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The concept of the internet of energy (IoE) emerged as an innovative paradigm to encompass all the complex and intertwined notions relevant to the transition of current smart grids towards more decarbonization, digitalization and decentralization. With a focus on the two last aspects, the amount of intelligent devices being connected in a scattered way to the existing power grid is ever-growing. Nevertheless, guaranteeing a cyber-secure and resilient control of these IoE components as well as a seamless and reliable delivery of electricity services, such as renewable energy exchange, electric vehicles charging, demand response, and so forth; might be the bottleneck of current power systems that are largely still functioning following a centralized approach. Thus, the future power grid would gradually incorporate a growing number of distributed-based control schemes to deal with this challenge. And many believe that blockchain could be a key-enabler in this transition, due to its consistent characteristics with multiple requirements of future power systems. In this paper, we provide an extensive state-of-the-art of blockchain-based additions to the IoE. Where, we first introduce various concepts related to blockchain and discuss the rationale behind its adoption in the context of IoE. Then, differently from the existing body of literature surveys, we do not only provide a taxonomy and evaluate a wide range of recent research outputs that integrated blockchain within modern power systems. But we also draw some valuable lessons learned for each studied category and discuss the intersection of blockchain with various emerging paradigms that have the potential of radically impacting the smart grid. In addition, we present some real-world industrial initiatives and ongoing projects built on top of blockchain, dedicated for offering diverse electricity services with a case study of a pilot project on energy trading in Amsterdam. Finally, we discuss the remaining challenges and worthwhile opportunities of deploying blockchain in this particular area, with a focus on the aspect of operational cyber-security.

This paper investigates the possibility of charging battery electric vehicles at workplace in Netherlands using solar energy. Data from the Dutch Meteorological Institute is used to determine the optimal orientation of PV panels for maximum energy yield in the Netherlands. The seasonal and diurnal variation in solar insolation is analyzed to determine the energy availability for EV charging and the necessity for grid connection. Due to relatively low solar insolation in Netherlands, it has been determined that the powerrating of the PV array can be oversized by 30% with respect to power rating of the converter. Various dynamic EV charging profiles are compared with an aim to minimize the grid dependency and to maximize the usage of solar power to directly charge the EV. Two scenarios are considered – one where the EVs have to be charged only on weekdays and the second case where EV have to be charged all 7 days/week. A priority mechanism is proposed to facilitate the charging of multiple EV from a single EV–PV charger. The feasibility of integrating a local storage to the EV–PV charger to make it grid independent is evaluated. The optimal storage size that reduces the grid dependency by 25% is evaluated.

These files must be understood in relation to the main text. EV2ndlife_workbook.m - Matlab code containing all input data and methodology used to generate and plot results. EV2ndlife_alldata.xlsx - data generated by the above Matlab code, whose plots are included as figures in the main text and Supplementary Information. BEV_PHEV_salesdata.xlsx - sales data of BEVs and PHEVs broken down by make/model for years 2013-2016, copied from the website EV Volumes, and their use in calculating the average EV battery capacity in years 2013-2016.

Growth of mobility for people and goods transportation has been increasing steadily over the years. With the perpetual increase in number of road vehicles, comes the inevitable problems of traffic and pollution. Various intelligent traffic technologies and strategies have been proposed and implemented over the years to overcome the road traffic problems. Platooning is identified as one of the ways to tackle transportation related problems efficiently. Even though platooning has been tested and implemented in highways, not much attention has been given to controlling the platoons at urban roads. When vehicles in the platoon are connected and automated, it helps them to understand the environment better and communicate efficiently for better performance. Platoons at the vicinity of signalized intersections need to accelerate and decelerate in a nonlinear manner which leads to higher energy consumption and longer travel time. Most of the previous research approaches focused on optimizing only the energy consumption or travel time of the platoons. However, studies show that the recommended driving advice from the controllers is not tracked by the drivers completely. The current research focuses on optimal trajectory planning of the electric vehicle platoons at signalized intersections. This is done by designing an energy efficient longitudinal controller for the platoons using optimal control method. The goal of this thesis is split between planning and tracking the trajectory. Thus the optimal speed profile is planned by the high level controller (i.e the optimal controller) and tracked using a battery electric vehicle model controlled by a low level controller (i.e a PID controller). The performance of the controlled platoon was verified using different scenarios and was found to perform positively under respective control objectives and constraints. The designed controlled and automated platoon by the optimal controller was able to achieve energy consumption and cost saving up to 67% when compared with ...

The included tests were performed at the University of Wisconsin-Madison by Dr. Phillip Kollmeyer (phillip.kollmeyer@gmail.com). If this data is utilized for any purpose, it should be appropriately referenced. The tests can be used to test Neural Network and Kalman Filter State of Charge algorithms, or to develop battery models, and are intended to be a reference so researchers can compare their algorithm and model performance for a standard data set. The test data, or similar data, has been used for numerous publications, including: E. Chemali, P. J. Kollmeyer, M. Preindl, R. Ahmed and A. Emadi, "Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries," in IEEE Transactions on Industrial Electronics, vol. 65, no. 8, pp. 6730-6739, Aug. 2018. doi: 10.1109/TIE.2017.2787586 R. Zhao, P. J. Kollmeyer, R. D. Lorenz and T. M. Jahns, "A compact unified methodology via a recurrent neural network for accurate modeling of lithium-ion battery voltage and state-of-charge," 2017 IEEE Energy Conversion Congress and Exposition (ECCE), Cincinnati, OH, 2017, pp. 5234-5241. doi: 10.1109/ECCE.2017.8096879 P.J. Kollmeyer, A. Hackl and A. Emadi, "Li-ion battery model performance for automotive drive cycles with current pulse and EIS parameterization," 2017 IEEE Transportation Electrification Conference and Expo (ITEC), Chicago, IL, 2017, pp. 486-492. doi: 10.1109/ITEC.2017.7993319 P. J. Kollmeyer, Development and Implementation of a Battery-Electric Light-Duty Class 2a Truck including Hybrid Energy Storage, PhD Dissertation, Dept. Elect. Eng., University of Wisconsin-Madison, 2015. For the tests, a brand new 2.9Ah Panasonic 18650PF cell was tested in an 8 cu.ft. thermal chamber with a 25 amp, 18 volt Digatron Firing Circuits Universal Battery Tester channel. A series of tests, including HPPC, drive cycles, and impedance spectroscopy, were performed at five different temperatures. The battery was charged after each test at a 1C rate to 4.2V, 50mA cut off, with battery temperature 12degC or greater). See the readme file for additional details.

Vehicle-2-Home (V2H) is a concept in which an electric vehicle (EV) is able to charge and discharge its battery pack to the building it is connected to. This concept enables EVs to store surplus solar energy, which is produced during the daytime and return in to buildings during the night time. For this system to work (cost) efficiently, the EVs location during the day must match the location of surplus solar energy. Also, at night, the EVs must discharge this electricity evenly over an area to reduce the strain on the electricity distribution grid. This thesis has analysed whether there exists a match between the spatial distribution of solar panels and EVs in the Amsterdam region under growth scenarios until 2040.

Electric vehicles (EVs) have the potential to play a crucial role in clean and intelligent power systems. The key to this potential lies in the flexibility that EVs provide by the ability to shift their electricity demand in time. This flexibility can be used to facilitate the integration of renewable energy sources by adjusting EV demand to the variable production of wind or solar energy. On the other hand, the same flexibility can be employed to reduce peaks in network load that could result from a massive adoption of EVs. This PhD thesis aims to improve the understanding of the value of flexible EV demand in the context of multi-actor power systems with a high share of renewable energy sources. We first explore flexible EV demand from a distribution network point of view, and then in the light of renewable energy integration. Moreover, we also bring these perspectives together and investigate mechanisms to align the different objectives related to the distribution networks and renewable energy integration. This thesis thus demonstrates the value of demand response in the sustainable power systems of the future.

DC systems, Energy conversion & Storage ; Electrical Sustainable Energy

Development of electric mobility and sustainable energy result in new technologies such as contactless electric vehicle charging and roadway energy harvesting methods, but also self-healing asphalt roads. By combining these technologies a new concept of Future Sustainable Roads for Electric Mobility is created and presented in the paper. This paper bridges the gap created by these unilateral technology developments using a multi-disciplinary approach including placing cautions when necessary and suggesting viable alternatives for optimal utilization of these energy transfer and conversion techniques. Through theoretical analysis, simulations, and tests on lab-scale experimental prototypes, the impact of our proposal is showcased. Thermal and loss models are developed for self-healing asphalt. Also, integration study of solar roads and contactless charging is performed. Applying the insight gained from the results, it is discussed how some challenges also pave a way towards interesting opportunities, for instance, infrastructure sharing for material use optimization and efficient mosaic integration. Finally, an economic viability case study is presented for a future Dutch highway with such newly emerging components.