Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

Design And Implementation Of Co-Operative Control Strategy For Hybrid AC/DC Microgrids

This thesis is mainly divided in two major sections: 1) Modelling and control of AC microgrid, DC microgrid, Hybrid AC/DC microgrid using distributed co-operative control, and 2) Development of a four bus laboratory prototype of an AC microgrid system. At first, a distributed cooperative control (DCC) for a DC microgrid considering the state-of-charge (SoC) of the batteries in a typical plug-in-electric-vehicle (PEV) is developed. In DC microgrids, this methodology is developed to assist the load sharing amongst the distributed generation units (DGs), according to their ratings with improved voltage regulation. Subsequently, a DCC based control algorithm for AC microgrid is also investigated to improve the performance of AC microgrid in terms of power sharing among the DGs, voltage regulation and frequency deviation. The results validate the advantages of the proposed methodology as compared to traditional droop control of AC microgrid. The DCC-based control methodology for AC microgrid and DC microgrid are further expanded to develop a DCC-based power management algorithm for hybrid AC/DC microgrid. The developed algorithm for hybrid microgrid controls the power flow through the interfacing converter (IC) between the AC and DC microgrids. This will facilitate the power sharing between the DGs according to their power ratings. Moreover, it enables the fixed scheduled power delivery at different operating conditions, while maintaining good voltage regulation and improved frequency profile. The second section provides a detailed explanation and step-by-step design and development of an AC/DC microgrid testbed. Controllers for the three-phase inverters are designed and tested on different generation units along with their corresponding inductor-capacitor-inductor (LCL) filters to eliminate the switching frequency harmonics. Electric power distribution line models are developed to form the microgrid network topology. Voltage and current sensors are placed in the proper positions to achieve a full visibility over the microgrid. A running average filter (RAF) based enhanced phase-locked-loop (EPLL) is designed and implemented to extract frequency and phase angle information. A PLL-based synchronizing scheme is also developed to synchronize the DGs to the microgrid. The developed laboratory prototype runs on dSpace platform for real time data acquisition, communication and controller implementation

Fast-timescale Control Strategies for Demand Response in Power Systems.

Concerns over climate change have spurred an increase in the amount of wind and solar power generation on the grid. While these resources reduce carbon emissions, the physical phenomena that they rely on - wind and sunlight - are highly stochastic, making their generated power less controllable. Demand-side strategies, which modulate load in a controllable manner, have been proposed as a way to add flexibility to the grid. Resources with innate flexibility in their load profile are particularly suited to demand response (DR) applications. This work examines two such loads: heating, ventilation, and air conditioning (HVAC) systems, and plug-in electric vehicle (PEV) fleets. HVAC systems can vary the timing of power consumption due to the thermal inertia inherent in their associated building(s). The first part of this thesis explores the efficacy of using commercial HVAC for DR applications. Results are presented from an experimental testbed that quantify performance, in terms of accuracy in perturbing the load in a desired manner, as well as the efficiency of this process. PEVs offer very fast response times and may eventually represent a significant load on the power system. The second part of this thesis develops several control strategies to manage PEV power consumption in an environment where communication resources are limited, both to prevent detrimental system effects such as transformer overload, and to provide ancillary services such as frequency regulation to the grid.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116627/1/ianbeil_1.pd

Optimal Online Charging Coordination of Plug in Electric Vehicles in Unbalanced Grids for Ancillary Voltage Support

This PhD thesis will propose an optimal online charge control through genetic algorithm for G2V coordination of PEVs (OL-C-TP) in unbalanced systems. Moreover the algorithm will be extended to also include V2G coordination and offer ancillary voltage support (OL-CD-TPQ) by considering two different methods based on the utility time-of-day prices for exporting reactive power and droop controller for decentralized exporting of reactive power. Then the performance of OL-CD-TPQ by switching PEVs in three phase unbalanced networks is improved

Online Coordinated Charging of Plug-In Electric Vehicles in Smart Grid to Minimize Cost of Generating Energy and Improve Voltage Profile

This Ph.D. research highlights the negative impacts of random vehicle charging on power grid and proposes four practical PEV coordinated charging strategies that reduce network and generation costs by integrating renewable energy resources and real-time pricing while considering utility constraints and consumer concerns

A heuristic approach for coordination of plug-in electric vehicles charging in smart grid

In this paper, a heuristic load management algorithm (H-LMA) is proposed for Plug-in Electric Vehicles (PEVs) charging coordination. The proposed approach is aimed to minimize system losses over a period T (e.g., 24 hours) through re-optimizing the system at time intervals (e.g., 15 minutes) while regulating bus voltages through future smart grid communication system by exchanging signals with individual PEV chargers. Scheduling is performed based on the allowable substation transformer loading level and taking into account PEV owner preference/priority within three designated charging time zones. Starting with the highest priority consumers, H-LMA will distribute charging of PEVs within the selected priority time zones to minimize total system losses over a period T while maintaining network operation criteria such as power generation and bus voltages within their permissible limits. Simulation results are presented for different charging scenarios and are compared to demonstrate the performance of H-LMA for the modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs. The main contribution of this paper lies in the detailed simulations / analyses of the smart grid under study and highlighting the impacts of and T values on the performance of the proposed coordination approach in terms of accuracy and coordination execution time

Control Strategies for Smart Charging and Discharging of Plug- In Electric Vehicles

This chapter aims to provide an overview of the plug-in electric vehicle (PEV) charging and discharging strategies in the electric power system and the smart cities, as well as an application benefiting both consumers and power utility. The electric vehicle technology will be introduced. Then, the main impacts, benefits and challenges related to this technology will be discussed. Following, the role of the vehicles in smart cities will be presented. Next, the major methods and strategies for charging and discharging of plug-in electric vehicles available in the literature will be described. Finally, a new strategy for the intelligent charging and discharging of electric vehicles will be presented, which aims to benefit the consumer and the power utility

Bi-directional coordination of plug-in electric vehicles with economic model predictive control

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-To-vehicle (G2V) and vehicle-To-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university's PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%