A. Shahbazi; H. Moradi CheshmehBeigi; H. Abdi; M. Shahbazitabar
Abstract
Nowadays, the use of electric vehicles (EVs), in the form of distributed generation, as an appropriate solution is considered to replace combustion vehicles by reducing fuel consumption and supplying needed power. In this regard, the incorporation of EVs charging stations (EVCSs) in the power network ...
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Nowadays, the use of electric vehicles (EVs), in the form of distributed generation, as an appropriate solution is considered to replace combustion vehicles by reducing fuel consumption and supplying needed power. In this regard, the incorporation of EVs charging stations (EVCSs) in the power network can affect the distribution networks in different ways. On the other hand, the location of EVCS in distribution networks changes operational parameters includes electrical losses, and voltage deviations. Also, the probabilistic and uncertain behaviour of the loads and their daily changes can play a significant role on power distribution networks. To this end, in this paper, first, the modelling of the EVCSs affected by the behaviour of the EVs’ owner in a power distribution network is discussed. Then, the optimal location and size of EVCSs to reduce their negative effects on the network, including network losses (active and reactive) and voltage deviations are addressed in the presence of uncertain loads. The probabilistic model is investigated based on using the Monte Carlo simulation (MCS) method. The simulation results in MATLAB software environment show a 10% increase in active and reactive power losses in most hours of the day, due to increased power flow, when EVCSs are located in the optimal placement. The power losses at 24:00-7:00. when the EVs load is very low, are reduced due to decreased power flow across the lines. The results also show that if the EVCSs are not optimally located, the voltage deviation will increase by an average of 30% over a day, while by optimal placement of EVCSs, the voltage deviation increases to a maximum of 8% of the nominal value.
V.D. Juyal; S. Kakran
Abstract
Nowadays, the centralized power system is changing to a distributed system, and various energy management systems are being installed for efficient functioning. Load side management is a vital aspect of the energy management of the power network. As residential demand is growing at a high rate, domestic ...
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Nowadays, the centralized power system is changing to a distributed system, and various energy management systems are being installed for efficient functioning. Load side management is a vital aspect of the energy management of the power network. As residential demand is growing at a high rate, domestic customers play a crucial role in the successful implementation of demand response (DR) programs. This paper considers a single customer having a home energy management system (HEMS) for thermostatic and non-thermostatic characteristics-based appliances, photovoltaic panels, an electric vehicle, and a battery energy storage system. The effect of various DR strategies has been discussed. A mixed-integer linear programming-based model of a HEMS is modulated and solved to minimize the electricity consumption cost by employing a real-time price-based DR program using dynamic power import limits. An incentive-based DR program is considered for reducing the energy demand and maintaining the energy balance during peak hours, and peak pricing-based dynamic power import limiting DR programs are included for load shaping. The effect of load shaping on the peak to average ratio is also discussed in different scenarios. Finally, the total electricity price is calculated and analyzed by considering other test cases based on the inclusion/rejection of the mentioned DR programs.
M.A. Baherifard; R. Kazemzadeh; A.S. Yazdankhah; M. Marzband
Abstract
With the development of electrical network infrastructure and the emergence of concepts such as demand response and using electric vehicles for purposes other than transportation, knowing the behavioral patterns of network technical specifications to manage electrical systems has become very important ...
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With the development of electrical network infrastructure and the emergence of concepts such as demand response and using electric vehicles for purposes other than transportation, knowing the behavioral patterns of network technical specifications to manage electrical systems has become very important optimally. One of the critical parameters in the electrical system management is the distribution network imbalance. There are several ways to improve and control network imbalances. One of these ways is to detect the behavior of bus imbalance profiles in the network using data analysis. In the past, data analysis was performed for large environments such as states and countries. However, after the emergence of smart grids, behavioral study and recognition of these patterns in small-scale environments has found a fundamental and essential role in the deep management of these networks. One of the appropriate methods in identifying behavioral patterns is data mining. This paper uses the concepts of hierarchical and k-means clustering methods to identify the behavioral pattern of the imbalance index in an unbalanced distribution network. For this purpose, first, in an unbalanced network without the electric vehicle parking, the imbalance profile for all busses is estimated. Then, by applying the penetration coefficient of 25% and 75% for electric vehicles in the network, charging\discharging effects on the imbalance profile is determined. Then, by determining the target cluster and using demand response, the imbalance index is improved. This method reduces the number of busses competing in demand response programs. Next, using the concept of classification, a decision tree is constructed to minimize metering time.
M. Shadnam Zarbil; A. Vahedi
Abstract
Due to the presence of power electronic converters in electric vehicle battery chargers, the electrical power drawn from the distribution system has severe distortions which pose many problems to the power quality. Herein, the impact of chargers in terms of indicators, e.g., penetration level, battery ...
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Due to the presence of power electronic converters in electric vehicle battery chargers, the electrical power drawn from the distribution system has severe distortions which pose many problems to the power quality. Herein, the impact of chargers in terms of indicators, e.g., penetration level, battery state of charge, type of charging stations, the time of connection of chargers to the network, and the location of charging stations was comprehensively studied on a sample distribution network. The effect of these chargers was investigated based on power quality parameters, e.g., total harmonic distortion (THD) and voltage profile, and the effect of each indicator on these parameters was determined. To minimize the effects of the chargers, an IEEE 33-bus distribution sample network was optimized with the objective functions of voltage drop and THD. Based on this optimization algorithm, the installation placement and the power capacity of the charging stations were obtained to achieve the lowest voltage drop and THD.
R.K. Avvari; V. Kumar D M
Abstract
In this paper, a new hybrid decomposition-based multi-objective evolutionary algorithm (MOEA) is proposed for the optimal power flow (OPF) problem including Wind, PV, and PEVs uncertainty with four conflicting objectives. The proposed multi-objective OPF (MOOPF) problem includes minimization of the total ...
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In this paper, a new hybrid decomposition-based multi-objective evolutionary algorithm (MOEA) is proposed for the optimal power flow (OPF) problem including Wind, PV, and PEVs uncertainty with four conflicting objectives. The proposed multi-objective OPF (MOOPF) problem includes minimization of the total cost (TC), total emission (TE), active power loss (APL), and voltage magnitude deviation (VMD) as objectives and a novel constraint handling method, which adaptively adds the penalty function and eliminates the parameter dependence on penalty function evaluation is deployed to handle several constraints in the MOOPF problem. In addition, summation-based sorting and improved diversified selection methods are utilized to enhance the diversity of MOEA. Further, a fuzzy min-max method is utilized to get the best-compromised values from Pareto-optimal solutions. The impact of intermittence of Wind, PV, and PEVs integration is considered for optimal cost analysis. The uncertainty associated with Wind, PV, and PEV systems are represented using probability distribution functions (PDFs) and its uncertainty cost is calculated using the Monte-Carlo simulations (MCSs). A commonly used statistical method called the ANOVA test is used for the comparative examination of several methods. To test the proposed algorithm, standard IEEE 30, 57, and 118-bus test systems were considered with different cases and the acquired results were compared with NSGA-II and MOPSO to validate the suggested algorithm's effectiveness
Smart Grid
H. Rashidizadeh-Kermani; H. R. Najafi; A. Anvari-Moghaddam; J. M. Guerrero
Abstract
Electric vehicle (EV) aggregator, as an agent between the electricity market and EV owners, participates in the future and pool market to supply EVs’ requirement. Because of the uncertain nature of pool prices and EVs’ behaviour, this paper proposed a two-stage scenario-based model to obtain ...
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Electric vehicle (EV) aggregator, as an agent between the electricity market and EV owners, participates in the future and pool market to supply EVs’ requirement. Because of the uncertain nature of pool prices and EVs’ behaviour, this paper proposed a two-stage scenario-based model to obtain optimal decision making of an EV aggregator. To deal with mentioned uncertainties, the aggregator’s risk aversion is applied using conditional value at risk (CVaR) method in the proposed model. The proposed two-stage risk-constrained decision-making problem is applied to maximize EV aggregator’s expected profit in an uncertain environment. The aggregator can participate in the future and pool market to buy the required energy of EVs and offer optimal charge/discharge prices to the EV owners. In this model, in order to assess the effects of EVs owners’ reaction to the aggregator’s offered prices on the purchases from electricity markets, a sensitivity analysis over risk factor is performed. The numerical results demonstrate that with the application of the proposed model, the aggregator can supply EVs with lower purchases from markets.
Power System Operation
A. Badri; K. Hoseinpour Lonbar
Volume 3, Issue 1 , June 2015, , Pages 34-46
Abstract
This paper proposes a novel decision making framework for an electricity retailer to procure its electric demand in a bilateral-pool market in presence of charging and discharging of electric vehicles (EVs). The operational framework is a two-stage programming model in which at the first stage, the retailer ...
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This paper proposes a novel decision making framework for an electricity retailer to procure its electric demand in a bilateral-pool market in presence of charging and discharging of electric vehicles (EVs). The operational framework is a two-stage programming model in which at the first stage, the retailer and EV aggregator do their medium-term planning. Determination of retailer's optimum selling price and the amount of energy that should be purchased from bilateral contracts are medium-term decisions that are made one month prior to real-time market. At the second stage, market agents deal with their activities in the short-term period. In this stage the retailer may modify its preliminary strategy by means of pool market option, interruptible loads (ILs), self-scheduling and EVs charging and discharging (V2G). Thus, a bi-level programming is introduced in which the upper sub-problem maximizes retailer profit, whereas the lower sub-problem minimizes the aggregated EVs charging and discharging costs. Final decision making is obtained in this stage that may be considered as a day-ahead market, keeping in mind the medium-term decisions. Due to the volatility of pool price and uncertainties associated with the consumers and EVs demand, the proposed framework is a mixed integer nonlinear stochastic optimization problem; therefore, Monte Carlo Simulation (MCS) is applied to solve it. Furthermore, a market quota curve is utilized to model the uncertainty of the rivals and obtaining retailer's actual market share. Finally, a case study is presented in order to show the capability and accuracy of the proposed framework.
M. Moazen; M. Sabahi
Volume 2, Issue 2 , December 2014, , Pages 141-150
Abstract
Using an Electric Differential (ED) in electric vehicle has many advantages such as flexibility and direct torque control of the wheels during cornering and risky maneuvers. Despite its reported successes and advantages, the ED has several problems limits its applicability, for instance, an increment ...
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Using an Electric Differential (ED) in electric vehicle has many advantages such as flexibility and direct torque control of the wheels during cornering and risky maneuvers. Despite its reported successes and advantages, the ED has several problems limits its applicability, for instance, an increment of control loops and an increase of computational effort. In this paper, an electric differential for an electric vehicle with four independent driven motors is proposed. The proposed ED is easy-to-implement and hasn’t the problems of previous EDs. This ED has been developed for four wheels steering vehicles. The synchronization action is achieved by using an improved fictitious master technique, and the Ackerman principle is used to compute an adaptive desired wheel speed. The proposed ED is simulated and the operation of the system is studied. The simulation results show that ED ensures both reliability and good path tracking.
