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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (24 February 2021) | Viewed by 52855

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Prof. Dr. Michael Short

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School of Computing, Engineering, and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
Interests: control and protection; optimization; embedded systems; real-time systems; industry 4.0; industrial digitization and smart factories; smart grids; smart energy systems; advanced robotics/process control
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Prof. Dr. Tracey Crosbie

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School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK
Interests: research methodologies; sustainability policy; sustainable urban development; smart cities and smart energy infrastructures
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Dr. Maher Al-Greer

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School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, Cleveland, UK
Interests: system identification and intelligent control; power converter design and control; battery characterization
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Special Issue Information

Dear Colleagues,

Smart grids are electrical grids that include a variety of interoperable communication and control devices, which optimally facilitate the production and distribution of electricity. Smart grids allow better integration of renewable energy sources, flexible transmission resources, energy storage devices, electric vehicles, microgrids and controllable loads; they are seen as key enablers in the decarbonisation of both industry and society. This Special Issue focuses on the analysis, design and implementation of future smart grid systems. Submissions are invited from researchers and practitioners working in related areas, and to promote a venue for cutting-edge fundamental and applied research related to future smart grid.

Dr. Michael Short
Dr. Tracey Crosbie
Dr. Maher Al-Greer
Guest Editors

Manuscript Submission Information

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Keywords

New and emerging paradigms for smart grid communication networks
Digitisation of the electrical grid
Smart grid cybersecurity
Power converters for smart grid applications
Automation, control and optimisation for smart grids
Integration and interoperability of smart grid devices
Modelling and simulation techniques for smart grids
Renewable technologies
Battery storage
Energy management systems and advanced control
Microgrids and sustainable communities
Regulatory and technoeconomic aspects of smart grids

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Published Papers (11 papers)

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Research

24 pages, 1522 KiB

Open AccessArticle

Parameter Selection for the Virtual Oscillator Control Applied to Microgrids

by Daniel Alves Costa, Leonardo A. B. Tôrres, Sidelmo Magalhães Silva, Alberto De Conti and Danilo Iglesias Brandão

Energies 2021, 14(7), 1818; https://doi.org/10.3390/en14071818 - 24 Mar 2021

Cited by 6 | Viewed by 2711

Abstract

Virtual Oscillator Control (VOC) is a promising technique that allows several inverters connected to a microgrid to naturally synchronize, without communication. However, the selection of the VOC parameters often require iterative or optimization procedures that render its practical use not straightforward. In this paper, this problem is overcome with the proposition of a novel methodology for determining the dead-zone type VOC parameters based on the describing function method. The methodology consists of a set of analytical equations that use as input data few basic electrical system parameters from the converter and from the microgrid, namely, the operating voltage and frequency ranges, besides rated power. The proposed set of equations is used to calculate the parameters required to control an inverter in voltage mode. The validity of the proposed approach is demonstrated in experiments that encompass different situations such as pre-synchronization, connection, and disconnection of a second inverter from a microgrid. Full article

(This article belongs to the Special Issue Future Smart Grid Systems)

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18 pages, 3574 KiB

Open AccessArticle

A Novel Intrusion Mitigation Unit for Interconnected Power Systems in Frequency Regulation to Enhance Cybersecurity

by Faisal R. Badal, Zannatun Nayem, Subrata K. Sarker, Dristi Datta, Shahriar Rahman Fahim, S. M. Muyeen, Md. Rafiqul Islam Sheikh and Sajal K. Das

Energies 2021, 14(5), 1401; https://doi.org/10.3390/en14051401 - 4 Mar 2021

Cited by 9 | Viewed by 2468

Abstract

Cyberattacks (CAs) on modern interconnected power systems are currently a primary concern. The development of information and communication technology (ICT) has increased the possibility of unauthorized access to power system networks for data manipulation. Unauthorized data manipulation may lead to the partial or complete shutdown of a power network. In this paper, we propose a novel security unit that mitigates intrusion for an interconnected power system and compensates for data manipulation to augment cybersecurity. The studied two-area interconnected power system is first stabilized to alleviate frequency deviation and tie-line power between the areas by designing a fractional-order proportional integral derivative (FPID) controller. Since the parameters of the FPID controller can also be influenced by a CA, the proposed security unit, named the automatic intrusion mitigation unit (AIMU), guarantees control over such changes. The effectiveness of the AIMU is inspected against a CA, load variations, and unknown noises, and the results show that the proposed unit guarantees reliable performance in all circumstances. Full article

(This article belongs to the Special Issue Future Smart Grid Systems)

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25 pages, 3223 KiB

Open AccessArticle

Two-Stage Optimal Microgrid Operation with a Risk-Based Hybrid Demand Response Program Considering Uncertainty

by Ho-Sung Ryu and Mun-Kyeom Kim

Energies 2020, 13(22), 6052; https://doi.org/10.3390/en13226052 - 19 Nov 2020

Cited by 11 | Viewed by 2103

Abstract

Owing to the increasing utilization of renewable energy resources, distributed energy resources (DERs) become inevitably uncertain, and microgrid operators have difficulty in operating the power systems because of this uncertainty. In this study, we propose a two-stage optimization approach with a hybrid demand response program (DRP) considering a risk index for microgrids (MGs) under uncertainty. The risk-based hybrid DRP is presented to reduce both operational costs and uncertainty effect using demand response elasticity. The problem is formulated as a two-stage optimization that considers not only the expected operation costs but also risk expense of uncertainty. To address the optimization problem, an improved multi-layer artificial bee colony (IML-ABC) is incorporated into the MG operation. The effectiveness of the proposed approach is demonstrated through a numerical analysis based on a typical low-voltage grid-connected MG. As a result, the proposed approach can reduce the operation costs which are taken into account uncertainty in MG. Therefore, the two-stage optimal operation considering uncertainty has been sufficiently helpful for microgrid operators (MGOs) to make risk-based decisions. Full article

(This article belongs to the Special Issue Future Smart Grid Systems)

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19 pages, 41806 KiB

Open AccessArticle

Time-Frequency Image Analysis and Transfer Learning for Capacity Prediction of Lithium-Ion Batteries

by Ma’d El-Dalahmeh, Maher Al-Greer, Mo’ath El-Dalahmeh and Michael Short

Energies 2020, 13(20), 5447; https://doi.org/10.3390/en13205447 - 19 Oct 2020

Cited by 31 | Viewed by 4854

Abstract

Energy storage is recognized as a key technology for enabling the transition to a low-carbon, sustainable future. Energy storage requires careful management, and capacity prediction of a lithium-ion battery (LIB) is an essential indicator in a battery management system for Electric Vehicles and Electricity Grid Management. However, present techniques for capacity prediction rely mainly on the quality of the features extracted from measured signals under strict operating conditions. To improve flexibility and accuracy, this paper introduces a new paradigm based on a multi-domain features time-frequency image (TFI) analysis and transfer deep learning algorithm, in order to extract diagnostic characteristics on the degradation inside the LIB. Continuous wavelet transform (CWT) is used to transfer the one-dimensional (1D) terminal voltage signals of the battery into 2D images (i.e., wavelet energy concentration). The generated TFIs are fed into the 2D deep learning algorithms to extract the features from the battery voltage images. The extracted features are then used to predict the capacity of the LIB. To validate the proposed technique, experimental data on LIB cells from the experimental datasets published by the Prognostics Center of Excellence (PCoE) NASA were used. The results show that the TFI analysis clearly visualised the degradation process of the battery due to its capability to extract different information on electrochemical features from the non-stationary and non-linear nature of the battery signal in both the time and frequency domains. AlexNet and VGG-16 transfer deep learning neural networks combined with stochastic gradient descent with momentum (SGDM) and adaptive data momentum (ADAM) optimization algorithms are examined to classify the obtained TFIs at different capacity values. The results reveal that the proposed scheme achieves 95.60% prediction accuracy, indicating good potential for the design of improved battery management systems. Full article

(This article belongs to the Special Issue Future Smart Grid Systems)

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30 pages, 9976 KiB

Open AccessArticle

A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services

by Sean Williams, Michael Short, Tracey Crosbie and Maryam Shadman-Pajouh

Energies 2020, 13(16), 4191; https://doi.org/10.3390/en13164191 - 13 Aug 2020

Cited by 7 | Viewed by 3203

Abstract

This paper presents a decentralized informatics, optimization, and control framework to enable demand response (DR) in small or rural decentralized community power systems, including geographical islands. The framework consists of a simplified lumped model for electrical demand forecasting, a scheduling subsystem that optimizes the utility of energy storage assets, and an active/pro-active control subsystem. The active control strategy provides secondary DR services, through optimizing a multi-objective cost function formulated using a weight-based routing algorithm. In this context, the total weight of each edge between any two consecutive nodes is calculated as a function of thermal comfort, cost (tariff), and the rate at which electricity is consumed over a short future time horizon. The pro-active control strategy provides primary DR services. Furthermore, tertiary DR services can be processed to initiate a sequence of operations that enables the continuity of applied electrical services for the duration of the demand side event. Computer simulations and a case study using hardware-in-the-loop testing is used to evaluate the optimization and control module. The main conclusion drawn from this research shows the real-time operation of the proposed optimization and control scheme, operating on a prototype platform, underpinned by the effectiveness of the new methods and approach for tackling the optimization problem. This research recommends deployment of the optimization and control scheme, at scale, for decentralized community energy management. The paper concludes with a short discussion of business aspects and outlines areas for future work. Full article

(This article belongs to the Special Issue Future Smart Grid Systems)

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12 pages, 1131 KiB

Open AccessArticle

Smart Grid Monitoring by Wireless Sensors Using Binary Logistic Regression

by Hariprasath Manoharan, Yuvaraja Teekaraman, Irina Kirpichnikova, Ramya Kuppusamy, Srete Nikolovski and Hamid Reza Baghaee

Energies 2020, 13(15), 3974; https://doi.org/10.3390/en13153974 - 2 Aug 2020

Cited by 25 | Viewed by 4912

Abstract

This article focuses on addressing the data aggregation faults caused by the Phasor Measuring Unit (PMU) by installing Wireless Sensor Networks (WSN) in the grid. All data that is monitored by PMU should be sent to the base station for further action. But the data that is sent from PMU does not reach the main server properly in many situations. To avoid this situation, a sensor-based technology has been introduced in the proposed method for sensing the values that are monitored by PMU. Also, the basic parameters that are necessary for determining optimal solutions like energy consumption, distance and cost have been calculated for wireless sensors, whereas, for PMU optimal placements with cost analysis have been restrained. For analyzing and improving the accuracy of the proposed method, an effective Binary Logistic Regression (BLR) algorithm has been integrated with an objective function. The sensor will report all measured PMU values to an Online Monitoring System (OMS). To examine the effectiveness of the proposed method, the examined values are visualized in MATLAB and results prove that the proposed method using BLR is more effective than existing methods in terms of all parametric values and the much improved results have been obtained at a rate of 81.2%. Full article

(This article belongs to the Special Issue Future Smart Grid Systems)

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19 pages, 1758 KiB

Open AccessArticle

Life Cycle Costing Analysis: Tools and Applications for Determining Hydrogen Production Cost for Fuel Cell Vehicle Technology

by Martin Khzouz, Evangelos I. Gkanas, Jia Shao, Farooq Sher, Dmytro Beherskyi, Ahmad El-Kharouf and Mansour Al Qubeissi

Energies 2020, 13(15), 3783; https://doi.org/10.3390/en13153783 - 23 Jul 2020

Cited by 79 | Viewed by 9997

Abstract

This work investigates life cycle costing analysis as a tool to estimate the cost of hydrogen to be used as fuel for Hydrogen Fuel Cell vehicles (HFCVs). The method of life cycle costing and economic data are considered to estimate the cost of hydrogen for centralised and decentralised production processes. In the current study, two major hydrogen production methods are considered, methane reforming and water electrolysis. The costing frameworks are defined for hydrogen production, transportation and final application. The results show that hydrogen production via centralised methane reforming is financially viable for future transport applications. The ownership cost of HFCVs shows the highest cost among other costs of life cycle analysis. Full article

(This article belongs to the Special Issue Future Smart Grid Systems)

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22 pages, 470 KiB

Open AccessArticle

Towards Self-Sustainable Island Grids through Optimal Utilization of Renewable Energy Potential and Community Engagement

by Marko Jelić, Marko Batić, Nikola Tomašević, Andrew Barney, Heracles Polatidis, Tracey Crosbie, Dana Abi Ghanem, Michael Short and Gobind Pillai

Energies 2020, 13(13), 3386; https://doi.org/10.3390/en13133386 - 1 Jul 2020

Cited by 20 | Viewed by 4886

Abstract

Solving the issue of energy security for geographical islands presents a one-of-a-kind problem that has to be tackled from multiple sides and requires an interdisciplinary approach that transcends just technical and social aspects. With many islands suffering in terms of limited and costly energy supply due to their remote location, providing a self-sustainable energy system is of utmost importance for these communities. In order to improve upon the status quo, novel solutions and projects aimed at increasing sustainability not only have to consider optimal utilization of renewable energy potentials in accordance with local conditions, but also must include active community participation. This paper analyzes both of these aspects for island communities and brings them together in an optimization scenario that is utilized to determine the relationship between supposed demand flexibility levels and achievable savings in a setting with variable renewable generation. The results, specifically discussed for a use case with real-world data for the La Graciosa island in Spain, show that boosting community participation and thus unlocking crucial demand flexibility, can be used as a powerful tool to augment novel generation technologies with savings from flexibility at around 7.5% of what is achieved purely by renewable sources. Full article

(This article belongs to the Special Issue Future Smart Grid Systems)

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15 pages, 860 KiB

Open AccessArticle

Stacked Boosters Network Architecture for Short-Term Load Forecasting in Buildings

by Tuukka Salmi, Jussi Kiljander and Daniel Pakkala

Energies 2020, 13(9), 2370; https://doi.org/10.3390/en13092370 - 9 May 2020

Cited by 13 | Viewed by 3343

Abstract

This paper presents a novel deep learning architecture for short-term load forecasting of building energy loads. The architecture is based on a simple base learner and multiple boosting systems that are modelled as a single deep neural network. The architecture transforms the original multivariate time series into multiple cascading univariate time series. Together with sparse interactions, parameter sharing and equivariant representations, this approach makes it possible to combat against overfitting while still achieving good presentation power with a deep network architecture. The architecture is evaluated in several short-term load forecasting tasks with energy data from an office building in Finland. The proposed architecture outperforms state-of-the-art load forecasting model in all the tasks. Full article

(This article belongs to the Special Issue Future Smart Grid Systems)

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20 pages, 1919 KiB

Open AccessArticle

On the Role of Regulatory Policy on the Business Case for Energy Storage in Both EU and UK Energy Systems: Barriers and Enablers

by Ahmed Gailani, Tracey Crosbie, Maher Al-Greer, Michael Short and Nashwan Dawood

Energies 2020, 13(5), 1080; https://doi.org/10.3390/en13051080 - 1 Mar 2020

Cited by 11 | Viewed by 7438

Abstract

This paper presents a SWOT analysis of the impact of recent EU regulatory changes on the business case for energy storage (ES) using the UK as a case study. ES technologies (such as batteries) are key enablers for increasing the share of renewable energy generation and hence decarbonising the electricity system. As such, recent regulatory changes seek to improve the business case for ES technologies on national networks. These changes include removing double network charging for ES, defining and classifying ES in relevant legislations, and clarifying ES ownership along with facilitating its grid access. However, most of the current regulations treat storage in a similar way to bulk generators without paying attention to the different sizes and types of ES. As a result, storage with higher capacity receives significantly higher payment in the capacity market and can be exempt from paying renewable energy promotion taxes. Despite the recent regulatory changes, ES is defined as a generation device, which is a barrier to a wide range of revenue streams from demand side services. Also, regulators avoid disrupting the current energy market structure by creating an independent asset class for ES. Instead, they are encouraging changes that co-exist with the current market and regulatory structure. Therefore, although some of the reviewed market and regulatory changes for ES in this paper are positive, it can be concluded that these changes are not likely to allow a level playing field for ES that encourage its increase on energy networks. Full article

(This article belongs to the Special Issue Future Smart Grid Systems)

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20 pages, 5018 KiB

Open AccessArticle

Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach

by Michael Short, Sergio Rodriguez, Richard Charlesworth, Tracey Crosbie and Nashwan Dawood

Energies 2019, 12(22), 4320; https://doi.org/10.3390/en12224320 - 13 Nov 2019

Cited by 19 | Viewed by 4592

Abstract

Demand response (DR) involves economic incentives aimed at balancing energy demand during critical demand periods. In doing so DR offers the potential to assist with grid balancing, integrate renewable energy generation and improve energy network security. Buildings account for roughly 40% of global energy consumption. Therefore, the potential for DR using building stock offers a largely untapped resource. Heating, ventilation and air conditioning (HVAC) systems provide one of the largest possible sources for DR in buildings. However, coordinating the real-time aggregated response of multiple HVAC units across large numbers of buildings and stakeholders poses a challenging problem. Leveraging upon the concepts of Industry 4.0, this paper presents a large-scale decentralized discrete optimization framework to address this problem. Specifically, the paper first focuses upon the real-time dispatch problem for individual HVAC units in the presence of a tertiary DR program. The dispatch problem is formulated as a non-linear constrained predictive control problem, and an efficient dynamic programming (DP) algorithm with fixed memory and computation time overheads is developed for its efficient solution in real-time on individual HVAC units. Subsequently, in order to coordinate dispatch among multiple HVAC units in parallel by a DR aggregator, a flexible and efficient allocation/reallocation DP algorithm is developed to extract the cost-optimal solution and generate dispatch instructions for individual units. Accurate baselining at individual unit and aggregated levels for post-settlement is considered as an integrated component of the presented algorithms. A number of calibrated simulation studies and practical experimental tests are described to verify and illustrate the performance of the proposed schemes. The results illustrate that the distributed optimization algorithm enables a scalable, flexible solution helping to deliver the provision of aggregated tertiary DR for HVAC systems for both aggregators and individual customers. The paper concludes with a discussion of future work. Full article

(This article belongs to the Special Issue Future Smart Grid Systems)

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