@inproceedings{Zhan2205:Deep,

title = {Deep Learning-based Energy Optimization for Electric Vehicles Integrated Smart Micro Grid},

author = {Jiajing {Zhang} and Huanzhuo {Wu} and Riccardo {Bassoli} and Riccardo {Bonetto} and Frank H. P. {Fitzek}},

doi = {10.1109/ICC45855.2022.9838771},

year  = {2022},

date = {2022-05-01},

urldate = {2022-05-01},

booktitle = {2022 IEEE International Conference on Communications (ICC): Green Communication Systems and Networks Symposium (IEEE ICC'22 - GCSN Symposium)},

address = {Seoul, Korea (South)},

abstract = {\"{A}pplying renewable energy in smart micro grid (MG) is increasingly receiving attention to reducing greenhouse gas emissions. However, the mismatch between supply and demand hinders the realization of this process. With the widespread use of Electrical Vehicles (EVs) and the development of emerging Mobile Edge Clouds (MECs), intelligent energy optimization becomes a way to the addressee the challenge. Therefore, in this paper, we propose a novel two-stage approach based on Deep Learning (DL) to reduce overall energy cost for sharing EVs integrated MG by forecasting its state and optimizing EVs scheduling. Our simulation results show that the joint design of forecasting and optimization reduces the overall energy consumption and the payment to the external grid."},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sychev2019,

title = {Closed Loop Benchmark for Timeseries Databases},

author = {Ilya {Sychev} and Abdelrahman {Abdelkader} and Wojciech {Kozak} and Riccardo {Bonetto} and Frank H. P. {Fitzek}},

year  = {2020},

date = {2020-01-10},

booktitle = {2020 IEEE 17th Annual Consumer Communications \& Networking Conference (CCNC) (CCNC 2020)},

address = {Las Vegas, USA},

abstract = {The ever growing hunger for high quality, up to 

date, and efficient datasets to be used in data driven models and 

control systems led to the diffusion of a number of database 

management systems specifically tailored to support operations 

on timeseries: Timeseries Databases. Here, we focus on the 

systematic performance assessment of some of the most popular 

TSDBs available. To this end we developed a tool specifically 

designed for the task at hand: SimpleMetric. By means of Simple 

Metric, we measure the performance of the selected TSDBs in 

terms of the computational overhead introduced by a number 

of aggregation functions in a closed loop control scenario (i.e., 

monitoring and actuation).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bonetto2020,

title = {Smart Grids for Smarter Cities},

author = {Riccardo {Bonetto} and Ilya {Sychev} and Oleksandr {Zhdanenko} and Abdelrahman {Abdelkader} and Frank H. P. {Fitzek}},

year  = {2020},

date = {2020-01-01},

booktitle = {2020 IEEE 17th Annual Consumer Communications \& Networking Conference (CCNC) (CCNC 2020)},

address = {Las Vegas, USA},

abstract = {Here we show how, based on 5G-enabled technologies, a connected power 

distribution grid can be split into self sufficient islands integrating 

self driving electric vehicles (EVs). End users form self sufficient (from 

the energy demand point of view) clusters called virtual power plants, and 

EVs serve as energy delivery devices between different VPPs. Moreover, we 

show how phase-to-ground faults can be detected and isolated even in the 

presence of distributed energy generation, thanks to low latency 

communication and massive IoT.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Abdelkader2019b,

title = {A Market Oriented, Reinforcement Learning Based Approach for Electric Vehicles Integration in Smart Micro Grids},

author = {Abdelrahman {Abdelkader} and Ilya {Sychev} and Riccardo {Bonetto} and Frank H. P. {Fitzek}},

year  = {2019},

date = {2019-10-21},

booktitle = {IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm 2019)},

address = {Beijing, China},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bone1810:Power,

title = {Power to the Future: Use Cases and Challenges for Mobile, Self Configuring, and Distributed Power Grids},

author = {Riccardo {Bonetto} and Ilya {Sychev} and Frank H. P. {Fitzek}},

year  = {2018},

date = {2018-10-01},

booktitle = {2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (IEEESmartGridComm'18)},

address = {Aalborg, Denmark},

abstract = {Smart Cities are calling for a major innovation in many strategic infrastructures that shape the daily life of citizens. Arguably one of the most strategic and resilient to changes is the power transmission and distribution infrastructure. And yet, the power sector, together with the communication network infrastructure, are the major enablers of future smart cities. 

In this paper, we present three fictional, yet likely use cases that highlight how the Information and Communication Technologies (ICT) and Infrastructures are ready to support this much needed innovation, while the power distribution infrastructure has still a long way to go to be able benefit from the opportunities offered by ICT. The resulting discussion will hopefully involve power grid operators and legislators to the end of promoting a thorough renovation of the power sector.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Bone1810:ARIES,

title = {ARIES: Low Voltage smArt gRid dIscrete Event Simulator to Enable Large Scale Learning in the Power Distribution Networks},

author = {Ilya {Sychev} and Oleksandr {Zhdanenko} and Riccardo {Bonetto} and Frank H. P. {Fitzek}},

year  = {2018},

date = {2018-01-01},

booktitle = {2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (IEEE SmartGridComm'18)},

abstract = {Accurate software based simulation of (complex) dynamic and, possibly, 

stochastic systems is a key component of the design and test of control 

strategies. Simulation tools developed according to software design best 

practices provide engineers and researchers with efficient and easy to use 

representations of the world. Hence, allowing for faster control algorithms 

design and test. Here we present ARIES, a (low voltage) smArt gRid dIscrete 

Event Simulator meant to enable large scale learning and easy smart grid 

applications design and testing. ARIES is designed according to object 

oriented best practices, and it is implemented in Python 3. ARIES is 

equipped with a REST API to actively interact with the simulations, it 

features a simulation results storage system based on a MongoDB database, 

and a event management system based on a redis in-memory data structure 

store used as message broker.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}