robustness against the failures of individual controllers, resilience against failures of supporting cyber systems, faster parallel computations, and increased privacy with limited data communicated to a subset of other controllers. Ongoing research efforts are working to improve distributed optimization algorithms by speeding up convergence rates, enhancing optimization accuracy, extending modeling flexibility, and enhancing robustness to both noisy data from nonideal communications and malicious data from cyberattacks. Demonstration projects are also illustrating the applicability of distributed optimization in practical settings, the need for distributed computing devices, and options for implementation architectures.
Working Group on Computational Challenges and Solutions for Implementing Distributed Optimization in Power Systems, part of the Analytic Methods for Power Systems (AMPS) Committee, Computing and Analytical Methods (CAMS) Subcommittee.
Working Group on Multi-Agent Systems (MAS), part of the Analytic Methods for Power Systems (AMPS) Committee, Intelligent Systems Applications (ISA) Subcommittee.
IEEE Xplore Articles:
- ‘Distributed Optimization in Distribution Systems: Use Cases, Limitations, and Research Needs,‘ N. Patari, V. Venkataramanan, A. Srivastava, D.K. Molzahn, N. Li, and A. Annaswamy. To appear in IEEE Transactions on Power Systems.
- ‘Learning-Accelerated ADMM for Distributed DC Optimal Power Flow,‘ D. Biagioni, P. Graf, X. Zhang, A. S. Zamzam, K. Baker, and J. King. IEEE Control Systems Letters, vol. 6, pp. 1-6, 2022.
- ‘Distributed Voltage Control for Three-Phase Unbalanced Distribution Systems With DERs and Practical Constraints,‘ N. Patari, A. K. Srivastava, G. Qu, and N. Li. IEEE Transactions on Industry Applications, vol. 57, no. 6, pp. 6622-6633, Nov.-Dec. 2021.
- ‘A Two-Level ADMM Algorithm for AC OPF With Global Convergence Guarantees,‘ K. Sun and X. A. Sun. IEEE Transactions on Power Systems, vol. 36, no. 6, pp. 5271-5281, Nov. 2021
- ‘Resilient Information Architecture Platform for the Smart Grid: A Novel Open-Source Platform for Microgrid Control,’ H. Tu, Y. Du, H. Yu, A. Dubey, S. Lukic and G. Karsai. IEEE Transactions on Industrial Electronics, vol. 67, no. 11, pp. 9393-9404, Nov. 2020.
- ‘Online Stochastic Optimization of Networked Distributed Energy Resources,‘ X. Zhou, E. Dall’Anese and L. Chen. IEEE Transactions on Automatic Control, vol. 65, no. 6, pp. 2387-2401, June 2020.
- ‘Resilient Cyber Infrastructure for the Minimum Wind Curtailment Remedial Control Scheme,’ V. V. G. Krishnan, S. Gopal, R. Liu, A. Askerman, A. Srivastava, D. Bakken, and P. Panciatici. IEEE Transactions on Industry Applications, vol. 55, no. 1, pp. 943-953, Jan.-Feb. 2019, doi: 10.1109/TIA.2018.2868257.
- ‘Toward Distributed/Decentralized DC Optimal Power Flow Implementation in Future Electric Power Systems,’ A. Kargarian, J. Mohammadi, J. Guo, S. Chakrabarti, M. Barati, G. Hug, S. Kar, and R. Baldick. IEEE Transactions on Smart Grid, vol. 9, no. 4, pp. 2574-2594, July 2018.
- ‘On the Role of Communications Plane in Distributed Optimization of Power Systems,’ J. Guo, G. Hug, and O. K. Tonguz. IEEE Transactions on Industrial Informatics, vol. 14, no. 7, pp. 2903-2913, July 2018.
- ‘A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems,’ D.K. Molzahn, F. Dörfler, H. Sandberg, S.H. Low, S. Chakrabarti, R. Baldick, and J. Lavaei. IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2941-2962, Nov. 2017.
- ‘Distributed and Decentralized Voltage Control of Smart Distribution Networks: Models, Methods, and Future Research,’ K. E. Antoniadou-Plytaria, I. N. Kouveliotis-Lysikatos, P. S. Georgilakis, and N. D. Hatziargyriou. IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2999-3008, Nov. 2017.
- ‘A Case for Nonconvex Distributed Optimization in Large-Scale Power Systems,’ J. Guo, G. Hug, and O. K. Tonguz. IEEE Transactions on Power Systems, vol. 32, no. 5, pp. 3842-3851, Sept. 2017.
- ‘Towards Enhanced Power Grid Management via More Dynamic and Flexible Edge Computations,’ D. Bakken, A. Askerman, A. Srivastava, P. Panciatici, M. Seewald, F. Columbus, and S. Jiang. IEEE Fog World Congress (FWC), 2017.
- IEEE Transactions on Smart Grid, vol. 8, no. 6, Nov. 2017 includes a special section titled “Distributed Control and Efficient Optimization Methods for Smart Grid,” that is dedicated to distributed optimization. See the guest editorial for this special issue here: https://ieeexplore.ieee.org/document/8075192
Power & Energy Magazine (P&E) Articles:
- ‘Autonomous Energy Grids: Controlling the Future Grid With Large Amounts of Distributed Energy Resources,’ B. Kroposki, A. Bernstein, J. King, D. Vaidhynathan, X. Zhou, C.-Y. Chang, and E. Dall’Anese, IEEE Power and Energy Magazine, vol. 18, no. 6, pp. 37-46, Nov.-Dec. 2020.
- A.K. Srivastava and D.K. Molzahn, “Distributed Optimization for Electric Power Systems: Needs, Algorithmic Developments, and Use Cases,” 2022 Half-Day Tutorial (March 1-2, 2022). [Tutorial registration available here]
- ‘Distributed Optimization and Control for Enabling Power Grid Resiliency,’ A.K. Srivastava and D.K. Molzahn, IEEE PES-CAMS Webinar, January 18, 2022. [Webinar available here]
- ‘Decomposing Transmission Constraints for Decentralized Power System Optimization,‘ F. Qiu, IEEE PES University Webinar, February 2, 2022. [Webinar available here]