TSO-DSO Operational Planning Coordination through "$l_1$-Proximal" Surrogate Lagrangian Relaxation

TL;DR

This paper introduces a novel $l_1$-proximal surrogate Lagrangian relaxation method for efficient TSO-DSO operational planning coordination, addressing nonlinear AC power flow challenges and enabling scalable, feasible solutions.

Contribution

The paper develops a new decomposition and coordination approach using $l_1$-proximal terms to handle nonlinearities and improve computational efficiency in TSO-DSO planning.

Findings

Method effectively coordinates TSO and DSO systems.

Numerical results show improved computational efficiency.

Approach maintains feasibility despite nonlinear AC constraints.

Abstract

The proliferation of distributed energy resources (DERs), located at the Distribution System Operator (DSO) level, bring new opportunities as well as new challenges to the operations within the grid, specifically, when it comes to the interaction with the Transmission System Operator (TSO). To enable interoperability, while ensuring higher flexibility and cost-efficiency, DSOs and the TSO need to be efficiently coordinated. Difficulties behind creating such TSO-DSO coordination include the combinatorial nature of the operational planning problem involved at the transmission level as well as the nonlinearity of AC power flow within both systems. These considerations significantly increase the complexity even under the deterministic setting. In this paper, a deterministic TSO-DSO operational planning coordination problem is considered and a novel decomposition and coordination approach is developed. Within the new method, the problem is decomposed into TSO and DSO subproblems, which are efficiently coordinated by updating Lagrangian multipliers. The nonlinearities at the TSO level caused by AC power flow constraints are resolved through a dynamic linearization while guaranteeing feasibility through "$l_1$-proximal" terms. Numerical results based on the coordination of the 118-bus TSO system with up to 32 DSO 34-bus systems indicate that the method efficiently overcomes the computational difficulties of the problem.