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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 (15 November 2020) | Viewed by 28082

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Special Issue Editor

Prof. Dr. Tamas Keviczky

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Delft Center for Systems and Control, Delft University of Technology, Mekelweg 2, 2628 CD, Delft, The Netherlands
Interests: distributed control and estimation; distributed optimization; model predictive control; distributed robotics; large-scale systems; networked control systems; embedded and real-time optimization based control; decision-making in autonomous greenhouses; thermal-/electric power-/water-networks and smart grids
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Special Issue Information

Dear Colleagues,

The energy transition—fueled by various non-negotiable societal trends, such as the electrification of all sectors, de-carbonisation, and awareness—has led to the concept of smart grids, which are expected to tackle pressing technological challenges due to the growing share of renewable sources, while leveraging the increasing digitalization of our energy networks. However, as the complexity of man-made engineering systems (such as electricity, heat, gas, and other types of energy networks, industrial processes, transport, and the built environment) rapidly evolve beyond the complete understanding and influence of their creators, new approaches are sought to understand, design, and control these pieces of critical infrastructure as integrated energy systems. Since they comprise both grid technology (generation, transmission, distribution hardware, conversion devices, pipes, storage, and more) and the underlying intelligence (in terms of ICT, algorithms, data, operations, controls, management, balancing, security and quality of supply, analytics, and planning), they are prime examples for what are called large-scale cyber–physical systems.

This Special Issue aims to publish articles that provide novel insights, theories, and solutions for smart grids viewed as cyber–physical systems. The subject areas may range from methods for the analysis of complex energy systems, where advanced mathematics and measurement techniques are used to tackle the complexity of future smart grids stemming from renewable generation, from the management of flexibility and storage, to vehicle-to-grid challenges, and planning and scheduling under increased uncertainty, to name a few.

Prof. Dr. Tamás Keviczky
Guest Editor

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Keywords

Cyber–Physical Systems
Smart Grids
Energy Conversion and Storage
Power-to-X Concept
Electric Vehicle Charging
Microgrids
Heat-, Power- and Gas-networks
Renewables
Distribution
Digitalization
Data Analytics
Control Systems
Algorithmic Design
Optimization, Planning, and Scheduling in Smart Grids

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Published Papers (7 papers)

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Research

25 pages, 643 KiB

Open AccessArticle

P2PEdge: A Decentralised, Scalable P2P Architecture for Energy Trading in Real-Time

by Jan Kalbantner, Konstantinos Markantonakis, Darren Hurley-Smith, Raja Naeem Akram and Benjamin Semal

Energies 2021, 14(3), 606; https://doi.org/10.3390/en14030606 - 25 Jan 2021

Cited by 18 | Viewed by 3448

Abstract

Current Peer-to-Peer (P2P) energy market models raise serious concerns regarding the confidentiality and integrity of energy consumption, trading and billing data. While Distributed Ledger Technology (DLT) systems (e.g., blockchain) have been proposed to enhance security, an attacker could damage other parts of the model, such as its infrastructure: an adversarial attacker could target the communication between entities by, e.g., eavesdropping or modifying data. The main goal of this paper is to propose a model for a decentralised P2P marketplace for trading energy, which addresses the problem of developing security and privacy-aware environments. Additionally, a Multi-Agent System (MAS) architecture is presented with a focus on security and sustainability. In order to propose a solution to DLT’s scalability issues (i.e., through transaction confirmation delays), off-chain state channels are considered for the energy negotiation and resolution processes. Additionally, a STRIDE (spoofing, tampering, repudiation, information disclosure, denial of service, elevation of privilege) security analysis is conducted within the context of the proposed model to identify potential vulnerabilities. Full article

(This article belongs to the Special Issue Cyber-Physical Systems for Smart Grids)

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26 pages, 428 KiB

Open AccessArticle

Distributed Computational Framework for Large-Scale Stochastic Convex Optimization

by Vahab Rostampour and Tamás Keviczky

Energies 2021, 14(1), 23; https://doi.org/10.3390/en14010023 - 23 Dec 2020

Viewed by 1788

Abstract

This paper presents a distributed computational framework for stochastic convex optimization problems using the so-called scenario approach. Such a problem arises, for example, in a large-scale network of interconnected linear systems with local and common uncertainties. Due to the large number of required scenarios to approximate the stochasticity of these problems, the stochastic optimization involves formulating a large-scale scenario program, which is in general computationally demanding. We present two novel ideas in this paper to address this issue. We first develop a technique to decompose the large-scale scenario program into distributed scenario programs that exchange a certain number of scenarios with each other to compute local decisions using the alternating direction method of multipliers (ADMM). We show the exactness of the decomposition with a-priori probabilistic guarantees for the desired level of constraint fulfillment for both local and common uncertainty sources. As our second contribution, we develop a so-called soft communication scheme based on a set parametrization technique together with the notion of probabilistically reliable sets to reduce the required communication between the subproblems. We show how to incorporate the probabilistic reliability notion into existing results and provide new guarantees for the desired level of constraint violations. Two different simulation studies of two types of interconnected network, namely dynamically coupled and coupling constraints, are presented to illustrate advantages of the proposed distributed framework. Full article

(This article belongs to the Special Issue Cyber-Physical Systems for Smart Grids)

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19 pages, 893 KiB

Open AccessArticle

Demand Flexibility Management for Buildings-to-Grid Integration with Uncertain Generation

by Vahab Rostampour, Thom S. Badings and Jacquelien M. A. Scherpen

Energies 2020, 13(24), 6532; https://doi.org/10.3390/en13246532 - 10 Dec 2020

Cited by 5 | Viewed by 2327

Abstract

We present a Buildings-to-Grid (BtG) integration framework with intermittent wind-power generation and demand flexibility management provided by buildings. First, we extend the existing BtG models by introducing uncertain wind-power generation and reformulating the interactions between the Transmission System Operator (TSO), Distribution System Operators (DSO), and buildings. We then develop a unified BtG control framework to deal with forecast errors in the wind power, by considering ancillary services from both reserves and demand-side flexibility. The resulting framework is formulated as a finite-horizon stochastic model predictive control (MPC) problem, which is generally hard to solve due to the unknown distribution of the wind-power generation. To overcome this limitation, we present a tractable robust reformulation, together with probabilistic feasibility guarantees. We demonstrate that the proposed demand flexibility management can substitute the traditional reserve scheduling services in power systems with high levels of uncertain generation. Moreover, we show that this change does not jeopardize the stability of the grid or violate thermal comfort constraints of buildings. We finally provide a large-scale Monte Carlo simulation study to confirm the impact of achievements. Full article

(This article belongs to the Special Issue Cyber-Physical Systems for Smart Grids)

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29 pages, 1901 KiB

Open AccessArticle

Configurable DDS as Uniform Middleware for Data Communication in Smart Grids

by Alaa Alaerjan, Dae-Kyoo Kim, Hua Ming and Hwimin Kim

Energies 2020, 13(7), 1839; https://doi.org/10.3390/en13071839 - 10 Apr 2020

Cited by 5 | Viewed by 3675

Abstract

Data Distribution Service (DDS) has emerged as a potential solution for data communication challenges in smart grids. DDS is designed to support quality communication for large scale real-time systems through a wide range of QoS policies. However, a smart grid involves various types of communication applications running on different computing environments. Some environments have limited computing resources such as small memory and low performance, which makes it difficult to accommodate DDS. In this paper, we present a feature-based approach for tailoring DDS to configure lightweight DDS by selecting only the necessary features for the application in consideration of the resource constraints of its running environment. This allows DDS to serve as a uniform communication middleware across the smart grid, which is critical for interoperability. We analyze DDS in terms of features and design them using Unified Modeling Language (UML) and Object Constraint Language (OCL) based on inheritance and overriding. We define a formal notion of feature composition to build DDS configurations. We implemented the approach in OpenDDS and demonstrate its application to different application environments. We also experimented the approach for the efficiency of configured DDS in terms of resource utilization. The results show that configured DDS is viable for efficient and quality data communication for applications that run on an environment with limited computing capability. Full article

(This article belongs to the Special Issue Cyber-Physical Systems for Smart Grids)

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26 pages, 1192 KiB

Open AccessFeature PaperArticle

Classical Failure Modes and Effects Analysis in the Context of Smart Grid Cyber-Physical Systems

by Andrés A. Zúñiga, Alexandre Baleia, João Fernandes and Paulo Jose Da Costa Branco

Energies 2020, 13(5), 1215; https://doi.org/10.3390/en13051215 - 6 Mar 2020

Cited by 32 | Viewed by 8828

Abstract

Reliability assessment in traditional power distribution systems has played a key role in power system planning, design, and operation. Recently, new information and communication technologies have been introduced in power systems automation and asset management, making the distribution network even more complex. In order to achieve efficient energy management, the distribution grid has to adopt a new configuration and operational conditions that are changing the paradigm of the actual electrical system. Therefore, the emergence of the cyber-physical systems concept to face future energetic needs requires alternative approaches for evaluating the reliability of modern distribution systems, especially in the smart grids environment. In this paper, a reliability approach that makes use of failure modes of power and cyber network main components is proposed to evaluate risk analysis in smart electrical distribution systems. We introduce the application of Failure Modes and Effects Analysis (FMEA) method in future smart grid systems in order to establish the impact of different failure modes on their performance. A smart grid test system is defined and failure modes and their effects for both power and the cyber components are presented. Preventive maintenance tasks are proposed and systematized to minimize the impact of high-risk failures and increase reliability. Full article

(This article belongs to the Special Issue Cyber-Physical Systems for Smart Grids)

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19 pages, 1016 KiB

Open AccessArticle

Robust Economic Model Predictive Control Based on a Zonotope and Local Feedback Controller for Energy Dispatch in Smart-Grids Considering Demand Uncertainty

by Mohamadou Nassourou, Joaquim Blesa and Vicenç Puig

Energies 2020, 13(3), 696; https://doi.org/10.3390/en13030696 - 5 Feb 2020

Cited by 20 | Viewed by 3406

Abstract

Electrical smart grids are complex MIMO systems whose operation can be noticeably affected by the presence of uncertainties such as load demand uncertainty. In this paper, based on a restricted representation of the demand uncertainty, we propose a robust economic model predictive control method that guarantees an optimal energy dispatch in a smart micro-grid. Load demands are uncertain, but viewed as bounded. The proposed method first decomposes control inputs into dependent and independent components, and then tackles the effect of demand uncertainty by tightening the system constraints as the uncertainty propagates along the prediction horizon using interval arithmetic and local state feedback control law. The tightened constraints’ upper and lower limits are computed off-line. The proposed method guarantees stability through a periodic terminal state constraint. The method is faster and simpler compared to other approaches based on Closed-loop min–max techniques. The applicability of the proposed approach is demonstrated using a smart micro-grid that comprises a wind generator, some photovoltaic (PV) panels, a diesel generator, a hydroelectric generator and some storage devices linked via two DC-buses, from which load demands can be adequately satisfied. Full article

(This article belongs to the Special Issue Cyber-Physical Systems for Smart Grids)

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24 pages, 1015 KiB

Open AccessArticle

Mitigating the Impacts of Covert Cyber Attacks in Smart Grids Via Reconstruction of Measurement Data Utilizing Deep Denoising Autoencoders

by Saeed Ahmed, YoungDoo Lee, Seung-Ho Hyun and Insoo Koo

Energies 2019, 12(16), 3091; https://doi.org/10.3390/en12163091 - 11 Aug 2019

Cited by 26 | Viewed by 3689

Abstract

As one of the most diversified cyber-physical systems, the smart grid has become more decumbent to cyber vulnerabilities. An intelligently crafted, covert, data-integrity assault can insert biased values into the measurements collected by a sensor network, to elude the bad data detector in the state estimator, resulting in fallacious control decisions. Thus, such an attack can compromise the secure and reliable operations of smart grids, leading to power network disruptions, economic loss, or a combination of both. To this end, in this paper, we propose a novel idea for the reconstruction of sensor-collected measurement data from power networks, by removing the impacts of the covert data-integrity attack. The proposed reconstruction scheme is based on a latterly developed, unsupervised learning algorithm called a denoising autoencoder, which learns about the robust nonlinear representations from the data to root out the bias added into the sensor measurements by a smart attacker. For a robust, multivariate reconstruction of the attacked measurements from multiple sensors, the denoising autoencoder is used. The proposed scheme was evaluated utilizing standard IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems. Simulation results confirm that the proposed scheme can handle labeled and non-labeled historical measurement data and results in a reasonably good reconstruction of the measurements affected by attacks. Full article

(This article belongs to the Special Issue Cyber-Physical Systems for Smart Grids)

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