Andrew P. Reiman

Shiva Poudel, Thiagarajan Ramachandran, Orestis Vasios et al.

The increasing use of LLM-based agents to support decision-making and control across diverse domains motivates the need for systematic deconfliction of their proposed actions. We present a deconfliction framework for coordinating multiple agents that formally encapsulate individual applications, each proposing potentially conflicting actions over shared resources. Conflicts are resolved through three deconfliction modes: bilateral negotiation, structured mediation, and procedural (deterministic) deconfliction. We define design principles for large language model-based client agents, including a chain-of-thought style reasoning process, and introduce an iterative weighted-consensus mechanism that does not require the applications themselves to solve optimization problems. The framework is domain agnostic and supports both numeric and non-numeric decisions. Its performance is demonstrated on a power distribution use case with conflicting advanced distribution management system applications for cost optimization and resilience, coordinating diesel generators and battery energy storage systems.

Deepthi Sen, Indrasis Chakraborty, Soumya Kundu et al.

With the expected rise in behind-the-meter solar penetration within the distribution networks, there is a need to develop time-series forecasting methods that can reliably predict the net-load, accurately quantifying its uncertainty and variability. This paper presents a deep learning method to generate probabilistic forecasts of day-ahead net-load at 15-min resolution, at various solar penetration levels. Our proposed deep-learning based architecture utilizes the dimensional reduction, from a higher-dimensional input to a lower-dimensional latent space, via a convolutional Autoencoder (AE). The extracted features from AE are then utilized to generate probability distributions across the latent space, by passing the features through a kernel-embedded Perron-Frobenius (kPF) operator. Finally, long short-term memory (LSTM) layers are used to synthesize time-series probability distributions of the forecasted net-load, from the latent space distributions. The models are shown to deliver superior forecast performance (as per several metrics), as well as maintain superior training efficiency, in comparison to existing benchmark models. Detailed analysis is carried out to evaluate the model performance across various solar penetration levels (up to 50\%), prediction horizons (e.g., 15\,min and 24\,hr ahead), and aggregation level of houses, as well as its robustness against missing measurements.