Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
The increasing penetrations of distributed energy resources (DERs) at the power distribution level augments the complexity of optimally operating the grid edge assets, primarily because of the nonlinearity and scale of the system. An alternative is to solve the relaxed convex or linear-approximated problem, but these methods lead to sub-optimal or power-flow infeasible solutions. This paper proposes a scalable and fast approach to solve the large nonlinear optimal power flow (OPF) problem using a developed distributed method. The full network-level OPF problem is decomposed into multiple smaller sub-problems that are easy to solve - the distributed method attains network-level optimal solutions upon consensus. This effective decomposition technique reduces the number of iterations required for consensus by order of magnitude compared to traditional distributed algorithms. We demonstrate the proposed approach by solving different nonlinear OPF problems (for different problem objectives) for a distribution system with more than fifty-thousands (50,000) problem variables.
Recommended Citation
Sadnan, Rabayet and Dubey, Anamika, "Distributed Computing for Scalable Optimal Power Flow in Large Radial Electric Power Distribution Systems with Distributed Energy Resources" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 8.
https://aisel.aisnet.org/hicss-56/es/monitoring/8
Distributed Computing for Scalable Optimal Power Flow in Large Radial Electric Power Distribution Systems with Distributed Energy Resources
Online
The increasing penetrations of distributed energy resources (DERs) at the power distribution level augments the complexity of optimally operating the grid edge assets, primarily because of the nonlinearity and scale of the system. An alternative is to solve the relaxed convex or linear-approximated problem, but these methods lead to sub-optimal or power-flow infeasible solutions. This paper proposes a scalable and fast approach to solve the large nonlinear optimal power flow (OPF) problem using a developed distributed method. The full network-level OPF problem is decomposed into multiple smaller sub-problems that are easy to solve - the distributed method attains network-level optimal solutions upon consensus. This effective decomposition technique reduces the number of iterations required for consensus by order of magnitude compared to traditional distributed algorithms. We demonstrate the proposed approach by solving different nonlinear OPF problems (for different problem objectives) for a distribution system with more than fifty-thousands (50,000) problem variables.
https://aisel.aisnet.org/hicss-56/es/monitoring/8