Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.14367

Grid Topology Optimization for Congestion Management Under High Renewable Penetrations and Discrete Load Growth

Giacomo Bastianel·Hakan Ergun·Line Roald
View original source ↗Read source document ↗Export metadata ↓

Abstract

Transmission grids are increasingly stressed by the fluctuating nature of renewable energy sources and by increasing electricity demand. Such grids were mainly built for a different generation fleet and load conditions. Grid topology optimization offers the possibility to redistribute power flows by modifying the busbar topology of substations in the grid. However, the combinatorial explosion of feasible busbar configurations makes topology optimization impractical for system operators. This paper proposes a methodology to identify a small subset of high-value busbar topologies to capture the economic benefit of topology optimization across a range of renewable-demand patterns. The optimization model is based on a LPAC approximation of the optimal power flow formulation, and AC-feasibility checks of the optimal topology applied to the IEEE 118-bus test case. In the test case, we select two distinct pairs of substations (46-49 and 24-69) and we optimize their topology separately over 365 clustered timesteps with different wind and load conditions. For each pair, we identify the four most recurrent optimal topologies and evaluate their performance under standard, and congested conditions, with and without a discrete load growth. Results show that selecting from this reduced set of topologies reduces total generation costs by up to 0.147% compared to a plain AC-OPF. In addition, we show the influence of topology optimization on the hosting capacity of selected busbars under discrete load growth. Our findings provide a practical methodology for system operators to select a subset of optimal busbar topologies to be used in their grid for different wind-load conditions, resulting in decreasing generation costs without the computational and operational risks of real-time switching decisions.

Explore related subjects

eess.SYcs.SY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Giacomo Bastianel, Hakan Ergun, Line Roald. 2026-09-15. Grid Topology Optimization for Congestion Management Under High Renewable Penetrations and Discrete Load Growth. https://arxiv.org/abs/2609.14367

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Constrained Feedback Control of Nonlinear Systems via Approximate HJB and Control Barrier Functions

This paper presents a two-stage framework for constrained feedback control of input-affine nonlinear systems. Offline, an approximate value function for the unconstrained problem is computed, for example using Hamilton--Jacobi--Bellman (HJB)-based policy iteration. Online, the proposed quadratic program (QP) minimizes the pre-Hamiltonian evaluated using the approximate value-function gradient subject to safety constraints enforced by control barrier functions (CBFs). This architecture decouples performance optimization from constraint enforcement, allowing constraints to be modified without recomputing the value function. As in CBF-QP architectures based on control Lyapunov functions (CLFs), safety is enforced as a hard constraint; however, the performance objective targets approximate optimality rather than a prescribed Lyapunov decay. Numerical results on a linear 2-state hovercraft and a nonlinear 9-state spacecraft attitude-control problem show agreement with the constrained open-loop optimal control problem (OCP) benchmark in the linear case, and performance close to the OCP benchmark, improving on CLF-based controllers, in the nonlinear case.

eess.SY↗
arXiv2026

Rao-Blackwellized Stein Gradient Descent for Joint State-Parameter Estimation

We present a filtering framework for online joint state estimation and parameter identification in nonlinear, time-varying systems. The algorithm uses a Rao-Blackwellization technique to infer joint state-parameter posteriors efficiently. In particular, conditional state distributions are computed analytically via Kalman filtering, while model parameters, including the measurement-noise covariance, are approximated using particle-based Stein Variational Gradient Descent (SVGD), enabling stable real-time inference. To handle parameters subject to physical constraints, we further introduce constrained variants that enforce them through an alternating direction method of multipliers (ADMM) splitting of the SVGD update, including nonlinear equality constraints that standard particle filters cannot readily handle. We derive a stability bound that relates the approximation error in the parameter posterior to the resulting error in the marginal state distribution. Performance of the proposed filters is validated on three case studies: a fed-batch bioreactor with Haldane kinetics and a damped pendulum, both under physical constraints, and a neural-network-augmented dynamic system. The examples cover parameter estimation under inequality and equality constraints and online neural-network training within a dynamical model.

eess.SY↗
arXiv2026

Firing Rate Neural Network Implementations of Model Predictive Control

Human and animal brains perform planning to enable complex movements and behaviors, a process that can be effectively described using model predictive control (MPC). How could the brain physically implement MPC? In this work, we translate model predictive controllers into firing rate neural networks, offering insights into the nonlinear neural dynamics that underpin planning. We propose a constructive method; no training is required. This is done first applying the projected gradient method to the dual problem to derive a baseline neural network implementation. We then use factorization and contraction analysis to systematically generate alternative network architectures; in other words, we systematically generate hypotheses for how planning is done in the brain via neural dynamics. Finally, we present numerical simulations to study different neural networks performing MPC to balance an inverted pendulum on a cart (i.e., balancing a stick on a hand), including one example in which imposing sparse connectivity (a property observed in brain networks) does not degrade control performance.

eess.SY↗