Improving voltage flexibility index in MGs by optimal charging scheduling of PHEVs

Impact of PHEV in active distribution network under gas station network attack

Charging performance of Plug-in Hybrid Electric Vehicles (PHEVs) under various charging approaches brings new challenges for the distribution grid, such as feeder overloading and increment of loss. In this paper, the impact of charging PHEVs on the load profile is evaluated considering Demand Response (DR) programs in order to minimize the impact of charging PHEVs on load profile. In the proposed DR strategy, all consumers can determine and control their loads. Also, the proposed DR strategy can satisfy the function of reducing peak with regard to different PHEV penetration, while preserving consumer comfort levels. For performing DR programs, time of use and real time pricing models are used to evaluate the impacts of charging of PHEVs on distribution system operations. A 33-node test feeder has been studied to assess different penetration schemes of PHEVs including 11.3%, 35% and 45% PHEV penetration in residential loads. Moreover, uncertainties associated with PHEV charging performances are handled with Monte Carlo simulation. Results validate that the amount of peak loss, and Peak to Average Ratio (PAR) increase owing to PHEVs penetration growth. Also, the results illustrate that scheduling for the operation of PHEVs with DR program reduces the distribution losses and PAR.

A population of drivers was simulated using a microsimulation model. Consistent with the 2001 National Household Travel Survey (NHTS), a wide range of daily driving distance was observed. This heterogeneity implies that some drivers will realize greater fuel savings from driving a plug-in hybrid electric vehicle (PHEV) than others, therefore, consumers who choose to purchase PHEVs may tend to be those who drive farther than average. The model was used to examine the effects of this difference in driving by estimating fuel use, electricity demand and GHG emissions by two populations, one assigned PHEVs at random to some fraction of drivers, and the other assigned PHEVs to drivers who realized operating cost savings at least as great as the amortized incremental cost of the PHEV relative to a comparable conventional vehicle. These two populations showed different distributions of daily driving distance, with the population of PHEV drivers selected on the basis of operating cost savings driving 40% farther per day on average than average drivers. This difference indicates the possible range of driving patterns of future PHEV drivers, which should be taken into account when estimating fuel savings and GHG reductions from PHEVs. For example, if 20% of U.S. vehicles were PHEVs, we find a potential reduction of fuel use of 0.17 gal per day per vehicle if PHEVs substitute randomly for conventional vehicles, whereas the fuel savings is as large as 0.26 gal per day per vehicle if PHEVs are substituted according to operating cost savings. Similar differences in GHG emissions were estimated as well. The effects of electricity demand management on charging PHEVs was examined for these two populations. It was found for both that only a small fraction of PHEVs were impacted by interruptible electricity service (no charging permitted during peak hours). Most PHEV drivers were able to charge sufficiently during off-peak hours and saw little change in operating costs. This implies that interruptible electricity service may impact operating costs of only a small fraction of PHEV drivers.

The impact of PHEVs charging and network topology optimization on bulk power system reliability

By using the 2017 National Household Travel Survey (NHTS) data, this study explores the status quo of ownership and usage of conventional vehicles (CVs) and alternative fuel vehicles (AFVs), i.e., Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs) and Battery Electric Vehicles (BEVs), in the United States. The young ages of HEVs (6.0 years), PHEVs (3.2 years) and BEVs (3.1 years) demonstrate the significance of the 2017 NHTS data. The results show that after two decades of development, AFVs only occupy about 5% of annual vehicle sales, and their share does not show big increases in recent years. Meanwhile, although HEVs still dominate the AFV market, the share of PHEVs & BEVs has risen to nearly 50% in 2017. In terms of ownership, income still seems to be a major factor influencing AFV adoption, with the median annual household incomes of CVs, HEVs, PHEVs and BEVs being $75,000, $100,000, $150,000 and $200,000, respectively. Besides, AFV households are more likely to live in urban areas, especially large metropolitan areas. Additionally, for AFVs, the proportions of old drivers are much smaller than CVs, indicating this age group might still have concerns regarding adopting AFVs. In terms of travel patterns, the mean and 85th percentile daily trip distances of PHEVs and HEVs are significantly larger than CVs, followed by BEVs. BEVs might still be able to replace CVs for meeting most travel demands after a single charge, considering most observed daily trip distances are fewer than 93.5 km for CVs. However, the observed max daily trip distances of AFVs are still much smaller than CVs, implying increasing the endurance to meet extremely long-distance travel demands is pivotal for encouraging consumers to adopt AFVs instead of CVs in the future.

Recently, plug-in hybrid electric vehicles (PHEVs) have attracted considerable attention as a sustainable transport system and also an essential component of the smart grid. With the rapid growth of PHEVs penetration, the charging and discharging of PHEVs will pose a significant impact on the residential electricity distribution network. For this reason, the management of PHEV charging and discharging has become one of the key issues in the research of PHEVs. In most existing work, PHEVs are supposed to operate individually for charging and discharging in the grid. However, we argue that, by leveraging the cooperation among PHEVs, the grid will efficiently stimulate PHEV users to charge in load valley and discharge in load peak. As a consequence, the electricity load is well balanced. Meanwhile, the PHEV users also achieve higher profit. The PHEV charging and discharging cooperation is a win-win strategy for both the grid and the PHEV users. We formulate and resolve the PHEV charging and discharging cooperation in the framework of coalition game. The simulation results indicate that the peak-valley difference in electricity load of the grid is significantly reduced. Besides, the PHEV users have better satisfaction in the vehicle battery status and the economic profit.

This paper examines the problem of predicting the aggregate grid load imposed by battery health-conscious plug-in hybrid electric vehicle (PHEV) charging. The paper begins by generating a set of representative daily PHEV trips using the National Household Travel Survey (NHTS) and a set of federal and real-world drive cycles. Each trip is then used in a multiobjective genetic optimizer, along with a PHEV model and a battery degradation model, to simultaneously minimize PHEV energy cost and battery degradation. The optimization variables include the parameters of the PHEV charge pattern, defined as the timing and rate with which the PHEV receives electricity from the grid. For several weightings of the optimization objectives, total PHEV power demand is predicted by accumulating the charge patterns for individual PHEVs. Two charging scenarios, i.e., charging at home only versus charging at home and work, are examined. Results indicate that the main PHEV peak load occurs early in the morning (between 5.00–6.00a.m.), with approximately 45%–60% of vehicles simultaneously charging from the grid. Moreover, charging at work creates additional peaks in this load pattern.

This paper develops a convex quadratic programming (QP) formulation for the demand response (DR) optimization of plug-in hybrid electric vehicles (PHEVs) under time-varying electricity price signals. The work is motivated by the need for a computationally-efficient PHEV DR model that accounts for the ohmic energy losses in PHEV batteries, and is scalable to large-scale vehicle-to-grid (V2G) optimization and control applications. We use a previously-developed power-split PHEV model with an optimal power management strategy to compute the average distance-based PHEV energy consumption characteristics. Moreover, we use an equivalent circuit battery model for the PHEV's charge and discharge process. We then derive the PHEV's total fuel and electric energy cost as a quadratic function of battery state-of-charge (SOC), and show that the cost function is convex. Finally, we use a standard QP solver to optimize the PHEV's demand response for a few sample trips obtained from the U.S. National Household Travel Survey (NHTS) dataset. The achieved optimization time for a 24-hour time window with 5 min. resolution is less than 0.1 s (using a single quad-core computer). The method can hence be easily scaled for large-scale smart grid optimization and control studies.

Plug-in hybrid electric vehicles (PHEVs) have a large potential to reduce greenhouse gases emissions and increase fuel economy and fuel flexibility. PHEVs are propelled by the energy from both gasoline and electric power sources. Penetration of PHEVs into the automobile market affects the electrical grid through an increase in electricity demand. This paper studies effects of the wide spread adoption of PHEVs on peak and base load demands in Ontario, Canada. Long-term forecasting models of peak and base load demands and the number of light-duty vehicles sold were developed. To create proper forecasting models, both linear regression (LR) and non-linear regression (NLR) techniques were employed, considering different ranges in the demographic, climate and economic variables. The results from the LR and NLR models were compared and the most accurate one was selected. Furthermore, forecasting the effects of PHEVs penetration is done through consideration of various scenarios of penetration levels, such as mild, normal and aggressive ones. Finally, the additional electricity demand on the Ontario electricity grid from charging PHEVs is incorporated for electricity production planning purposes.

Vehicle emissions are a major concern in the development of new automobiles. Plug-in hybrid electric vehicles (PHEVs) have a large potential to reduce greenhouse gases emissions and increase fuel economy and fuel flexibility. PHEVs are propelled by the energy from both gasoline and electric power sources. Penetration of PHEVs into the automobile market affects the electrical grid and increasing the electricity demand has not been fully investigated. This paper studies effects of the wide spread adoption of PHEVs on peak and base load demands in Ontario, Canada. Long-term forecasting models of peak and base load demands and the number of light-duty vehicles sold are developed. To create proper forecasting models, both linear regression (LR) and non-linear regression (NLR) techniques are employed, considering different ranges in the demographic, climate and economic variables. The results from the LR and NLR models (LRM and NLRM) are compared and the most accurate one is selected. Furthermore, forecasting the effects of PHEVs penetration is done through consideration of various scenarios of penetration levels, such as mild, normal and aggressive ones. Finally, the additional electricity demand on the Ontario electricity grid from charging PHEVs is incorporated for electricity production planning purposes.

This paper presents the harmonic effects of plug-in hybrid electric vehicles (PHEV) on the IEEE 37-bus distribution system at different PHEV penetration levels considering a practical daily residential load shape. The PHEV is modeled as a current harmonic source by using the Open-Source Distribution System Simulator (OpenDSS) and DSSimpc software. Time series harmonic simulation was conducted to investigate the harmonic impact of PHEV on the system by using harmonic data obtained from a real electric vehicle. Harmonic effects on the system voltage profile and circuit power losses are also investigated by using OpenDSS and MATLAB software. Current/voltage total harmonic distortion (THD) produced from the large scale of PHEV is investigated. Test results show that the voltage and current THDs are increased up to 9.5% and 50%, respectively, due to high PHEV penetrations and these THD values are significantly larger than the limits prescribed by the IEEE standards.

Concerns over the adverse environmental impact of conventional vehicles have led to cleaner and more efficient vehicle technology. Plug-in hybrid electric vehicles (PHEVs) are the most promising approach to reducing petroleum use. While PHEVs partially rely on the electricity from the power grid, they raise concerns about their negative impacts on power generation, transmission, and distribution installations. The impacts of PHEVs on the power grid cannot be examined thoroughly unless extensive data on the utilization of each individual PHEV are available. For instance, in order to estimate the aggregated impact of PHEVs on the electricity demand profile, one needs to know i) when each PHEV would begin its charging process, ii) how much electrical energy it would require, and iii) what level of power would be available. Uncertainty regarding the above factors makes the researchers to come up with a series of probability distribution functions (PDFs). This paper extracts and analyzes the data that are available through national household travel surveys (NHTS). Based on 40,000 vehicle trips, probability distribution functions for arriving time and the required energy of PHEVs are extracted. Also, the probabilistic and deterministic PHEV charging load profiles are built and compared.

In future the usage of Plug-in hybrid electric vehicles (PHEV) will be in wide range, which will impose huge burden to the distributive system. The peak load at the distribution system can be controlled by Demand Side Management (DSM) strategy. In the proposed study, the load curve of Low-voltage Transformers (LVTs) is made to be flatten, on satisfying the requirement of charging PHEV at given time to the required level. The proposed problem statement is formulated as convex optimization problem, and then the random arrival of PHEV is handled by introducing the moving horizon strategy. Based on this, the PHEV are being disconnected from the LVTs beyond their respective exit times. Such that the demand curve of the LVTs is flattened. This problem is solved using MATLAB and the power demand curves of the LVTs, power curves of the PHEVs and non- PHEV load are compared over a time of 24 hours to show that the power curve is flattened with the penetration of PHEV.

With the improved awareness of negative environmental impact from traditional automobile fuel consumption and the fluctuating increase of gas price, fuel demand and supply in the transportation sector and strategies of securing it has gained governmental and public attentions. Plug-in hybrid electric vehicles (PHEV), as an alternative to the conventional vehicles, become appealing. A PHEV is a hybrid electric vehicle with sufficient battery to support its daily travel and an internal combustion engine to allow overtime travel, which is expected to help reduce obnoxious chemicals emission and oil dependency caused by the usage of conventional vehicles. In this paper, we employed the National Energy Modeling System (NEMS) developed by the Energy Information Administration (EIA) to study the impact from PHEV penetration into the transportation sector on the future electric generation capacity expansion in the United States. Arbitrary penetration trajectories of PHEVs for light duty vehicles (i.e., cars, pickup trucks, SUVs) were postulated that started penetration in 2006 and projected 110 million PHEV vehicles till 2030. It shows that large volume of PHEV penetration can reduce most negative impacts.

As a new form of distributed energy resources, massive plug-in hybrid electric vehicles (PHEVs) could affect the power distribution system adequacy considering their intermittent charging loads and the load recovery ability during system outages. This paper proposes a comprehensive framework for adequacy evaluation of power distribution networks with PHEVs penetration. A condition-dependent outage model is used in this paper to obtain the time sequential failure rate of the transformer. Also, a business model for the PHEVs is developed to encourage the PHEV owners to charge their vehicles in such a way that the distribution system adequacy is enhanced. Based on this model, a smart charging algorithm is proposed for the PHEVs to minimize their charging cost and enhance the adequacy of the distribution network at the same time. Various simulation studies are carried out to verify the effectiveness of the proposed smart charging approach. The simulation results show that the proposed approach is effective in enhancing both the adequacy of the distribution network and economic profits of PHEVs.