On optimal charging scheduling for electric vehicles with wind power generation

We consider the scheduling of battery charging of electric vehicles(EVs)integrated with renewable power generation.The increasing adoption of EVs and the development of renewable energies contribute importance to this research.The optimization of charging scheduling is challenging because of the large action space,the multi-stage decision making,and the high uncertainty.To solve this problem is time-consuming when the scale of the system is large.It is urgent to develop a practical and efficient method to properly schedule the charging of EVvs.The contribution of this work is threefold.First,we provide a sufficient condition on which the charging of EVs can be completely self-sustained by distributed generation.An algorithm is proposed to obtain the optimal charging policy when the sufficient condition holds.Second,the scenario when the supply of the renewable power generation is deficient is investigated.We prove that when the renewable generation is deterministic there exists an optimal policy which follows the modified least laxity and longer remaining processing time first(mLLLP)rule.Third,we provide an adaptive rule-based algorithm which obtains a near-optimal charging policy efficiently in general situations.We test the proposed algorithm by numerical experiments.The results show that it performs better than the other existing rule-based methods.

A review on charging behavior of electric vehicles:data,model,and control

The adoption and usage of electric vehicles(EVs)have emerged recently due to the increasing concerns on the greenhouse gas issues and energy revolution.As a part of the smart grid,EVs can provide valuable ancillary services beyond consumers of electricity.However,EVs are gradually considered as nonnegligible loads due to their increasing penetration,which may result in negative effects such as voltage deviations,lines saturation,and power losses.Relationship and interaction among EVs,charging stations,and micro grid have to be considered in the next generation of smart grid.Therefore,the topic of smart charging has been the focus of many works where a wide range of control methods have been developed.As one of the bases of simulation,the EV charging behavior and characteristics have also become the focus of many studies.In this work,we review the charging behavior of EVs from the aspects of data,model,and control.We provide the links for most of the data sets reviewed in this work,based on which interested researchers can easily access these data for further investigation.

Matching Uncertain Renewable Supply with Electric Vehicle Charging Demand-A Bi-Level Event-Based Optimization Method

The matching between dynamic supply of renewable power generation and flexible charging demand ofthe Electric Vehicles (EVs) can not only increase the penetration of renewables but also reduce the load to the stateelectric power grid. The challenges herein are the curse of dimensionality, the multiple decision making stagesinvolved, and the uncertainty of both the supply and demand sides. Event-Based Optimization (EBO) provides a newway to solve large-scale Markov decision process. Considering different spatial scales, we develop a bi-level EBOmodel in this paper which can both catch the changes on the macro and micro levels. By proper definition, the sizeof event space stays fixed with the scale of the problem, which shows good scalability in online optimization. Then abi-level Q-learning method is developed to solve the problem iteratively. We demonstrate the performance of themethod by numerical examples. Our method outperforms other methods both in performance and scalability.

Remanufacturing planning based on constrained ordinal optimization

Resource planning for a remanufacturing system is in general extremely difficult in terms of problem size,uncertainties,complicated constraints,etc.In this paper,we present a new method based on constrained ordinal optimization(COO)for remanufacturing planning.The key idea of our method is to estimate the feasibility of plans by machine learning and to select a subset with the estimated feasibility based on the procedure of horse racing with feasibility model(HRFM).Numerical testing shows that our method is efficient and effective for selecting good plans with high probability.It is thus a scalable optimization method for large scale remanufacturing planning problems with complicated stochastic constraints.