arXiv subjects

Qing-Shan Jia

Publications and source records attributed to Qing-Shan Jia.

21 records · Page 2Linked to original sources

arXiv2021

Joint Optimization for Coordinated Charging Control of Commercial Electric Vehicles Under Distributed Hydrogen Energy Supply

The transition to the zero-carbon power system is underway accelerating recently. Hydrogen energy and electric vehicles (EVs) are promising solutions on the supply and demand sides. This paper presents a novel architecture that includes hydrogen production stations (HPSs), fast charging stations (FCSs), and commercial EVs. The proposed architecture jointly optimizes the distributed hydrogen energy dispatch and the EV charging location selection, and is formulated by a time-varying bi-level bipartite graph (T-BBG) model for real-time operation. We develop a bi-level iteration optimization method combining linear programming (LP) and Kuhn-Munkres (KM) algorithm to solve the joint problem whose optimality is proved theoretically. The effectiveness of the proposed architecture on reducing the operating cost is verified via case studies in Shanghai. The proposed method outperforms other strategies and improves the performance by at least 13% which shows the potential economic benefits of the joint architecture. The convergence and impact of the pile number, battery capacity, EV speed and penalty factor are assessed.

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arXiv2019

Learning Optimal Scheduling Policy for Remote State Estimation under Uncertain Channel Condition

We consider optimal sensor scheduling with unknown communication channel statistics. We formulate two types of scheduling problems with the communication rate being a soft or hard constraint, respectively. We first present some structural results on the optimal scheduling policy using dynamic programming and assuming the channel statistics is known. We prove that the Q-factor is monotonic and submodular, which leads to the threshold-like structures in both types of problems. Then we develop a stochastic approximation and parameter learning frameworks to deal with the two scheduling problems with unknown channel statistics. We utilize their structures to design specialized learning algorithms. We prove the convergence of these algorithms. Performance improvement compared with the standard Q-learning algorithm is shown through numerical examples.

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arXiv2012

Efficient Computing Budget Allocation for Simulation-based Optimization with Stochastic Simulation Time

The dynamics of many systems nowadays follow not only physical laws but also man-made rules. These systems are known as discrete event dynamic systems and their performances can be accurately evaluated only through simulations. Existing studies on simulation-based optimization (SBO) usually assume deterministic simulation time for each replication. However, in many applications such as evacuation, smoke detection, and territory exploration, the simulation time is stochastic due to the randomness in the system behavior. We consider the computing budget allocation for SBO's with stochastic simulation time in this paper, which has not been addressed in existing literatures to the author's best knowledge. We make the following major contribution. The relationship between simulation time and performance estimation accuracy is quantified. It is shown that when the asymptotic performance is of interest only the mean value of individual simulation time matters. Then based on the existing optimal computing budget allocation (OCBA) method for deterministic simulation time we develop OCBA for stochastic simulation time (OCBAS), and show that OCBAS is asymptotically optimal. Numerical experiments are used to discuss the impact of the variance of simulation time, the impact of correlated simulation time and performance estimation, and to demonstrate the performance of OCBAS on a smoke detection problem in wireless sensor network. The numerical results also show that OCBA for deterministic simulation time is robust even when the simulation time is stochastic.

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