Sara Deilami

Real-Time Coordination of Plug-In Electric Vehicle Charging in Smart Grids to Minimize Power Losses and Improve Voltage Profile

This paper proposes a novel load management solution for coordinating the charging of multiple plug-in electric vehicles (PEVs) in a smart grid system. Utilities are becoming concerned about the potential stresses, performance degradations and overloads that may occur in distribution systems with multiple domestic PEV charging activities. Uncontrolled and random PEV charging can cause increased power losses, overloads and voltage fluctuations, which are all detrimental to the reliability and security of newly developing smart grids. Therefore, a real-time smart load management (RT-SLM) control strategy is proposed and developed for the coordination of PEV charging based on real-time (e.g., every 5 min) minimization of total cost of generating the energy plus the associated grid energy losses. The approach reduces generation cost by incorporating time-varying market energy prices and PEV owner preferred charging time zones based on priority selection. The RT-SLM algorithm appropriately considers random plug-in of PEVs and utilizes the maximum sensitivities selection (MSS) optimization. This approach enables PEVs to begin charging as soon as possible considering priority-charging time zones while complying with network operation criteria (such as losses, generation limits, and voltage profile). Simulation results are presented to demonstrate the performance of SLM for the modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs.

Coordination of Generation Scheduling with PEVs Charging in Industrial Microgrids

Conventional industrial microgrids (IMGs) consist of factories with distributed energy resources (DERs) and electric loads that rely on combined heat and power (CHP) systems while the developing IMGs are expected to also include renewable DERs and plug-in electric vehicles (PEVs) with different vehicle ratings and charging characteristics. This paper presents an electricity and heat generation scheduling method coordinated with PEV charging in an IMG considering photovoltaic (PV) generation systems coupled with PV storages. The proposed method is based on dynamic optimal power flow (DOPF) over a 24-hour period and includes security-constrained optimal power flow (SCOPF), IMGs factories constraints, PV storage constraints and PEVs dynamic charging constraints. It will utilize the generators waste heat to fulfill thermal requirements while considering the status of renewable DERs to decrease the overall cost of IMGs. To demonstrate the effectiveness of the proposed method, detailed simulation results are presented and analyzed for an 18-bus IMG consisting of 12 factories and 6 types of PEVs without/with PV generation systems operating in grid-connected and stand-alone modes. The main contribution is including PEVs with dynamic constraints that have changed the nature of scheduling formulation from a simple hourly OPF to a dynamic OPF.

Power quality of smart grids with Plug-in Electric Vehicles considering battery charging profile

The impact of different battery charging rates of Plug-in Electric Vehicles (PEVs) on the power quality of smart grid distribution systems is studied in this paper. PEV battery chargers are high power nonlinear devices that can generate significant amount of current harmonics. PEVs will be an integral component to the operation of smart grids and therefore their power quality impacts must be thoroughly analyzed. Based on decoupled harmonic load flow analysis, different PEV charging scenarios (e.g., time zone scheduling, charging rate and penetration level) are tested for a typical large distribution network topology. The impacts of PEV charge rate on voltage profile, fundamental and harmonic losses, transformer loading and total harmonic distortions are demonstrated.

Impacts of battery charging rates of Plug-in Electric Vehicle on smart grid distribution systems

Plug-in Electric Vehicles (PEVs) will be an integral part of smart grids in the near future. This paper studies the impacts of different PEV battery charging profiles on the performance of smart grid distribution systems. PEVs are already growing in popularity as a low emission mode of transport versus conventional petroleum based vehicles. Utilities are becoming concerned about the potential stresses and overloads that may occur with multiple domestic PEV charging activity. Smart grids will play an important role in PEV operation because the battery chargers can be coordinated by the utility and harnessed for storing surplus grid energy and reused to support the grid during peak times. Therefore, an analysis is performed for a smart grid distribution system to demonstrate the impacts of different PEV charging scenarios. The paper compares charging rates (e.g., slow, medium and fast charging), PEV penetration levels as well as different charging periods over a 24 hour period considering existing system load profiles, and evaluates the overall performance of the distribution system. The impact on system load profile, total losses, transformer loading and voltage profile is examined.

Load management in smart grids considering harmonic distortion and transformer derating

This paper addresses the important issue of power quality management for smart grids and proposes a load management strategy based on transformer derating for minimizing harmonic distortion in distribution feeders and transformers. Ongoing development of smart grid technologies such as smart metering and smart appliances are creating new opportunities for improving distribution system performance. One area undergoing study is effective control of demand response through (semi)automated load management practices (e.g., smart appliances). Despite these developments, the impact on power quality has not been taken into consideration from a demand side management point of view. Smart grids provide an excellent opportunity to better manage power quality and reduce harmonic distortions present in power networks. In this paper, it is proposed that the impact of harmonics generated by nonlinear loads should be factored into overall load control strategies of smart appliances. This work focuses on the impact on residential distribution transformers which are adversely impacted by harmonic current distortions. A growing concern is the potentially high penetration of plug-in electric vehicles in smart grids. Load management of electric vehicles is studied for an IEEE 30-bus 23 kV distribution system to demonstrate the benefits of the proposed power quality and load management strategy. This paper proposes computing transformer K-Factor derating to control scheduling of smart appliances/loads to reduce harmonic stresses.

Voltage profile and THD distortion of residential network with high penetration of Plug-in Electrical Vehicles

This paper analyzes the potential impacts of Plug-in Electric Vehicles (PEVs) on the voltage profile, losses, power quality and daily load curve of low voltage residential network. PEVs are soon expected to grow in popularity as a low emission mode of transport compared to conventional petroleum based vehicles. Utilities are concerned about the potential detrimental impacts that multiple domestic PEV charging may have on network equipment (e.g., transformer and cable stresses). To address these issues, two charging regimes including uncoordinated (random) and coordinated (uniformly distribution) are considered. Based on harmonic analysis of a typical 19 bus low voltage (415V) residential network, different charging scenarios over a 24 hour period are compared considering voltage deviations, system losses, transformer overloading and harmonic distortions. Simulation results are used to highlight the advantages of the coordinated uniformly distributed charging of PEV in residential systems.

Fuzzy Approach for Online Coordination of Plug-In Electric Vehicle Charging in Smart Grid

This paper proposes an online fuzzy coordination algorithm (OL-FCA) for charging plug-in electric vehicles (PEVs) in smart grid networks that will reduce the total cost of energy generation and the associated grid losses while maintaining network operation criteria such as maximum demand and node voltage profiles within their permissible limits. A recently implemented PEV coordination algorithm based on maximum sensitivity selection (MSS) optimization is improved using fuzzy reasoning. The proposed OL-FCA considers random plug-in of vehicles, time-varying market energy prices, and PEV owner preferred charging time zones based on priority selection. Impacts of uncoordinated, MSS, and fuzzy coordinated charging on total cost, gird losses, and voltage profiles are investigated by simulating different PEV penetration levels on a 449-node network with three wind distributed generation (WDG) systems. The main advantage of OL-FCA compared with the MSS PEV coordination is the reduction in the total cost it introduces within the 24h.

Mitigation of harmonics in smart grids with high penetration of plug-in electric vehicles

Smart grids provide an excellent opportunity to improve the performance and the power quality of distribution system. This paper addresses a simple and effective approach to improve the quality of electric power in smart grids with high penetration of smart appliances such as plug-in electric vehicles (PEV). Assuming a typical daily load curve, decupled harmonic load flow (DHLF) formulation is used to model the distorted grid, identify the worst bus and calculate the total harmonic distortion (THD) over a period of 24 hours. The possibility of reducing THD (e.g., to 5%, as recommended by IEEE-519) by optimal dispatch of LTC and shunt capacitors is first investigated. Otherwise, for systems with high penetration of smart appliances, passive filter banks are placed at the worst buses and the switching devices are rescheduled to improve the overall quality of electric power. This approach is demonstrated for the IEEE 30-bus 23 kV distribution system and case studies are presented to demonstrate its effectiveness.

Rooftop PV with battery storage for constant output power production considering load characteristics

One of the main limitations of rooftop photovoltaic generation systems (rooftop PVs) is the dependency of their output power to environmental factors such as sun radiation, panel temperature, passing clouds and shading, as well as loading level (operating point on v-i characteristics). This dependency may result in sudden output power variations of rooftop PVs particularly during cloudy periods. This paper investigates application and control of battery storage (BS) systems to overcome the sudden output power variations of rooftop PVs. A practical battery storage energy management strategy (BS-EMS) for operating grid-connected rooftop PVs at point of common coupling (PCC) is presented such that the delivered output power to the grid is constant under various operating conditions. The power balance between rooftop PV, BS and grid is considered by dynamic control of the BS converter to achieve constant output power to the grid during daylight. Simulation results for a 24-hour period will be presented and analyzed to investigate the performance of BS-EMS for a system comprising of a single-phase rooftop PV with BS and linear loads connected to power grid.

Forecasting plug-in electric vehicles load profile using artificial neural networks

Plug-in electric vehicles (PEVs) are becoming very popular these days and consequently, their load management will be a challenging issue for the network operators in the future. This paper proposes an artificial intelligence approach based on neural networks to forecast daily load profile of individual and fleets of randomly plugged-in PEVs, as well as the upstream distribution transformer loading. An artificial neural network (ANN) model will be developed to forecast daily arrival time (Ta) and daily travel distance (Dtr) of individual PEV using historical data collected for each vehicle in the past two years. The predicted parameters are then will be used to forecast transformer loading with PEV charging activities. The results of this paper will be very beneficial to coordination and charge/discharge management of PEVs as well as demand load management, network planning and operation proposes. Detailed simulations are presented to investigate the feasibility and accuracy of the proposed forecasting strategy.