Related papers: Risk Limiting Dispatch with Fast Ramping Storage
We consider the problem of look-ahead economic dispatch (LAED) with uncertain renewable energy generation. The goal of this problem is to minimize the cost of conventional energy generation subject to uncertain operational constraints. The…
Load balancing systems, comprising a central dispatcher and a scheduling policy at each server, are widely used in practice, and their response time has been extensively studied in the theoretical literature. While much is known about the…
Operational constraint violations may occur when deep reinforcement learning (DRL) agents interact with real-world active distribution systems to learn their optimal policies during training. This letter presents a universal…
Dispatching strategies for gas turbines (GTs) are changing in modern electricity grids. A growing incorporation of intermittent renewable energy requires GTs to operate more but shorter cycles and more frequently on partial loads. Deep…
This study focuses on relay transport carriers (RTCs) that contract with hub providers to lease hub capacity and employ relay transportation via hubs. It enables long-haul freight shipments to be transported by multiple short-haul drivers…
In discussions at the 2015 HICSS meeting, it was argued that loads can provide most of the ancillary services required today and in the future. Through load-level and grid-level control design, high-quality ancillary service for the grid is…
On-demand delivery has become increasingly popular around the world. Motivated by a large grocery chain store who offers fast on-demand delivery services, we model and solve a stochastic dynamic driver dispatching and routing problem for…
The growing penetration of renewable generation in distribution networks, primarily deployed by end-use electricity customers, is changing the traditional load profile and inevitably makes supply-load balancing more challenging for grid…
Distributed resource allocation (DRA) is fundamental to modern networked systems, spanning applications from economic dispatch in smart grids to CPU scheduling in data centers. Conventional DRA approaches require reliable communication, yet…
To foster the adoption of active disturbance rejection control (ADRC) and support its deployment even on low-cost embedded systems, this article introduces the most efficient implementation of linear discrete-time ADRC to date. While…
We consider the problem of Robust Dynamic Coded Distributed Storage (RDCDS) with partially storage constrained servers where the goal is to enable robust (resilient to server dropouts) and efficient (as measured by the communication costs)…
The recently developed dynamic discretization discovery (DDD) is a powerful method that allows many time-dependent problems to become more tractable. While DDD has been applied to a variety of problems, one particular challenge has been to…
In this paper, we focus on the problem of robustifying reinforcement learning (RL) algorithms with respect to model uncertainties. Indeed, in the framework of model-based RL, we propose to merge the theory of constrained Markov decision…
The Storage Location Assignment Problem (SLAP) and the Picker Routing Problem (PRP) have received significant attention in the literature due to their pivotal role in the performance of the Order Picking (OP) activity, the most…
Mobility on demand (MoD) systems show great promise in realizing flexible and efficient urban transportation. However, significant technical challenges arise from operational decision making associated with MoD vehicle dispatch and fleet…
Remote state estimation, where sensors send their measurements of distributed dynamic plants to a remote estimator over shared wireless resources, is essential for mission-critical applications of Industry 4.0. Existing algorithms on…
Due to its high density and close-to-SRAM read latency, spin transfer torque RAM (STT-RAM) is considered one of the most-promising emerging memory technologies for designing large last level caches (LLCs). However, in deep sub-micron…
The solution of multistage stochastic linear problems (MSLP) represents a challenge for many application areas. Long-term hydrothermal dispatch planning (LHDP) materializes this challenge in a real-world problem that affects electricity…
Distributional reinforcement learning (DRL) enhances the understanding of the effects of the randomness in the environment by letting agents learn the distribution of a random return, rather than its expected value as in standard…
Safe Reinforcement Learning from Human Feedback (RLHF) typically enforces safety through expected cost constraints, but the expectation captures only a single statistic of the cost distribution and fails to account for distributional…