Stochastic analysis of the impact of plug-in hybrid electric vehicles on the distribution grid (original) (raw)
Related papers
2009
The charging of batteries of plug-in hybrid electric vehicles at home at standard outlets has an impact on the distribution grid which may require serious investments in grid expansion. The coordination of the charging gives an improvement of the grid exploitation in terms of reduced power losses and voltage deviations with respect to uncoordinated charging. The vehicles must be dispatchable to achieve the most efficient solution. As the exact forecasting of household loads is not possible, stochastic programming is introduced. Two main techniques are analyzed in this paper: quadratic and dynamic programming. Both techniques are compared in results and storage requirements. The charging can be coordinated directly or indirectly by the grid utility or an aggregator who will sell the aggregated demand of PHEVs at the utility. PHEVs can also discharge and so inject energy in the grid to restrict voltage drops. The amount of energy that is injected in the grid depends on the price tariffs, the charging and discharging efficiencies and the battery energy content. The impact of a voltage controller embedded in a PHEV charger is regarded in this paper. A day and night tariff are applied. The charging and discharging of vehicles can respond to real-time pricing or on a price-schedule as well. Voltage control is the first step in the utilization of distributed resources like PHEVs for ancillary services.
The charging of batteries of plug-in hybrid electric vehicles at home at standard outlets has an impact on the distribution grid which may require serious investments in grid expansion. The coordination of the charging gives an improvement of the grid exploitation in terms of reduced power losses and voltage deviations with respect to uncoordinated charging. The vehicles must be dispatchable to achieve the most efficient solution. As the exact forecasting of household loads is not possible, stochastic programming is introduced. Two main techniques are analyzed in this paper: quadratic and dynamic programming. Both techniques are compared in results and storage requirements. The charging can be coordinated directly or indirectly by the grid utility or an aggregator who will sell the aggregated demand of PHEVs at the utility. PHEVs can also discharge and so inject energy in the grid to restrict voltage drops. The amount of energy that is injected in the grid depends on the price tariffs, the charging and discharging efficiencies and the battery energy content. The impact of a voltage controller embedded in a PHEV charger is regarded in this paper. A day and night tariff are applied. The charging and discharging of vehicles can respond to real-time pricing or on a price-schedule as well. Voltage control is the first step in the utilization of distributed resources like PHEVs for ancillary services.
Impacts of Stochastic Residential Plug-In Electric Vehicle Charging on Distribution Grid
In this paper, the impacts of residential Plug-In Electric Vehicles (PEVs) charging on a distribution grid are investigated. A stochastic charging model is developed and used to study the impacts on distribution transformer loading, hotspot temperature variation and Accelerated Aging Factor (AAF) of the transformer. Different penetration levels of PEVs are considered in a typical distribution system. Furthermore, distribution of State of Charge (SOC) is discussed which can be used to optimize battery capacity and required charging infrastructure. Distribution of parking time interval is also discussed which can be used to evaluate availability of PEVs for overnight charging. The merit of stochastic approach compared with deterministic approach is also illustrated. The main contribution of this paper is the stochastic approach to evaluate the impact of residential PEV charging on the distribution grid.
—Rising fuel prices and environmental concerns are threatening the stability of current electrical grid systems. These factors are pushing the automobile industry towards more efficient, hybrid vehicles. Current trends show petroleum is being edged out in favor of electricity as the main vehicular motive force. The proposed methods create an optimized charging control schedule for all participating Plug-in Hybrid Electric Vehicles in a distribution grid. The optimization will minimize daily operating costs, reduce system losses, and improve power quality. This requires participation from Vehicle-to-Grid capable vehicles, load forecasting, and Locational Marginal Pricing market predictions. Vehicles equipped with bidirectional chargers further improve the optimization results by lowering peak demand and improving power quality. Index Terms—Distributed Generation (DG), Distribution Market Price, intelligent Energy Management System (iEMS), Plug-in Hybrid Electric Vehicle (PHEV), stochastic optimization, Vehicle-to-Grid (V2G).
Modeling of Uncertainties in Electric Vehicle Charging and Its Impact on the Electric Grid
2015
Electric Vehicles represent an important and futuristic alternative to conventional vehicles. However, the effect of charging electric vehicles on electric grids must be taken into account to prevent accidental overloading of the distribution grid due to simultaneous charging/discharging of electric vehicles. As electric vehicles gain more popularity with consumers, power companies and utilities must be prepared for the added load. To assist in this preparation, power companies will need accurate load forecasting algorithms. This paper presents the development of an algorithm that forecasts the load for Battery Electric Vehicles, or BEVs at 15 minute intervals for any day between January 1, 2011 and December 31, 2023. The forecast algorithm uses the projected BEV growth rate, the population of the parking lot or garage of inquiry, and a probability distribution which relates the state of charge (SOC) of the vehicle’s battery to the percent of EV owners that require such charging dai...
Probabilistic modeling of electric vehicle charging pattern in a residential distribution network
Electric Power Systems Research
It has been recognized that an increased penetration of electric vehicles (EVs) may potentially alter load profile in a distribution network. Charging pattern of EVs and its corresponding electrical load pattern may be assessed and quantified by using either a deterministic method or stochastic approach. However, deterministic method does not account for stochastic nature of EV users which affects the load pattern and of stochastic nature of grid condition. Thus, a stochastic method is applied to develop a probabilistic model of EVs charging pattern that takes into account various factors such as vehicle class, battery capacity, state of charge (SOC), driving habit/need, i.e. involving trip type and purpose, plug-in time, mileage, recharging frequency per day, charging power rate and dynamic EV charging price under controlled and uncontrolled charging schemes. The probabilistic model gives EV charging pattern over a period of day for different months to represent the load pattern during different seasons of a year. The presented model gives a rigorous estimation of EV charging load pattern in a distribution network which is considered important for network operators.
2021 IEEE Madrid PowerTech
The plug-in hybrid electric vehicles (PHEVs) integration into the electrical network introduces new challenges and opportunities for operators and PHEV owners. On the one hand, PHEVs can decrease environmental pollution. On the other hand, the high penetration of PHEVs in the network without charging management causes harmonics, voltage instability, and increased network problems. In this study, a charging management algorithm is presented to minimize the total cost and flatten the demand curve. The behavior of the PHEV owner in terms of arrival time and leaving time is modeled with a stochastic distribution function. The battery model and hourly power consumption of PHEV are modeled, and the obtained models are applied to determine the battery's state of charge. The proposed method is tested on a sample demand curve with and without a charging management algorithm to verify the efficiency. The results verify the efficiency of the proposed method in decreasing the total cost using the management algorithm for PHEVs, especially when the PHEVs sell the electricity to the network.
2016
Nowadays, one of the dominant reasons of excessive energy consumption is the high energy demand in corporate and/or public buildings. At the same time, electric vehicles (EVs) are becoming more and more popular worldwide being a considerable alternative power source when parked. In this work we initially propose an energy management framework which optimizes the control of the charging-discharging schedule of a fleet of EVs arriving at a university building for two typical loaddays in February and May aiming at the minimization of the energy demand and, thus, the electricity cost of the building. To this end, a mixed integer linear programing (MILP) model containing binary and continuous variables was developed. Uncertainties in load, generation, and cost require modeling power systems with a probabilistic approach. In such a way, the probabilistic nature of demand side management (DSM) problem is also possible to be addressed. The integration of the EVs in the Low Voltage (LV) grid is simulated with a probabilistic analysis framework that uses real smart metering (SM) data. The stochastic character of the loading parameters at the network nodes is studied taking into account the charging energy needs of the corresponding EVs fleet. Index Terms-Plug-in electric vehicles; energy management; coordinated charging; Monte Carlo, low-voltage network, MILP I.
This paper presents a methodology for modeling the load demand of plug-in hybrid electric vehicles (PHEVs). Due to the stochastic nature of vehicle arrival time, departure time and daily mileage, probabilistic methods are chosen to model the driving pattern. However, these three elements of driving pattern are correlated with each other, which makes the probability density functions (PDFs) based probabilistic methods inaccurate. Here a fuzzy logic based stochastic model is built to study the relationship between the three elements of driving pattern. Moreover, a load profile modeling framework (LPMF) for PHEVs is proposed to synthesize both the characteristics of driving pattern and vehicle parameters into a load profile prediction system. Based on this stochastic model of PHEV, a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm is proposed to integrate PHEVs into a residential distribution grid. A novel business model is developed for PHEVs to provide ancillary service and participate in peak load shaving. A virtual time-of-use rate is used to reflect the load deviation of the system. Then, an objective function is developed to aggregate the peak load shaving, power quality improvement, charging cost, battery degradation cost and frequency regulation earnings into one cost function. The ESPSO approach can benefit the system in four major aspects by: (1) improving the power quality; (2) reducing the peak load; (3) providing frequency regulation service; and (4) minimizing the total virtual cost. Finally, simulations are carried out based on different control strategies and the results have demonstrated the effectiveness of the proposed algorithm.
Plug-in Hybrid Electric Vehicles and Smart Grid: Investigations Based on a Micro Simulation
Introduction of Plug-in Hybrid Electric Vehicles (PHEVs) could potentially trigger a stepwise electrification of the whole transportation sector. But the impact on the electric grid by electrical vehicle charging is still not fully known. This paper investigates several PHEV charging schemes, including smart charging, using a novel iterative approach. An agent based traffic demand model is used for modeling the electrical demand of PHEVs over the day. For modeling the different parts of the electric grid, an approach based on interconnected multiple energy carrier systems is used. For a given charging scheme the power system simulation gives back a price signal indicating whether grid constraints, such as maximum power output at hub transformators, have been violated. This leads to a corrective step in the iterative process, until a charging pattern is found, which does not violate grid constraints. The proposed system allows to investigate existing electric grids, whether they are capable of meeting increased electricity demand by certain future PHEV penetration. Furthermore, in the future, different types of smart charging schemes can be added into the system for comparison.