A comprehensive study of economic unit commitment of power systems integrating various renewable generations and plug-in electric vehicles

Yang, Zhile; Li, Kang; Niu, Qun; Xue, Yusheng

doi:10.1016/j.enconman.2016.11.050

Cited by 99 publications

(49 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, a reliable optimization algorithm [20][21][22][23] is more suitable for the trajectory planning. Furthermore, both of the overshoot and error should be considered in the optimization object, so a multiobjective optimization based on PSO [24][25][26][27][28][29] (particle swarm optimization) is adopted in this paper.…”

Section: Introductionmentioning

confidence: 99%

Online‐Offline Optimized Motion Profile for High‐Dynamic Positioning of Ultraprecision Dual Stage

2018

View full text Add to dashboard Cite

The wafer stage in dual-stage lithographic system is an air-bearing servo motion platform requiring high positioning accuracy and high transient performance. However, the residual vibration, resulting from almost zero damping, high velocity, parallel decoupling structure, and direct drive, brings about unacceptable overshoot and settling time. To suppress these unfavorable elements in high dynamic motion, a novel motion profile planning method combined with input shaping is proposed in this paper. Firstly, a trajectory named all free S-curve (AFS-curve) is derived, which has less constraints and better performance than traditional S-curve profile. Then, AFS-curve combined with a zero vibration shaper (ZV) is developed to further suppress residual vibration. Due to the very complex parameter adjustment, the online tuning may cause system oscillation that leads to damage of the precision stage. This paper, furthermore, proposes an online-offline method to optimize the parameters in the motion profile. Online step is performed to collect input and output data. Offline step includes the system model identification based on I/O data and parameter self-learning based on particle swarm optimization (PSO). The simulation and experimental results indicate that the proposed method achieves significant reduction of the positioning time and the overshoot in the dual-stage system.

show abstract

Section: Introductionmentioning

confidence: 99%

Online‐Offline Optimized Motion Profile for High‐Dynamic Positioning of Ultraprecision Dual Stage

2018

View full text Add to dashboard Cite

show abstract

“…If an existing infrastructure 142 does not permit such access, the SOCs can still be monitored by the EV supply equipment by measuring the export/import power. This power can be estimated from the measured voltage and current in accordance with the standard BS EN 61851 [69].…”

Section: Approach 1: Day-ahead Scheduling By Estimating the Parametermentioning

confidence: 99%

“…PV output for such charging approaches is predicted by statistical smoothing techniques and quantile regression, a short-term forecasting engine supported by machine learning methods, and MZS-s algorithm [142]. However, these methods require a range of meteorological and exogenous data, which are not only expensive to obtain but also prone to errors [164].…”

Section: Real-time Ev Load Dispatching Addressing Pv Output Variabilimentioning

confidence: 99%

“…Therefore, the probabilistic charging strategies are incompatible with the charging facilities where frequent real-time control of charging is required. From historical solar irradiation [97,98,115,145] From historical data [125,167] By prediction [142,168] PV panel's tilt, azimuth, and seven other relevant weight coefficients [169] Estimated from a supplied solar intensity [56,98,123,125,145,168,170] Directly obtained from the historical data [97,118,167] Probabilistic PV output…”

Section: Real-time Ev Load Dispatching Addressing Pv Output Variabilimentioning

confidence: 99%

“…Random PV output and EV load samples are generated from their supplied PDF [29,75,142] Predicted PV output Statistical smoothing techniques and quantile regression [171] Short-term forecasting engine supported by machine learning methods [149] MZS-s algorithm [142] Real-time PV output - [33,74,119,122] DC link voltage measurements [172][173][174] Since the inherent drawbacks limit the applicability of the direct measurement and probabilistic charging strategies as above, predicted PV output based charging strategies could be the appropriate avenues [142,168]. PV output for such charging approaches is predicted by statistical smoothing techniques and quantile regression, a short-term forecasting engine supported by machine learning methods, and MZS-s algorithm [142].…”

Section: Real-time Ev Load Dispatching Addressing Pv Output Variabilimentioning

confidence: 99%

See 2 more Smart Citations

PV-based EV charging station with vehicle-to-grid services for business premises

Islam¹

View full text Add to dashboard Cite

The transportation sector is the second major contributor to the global greenhouse gas (GHG) emissions, next to electricity generation sector. GHG emissions are projected to grow in the future due to the over-reliance on fossil fuels for both electricity generation and transportation sectors.However, the growth can be stalled by gradually introducing electric vehicles (EVs) as they produce less GHG compared to their conventional counterparts. Despite that, their proliferation has been stunted by a completely new set of challenges in addition to a very high capital cost. These challenges include low driving ranges, scarcity of electric vehicle charging stations (EVCS), long charging time and inability to provide charging service readily. Nevertheless, some of these challenges can be addressed by installing EVCS at business premises (i.e., offices, universities, etc.) and augmenting EVCS with onsite PV and battery energy storage (BES). In that case, the impacts of EV charging on the grid will be less significant; charging services will be available readily, and GHG emissions growth can be reduced significantly. Moreover, EVCS, under suitable conditions, would be able to provide vehicle-to-grid (V2G) services, which makes EVCS more attractive.Despite such prospective opportunities, offhand planning and operational planning of EVCS would deem them unachievable. In addition, the inherent intermittency of PV, and EV and grids loads will pose considerable hindrance on availing those benefits.Hence, this research focusses on an EVCS with onsite PV and BES, in the perspective of planning and operational planning including V2G services. The cornerstone of the planning and operational planning is an appropriate EV load model that captures the uncertainties of EV charging. In addition, the EV load model reflects the grid voltage and EV battery's state of charge (SOC)-dependency of EV charging. Moreover, it accounts for the diversity of the EV population embodied by market shares, battery sizes, charging levels, and charging voltages, currents, and power factors. Furthermore, it is scalable to facilitate seamless assimilation of a large EV population. Therefore, a new EV load model is first proposed with MATLAB/Simulink validation reflecting the factors above along with the recommendations by the standard BS EN 61851-23:2014. This EV model is then incorporated into the planning activities, which include four chronological exercises. Firstly, the impact (i.e., grid voltage, current, losses, etc.) of the EV charging on the grid at different locations, namely home, office, public charging, is investigated considering the uncertainties. Secondly, the optimal location of the PV and BES based EVCS is divulged regarding the reduction of the impact and costs involved while enhancing the charging quality of service (QoS). Thirdly, a suitability analysis is performed for the obtained optimal location to find the most suitable combination of the grid upgrading, PV deployment, and BES iii deployment to satisfy the QoS, ...

show abstract

Energy storage usages: Engineering reactions,economic‐technologicalvalues for electric vehicles—A technological outlook

Arfeen

Abdullah

Hassan

et al. 2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary At present with the massive induction of distributed renewable energy sources (RES), energy storage systems (ESS) have the potential to curb the intermittent nature of micro sources and provide a steady supply of power to the load. It gives an optimum solution and considers as a major part of intelligent grids. For making a green environment, Electric Vehicle (EV) is the best option that emits zero exhaust gases, cleaner, less noisy and eco‐friendly compared to engine‐based vehicles. It could embark power sanctuary by allowing open access to RES. Nonetheless, EVs presently face encounters in the deployment of ESSs, inroad to their reliability, capacity, price, and online management issues. This study comprehensive review about technical advancements of ESSs, its detailed taxonomy, features, implementation, possibilities with system differences, and additional features of particularly EV applications. Hence, in this current study, technical analysis of Energy storage systems, its leading technologies, core assets, global energy stakeholders, economic merits and techniques on energy conversion is provided. Besides, the way of deploying energy storage techniques, the barriers and assessments are also presented to give a wider scope in this particular area.

show abstract

A comprehensive study of economic unit commitment of power systems integrating various renewable generations and plug-in electric vehicles

Cited by 99 publications

References 49 publications

Online‐Offline Optimized Motion Profile for High‐Dynamic Positioning of Ultraprecision Dual Stage

Online‐Offline Optimized Motion Profile for High‐Dynamic Positioning of Ultraprecision Dual Stage

PV-based EV charging station with vehicle-to-grid services for business premises

Energy storage usages: Engineering reactions,economic‐technologicalvalues for electric vehicles—A technological outlook

Contact Info

Product

Resources

About