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![Average commuting distance in Canada and Ontario [12].](profile/A-Elkamel/publication/265050835/figure/tbl2/AS:670449184800770@1536858950142/Average-commuting-distance-in-Canada-and-Ontario-12.png)
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![Table 3 . Average commuting distance in Canada and Ontario [12].](profile/A-Elkamel/publication/265050835/figure/tbl2/AS:670449184800770@1536858950142/Average-commuting-distance-in-Canada-and-Ontario-12_Q320.jpg)
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. T...
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Short-term electricity load forecasting is critical and challenging for scheduling operations and production planning in modern power management systems due to stochastic characteristics of electricity load data. Current forecasting models mainly focus on adapting to various load data to improve the accuracy of the forecasting. However, these model...
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... EV load modeling is defined in many load types, such as power constant (P), current constant (I), polynomial load or ZIP load, and voltage source converter modeling [17]. Large-scale FCSs are affected by the electrical power system in many ways, so the grid needs to be improved [18]. It can be adapted to solve the electrical power system's problem, as revealed in many research areas. ...
In this study, the concept of symmetry is introduced by finding the optimal state of a power system. An electric vehicle type load is present, where the supply stores’ electrical energy causes an imbalance in the system. The optimal conditions are related by adjusting the voltage of the bus location. The key variables are the load voltage deviation (LVD), the variation of the load and the power, and the sizing of the distributed photovoltaic (DPV), which are added to the system for power stability. Here, a method to optimize the fast-charging stations (FCSs) and DPV is presented using an optimization technique comparison. The system tests the distribution line according to the bus grouping in the IEEE 33 bus system. This research presents a hypothesis to solve the problem of the voltage level in the system using metaheuristic algorithms: the cuckoo search algorithm (CSA), genetic algorithm (GA), and simulated annealing algorithm (SAA) are used to determine the optimal position for DPV deployment in the grid with the FCSs. The LVD, computation time, and total power loss for each iteration are compared. The voltage dependence power flow is applied using the backward/forward sweep method (BFS). The LVD is applied to define the objective function of the optimization techniques. The simulation results show that the SAA showed the lowest mean computation time, followed by the GA and the CSA. A possible location of the DPV is bus no. 6 for FCSs with high penetration levels, and the best FCS locations can be found with the GA, with the best percentage of best hit counter on buses no. 2, 3, 13, 14, 28, 15, and 27. Therefore, FCSs can be managed and handled in optimal conditions, and this work supports future FCS expansion.
... After the analyses of EV electrical behavior, different studies (Ahmadi et al. 2012;Deb et al. 2018;Galiveeti et al. 2018;Gong et al. 2018) claim that a massive introduction of EV would create negative impacts on the grid, leading to new power network challenges (Clairand et al. 2018). With this problem in mind, several studies have lately proposed different solutions to minimize the impact of EV on the grid (Wang and Chen 2019). ...
The alarming increase in the average temperature of the planet due to the massive emission of greenhouse gases has stimulated the introduction of electric vehicles (EV), given transport sector is responsible for more than 25% of the total global CO2 emissions. EV penetration will substantially increase electricity demand and, therefore, an optimization of the EV recharging scenario is needed to make full use of the existing electricity generation system without upgrading requirements. In this paper, a methodology based on the use of the temporal valleys in the daily electricity demand is developed for EV recharge, avoiding the peak demand hours to minimize the impact on the grid. The methodology assumes three different strategies for the recharge activities: home, public buildings, and electrical stations. It has been applied to the case of Spain in the year 2030, assuming three different scenarios for the growth of the total fleet: low, medium, and high. For each of them, three different levels for the EV penetration by the year 2030 are considered: 25%, 50%, and 75%, respectively. Only light electric vehicles (LEV), cars and motorcycles, are taken into account given the fact that batteries are not yet able to provide the full autonomy desired by heavy vehicles. Moreover, heavy vehicles have different travel uses that should be separately considered. Results for the fraction of the total recharge to be made in each of the different recharge modes are deduced with indication of the time intervals to be used in each of them. For the higher penetration scenario, 75% of the total park, an almost flat electricity demand curve is obtained. Studies are made for working days and for non-working days.
... From a power system operation point of view, plug in electrical vehicles (EVs), can play a similar role, if seen as distributed energy storage devices. Due to the increasing commercial utilization of EVs since the end of 2010, electricity demand has subsequently increased [6][7][8][9][10][11]. The energy sector must anticipate and prepare for this extra demand and implement optimal planning for electricity producer. ...
... The energy sector must anticipate and prepare for this extra demand and implement optimal planning for electricity producer. Furthermore, similar to the demand side, the random behavior of EVs will bring significant uncertainties to the power system and impose new complexities and challenges to the power systems studies [7,[12][13][14][15]. Determining and formulating the charging and discharging of EVs in the operation period (for example, in the next 24 h) are the main challenges for the power system operator. ...
... Thus, multitypes of distributed generator (DG) have been used to represent each energy source and to solve the problems of the proposed system [3]. Therefore, the impact of the emergence of PEVs has created interest in the study of ways to reduce the impact and provide optimal conditions to the power system networks [4][5]. Many researchers have studied the impact of PEVs on the power system networks. ...
... The element and component consists of PDElement, PCElement, controls, meter and general. Direct connection shared libraries (DLL) is an OpenDSS COM interface that consists of classes, properties and methods.Meanwhile, the power flow solution of OpenDSS is defined based on the nodal admittance formulation in the nonlinear system admittance equation, as shown in Eq. (5). ...
The integration of plug-in electric vehicles (PEVs) to the conventional distribution system has had a major impact upon consumption of energy in the past year. This paper presents optimal distributed generator (DG) sizing and location in the power system using PEVs load demand probability. The MATLAB m-file scripts and OpenDSS were applied to solve the proposed study by varying the percentage penetration level of PEVs. A genetic algorithm optimization technique was used to find the best solution of DG installation. The simulation results showed that the PEVs were directly connected to the power grid with 100 PEVs (13.84%), 200 PEVs (27.68%) and 500 PEVs (69.19%), respectively. It was found that the DG sizing also varied with 1.773 MW, 1.663 MW and 1.996 MW, respectively. While the position of the DG also changes according to the sizing of DG. The position of DG was installed at bus No.738, bus No.741 and bus No.711, respectively. Therefore, the optimal DG placement helped to improve and reduce the total line loss and total energy demand from the power grid. The grid increased the power system stability and reduced the impact from the large scale of PEV penetration.
... This generates zero emissions while driving, and electricity production causes its only footprint. However, a massive introduction to the grid could create negative impacts [5][6][7][8], and create new challenges for the power systems [9,10]. In particular, a massive introduction of EVs in distribution networks that have a high penetration of renewable electricity generation is even more complicated because of some issues, such as impacts on the performance of parking lot operators [11], power systems security [12], and planning of RES Sources [13]. ...
Inhabited islands depend primarily on fossil fuels for electricity generation and they also present frequently a vehicle fleet, which result in a significant environmental problem. To address this, several governments are investing in the integration of Renewable Energy Sources (RESs) and Electric Vehicles (EVs), but the combined integration of them creates challenges to the operation of these isolated grid systems. Thus, the aim of this paper is to propose an Electric Vehicle charging strategy considering high penetration of RES. The methodology proposes taxing CO2 emissions based on high pricing when the electricity is mostly generated by fossil fuels, and low pricing when there is a RES power excess. The Smart charging methodology for EV optimizes the total costs. Nine scenarios with different installed capacity of solar and wind power generation are evaluated and compared to cases of uncoordinated charging. The methodology was simulated in the Galapagos Islands, which is an archipelago of Ecuador, and recognized by the United Nations Educational, Scientific and Cultural Organization (UNESCO) as both aWorld Heritage site and a biosphere reserve. Simulations results demonstrate that the EV aggregator could reduce costs: 7.9% for a case of 5 MW installed capacity (wind and PV each), and 7% for a case of 10 MW installed (wind and PV each). Moreover, the use of excess of RES power for EV charging will considerably reduce CO2 emissions
... 531). The technologies and policies for alternative fuels and vehicles are discussed in [41] and [42]. ...
... It has the potential to reduce Green House Gases (GHG) emissions and carbon footprint [38][39][40]. PHEV facilitates flexibility as well as economy in fuel usage [41][42][43]. WSN can be used to communicate PHEV statistics to upstream network layers for operation and control of Smart Grid components. This information will be online available to various stakeholders through a web of sensor nodes [44][45][46][47][48]. ...
The existing power grid is going through a massive transformation. Smart grid technology is a radical approach for improvisation in prevailing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of Smart grid network. Smart grid technology is characterized by full duplex communication, automatic metering infrastructure, renewable energy integration, distribution automation and complete monitoring and control of entire power grid. Wireless sensor networks (WSNs) are small micro electrical mechanical systems that are deployed to collect and communicate the data from surroundings. WSNs can be used for monitoring and control of smart grid assets. Security of wireless sensor based communication network is a major concern for researchers and developers. The limited processing capabilities of wireless sensor networks make them more vulnerable to cyber-attacks. The countermeasures against cyber-attacks must be less complex with an ability to offer confidentiality, data readiness and integrity. The address oriented design and development approach for usual communication network requires a paradigm shift to design data oriented WSN architecture. WSN security is an inevitable part of smart grid cyber security. This paper is expected to serve as a comprehensive assessment and analysis of communication standards, cyber security issues and solutions for WSN based smart grid infrastructure.
... Besides dependence on profile type and weather zone, changes depending on the time scale considered: the hour within a given day, business or non-working day, month and season [8]. ERCOT data measured at a cluster of households located in north Texas during four months in 2014 (January, April, July and October) were considered in order to cover all weather scenarios [17]. ...
Replacing a portion of current light duty vehicles (LDV) with plug-in hybrid electric vehicles (PHEVs) offers the possibility to reduce the dependence on petroleum fuels together with environmental and economic benefits. The charging activity of PHEVs will certainly introduce new load to the power grid. In the framework of the development of a smarter grid, the primary focus of the present study is to propose a model for the electrical daily demand in presence of PHEVs charging. Expected PHEV demand is modeled by the PHEV charging time and the starting time of charge according to real world data. A normal distribution for starting time of charge is assumed. Several distributions for charging time are considered: uniform distribution, Gaussian with positive support, Rician distribution and a non-uniform distribution coming from driving patterns in real-world data. We generate daily demand profiles by using real-world residential profiles throughout 2014 in the presence of different expected PHEV demand models. Support vector machines (SVMs), a set of supervised machine learning models, are employed in order to find the best model to fit the data. SVMs with radial basis function (RBF) and polynomial kernels were tested. Model performances are evaluated by means of mean squared error (MSE) and mean absolute percentage error (MAPE). Best results are obtained with RBF kernel: maximum (worst) values for MSE and MAPE were about 2.89 10-8 and 0.023, respectively.
... In our previous study [5], we considered PHEVs' penetration and its impact on Ontario's electricity grid. For this purpose, long-term regression models, both linear and non-linear, of electricity load demands were forecasted for the years 2012-2030. ...
... The Ontario energy planning is optimized to minimize the value of the cost of the electricity considering the effect of PHEV charging over sixteen years (2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024)(2025)(2026)(2027)(2028)(2029)(2030). Based on our previous work [5], after PHEV penetration in Ontario, peak load demands and base load demands in December 2030 would be increased by ~13% and 4% compared to the 2013 demand. Consequently, supply is less than the peak load demand. ...
One of the main challenges for widespread penetration of plug-in hybrid electric vehicles (PHEVs) is their impact on the electricity grid. The energy sector must anticipate and prepare for this extra demand and implement long-term planning for electricity production. In this paper, the additional electricity demand on the Ontario electricity grid from charging PHEVs is incorporated into an electricity production planning model. A case study pertaining to Ontario energy planning is considered to optimize the value of the cost of the electricity over sixteen years (2014-2030). The objective function consists of the fuel costs, fixed and variable operating and maintenance costs, capital costs for new power plants, and the retrofit costs of existing power plants. Five different case studies are performed with different PHEVs penetration rates, types of new power plants, and CO2 emission constraints. Among all the cases studied, the one requiring the most new capacity, (~8748 MW), is assuming the base case with 6% reduction in CO2 in year 2018 and high PHEV penetration. The next highest one is the base case, plus considering doubled NG prices, PHEV medium penetration rate and no CO2 emissions reduction target with an increase of 34.78% in the total installed capacity in 2030. Furthermore, optimization results indicate that by not utilizing coal power stations the CO2 emissions are the lowest: ~500 tonnes compared to ~900 tonnes when coal is permitted.
... There are many researches concerning the estimation of EV load demand (including the possible discharging load under incentive policies) and charging strategy of the EV. Works on these topics usually estimated the number of EV in the first step then estimated their electricity demand and the new peak load and base load [5][6][7]. The power demand of EV relates to many different factors like battery, charging facility and user habits etc. Ref. [8] analyzed various factors related to the power demand of EV and established a statistical model of the power demand. ...
The moment when Electrical Vehicle (EV) starts charging or discharging is one of the most important parameters in estimating the impact of EV load on the grid. In this paper, a decision-making problem of determining the start time of charging and discharging during allowed period is proposed and studied under the uncertainty of real-time price. Prospect theory is utilized in the decision-making problem of this paper for it describes a kind of decision making behaviors under uncertainty. The case study uses the parameters of Springo SGM7001EV and adopts the historical realtime locational marginal pricing (LMP) data of PJM market for scenario reduction. Prospect values are calculated for every possible start time in the allowed charging or discharging period. By comparing the calculated prospect values, the optimal decisions are obtained accordingly and the results are compared with those based on Expected Utility Theory. Results show that with different initial State-of-Charge () and different reference points, the optimal start time of charging can be the one between 12 a.m. to 3 a.m. and optimal discharging starts at 2 p.m. or 3p.m. Moreover, the decision results of Prospect Theory may differ from that of the Expected Utility Theory with the reference points changing.
Transition to electric vehicles (EVs) is already under way. EVs have demonstrated to be the most fuel economic and emission free among other propulsion technologies. EVs can have a large impact on greenhouse gases (GHGs) reduction, increase in fuel economy, and higher fuel efficiency. The main idea behind this chapter is to analyse step-by-step energy efficiency, which is one of the key factors for technology acceptance. Penetration of EVs into the vehicle fleet affects load demand as well as electricity markets. Smart charging of EVs can remove a tremendous amount of stress from the continually evolving smart grid. Effect of home charging of EVs on electricity demand has been analyzed. More recently, EVs have been looked at as distributed sources of energy, whereby they could back up the power grid during critical high demand periods. With the help of an on-board battery pack, EVs can act as distributed generators and feedback energy to the grid. However, efficiency of energy conversion could become an issue in this power flow. Hence, in this chapter stage-by-stage efficiency of vehicle-to-grid (V2G) power flow has been evaluated.
