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This article proposes a comprehensive technical review on advanced approaches for electric vehicle charging demand management. The survey is divided into two parts. Part I consists of two major sections. In the former section, the applications for participating in electric power markets, including energy market and ancillary service market, are reviewed. The applications for enhancing renewable energy integration as single energy and multienergy sources are reviewed in the latter section.

This article proposes a comprehensive technical review on advanced approaches for electric vehicle charging demand management. The survey is divided in two parts. The part II of this survey has two major sections that summarize the charging demand management applications on transportation system coordination and infrastructure planning. The transportation system coordination section discusses the coordinated planning and operation of transportation network and power network, while the infrastructure planning section reviews the charging infrastructure planning and coplanning applications.

this paper presents a novel technique to define proper locations for distributed renewable generation (DRG) integration to minimize contingency impact. The contingency (n1) analysis and the repeated power flow technique are used to investigate the Critical Voltage Buses (CVB). Then priority rankings are defined in two considerations, the number of impacts and voltage level at the CVB. The POWERWORLD simulator is used in this study to analyze the modified Thailand power system during peak load period. The results show that the proposed technique can identify the priority CVBs, which are the proper locations for DRG installation. With more DRG installations at the CVBs, the numbers of CVB, generator capacity and power system loss can be decreased and the load demand can be increased. This proposed novel technique can be applied effectively for the power system security enhancement.

In order to support the increasing penetration of plug-in electric vehicle (PEV) users, a novel regional PEV charging station system with dc level 3 fast charging is proposed in this paper. To promote sustainable development, the proposed system is designed to be equipped with a distributed energy storage system charged by wind generation, solar photovoltaic (PV) generation, and electricity from the power grid, which can simultaneously charge multiple PEVs. The objective of the proposed system is to minimize operational cost. Wind/solar PV generation and electricity market price are input state variables in this problem, and are predicted by support vector regression (SVR). The uncertainties of the SVR models are analyzed using a martingale model forecast evolution. Finally, bounds of the optimal operational cost in this problem are evaluated with two stochastic measures, which can be solved using the expected value problem and the wait-and-see solution. Bounds from experiments simulating models of the Dallas–Fort Worth metroplex show that the largest uncertainty in the system occurs during weekdays in the summer.

The papers in this special issue focus on advanced approaches and applications for electric vehicle charging. Due to the concern of carbon emission, pollution, and oil depletion, transportation electrification has drawn more and more attention in both industrial and academic sectors. As one type of rapidly increasing power demands, electric vehicle (EV) charging loads have unique characteristics, as they are typically equipped with battery storage and moving capability. With those energy storage equipment, EV charging loads obtain flexibility in the temporal dimension by strategically schedule their charging and discharging states in different time periods. Meanwhile, with the mobility feature of vehicles, EV charging demands get flexibility in the spatial dimension by optimally finding their charging and discharging locations. Considering the unique characteristics of charging behaviors, EV charging demands can be utilized as flexible demand resources to enhance the operation and planning efficiency for both power system and transportation system.

Electrical machine efficiency improvement has been paid more attention over the past years. Using Permanent Magnets (PMs) in various modern electrical machines is one of the most favorable approaches to increase machine efficiency. However, PMs still have a significant disadvantage that is the demagnetization issue. Therefore, these demagnetized PMs need to magnetize for regaining their initial capability considering the demagnetization level. Therefore, first of all, this paper proposes the demagnetization behavior of NdFeB PMs due to thermal effect. Then, these PMs are magnetized using a proposed simple magnetizing technique considering various demagnetization conditions. Finally, the results indicate that the most complete demagnetized PMs are able to significantly recover to their initial capability.

Among various types of electric motor, selecting an induction motor for electric vehicle (EV) is still being discussed. Efficiency and weight of induction motor are two significant disadvantages compared to other motors. To solve these two concerns, this paper proposes a study to effectively improve induction motor efficiency and its power density. The stator winding of proposed three phase induction motor is rewound by decreasing number of turn and by increasing stator coil size. This proposed motor is suitable for EV driven by inverter in high speed operation which the rated power of this motor is approximately two times more than that of its original. As the results, the motor efficiency is improved since its total loss is slightly increased. In addition, as an increasing twice of rated motor power approximately with the same motor weight, the power density of the proposed motor is also enhanced. Three sizes of three-phase inductor motor including 0.75, 2.25, and 3.7 kW are tested to illustrate the proposed approach in this study.

In order to use a generator-set for charging battery of the stand-alone hybrid renewable power generation system in remote area efficiently, the generator-set needs to be operated in various speeds considering battery voltage. The conventional field excited synchronous generator, which is used to convert mechanical power to electrical power from internal combustion engine (ICE), cannot handle this important function. Therefore, this paper presents an effective technique to improve the efficiency of the generator-set using interior permanent magnet synchronous generator (IPMSG). Two designed IPMSGs including 6 cm and 8 cm stator core lengths, which are be able to operate in the maximum efficiency speed region of the ICE, are proposed for selecting the appropriate generator in this study. Finally, the testing results show that the proposed 8 cm stator core length IPMSG with suitable operation can be effectively and efficiently used as the generator-set for charging the 48 V battery systems.

An electrical energy monitoring system helps improve the security and reliability of a hospital operating system which can also indirectly enhance energy efficiency itself. However, a large hospital has a complex electrical system which leads to the big data issue from the installed monitoring system in this size-scaled hospital. Therefore, this paper proposes K-mean clustering technique, which is one of the effective data mining techniques, to analyze the big data from the electrical energy monitoring system in hospital. The case study is a large hospital with 200 in-patient beds in Thailand. Without loss of generality, electrical load profile is used for analyzing instead of using the electrical energy. Finally, the proposed data clustering technique is able to characterize electrical load profiles effectively for each hospital floor. This technique also identify the abnormality of these characterized electrical load profiles in various scenarios which hospital system operators can use them to consider the security, reliability, and energy efficiency of their operating systems.

Microemulsiflcation is one of the novel techniques to reduce viscosity of vegetable oils to avoid durability problems in diesel engines. Microemulsion biofuels are transparent, thermodynamically stable, and single-phase mixtures of vegetable oils and ethanol in the presence of surfactants and co-surfactants. Additives have also been included in microemulsion biofuel formulations to improve their stability and fuel properties; however, there is limited research on the effect of additives on emission characteristics of microemulsion biofuels. In this study, microemulsion biofuels were formulated from palm oil/diesel blend (1:1 v/v), ethanol, surfactant, and co-surfactants. Five additives, ethylene glycol butyl ether (EGBE), diethylene glycol ethyl ether (DEGEE), propylene glycol ethyl ether (PGEE), dipropylene glycol methyl ether (DPGME), and ethyl acetate (EA), were used to study the effect of additives on phase behaviors, fuel properties, and emission characteristics. The results showed that studied additives could improve some fuel properties of microemulsion biofuels with negligible effect on phase stability. Additionally, it was found that carbon monoxide (CO) emissions from microemulsion biofuels with DEGEE and EA, and nitrogen oxide (NOx) emissions from microemulsion biofuels with all additives were lower than those from diesel and biodiesel. Therefore, DEGEE and EA can be considered as promising additives for microemulsion biofuel formulations, which can improve fuel properties as well as can significantly reduce CO and NO x emissions below the levels of diesel and biodiesel. These encouraging results offer options of additives for biofuel applications.

This research studies dynamic control of a system of plug-in hybrid electric vehicle (PHEV) charging stations. A finite horizon dynamic problem is presented. Based upon the 15-minute updated period of the electricity market price, the objective function is to maximize profit, which is the revenue benefit from selling back to the grid and the charging of the vehicles minus the cost of buying electricity from the grid. The state variables in each 15-minute time period consist of the total wind purchased from the system, solar power generation at each charging station, total demand at each station, and nodal market price at stations' location. This mean value problem is formulated as a deterministic linear program and solved. Potential strategies are presented to provide insight into the behavior of the system.

In addition to providing charging service, an electric vehicle charging station equipped with a distributed energy storage system can also participate in the deregulated market to optimize the cost of operation. To support this function, it is necessary to achieve sufficient accuracy on the forecasting of energy resources and market prices. The deregulated market price prediction presents challenges since the occurrence and magnitude of the price spikes are difficult to estimate. This paper proposes a hybrid method for very short term market price forecasting to improve prediction accuracy on both nonspike and spike wholesale market prices. First, support vector classification is carried out to predict spike price occurrence, and support vector regression is used to forecast the magnitude for both nonspike and spike market prices. Additionally, three clustering techniques including classification and regression trees, K-means, and stratification methods are introduced to mitigate high error spike magnitude estimation. The performance of the proposed hybrid method is validated with the Electric Reliability Commission of Texas wholesale market price. The results from the proposed method show a significant improvement over typical approaches.

To compensate for “missing money”, the Electric Reliability Council of Texas (ERCOT) has continued to increase the offer price cap since the nodal market opened in 2010. This provides financial opportunities for Load Serving Entities (LSE) to deploy demand response. This paper proposes a quasi-real time incentive based demand response (IBDR) scheme for LSE to take advantage of scarcity prices. Combined with the scheme is a hybrid spike price forecasting model also presented by this paper. Numerical case studies are conducted based on the realistic ERCOT load zone price data. The results illustrate the financial benefits achieved by this proposed IBDR scheme.

Plug-in hybrid electric vehicle is one of the most feasible approaches towards significantly lowering the consumption of oil and improve fuel economy with today's existing technology. The paper focuses the design optimization of the PHEV charging station with battery storage units. The battery units conserve the low price clean energy and discharge when demanded. The on-site installed photovoltaic (PV) and off-site (virtual) wind farm are the main supply of charging station to charge the battery units. The grid electricity plays an auxiliary role in the station when the renewable sources and the demands are unbalance or the trading provides economic benefits to the charging station. The paper examines the PHEVs charging trend, and proposes an optimized operation for the battery storage schedule, and strategy of power trading to minimize the cost of station. Analysis based on level of forecast uncertainty is utilized to evaluate the optimization.

In order to enhance energy security while reducing environmental impact, the government of Thailand has established policy to develop domestic energy resources to increase energy stability and to sufficiently meet the future demand. Aiming to achieve a 20% share of renewable energy by 2022 according to the 15-Year Renewable Energy Development Plan (REDP), it is necessary to work out a plan to explore new renewable energy resources and technologies, encourage more investments from both public and private sectors, and promote entrepreneurships to transfer laboratory work to commercial production in manufacturing scale. In this paper, first the situation of Thailand's energy is presented. The energy plan in next 15 years will also be mentioned. Next, the potential of renewable energy resources are also discussed. Finally, the study of the technology roadmap of the renewable energy industry in Thailand is proposed to emphasize the importance of the present and upcoming technology and product trend.

This paper presents a phase to ground leakage current in 3-phase induction motor caused by switching voltage source. The phase to ground leakage current of induction motor is effected by impedance of motor insulator which varied with switching frequency from voltage source. So various testing voltage sources included variable frequency square wave, variable frequency sine wave and dc source are supplied to induction motor in order to find out the impedance of motor insulator. Finally, test results of 1hp, 3hp and 5hp induction motors are shown and carried out to evaluate the phase to ground insulation motor impedance model.

Under a deregulated market environment, industrial customers can participate in multiple markets with different time ranges to purchase electricity. Transactions in different markets make the industrial customer involve in different levels of cost uncertainties and risks. To solve this energy procurement portfolio problem, a medium-term operation model is proposed. The risk term is measured and managed by a mean-variance approach. The uncertainties in the proposed model are characterized by stochastic day-ahead and real-time prices generated based on ERCOT historical data. A sample case study is provided to illustrate and verify the proposed model.