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Smart Cities
Special Issues
Innovative Energy Systems for Smart Cities
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Innovative Energy Systems for Smart Cities

A special issue of Smart Cities (ISSN 2624-6511). This special issue belongs to the section "Energy and ICT".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 13214

Special Issue Editors

Dr. Pietro Colella

E-Mail Website
Guest Editor
Department of Energy “Galileo Ferraris”, Politecnico di Torino, Turin, 10129, Italy
Interests: distribution network; transmission network; electrical market; electrical safety
Dr. Pasquale Montegiglio

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Elettrica e dell'Informazione, Politecnico di Bari, Bari, 70125, Italy
Interests: distribution network; DC microgrids; electrical safety

Special Issue Information

Dear Colleagues,

The increased awareness of environmental and energetic issues is changing our cities and our habits. The widespread increment of renewable energy sources, energy monitoring platforms, and electrical vehicles are some examples of the technological transition that we are experiencing at present. Electrical grids and the adopted relative strategies to manage them are quickly evolving as well, with an increasing number of installed sensors and actuators, and the usage of more and more sophisticated management algorithms, for example, to locate faults or to predictively maintain electrical components. The objective of distributor system operators is to support the technological transition, guaranteeing a high-quality level of the energy dispatch service.

The challenges imposed by this scenario often require an interdisciplinary approach. Electrical, computer, and information technology engineering are tightly interconnected research areas. The journal Smart Cities is therefore the right place to describe this technology transition.

With this Special Issue on “Innovative Energy Systems for Smart Cities”, we are interested in grouping together all the contributions that describe the changes in energy systems, both from a technical and conceptual point of view.

We kindly invite you to submit a manuscript to this Special Issue. Full papers, communications, and reviews are all welcome.

Dr. Pietro Colella
Dr. Pasquale Montegiglio
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Smart Cities is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Energy systems
  • Smart grids
  • Renewable energy sources
  • Smart metering
  • Predictive maintenance
  • Distributed generators

Published Papers (4 papers)

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Research

24 pages, 4325 KiB  
Article
The Role of Local Citizen Energy Communities in the Road to Carbon-Neutral Power Systems: Outcomes from a Case Study in Portugal
by Hugo Algarvio
Smart Cities 2021, 4(2), 840-863; https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4020043 - 22 May 2021
Cited by 26 | Viewed by 3780
Abstract
Global warming contributes to the worldwide goal of a sustainable carbon-neutral society. Currently, hydroelectric, wind and solar power plants are the most competitive renewable technologies. They are limited to the primary resource availability, but while hydroelectric power plants (HPPs) can have storage capacity [...] Read more.
Global warming contributes to the worldwide goal of a sustainable carbon-neutral society. Currently, hydroelectric, wind and solar power plants are the most competitive renewable technologies. They are limited to the primary resource availability, but while hydroelectric power plants (HPPs) can have storage capacity but have several geographical limitations, wind and solar power plants have variable renewable energy (VRE) with stochastic profiles, requiring a substantially higher investment when equipped with battery energy storage systems. One of the most affordable solutions to compensate the stochastic behaviour of VRE is the active participation of consumers with demand response capability. Therefore, the role of citizen energy communities (CECs) can be important towards a carbon-neutral society. This work presents the economic and environmental advantages of CECs, by aggregating consumers, prosumers and VRE at the distribution level, considering microgrid trades, but also establishing bilateral agreements with large-scale VRE and HPPs, and participating in electricity markets. Results from the case-study prove the advantages of CECs and self-consumption. Currently, CECs have potential to be carbon-neutral in relation to electricity consumption and reduce consumers’ costs with its variable term until 77%. In the future, electrification may allow CECs to be fully carbon-neutral, if they increase their flexibility portfolio. Full article
(This article belongs to the Special Issue Innovative Energy Systems for Smart Cities)
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24 pages, 1565 KiB  
Article
Transfer Learning by Similarity Centred Architecture Evolution for Multiple Residential Load Forecasting
by Santiago Gomez-Rosero, Miriam A. M. Capretz and Syed Mir
Smart Cities 2021, 4(1), 217-240; https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4010014 - 01 Feb 2021
Cited by 8 | Viewed by 2831
Abstract
The development from traditional low voltage grids to smart systems has become extensive and adopted worldwide. Expanding the demand response program to cover the residential sector raises a wide range of challenges. Short term load forecasting for residential consumers in a neighbourhood could [...] Read more.
The development from traditional low voltage grids to smart systems has become extensive and adopted worldwide. Expanding the demand response program to cover the residential sector raises a wide range of challenges. Short term load forecasting for residential consumers in a neighbourhood could lead to a better understanding of low voltage consumption behaviour. Nevertheless, users with similar characteristics can present diversity in consumption patterns. Consequently, transfer learning methods have become a useful tool to tackle differences among residential time series. This paper proposes a method combining evolutionary algorithms for neural architecture search with transfer learning to perform short term load forecasting in a neighbourhood with multiple household load consumption. The approach centres its efforts on neural architecture search using evolutionary algorithms. The neural architecture evolution process retains the patterns of the centre-most house, and later the architecture weights are adjusted for each house in a multihouse set from a neighbourhood. In addition, a sensitivity analysis was conducted to ensure model performance. Experimental results on a large dataset containing hourly load consumption for ten houses in London, Ontario showed that the performance of the proposed approach performs better than the compared techniques. Moreover, the proposed method presents the average accuracy performance of 3.17 points higher than the state-of-the-art LSTM one shot method. Full article
(This article belongs to the Special Issue Innovative Energy Systems for Smart Cities)
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34 pages, 9061 KiB  
Article
Interdependencies of Infrastructure Investment Decisions in Multi-Energy Systems—A Sensitivity Analysis for Urban Residential Areas
by Daniel Then, Johannes Bauer, Tanja M. Kneiske and Martin Braun
Smart Cities 2021, 4(1), 112-145; https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4010007 - 08 Jan 2021
Cited by 5 | Viewed by 3340
Abstract
Considering the European Union (EU) climate targets, the heating sector should be decarbonized by 80% to 95% up to 2050. Thus, the macro-trends forecast increasing energy efficiency and focus on the use of renewable gas or the electrification of heat generation. This has [...] Read more.
Considering the European Union (EU) climate targets, the heating sector should be decarbonized by 80% to 95% up to 2050. Thus, the macro-trends forecast increasing energy efficiency and focus on the use of renewable gas or the electrification of heat generation. This has implications for the business models of urban electricity and in particular natural gas distribution network operators (DNOs): When the energy demand decreases, a disproportionately long grid is operated, which can cause a rise of grid charges and thus the gas price. This creates a situation in which a self-reinforcing feedback loop starts, which increases the risk of gas grid defection. We present a mixed integer linear optimization model to analyze the interdependencies between the electricity and gas DNOs’ and the building owners’ investment decisions during the transformation path. The results of the investigation in a real grid area are used to validate the simulation setup of a sensitivity analysis of 27 types of building collectives and five grid topologies, which provides a systematic insight into the interrelated system. Therefore, it is possible to identify building and grid configurations that increase the risk of a complete gas grid shutdown and those that should be operated as a flexibility option in a future renewable energy system. Full article
(This article belongs to the Special Issue Innovative Energy Systems for Smart Cities)
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24 pages, 8320 KiB  
Article
Sparse Measurement-Based Coordination of Electric Vehicle Charging Stations to Manage Congestions in Low Voltage Grids
by Daniel-Leon Schultis
Smart Cities 2021, 4(1), 17-40; https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4010002 - 22 Dec 2020
Cited by 2 | Viewed by 2457
Abstract
The increasing use of distributed generation and electric vehicle charging stations provokes violations of the operational limits in low voltage grids. The mitigation of voltage limit violations is addressed by Volt/var control strategies, while thermal overload is avoided by using congestion management. Congestions [...] Read more.
The increasing use of distributed generation and electric vehicle charging stations provokes violations of the operational limits in low voltage grids. The mitigation of voltage limit violations is addressed by Volt/var control strategies, while thermal overload is avoided by using congestion management. Congestions in low voltage grids can be managed by coordinating the active power contributions of the connected elements. As a prerequisite, the system state must be carefully observed. This study presents and investigates a method for the sparse measurement-based detection of feeder congestions that bypasses the major hurdles of distribution system state estimation. Furthermore, the developed method is used to enable congestion management by the centralized coordination of the distributed electric vehicle charging stations. Different algorithms are presented and tested by conducting load flow simulations on a real urban low voltage grid for several scenarios. Results show that the proposed method reliably detects all congestions, but in some cases, overloads are detected when none are present. A minimal detection accuracy of 73.07% is found across all simulations. The coordination algorithms react to detected congestions by reducing the power consumption of the corresponding charging stations. When properly designed, this strategy avoids congestions reliably but conservatively. Unnecessary reduction of the charging power may occur. In total, the presented solution offers an acceptable performance while requiring low implementation effort; no complex adaptations are required after grid reinforcement and expansion. Full article
(This article belongs to the Special Issue Innovative Energy Systems for Smart Cities)
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