Bin Wang

With the increasing of electric vehicle (EV) adoption in recent years, the impact of EV charging activities to the power grid becomes more and more significant. In this article, an optimal scheduling algorithm which combines smart EV charging and V2G gird service is developed to integrate EVs into power grid as distributed energy resources, with improved system cost performance. Specifically, an optimization problem is formulated and solved at each EV charging station according to control signal from aggregated control center and user charging behavior prediction by mean estimation and linear regression. The control center collects distributed optimization results and updates the control signal, periodically. The iteration continues until it converges to optimal scheduling. Experimental result shows this algorithm helps fill the valley and shave the peak in electric load profiles within a microgrid, while the energy demand of individual driver can be satisfied.

Un-coordinated Electric Vehicle (EV) charging behaviors will impact the power grid and degrade power quality. Smart charging system can optimally allocate the energy among EVs and minimize the impact on grid. IEC 61850 is an international standard for distribution & substation automation and intelligence device communication in smart grid. This paper introduces the extension of IEC 61850 with smart EV scheduling algorithms, considering current multiplexing, power sharing strategies and user behaviors. We have developed an IEC 61850 abstract data model for the information exchanged among components of smart EV charging infrastructures, including the EV charging control center, intelligent Electric Vehicle Supply Equipment (EVSE) and the EV users with mobile applications. Real-world EV usage data on UCLA campus is utilized in the simulation experiment, which is based on the predictive control paradigm. The data model is instantiated by a web service on EV control center by converting the raw data streams from EVSEs in various formats, such as JSON or raw string, etc. into a standardized IEC 61850 SCL file, which contains the critical meter data and charging session parameters. The system cost performance, the interoperability and scalability of smart EV charging infrastructure are greatly improved.

This paper proposes a new hybrid method for super short-term solar power prediction. Solar output power usually has a complex, nonstationary, and nonlinear characteristic due to intermittent and time varying behavior of solar radiance. In addition, solar power dynamics is fast and is inertia less. An accurate super short-time prediction is required to compensate for the fluctuations and reduce the impact of solar power penetration on the power system. The objective is to predict one step-ahead solar power generation based only on historical solar power time series data. The proposed method incorporates discrete wavelet transform (DWT), Auto-Regressive Moving Average (ARMA) models, and Recurrent Neural Networks (RNN), while the RNN architecture is based on Nonlinear Auto-Regressive models with eXogenous inputs (NARX). The wavelet transform is utilized to decompose the solar power time series into a set of richer-behaved forming series for prediction. ARMA model is employed as a linear predictor while NARX is used as a nonlinear pattern recognition tool to estimate and compensate the error of wavelet-ARMA prediction. The proposed method is applied to the data captured from UCLA solar PV panels and the results are compared with some of the common and most recent solar power prediction methods. The results validate the effectiveness of the proposed approach and show a considerable improvement in the prediction precision.

This paper studies energy management in a Microgrid (MG) with solar generation, Battery Energy Management System (BESS) and gridable (V2G) Electric Vehicles (EVs). A two-stage stochastic optimization method is proposed to capture the intermittent solar generation and random EV user behaviors. It is subsequently formulated as a Mixed Integer Linear Programming (MILP) problem. To evaluate the proposed method, real solar generation, loads, BESS and EV data is used in Sample Average Approximation (SAA). Computational results show the correctness of the proposed method as well as steady and tightly bounded optimality gap. Comparisons demonstrate that the proposed stochastic method outperforms its deterministic counterpart at the expense of higher computational cost. It is also observed that moderate number of EVs helps to reduce the overall operational cost of the MG, which sheds light on future EV integration to the smart grid.

This paper considers a typical solar installations scenario with limited sensing resources. In the literature, there exist either day-ahead solar generation prediction methods with limited accuracy, or high accuracy short timescale methods that are not suitable for applications requiring longer term prediction. We propose a two-tier (global-tier and local-tier) prediction method to improve accuracy for long term (24 hour) solar generation prediction using only the historical power data. In global-tier, we examine two popular heuristic methods: weighted k-Nearest Neighbors (k-NN) and Neural Network (NN). In local-tier, the global-tier results are adaptively updated using real-time analytical residual analysis. The proposed method is validated using the UCLA Microgrid with 35kW of solar generation capacity. Experimental results show that the proposed two-tier prediction method achieves higher accuracy compared to day-ahead predictions while providing the same prediction length. The difference in the overall prediction performance using either weighted k-NN based or NN based in the global-tier are carefully discussed and reasoned. Case studies with a typical sunny day and a cloudy day are carried out to demonstrate the effectiveness of the proposed two-tier predictions.

Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimization module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.

Uncontrolled Electric Vehicle (EV) and Plug-in Hybrid Electric Vehicle (PHEV) charging within a local distribution grid may cause unexpected high load, which further results in power quality degradation. However, coordinating charging behaviors of a number of EVs is a challenging task, which involves not only the deterministic schedule computing but also nondeterministic EV driver behaviors with random arrival time and energy demands. Previous researches in this area rarely consider these random behaviors for real EV users. In this paper, an implementable event-based cost optimal scheduling algorithm (ECSA) is developed, which solves EV scheduling problem by dynamically estimating the stay duration and energy demand for each participating EV user. Datasets, including users' historical charging records and time series meter data collected from Electric Vehicle Supply Equipments (EVSEs) in UCLA campus, are utilized for feature extraction. Based on that, proper inference technique is employed to determine parameters within each charging session. In addition, solar generation integration into EVSEs is also considered in our problem formulation. The proposed approaches are tested and validated by real EV charging schedules of users in UCLA campus. The results from simulation experiment demonstrate that the proposed algorithm has a better performance in cost minimization and load shifting compared to existing equal-sharing scheduling algorithm (ESSA).