This paper proposes the use of annealing-based quantum computing for solving
combinatorial optimal power flow problems. Quantum annealers provide a physical …

We consider the problem of scheduling complex-valued demands over a discretized time
horizon. Given a set of users, each user is associated with a set of demands representing …

Abstract The Partially Observable Markov Decision Process (POMDP) is widely used in
probabilistic planning for stochastic domains. However, current extensions, such as …

Abstract Partially Observable Markov Decision Process (POMDP) is a fundamental model for
probabilistic planning in stochastic domains. More recently, constrained POMDP and …

To meet the substantial demand for electrified transportation, a high level of penetration of
electric vehicles (EVs) will be expected to incur considerable impacts on the reliability of …

Rapid penetration of renewable energy based Distributed Energy Resources (DER) has the
potential to exacerbate the challenges inherent in grid frequency and voltage regulation …

This paper studies the scheduling optimization problem of electric vehicle (EV) charging
considering two salient characteristics:(1) discrete charging rates with minimum power …

In the era of dynamic smart grid with fluctuating demands and uncertain renewable energy
supplies, it is crucial to continuously optimize the operational cost and performance of …

Abstract The Unsplittable Flow on a Path (UFP) problem has garnered considerable
attention as a challenging combinatorial optimization problem with notable practical …