A massive introduction of plug-in electric vehicles (PEVs) in the transportation sector would likely increase the total electricity consumption. Depending on how and when the PEVs are charged, the effects on the power demand will vary. Previous studies have shown that uncontrolled charge of PEVs can cause problems for the distribution system in some areas. This paper proposes an approach to control PEVs charging based on the charging behavior estimated from the demographical statistical data. In this approach, three different charge strategies are designed and the impacts of PEVs charging on the distribution system is assess using standard load flow calculations. A case study using the proposed approach examines the real situation in Gothenburg, a city on the west coast of Sweden. The results from the study show that the impacts on the distribution system due to PEVs vary between different areas. By controlling the charging, the impacts can be reduced but the choice of control methods must be chosen carefully. Furthermore, the results indicate that a well-developed public charging infrastructure could reduce the stress on the residential distribution systems since part of the charging can be done in commercial areas. Index Terms-Electric distribution system, electric vehicle (EV), open loop radial distribution system, plug-in electric vehicle (PEV), plug-in hybrid electric vehicle (PHEV). LIST OF NOMENCLATURES AND SYMBOLS EPSElectric power system. DSO Distribution system operator. DS Electrical distribution system. OLR-DS Open loop radial distribution system. EV Electric vehicle. PHEV Plug-in hybrid electric vehicle. PEV Plug-in electric vehicle. DG Distributed generation. DES Distributed energy storage. V2G Vehicle To grid. Maximum number of PEVs.

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In this study, we have investigated the influence of crystallinity, phase ratio and heterojunction formation on the sensing performance of ZnO/ZnFe2O4 (ZnO/ZFO) nanocomposites-based electrochemical sensors for detection of furazolidone (FZD)...

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A 3D overhead crane is an underactuated system consisting of five outputs: trolley position, bridge translation, cable length, and two cargo swings. These outputs are controlled by three actuators for cargo hoisting, trolley motion, and bridge traveling. This study proposes the use of a nonlinear controller that performs five tasks concurrently: cargo hoisting, trolley tracking, bridge motion, payload vibration suppression during transport, and cargo swing elimination at the destination. The proposed algorithm is combined with two control components: (i) partial feedback linearization, which is a precursor to controller design, to suppress cargo vibration; and (ii) sliding mode method, which provides robust control in lifting the payload and driving trolley and bridge motions against model imprecision and uncertainty. These two control mechanisms are successfully merged into a combined controller because the kinematic relationships between the state variables are made apparent in the system dynamics. Simulation and experimental results show that the proposed controller asymptotically stabilizes all system responses.COMBINED CONTROL WITH SLIDING MODE AND PFL 3373 parameter-varying model, Giua [13] designed a controller-observer with state-feedback stabilization for time-varying systems. Park [14] proposed a nonlinear controller for a 2D container crane to suppress cargo swing, track the trolley, and lift the cargo. FLC was applied to the trolley and cargo hoisting dynamics to obtain one controller component; the antiswing component was obtained with an energy-based nonlinear control design. Cho [15] proposed a scheme linearly composed of two controls: nominal proportional-derivative (PD) control designed through feedback linearization and corrective control. Cho expanded this so-called PFL controller with an adaptive component in [16].Studies on SMC for crane systems have also been published. The general theory of SMC for a class of underactuated systems was first presented by Bergeman [17], developed by Lee [18], and completed by Ashrafiuon [19] and Sankaranarayanan [20]. Focusing on overhead crane control, Karkoub [21] introduced a variable structure controller in conjunction with state feedback control andţ-synthesis control. Bartolini [22] proposed a simple control scheme based on second-order sliding modes for a payload-cart system with constant cable length. In another study by Bartolini [23], two SMC laws were constructed: a proportional-integral controller and a linear, observer-based, time-varying feedback scheme. Liu [24] combined fuzzy logic with SMC for an overhead crane based on a linearized mathematical model. Lee [25] developed an SMC controller by analyzing sliding surface stability to concurrently control cargo swing and trolley motion. With the simplest crane model wherein cable length is constant, an adaptive fuzzy SMC control was proposed by Park [26] for cargo antiswing and trolley tracking. Almutrairi [27] adopted the SMC scheme developed by Lee [25] to develop a 3D crane control system. An ...

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