Jip Kim

Jip Kim, Yury Dvorkin

Peer-to-peer interactions between small-scale energy resources exploit distribution network infrastructure as an electricity carrier, but remain financially unaccountable to electric power utilities. This status-quo raises multiple challenges. First, peer-to-peer energy trading reduces the portion of electricity supplied to end-customers by utilities and their revenue streams. Second, utilities must ensure that peer-to-peer transactions comply with distribution network limits. This paper proposes a peer-to-peer energy trading architecture, in two configurations, that couples peer-to-peer interactions and distribution network operations. The first configuration assumes that these interactions are settled by the utility in a centralized manner, while the second one is peer-centric and does not involve the utility. Both configurations use distribution locational marginal prices to compute network usage charges that peers must pay to the utility for using the distribution network.

Geon Roh, Jip Kim

Dynamic line rating (DLR) enables greater utilization of existing transmission lines by leveraging real-time weather data. However, the elevated temperature operation (ETO) of conductors under DLR, particularly in the presence of uncertainty, is often overlooked, despite its long-term impact on conductor health. This paper addresses ETO under DLR and wind power uncertainty by 1) quantifying risk-based depreciation costs associated with ETO, 2) characterizing correlation-driven ETO risk from wind power and DLR forecast errors, and 3) proposing a Conductor Health-Aware Unit Commitment (CHA-UC) that internalizes these costs in operational decisions. CHA-UC incorporates a robust linear approximation of conductor temperature and integrates expected depreciation costs due to hourly ETO into the objective function. Case studies on the Texas 123-bus backbone test system demonstrate that the proposed CHA-UC model reduces the total cost by 0.75\% and renewable curtailment by 82\% compared to static line rating (SLR) and outperforms quantile regression forest-based methods, while conventional DLR operation without risk consideration resulted in higher costs due to excessive ETO. Further analysis shows that CHA-UC achieves safer line utilization by shifting generator commitments and endogenously adapting to uncertainty correlation, relaxing flows under risk-hedging conditions and tightening flows under risk-amplifying conditions.

Minsoo Kim, Jip Kim

Optimal transmission switching (OTS) improves optimal power flow (OPF) by selectively opening transmission lines, but its mixed-integer formulation increases computational complexity, especially on large grids. To deal with this, we propose a dispatch-aware deep neural network (DA-DNN) that accelerates DC-OTS without relying on pre-solved labels. DA-DNN predicts line states and passes them through a differentiable DC-OPF layer, using the resulting generation cost as the loss function so that all physical network constraints are enforced throughout training and inference. In addition, we adopt a customized weight-bias initialization that keeps every forward pass feasible from the first iteration, which allows stable learning on large grids. Once trained, the proposed DA-DNN produces a provably feasible topology and dispatch pair in the same time as solving the DCOPF, whereas conventional mixed-integer solvers become intractable. As a result, the proposed method successfully captures the economic advantages of OTS while maintaining scalability.