Multi-Period Optimization of Energy Demand Control for Electric Vehicles in Unbalanced Electrical Power Systems Considering the Center Load Distance of Charging Station Areas

The rise of plug-in electric vehicles (EVs) impacts the energy demand of power systems. This study employed a multi-period power flow analysis on the IEEE 123 node test system, which was optimized for the installation of 6-position EV charging stations. Temporal load shifting was utilized to control the charging intervals of electric vehicles. Non-dominated Sorting Genetic Algorithm (NSGA-II) was applied to determine the optimal locations for installing EV charging stations, considering target functions, such as total energy loss, voltage unbalance factor (VUF), and center load distance. The results showed that the center load distance resulted in the optimal charging station location in the central area of the system, different from conventional considerations. The results showed that installing the charging station in the center of the load group (case 4) increased the total energy loss and VUF compared to installing it at the root of the load group (case 3) by about 2.1134 and 1.2287%, respectively. However, EVs reduced impacts during periods of system weakness. By controlling charging intervals during off-peak times (case 6), total energy loss and VUF were decreased by 4.7070 and 5.6896%, respectively, which effectively reduced energy demand during peak periods

Impact of Plug-in Electric Vehicles Integrated into Power Distribution System Based on Voltage-Dependent Power Flow Analysis

This paper proposes the impact of plug-in electric vehicles (PEVs) integrated into a power distribution system based on voltage-dependent control. The gasolinegate situation has many people turning to electric vehicles as a more environmentally friendly option, especially in smart community areas. The advantage of PEVs is modern vehicles that can use several types of fuel cells and batteries as energy sources. The proposed PEVs model was developed as a static load model in power distribution systems under balanced load conditions. The power flow analysis was determined by using certain parameters of the proposed electrical network. The main research objective was to determine the voltage magnitude profiles, the load voltage deviation, and total power losses of the electrical power system by using the new proposed methodology. Furthermore, it investigated the effects of the constant power load, the constant current load, the constant impedance load, and the plug-in electric vehicles load model. The IEEE 33 bus system was selected as the test system. The proposed methodology assigned the balanced load types in a steady state condition and used the new methodology to solve the power flow problem. The simulation results showed that increasing the plug-in electric vehicles load had an impact on the grids when compared with the other four load types. The lowest increased value for the plug-in electric vehicles load had an effect on the load voltage deviation (0.062), the total active power loss (120 kW) and the total reactive power loss (80 kVar), respectively. Therefore, this study verified that the load of PEVs can affect the electrical power system according to the time charging and charger position. Therefore, future work could examine the difference caused when PEVs are attached to the electrical power system by means of the conventional or complex load type

Vertical Transportation System Power Usage: Behavioural Case Study of Regulated Buildings in Bangkok

Sustainable urban development worldwide is crucial for the development of living spaces in high-rise buildings and infrastructures, which leads to the inevitability of increased energy consumption and demand of vertical transportation systems. The evaluation of the energy consumption of transportation systems is needed to verify and analyse the power usage related to traffic demands and patterns. In addition, efficient vertical transportation systems are central to the formulation of more sustainable cities. Therefore, this trend represents a substantial portion of the overall energy consumption of the building types. The benchmarking of the energy needs of the vertical transportation systems in five different building types via the comparison of granular load profile patterns (in conjunction with population densities) to the energy consumed was conducted, and it will be used to infer some impactful design strategies for the future. This study demonstrated a systematic approach to determine the power usage patterns in vertical transportation systems by actual measurement and traffic data collection from elevator monitoring. This may be used to develop a prediction for other cases in different types of installed vertical transportation systems. Therefore, the power usage of the vertical transportation systems can be used to determine the correlation between energy consumption and load pattern based on building characteristics and the overall energy consumption of each presented system

Optimal DG sizing and location in modern power grids using PEVs load demand probability

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

Optimal Battery Energy Storage System Based on VAR Control Strategies Using Particle Swarm Optimization for Power Distribution System

We designed a battery energy storage system (BESS) based on the symmetrical concept where the required control is by the symmetrical technique known as volt/var control. The integration of BESS into the conventional distribution has significantly impacted energy consumption over the past year. Load demand probability was used to investigate optimal sizing and location of BESS in an electrical power system. The open electric power distribution system simulator (OpenDSS) was interfaced with MATLAB m-file scripts and presented by using time series analysis with load demand. The optimal BESS solution was adapted by using a genetic algorithm (GA) optimization technique and particle swarm optimization (PSO). The simulation results showed that the BESS was directly connected to the power grid with GA and PSO, and it was observed that BESS sizing also varied for these two values of 1539 kW and 1000 kW, respectively. The merit of those values is the power figure of the system, which is necessary for installation. Therefore, optimal sizing and location of the BESS are helpful to reduce the impact from the load demand to the total system loss and levelling of the energy demand from the power system network. The integration of the BESS can be applied to improve grid stability and store surplus energy very well. The grid increased the stability of the power system and reduced the impact from the large scale of BESS penetration

Vertical Transportation System Power Usage: Behavioural Case Study of Regulated Buildings in Bangkok

Sustainable urban development worldwide is crucial for the development of living spaces in high-rise buildings and infrastructures, which leads to the inevitability of increased energy consumption and demand of vertical transportation systems. The evaluation of the energy consumption of transportation systems is needed to verify and analyse the power usage related to traffic demands and patterns. In addition, efficient vertical transportation systems are central to the formulation of more sustainable cities. Therefore, this trend represents a substantial portion of the overall energy consumption of the building types. The benchmarking of the energy needs of the vertical transportation systems in five different building types via the comparison of granular load profile patterns (in conjunction with population densities) to the energy consumed was conducted, and it will be used to infer some impactful design strategies for the future. This study demonstrated a systematic approach to determine the power usage patterns in vertical transportation systems by actual measurement and traffic data collection from elevator monitoring. This may be used to develop a prediction for other cases in different types of installed vertical transportation systems. Therefore, the power usage of the vertical transportation systems can be used to determine the correlation between energy consumption and load pattern based on building characteristics and the overall energy consumption of each presented system

Optimal Placement of Distributed Photovoltaic Systems and Electric Vehicle Charging Stations Using Metaheuristic Optimization Techniques

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

Optimal sizing and location of the charging station for plug-in electric vehicles using the particle swarm optimization technique

This paper had presented the optimal battery charging station for Plug-in Electric Vehicles (PEVs) in the electrical power system, by using the Particle Swarm Optimization technique (PSO). The PEVs are represented using the Voltage Source Converter (VSC) as the group of PEVs that were installed in the charging station. The electrical power system was analyzed in a steady state, by using the IEEE 30 bus test system. The installed PEVs site in the power system aimed to minimize system power loss. The results were shown that the optimal site is bus N0.30. The total power loss and percentage of power loss reduction were 0.0994 p.u. and 6.4%, respectively

Impact of fast charging on lithium-ion battery in electric vehicle application

This paper presents the impact of fast charging on Lithium-ion batteries in electric vehicles (EVs) application. This impact occurred the charging accident based on chemical gas components of the Lithium-ion battery. According to the lithium-ion battery is popular used to be the primary energy for electric driving destination target and defined in high volume per energy density. The scheme of the charging station is used to find the gas volume of the lithium-ion battery component from the EVs. ALOHA software was applied to analyze the charging accident. The gas releases from the lithium-ion battery were selected to analyze the impact on the surrounding area and the environment of the fast charging station. The fast charging units are divided into 3 scenarios with 1, 5 or 10 EVs for the charging process. The simulation results for the Carbon monoxide (CO) showed the most impact to the thermal radiation treat zone, the flammable treat zone of 10 m and 54 m. Meanwhile, the toxic treat zone from the smoke generation showed the large scale of the free space area and concerned the wind flow direction. Therefore, the impact from the EVs during charging accidents needs to be studied to provide vital information for emergency situations and to advise on the preparation of optimal conditions for EV users and participants

Design of solar-powered charging station for electric vehicles in power distribution system

This paper presents an analysis of installation of solar powered charging station in power distribution system. The 9-bus primary distribution system was used to test the power flow using the Newton Raphson method, comparing the size and voltage angle with the DIgSILENT program. The 3-bus test system is used to analyze the installation of the charging station for a solar electric vehicle in distribution system. The power flow was analyzed by determining the solar energy source and the electric vehicle load in time series. The model of solar charge stations and the size of electric vehicles are determined at 100 kWh. The results of the power flow test using the Newton Raphson method found that the test results were in error by comparing the size and voltage angle with the DIgSILENT program that maximum value of at Bus no. 9 is 2.04% and -3.91%, respectively. While testing the analysis of solar powered charging station, it is found that the charging time will affect the power loss of the system and the maximum energy demand. The simulation, it is found that the suitable solar panel size must be greater than 7.39 kWp. Therefore, the electrical system design study using the PyPSA program to analyze of the solar powered charging station in distribution system shows the application in the design of the electrical system to support the load expansion from of electric vehicles and solar powered charging stations in the future