Wencong Su

A Survey on the Electrification of Transportation in a Smart Grid Environment

Economics and environmental incentives, as well as advances in technology, are reshaping the traditional view of industrial systems. The anticipation of a large penetration of plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) into the market brings up many technical problems that are highly related to industrial information technologies within the next ten years. There is a need for an in-depth understanding of the electrification of transportation in the industrial environment. It is important to consolidate the practical and the conceptual knowledge of industrial informatics in order to support the emerging electric vehicle (EV) technologies. This paper presents a comprehensive overview of the electrification of transportation in an industrial environment. In addition, it provides a comprehensive survey of the EVs in the field of industrial informatics systems, namely: 1) charging infrastructure and PHEV/PEV batteries; 2) intelligent energy management; 3) vehicle-to-grid; and 4) communication requirements. Moreover, this paper presents a future perspective of industrial information technologies to accelerate the market introduction and penetration of advanced electric drive vehicles.

Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources

Renewable energy resources such as wind and solar are an important component of a microgrid. However, the inherent intermittency and variability of such resources complicates microgrid operations. Meanwhile, more controllable loads (e.g., plug-in electric vehicles), distributed generators (e.g., micro gas turbines and diesel generators), and distributed energy storage devices (e.g., battery banks) are being integrated into the microgrid operation. To address the operational challenges associated with these technologies and energy resources, this paper formulates a stochastic problem for microgrid energy scheduling. The proposed problem formulation minimizes the expected operational cost of the microgrid and power losses while accommodating the intermittent nature of renewable energy resources. Case studies are performed on a modified IEEE 37-bus test feeder. The simulation results demonstrate the effectiveness and accuracy of the proposed stochastic microgrid energy scheduling model.

Performance evaluation of a PHEV parking station using Particle Swarm Optimization

There is expected to be a large penetration of Plug-in Hybrid Electric Vehicles (PHEVs) into the market in the near future. As a result, many technical problems related to the impact of this technology on the power grid need to be addressed. The anticipating large penetration of PHEV into our societies will add a substantial energy load to power grids, as well as add substantial energy resources that can be utilized. There is also a need for in-depth study on PHEVs in term of Smart Grid environment. In this paper, we propose an algorithm for optimally managing a large number of PHEVs (i.e., 500) charging at a municipal parking station. We used Particle Swarm Optimization (PSO) to intelligently allocate energy to the PHEVs. We considered constraints such as energy price, remaining battery capacity, and remaining charging time. A mathematical framework for the objective function (i.e., maximizing the average State-of-Charge at the next time step) is also given. We characterized the performance of our PSO algorithm using a MATLAB simulation, and compared it with other techniques.

Investigating a large-scale PHEV/PEV parking deck in a smart grid environment

There is a need to address the potential problems caused by the emergence of plug-in hybrid electric vehicles (PHEVs) within the next 20 years. The penetration of these vehicles into the marketplace poses a potential threat to the existing power grid. Adding a large number of PHEVs/PEVs into our society will create a large-scale aggregated load, as well as acting as a substantial energy resource. In this paper, we evaluate the impact of the integration of PHEVs/PEVs into the grid. First, we simulate the aggregated load pattern at a municipal PHEV/PEV parking deck, taking into account real-world parking deck scenarios. Then we propose two smart charging programs to optimally allocate available power from the utility to a large number of PHEVs/PEVs at a municipal parking deck. In a smart grid environment, the proposed energy management programs can improve the stability and reliability of the power grid. We characterize the system performance and illustrate the potential improvement using several steady-state simulations. The simulation results provide a general overview of the impact of the proposed charging scenarios in terms of voltage profiles, peak demand, and charging cost.

Microgrid planning and operation: Solar energy and wind energy

Economic, technology and environmental incentives are changing the features of electricity generation and transmission. Centralized power systems are giving way to local scale distributed generations. At present, there is a need to assess the effects of large numbers of distributed generators and short-term storage in Microgrid. To accommodate the high demand of renewable energy and the environment policy, the planning and operation of Micro-source generators has been studied using HOMER. Simulation results show a case study of an optimal microgrid configuration on Ontario area in Canada. Sensitivity variables are specified to examine the effect of uncertainties (e.g. diesel price and average wind speed), especially in a long-term planning. The effect of air emission penalties on Microgrid planning is also well presented.

Two-Stage Economic Operation of Microgrid-Like Electric Vehicle Parking Deck

This paper addresses a two-stage framework for the economic operation of a microgrid-like electric vehicle (EV) parking deck with on-site renewable energy generation (roof-top photovoltaic panel). This microgrid-like EV parking deck is a localized grouping of distributed generation (solar), energy storage (EV batteries), and load (EV charging load). Although EV parking decks can enable greater adoption of renewable energy sources by scheduling charging loads to coincide with periods of strong sun, the inherent intermittency of renewable energy resources and variable EV parking behaviors complicates the economic operation. In this paper, the proposed first stage of this framework provides the parking deck operators with a stochastic approach for dealing with the uncertainty of solar energy so as to make an optimal price decision (marginal electricity sale price and parking fee rebate) at the day-ahead time scale. The second stage introduces a model predictive control-based operation strategy of EV charging dealing with the uncertainty of parking behaviors within the real-time operation. Case studies demonstrate the better performance of the proposed framework, offering an effective day-ahead marginal electricity price for tomorrows operation and increasing the microgrid-like EV parking decks revenue during the real-time operation.

A Hierarchical Framework for Coordinated Charging of Plug-In Electric Vehicles in China

Plug-in electric vehicle (PEV) technology has drawn increasing amounts of attention in the last decade. As the worlds largest automotive market, China has recently made the electrification of transportation central to its national strategic plan. Because of the unique nature of the vertically regulated power industry, Chinas massive deployment of PEVs has to face unique challenges that may not be encountered by any other country/region. Therefore, a comprehensive coordinated PEV charging scheme is urgently needed to facilitate the smooth grid integration of PEVs at all levels (e.g., transmission systems, distribution systems, and charging stations). This paper presents detailed mathematical modeling of a novel hierarchical framework for coordinated PEV charging at multiple timescales (i.e., day-ahead and real-time). The proposed three-level (e.g., provincial level, municipal level, and charging station level) PEV charging strategy jointly optimizes system load profile and charging costs while satisfying customer charging requirements. The interrelationships between various levels in terms of energy transaction and information exchange are clearly identified. Case studies on Guangdong Province, China, are carried out and simulation results demonstrate the effectiveness of our proposed hierarchical control framework in reducing system peak demand and charging costs.

A digital testbed for a PHEV/PEV enabled parking lot in a Smart Grid environment

The anticipation of a large penetration of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) into the market brings up many technical problems that need to be addressed within the next 10 years. In the future, electric-powered vehicles would be plugged into the grid, and their onboard energy storage systems would be recharged using clean, renewable electricity. One of the key missions is to facilitate the smooth interaction between the plug-in vehicle and the power grid. This paper presents a digital testbed for a PHEV/PEV enabled parking lot in a Smart Grid environment. This digital testbed allows us to evaluate a wide range of PHEV/PEV charging scenarios and the corresponding control strategies. In addition, this digital testbed allows us to explore a variety of communication technologies for a PHEV/PEV charging facility such as a parking lot. Moreover, this digital testbed offers more flexibility to prepare for the emergence of new technologies (e.g., Vehicle-to-Grid, Vehicle-to-Building, and Smart Charging), which will become a reality in the near future.

Sensitivity analysis on battery modeling to large-scale PHEV/PEV charging algorithms

An in-depth need exists to address the potential problems caused by the emergence of plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) within in the next 20 years. The large penetration of these vehicles into the marketplace poses a potential threat to the existing power grid. A large number of PHEVs/ PEVs may cause serious system instability without a sophisticated control strategy. Energy storage is the key enabling technology for PHEVs/PEVs. The battery state information is critical to ensure optimal utilization of the available energy. It enables optimal control over the batterys charging and discharging process, thereby reducing the risk of overcharge or undercharge and prolonging battery life. In this paper, we first simulate real-world parking deck scenarios and implement four types of battery models (i.e., the linear model, relaxation model, hysteresis model, and combined model). We then evaluate optimal performance of the proposed large-scale PHEV/PEV charging algorithms under certain operating conditions. We characterize system performance and illustrate the importance of battery modeling to large-scale charging algorithms. The simulation results provide a general overview of the impact of battery modeling on optimal performance.

Framework for investigating the impact of PHEV charging on power distribution system and transportation network

Plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) have received increasing attention because of their low pollution emissions, petroleum independence, and high fuel economy. The large market penetration of these vehicles is dramatically changing the view of the power distribution system. Unlike other power loads, these vehicles can be connected to power grids anywhere and anytime, which brings more spatial and temporal diversity and uncertainty. There is an urgent need to investigate the impact of PHEV/PEV charging on the power distribution system considering multidisciplinary complexities (e.g., driving behavior, route and departure time choice, charging station location, engineering, policy, economic, environment, technology, and social impact). This paper consolidates the modeling and simulation of power distribution system and transportation network in order to assess the emerging electric vehicle technologies. Moreover, this paper proposes a comprehensive co-modeling/simulation framework for investigating the impact of the electrification of transportation in the real world.