Piampoom Sarikprueck

Design optimization of PHEV charging station

Plug-in hybrid electric vehicle is one of the most feasible approaches towards significantly lowering the consumption of oil and improve fuel economy with todays 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.

Medium-term operation for an industrial customer considering demand side management and risk management

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.

Developing important renewable energies in Thailand

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 Thailands 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.

A Linear Program for System-Level Control of Regional PHEV Charging Stations

This paper studies dynamic control of a system of plug-in hybrid electric vehicle (PHEV) charging stations. A finite horizon stochastic program is presented. Based upon the 15-min 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-min 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. A stochastic program is formulated, and the mean value problem as a deterministic linear program is solved. Potential strategies are presented to provide insight into the behavior of the system.

DESIGNING A SYSTEM OF PLUG-IN HYBRID ELECTRIC VEHICLE CHARGING STATIONS

In this paper we present a two-step approach for the design of a system of Plug-in Hybrid Electric Vehicle (PHEV) charging stations. Our approach consists of a simulation model and a mathematical model. The simulation model formulates the charging stations ability to meet charging demand by using discrete event simulation. The mathematical model formulates the design decisions made when designing the charging stations, i.e. locations and configurations of charging stations, using the compromise Decision Support Problem (cDSP). Waiting time, service time, number of slots (chargers) and demand are key inputs for the simulation model. Output of the simulation model, which is the service level of the charging stations, is used as an input for the mathematical model. By compromising between maximizing the service level, maximizing the demand coverage, minimizing the installation cost for slots and minimizing distance between charging stations and demand nodes, design decisions are taken in the mathematical model. Our focus in this paper is on the method which is widely applicable. However the approach is presented and evaluated for a data set from Dallas County, Texas.

Bounds for optimal control of a regional plug-in electric vehicle charging station system

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.

Financial opportunities for LSE under scarcity price environment

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.

Novel hybrid market price forecasting method with data clustering techniques for EV charging station application

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.

Effect of additives on fuel properties and emission characteristics of micromulsion biofuels from palm oil

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 NOx emissions below the levels of diesel and biodiesel. These encouraging results offer options of additives for biofuel applications.

An effective technique to improve generator-set efficiency for charging battery using IPMSG

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.