Session: Optimization Applications in Energy Systems
Chair: Sungho Shin
Cluster: Optimization Applications (Communication, Energy, Health, ML, ...)
Talk 1: Multiperiod Optimization for Power Grid Applications
Speaker: Mihai Anitescu
Abstract: There has been a growing interest in solving multi-period AC OPF problems, as the increasingly fluctuating electricity market requires operation planning over multiple periods. These problems, formerly deemed intractable, are now becoming technologically feasible to solve thanks to the advent of high-memory GPU hardware and accelerated NLP tools. This study evaluates the capability of the ExaModels.jl and MadNLP.jl tools for GPU-centered nonlinear programming to tackle previously unsolvable multi-period AC OPF instances. Our numerical experiments, run on an NVIDIA GH200, demonstrate that we can solve a multi-period OPF instance with more than 10 million variables up to 10−4 precision in less than 10 minutes.
Talk 2: Optimal Power Flow Under Constraint-Informed Uncertainty
Speaker: Anirudh Subramanyam
Abstract: Chance-constrained optimization has emerged as a promising framework for managing uncertainties in power systems. This work advances its application to DC Optimal Power Flow (DC-OPF) problems, developing a novel approach to uncertainty modeling. Current methods tackle these problems by first modeling random variables using high-dimensional statistical distributions that scale with the number of system buses, followed by deriving convex reformulations of the probabilistic constraints. We propose an alternative methodology that uses the probabilistic constraints themselves to inform the structure of uncertainty, enabling significant dimensionality reduction. Rather than learning joint distributions of wind generation forecast errors across all units, we model two key distributions: system-wide aggregate forecast errors and individual unit errors weighted by transmission line flow sensitivities. We evaluate our approach under both Gaussian and non-Gaussian uncertainty distributions, demonstrating improvements over state-of-the-art in both statistical accuracy and optimization performance.
Talk 3: Characterizing marginal value of storage in distribution grid operations
Speaker: Dirk Lauinger
Abstract: Electricity distribution companies invest in storage to shave peak load and reduce investments into substation and distribution line upgrades. In deregulated electricity markets, storage assets owned by distribution companies are not allowed to participate in electricity markets, which leads to the assets sitting idle most of the time. Contracting for storage could provide investors with additional value streams, distribution companies with cheaper storage, and rate payers with reduced prices. We integrate contracted storage into distribution company investment planning problems and find that peak shaving reduces profits from market participation by about 1% in a Massachusetts case study. Capital investment savings from contracted storage more than compensate for this reduction. Both distribution companies and storage investors could thus benefit from contracted storage.
