Di Wu

Data-driven Chance Constrained Programming based Electric Vehicle Penetration Analysis

Tracy Can Cui

Transportation electrification has been growing rapidly in recent years. The adoption of electric vehicles (EVs) could help to release the d… (voir plus)ependency on oil and reduce greenhouse gas emission. However, the increasing EV adoption will also impose a high demand on the power grid and may jeopardize the grid network infrastructures. For certain high EV penetration areas, the EV charging demand may lead to transformer overloading at peak hours which makes the maximal EV penetration analysis an urgent problem to solve. This paper proposes a data-driven chance constrained programming based framework for maximal EV penetration analysis. Simulation results are presented for a real-world neighborhood level network. The proposed framework could serve as a guidance for utility companies to schedule infrastructure upgrades.

2018-12-31

(publié)

www.semanticscholar.org

Optimizing Home Energy Management and Electric Vehicle Charging with Reinforcement Learning

Di Wu

Guillaume Rabusseau

Vincent François-Lavet

Doina Precup

Benoit Boulet

Smart grids are advancing the management efficiency and security of power grids with the integration of energy storage, distributed controll… (voir plus)ers, and advanced meters. In particular, with the increasing prevalence of residential automation devices and distributed renewable energy generation, residential energy management is now drawing more attention. Meanwhile, the increasing adoption of electric vehicle (EV) brings more challenges and opportunities for smart residential energy management. This paper formalizes energy management for the residential home with EV charging as a Markov Decision Process and proposes reinforcement learning (RL) based control algorithms to address it. The objective of the proposed algorithms is to minimize the long-term operating cost. We further use a recurrent neural network (RNN) to model the electricity demand as a preprocessing step. Both the RNN prediction and latent representations are used as additional state features for the RL based control algorithms. Experiments on real-world data show that the proposed algorithms can significantly reduce the operating cost and peak power consumption compared to baseline control algorithms.

2017-12-31

(publié)

www.semanticscholar.org

Boosting Based Multiple Kernel Learning and Transfer Regression for Electricity Load Forecasting

Di Wu

Boyu Wang

Doina Precup

Benoit Boulet

2017-12-29

Machine Learning and Knowledge Discovery in Databases (publié)

doi.org