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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.

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.

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.

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.