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Investigating a large-scale PHEV/PEV parking deck in a smart grid environment,” presented at the 43rd North Amer. Power Symp
, 2011
"... Abstract — 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 i ..."
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Abstract — 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.
ACKNOWLEDGEMENTS
"... This is the final report of a systematic review conducted as part of the Australian Primary Health Care Research Institute (APHCRI) Stream Four funding. The aim of Stream Four was to systematically identify, review, and synthesise knowledge about primary health care organisation, funding, delivery a ..."
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This is the final report of a systematic review conducted as part of the Australian Primary Health Care Research Institute (APHCRI) Stream Four funding. The aim of Stream Four was to systematically identify, review, and synthesise knowledge about primary health care organisation, funding, delivery and performance and then consider how this knowledge might be applied in the Australian context. This particular review focussed on the management of chronic diseases in the primary care setting. THE RESEARCH TEAM This review was undertaken by the Centre for Primary Health Care and Equity, School
Joint Optimization of Electric Vehicle and Home Energy Scheduling Considering User Comfort Preference
"... Abstract—In this paper, we investigate the joint optimization of electric vehicle (EV) and home energy scheduling. Our objective is to minimize the total electricity cost while considering user com-fort preference. We take both household occupancy and EV travel patterns into account. The novel contr ..."
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Abstract—In this paper, we investigate the joint optimization of electric vehicle (EV) and home energy scheduling. Our objective is to minimize the total electricity cost while considering user com-fort preference. We take both household occupancy and EV travel patterns into account. The novel contributions of this paper lie in the exploitation of EVs as dynamic storage facility as well as de-tailed modeling of user comfort preference, thermal dynamics, EV travel, and customer occupancy patterns in a concrete optimiza-tion framework. Extensive numerical results are presented to illus-trate the efficacy of the proposed design. Specifically, we show that the proposed design can achieve significant saving in electricity cost, allow more flexibility in setting the tradeoff between cost and user comfort, and enable to reduce energy demand during peak hours. We also demonstrate the benefits of applying the proposed framework to a residential community compared to optimization of individual household separately. Index Terms—Electric vehicle, HVAC system, energy manage-ment system, aggregator, day-ahead electricity price, occupancy pattern, travel pattern, cost minimization, user comfort. NOMENCLATURE Duration of time slot (hours). Maximum acceptable temperature deviation when house is occupied ( C). Coefficient of performance (COP) of heater/AC in house. Charging efficiency of EV. Discharging efficiency of EV. Travel time of EV during trip. Solar irradiance in time slot (kW/m). Dummy variable, “ ” for AC cooling, “1 ” for heating. The effective window area of house.
Quantifying the Effect of Plug-in Electric Vehicles on Future Grid Operations and Ancillary Service Procurement Requirements
- In Proceedings of the ASME 2013 International Mechanical Engineering Congress & Exposition
, 2013
"... ABSTRACT As plug-in electric vehicles (PEVs) grow in popularity, there is increasing research interest in the interaction between PEVs and the electric grid. Much of the previous work in the literature relies on an assumption that PEV charging will be scheduled, and that the duration and magnitude ..."
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ABSTRACT As plug-in electric vehicles (PEVs) grow in popularity, there is increasing research interest in the interaction between PEVs and the electric grid. Much of the previous work in the literature relies on an assumption that PEV charging will be scheduled, and that the duration and magnitude of charging loads can be modulated to suit the needs of the utility and the system operator. While access to the data or owner input necessary for charge scheduling and management might be technically feasible today, it is unclear whether vehicle owners will be amenable to providing these data or accepting utility control of their charging choices. Because of these uncertainties in the future relationship between electric utilities and PEV owners, this study seeks to examine the market effects of vehicles in the absence of the additional data utilities would need to realize these alternate, "optimal" PEV charging scenarios. In particular, this study focuses on quantifying the potential uncertainty in vehicle charging loads on an energy and power basis. Monte Carlo methods were applied to vehicle trip data from the National Household Travel Survey (NHTS) to generate simulated driving profiles for individual vehicles. Using these profiles, six PEV fleet sizes were studied, ranging from 1,000 to 500,000 vehicles, to determine whether fleet size had a linear effect on the stochasticity of vehicle charging loads. Following the Monte Carlo simulations, these loads were examined independent of and compared to net load (load minus wind generation). Results from the Monte Carlo simulations indicate that even for the largest PEV fleet sizes studied, variability in average charging loads is on the order of 10 MW, less than 0.2% of the magnitude of charging load for those fleet sizes. In comparison with electricity demand in the Electric Reliability Council of Texas' (ERCOT) operating area, these charging loads represent a 1% increase above typical summer peak loads. Unfortunately, while the relative increase in demand is small, the timing of peak charging load is nearly coincident with summer peak demand. The simulation approach was validated by comparing the results against empirical vehicle charging data collected by the Pecan Street Research Consortium from households in Austin, Texas. Simulated and empirical vehicle charging trends showed generally good agreement, with similar charging times but slightly different charging durations. The alignment between the two charging profiles indicates that the simulation methodology applied here with NHTS travel data can be used to predict electric load for vehicle charging when empirical historical charging data are not available.
1Estimating the Impact of Electric Vehicle Smart Charging on Distribution Transformer Aging
"... Abstract—This paper describes a method for estimating the impact of plug-in electric vehicle (PEV) charging on overhead distribution transformers, based on detailed travel demand data and under several different schemes for mitigating overloads by shifting PEV charging times (smart charging). The pa ..."
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Abstract—This paper describes a method for estimating the impact of plug-in electric vehicle (PEV) charging on overhead distribution transformers, based on detailed travel demand data and under several different schemes for mitigating overloads by shifting PEV charging times (smart charging). The paper also presents a new smart charging algorithm that manages PEV charging based on estimated transformer temperatures. We simulated the varied behavior of drivers from the 2009 National Household Transportation Survey, and transformer temperatures based an IEEE standard dynamic thermal model. Results are shown for Monte Carlo simulation of a 25kVA overhead dis-tribution transformer, with ambient temperature data from hot and cold climate locations, for uncontrolled and several smart-charging scenarios. These results illustrate the substantial impact of ambient temperatures on distribution transformer aging, and indicate that temperature-based smart charging can dramatically reduce both the mean and variance in transformer aging without substantially reducing the frequency with which PEVs obtain a full charge. Finally, the results indicate that simple smart charging schemes, such as delaying charging until after midnight can actually increase, rather than decrease, transformer aging. Index Terms—Plug-in hybrid electric vehicles, transformer aging, smart charging I.
Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles
"... Recent researches towards the use of green technologies to reduce pollution and higher penetration of renewable energy sources in the transportation sector have been gaining popularity. In this wake, extensive participation of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allo ..."
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Recent researches towards the use of green technologies to reduce pollution and higher penetration of renewable energy sources in the transportation sector have been gaining popularity. In this wake, extensive participation of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. Daytime charging stations will be needed for daily usage of PHEVs due to the limited all-electric range. Intelligent energy management is an important issue which has already drawn much attention of researchers. Most of these works require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. In this paper, gravitational search algorithm (GSA) has been applied and compared with another member of swarm family, particle swarm optimization (PSO), considering constraints such as energy price, remaining battery capacity, and remaining charging time. Simulation results obtained for maximizing the highly nonlinear objective function evaluate the performance of both techniques in terms of best fitness.
Optimal Energy Management Algorithm for Plug in Hybrid Electric Vehicles
"... ABSTRACT: Plug in Hybrid Electric Vehicles (PHEVs) charging and discharging, renewable energy resource generation and utilization is most important in future power system control. Proper integration of these energy sources gives solution to the challenges. In this paper Mixed Integer Linear Program ..."
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ABSTRACT: Plug in Hybrid Electric Vehicles (PHEVs) charging and discharging, renewable energy resource generation and utilization is most important in future power system control. Proper integration of these energy sources gives solution to the challenges. In this paper Mixed Integer Linear Programming (MILP) was proposed for plug in Hybrid Electric Vehicles charging and discharging in a charging station. Charging station consists of Photo Voltaic (PV) system with practical constraints, power balance constraints, battery charging and discharging constraints are considered. The results proposed that the proposed algorithm minimize the charging cost of PHEVs and optimal power flow for the grid connected PHEVs systems. Likewise, PHEVs owners could yield more profit by discharging their vehicles to the grid in addition to having preferred charge in the departure time.
Decentralised online charging scheduling for large populations of electric vehicles: a cyber-physical system approach
, 2012
"... As the number of electric vehicles (EVs) grows, their electricity demands may have significant detrimental impacts on electric power grid when not scheduled properly. In this paper, we model an EV charging system as a cyber-physical system, and design a decentralised online EV charging scheduling al ..."
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As the number of electric vehicles (EVs) grows, their electricity demands may have significant detrimental impacts on electric power grid when not scheduled properly. In this paper, we model an EV charging system as a cyber-physical system, and design a decentralised online EV charging scheduling algorithm for large populations of EVs, where the EVs can be highly heterogeneous and may join the charging system dynamically. The algorithm couples a clustering-based strategy that dynamically classifies heterogeneous EVs into mUltiple groups and a sliding-window iterative approach that schedules the charging demand for the EVs in each group in real time. Extensive simulation results demonstrate that our approach provides near-optimal solutions at significantly reduced complexity and communication overhead. It flattens the aggregated load on the power grid and reduces the costs of both the users and the utility.
Scheduling in Cyber-Physical Systems
, 2012
"... Cyber-physical systems (CPS) refer to a promising class of systems featuring intimate coupling between the ‘cyber’ intelligence and the ‘physical ’ world. Enabled by the ubiquitous availability of computation and communication capabilities, such systems are widely envisioned to redefine the way that ..."
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Cyber-physical systems (CPS) refer to a promising class of systems featuring intimate coupling between the ‘cyber’ intelligence and the ‘physical ’ world. Enabled by the ubiquitous availability of computation and communication capabilities, such systems are widely envisioned to redefine the way that people interact with the physical world, similar to the revolutionary role of internet in transforming how people interact with each other. As the whole society becomes increasingly dependent on such systems, it is crucial to develop a theory to understand and optimize the CPS in a systematic manner. This thesis contributes to the foundations of CPS by identifying and addressing a general class of scheduling-type applications for a vital class of CPS, the physical networks (PhyNets). Different from the abstract CPS, a PhyNet has a graph-type physical part, which represents the local interactions among users in the system, as specified by certain well-known physical laws. Thus, it is very promising to develop efficient distributed algorithms in PhyNets with proper communication infrastructure and protocols, due to the physical graph structure. The ‘scheduling’ refers to the applications where joint actions of all users are coordinated, in order to allocate system resources