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1. Analyzing the Impact of Electric Vehicles on Power Losses and Voltage Profile in Power Distribution Systems

   Majumdar, Maitreyee ; Spencer, Delton ; Arefifar, S. A. ( April 2022 , 2022 WCX™ World Congress Experience - SAE International)

   As the number of electric vehicles (EVs) within society rapidly increase, the concept of maximizing its efficiency within the electric smart grid becomes crucial. This research presents the impacts of integrating EV charging infrastructures within a smart grid through a vehicle to grid (V2G) program. It also observes the circulation of electric charge within the system so that the electric grid does not become exhausted during peak hours. This paper will cover several different case studies and will analyze the best and worst scenarios for the power losses and voltage profiles in the power distribution system. Specifically, we seek to find the optimal location as well as the ideal number of EVs in the distribution system while minimizing its power losses and optimizing its voltage profile. Verification of the results are primarily conducted using GUIs created on MATLAB. These simulations aim to develop a better understanding of the potential impacts of electric vehicles in smart grids, such as power quality and monetary benefits for utility companies and electric vehicle users
2. Optimal Grid – Distributed Energy Resource Coordination: Distribution Locational Marginal Costs and Hierarchical Decomposition

   https://doi.org/10.1109/ALLERTON.2019.8919689  

   Andrianesis, Panagiotis ; Caramanis, Michael C. ( September 2019 , 57th Allerton Conference on Communication, Control, and Computing. Allerton, September 24-27, 2019)

   We consider radial distribution networks hosting Distributed Energy Resources (DERs), including Solar Photo­voltaic (PV) and storage-like loads, such as Electric Vehicles (EVs). We employ short-run dynamic Distribution Locational Marginal Costs (DLMCs) of real and reactive power to co­optimize distribution network and DER schedules. Striking a balance between centralized control and distributed self­dispatch, we present a novel hierarchical decomposition ap­proach that is based on centralized AC Optimal Power Flow (OPF) interacting with DER self-dispatch that adapts to real and reactive power DLMCs. The proposed approach is designed to be highly scalable for massive DER Grid integration with high model fidelity incorporating rigorous network component dynamics and costs and reffecting them in DLMCs. We illustrate the use of an Enhanced AC OPF to discover spatiotemporally varying DLMCs enabling optimal Grid-DER coordination in­corporating congestion and asset (transformer) degradation. We employ an actual distribution feeder to exemplify the use of DLMCs as financial incentives conveying sufficient information to optimize Distribution Network and DER (PV and EV) operation, and we discuss the applicability and tractability of the proposed approach, while modeling the full complexity of spatiotemporal DER capabilities and preferences.
3. Joint Capacity Modeling for Electric Vehicles in V2I-enabled Wireless Charging Highways

   https://doi.org/10.1109/SmartGridComm47815.2020.9302993  

   Sikeridis, Dimitrios ; Devetsikiotis, Michael ( November 2020 , 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm))

   Wireless Charging Highways (WCHs) have been introduced by industry and academia to enable charging-while-driving for electric vehicles (EVs) and to combat range anxiety. While detailed planning and performance evaluation of such systems are crucial due to high cost and long life expectancy, most existing works assume a perfect communication environment. In this paper, we introduce a joint capacity model that takes into account both power and communication resources for WCH construction planning, and optimal day-to-day operation. The vehicle-to-infrastructure (V2I) communication and grid power capacities, along with the EV’s average service rate are formulated following technology requirements, EV speed-density characteristics, and the EV’s energy needs and consumption. In addition, a two-dimension Markov chain-based model is designed to capture the WCH power and connectivity dynamics. The proposed model can be used to calculate the system’s Quality of Service (QoS) and profit, provide design insights, and assess the impact of speed regulation, or admission control on the WCH lane. Finally, the performance of the proposed model is evaluated using real US highway data with the results demonstrating its ability to accurately capture the service provision dynamics, and to identify trade-offs between system parameters.
4. Willingness to delay charging of electric vehicles

   Daziano, R.A. ( July 2020 , 25th Annual Conference of the European Association Environmental and Resource Economists)

   Electrification of vehicles is becoming one of the main avenues for decarbonization of the transportation market. To reduce stress on the energy grid, large-scale charging will require optimal scheduling of when electricity is delivered to vehicles. Coordinated electric-vehicle charging can produce optimal, flattened loads that would improve reliability of the power system as well as reduce system costs and emissions. However, a challenge for successful introduction of coordinated deadline-scheduling of residential charging comes from the demand side: customers would need to be willing both to defer charging their vehicles and to accept less than a 100% target for battery charge. Within a coordinated electric-vehicle charging pilot run by the local utility in upstate New York, this study analyzes the necessary incentives for customers to accept giving up control of when charging of their vehicles takes place. Using data from a choice experiment implemented in an online survey of electric-vehicle owners and lessees in upstate New York (N=462), we make inference on the willingness to pay for features of hypothetical coordinated electric-vehicle charging programs. To address unobserved preference heterogeneity, we apply Variational Bayes (VB) inference to a mixed logit model. Stochastic variational inference has recently emerged as a fast and computationally-efficientmore »alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of discrete choice models. Our results show that individuals negatively perceive the duration of the timeframe in which the energy provider would be allowed to defer charging, even though both the desired target for battery charge and deadline would be respected. This negative monetary valuation is evidenced by an expected average reduction in the annual fee of joining the charging program of $2.64 per hour of control yielded to the energy provider. Our results also provide evidence of substantial heterogeneity in preferences. For example, the 25% quantile of the posterior distribution of the mean of the willingness to accept an additional hour of control yielded to the utility is $5.16. However, the negative valuation of the timeframe for deferring charging is compensated by positive valuation of emission savings coming from switching charging to periods of the day with a higher proportion of generation from renewable sources. Customers also positively valued discounts in the price of energy delivery.« less
5. Evaluating Coupling Models for Cloud Datacenters and Power Grids

   https://doi.org/10.1145/3447555.3464868  

   Lin, Liuzixuan ; Zavala, Victor M. ; Chien, Andrew A. ( June 2021 , InThe TwelfthACM International Conference on Future Energy Systems (e-Energy ’21),)

   Ardakanian, Omid ; Niesse, Astrid (Ed.)

   The rapid growth of datacenter (DC) loads can be leveraged to help meet renewable portfolio standard (RPS, renewable fraction)targets in power grids. The ability to manipulate DC loads over time(shifting) provides a mechanism to deal with temporal mismatch between non-dispatchable renewable generation (e.g. wind and solar) and overall grid loads, and this flexibility ultimately facilitates the absorption of renewables and grid decarbonization. To this end, we study DC-grid coupling models, exploring their impact on grid dispatch, renewable absorption, power prices, and carbon emissions.With a detailed model of grid dispatch, generation, topology, and loads, we consider three coupling approaches: fixed, datacenter-local optimization (online dynamic programming), and grid-wide optimization (optimal power flow). Results show that understanding the effects of dynamic DC load management requires studies that model the dynamics of both load and power grid. Dynamic DC-grid coupling can produce large improvements: (1) reduce grid dispatch cost (-3%), (2) increase grid renewable fraction (+1.58%), and (3) reduce DC power cost (-16.9%).It also has negative effects: (1) increase cost for both DCs and non-DC customers, (2) differentially increase prices for non-DC customers, and (3) create large power-level changes that may harm DC productivity.