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Dynamic Scheduling, Optimisation and Control of Futures Smart Grids

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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 (30 November 2020) | Viewed by 12535

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Dr. Mouloud Denai

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School of Engineering and Computer Science, University of Hertfordshire, Hatfield, Hertfordshire, UK
Interests: modelling, intelligent control and optimisation of renewable energy systems; energy management of smart homes; optimisation and control of future smart grids; electric vehicles, charging management and demand response (V2G and G2V); dynamic wireless charging of electric vehicles; smart mobility
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Dear Colleagues,

The future grid will be largely automated and will incorporate a host of advanced sensing, communication, information processing and control systems distributed across the physical transmission and distribution infrastructures. It will enable a higher penetration of distributed generation from renewable energy sources, energy storage and an unprecedented interaction between electricity providers and a new class of customers who can both consume and produce (prosumers), store or exchange energy with the grid. In addition, the future grid will also enable the integration of key digital technologies that enhance grid automation and improve asset management, smart homes and buildings with intelligent appliances that can adjust, curtail or defer their energy consumption to off-peak times and electric vehicles, which are set to play an active role in grid management and demand response.

The potential and promise of the future electricity system to be cost-effective, reliable, secure (cyber and physical) and resilient against disruption, and to deliver improved power quality, will require more advanced monitoring, energy scheduling and efficient assets utilisation with distributed optimisation, control and intelligence at all levels.

This aim of this Special Issue is to disseminate the latest and ongoing research advances in this emerging interdisciplinary area of smart electricity grids and related digital technologies focusing on the application of energy scheduling, data analytics, optimisation and control strategies.

We invite submissions of original, unpublished, high-quality technical and survey papers on the following topics:

Renewable energy generation technologies and grid ancillary services;
Control and optimisation of ac/dc microgrids;
Optimal sizing, placement and coordinated control of grid-scale energy storage systems;
Electric vehicle charging management, electrical vehicle-based grid services;
Demand-response, energy demand and price forecasting techniques;
Energy community and peer-to-peer energy trading;
Energy data analytics for grid monitoring, planning and management;
Advanced condition monitoring and diagnosis of grid assets;
Cyber-physical security of digital substations and future electricity grid;
Smart cities applications and services.

Dr. Mouloud Denai
Guest Editor

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. Energies 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 2600 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

smart grids
renewable energies
energy storage
energy data analytics
demand-response
condition monitoring.

Published Papers (4 papers)

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Research

16 pages, 2049 KiB

Open AccessArticle

Approximation Algorithm-Based Prosumer Scheduling for Microgrids

by Incheol Shin

Energies 2020, 13(21), 5853; https://doi.org/10.3390/en13215853 - 09 Nov 2020

Cited by 4 | Viewed by 1901

Abstract

Since the inherent intermittency and uncertainty of renewable energy resources complicates efficient Microgrid operations, a Demand Response (DR) scheme is implemented for customers in the grid to alter their power-usage patterns. However, for a real-time pricing model at the current DR, the automated decision on the energy price is not trustworthy because of artificial interferences to the power generation. As opposed to energy price, an operational cost-based prosumer scheduling approach would be able to protect the integrity of the power grid operations from deceptive market transactions and assist in robust energy management. To investigate the operational challenges associated with the costs and prosumers in the Microgrid, we focus on formulating the problem mathematically and designing approximation algorithms to solve the problem of how to optimally identify suppliers to minimize the total operational costs associated with providing electricity. We prove the hardness of the scheduling as one of the NP-Hard problems and propose polynomial time algorithms for approximating optimal solutions. With a proper resilience level for reliable power services, the scheduling algorithms include ways to construct not only robust supplier networks, but also group energy communities in terms of black start while minimizing the operational costs. A series of theoretical performances and experimental evaluations also demonstrates the practical effectiveness of this scheduling model for the operations. Full article

(This article belongs to the Special Issue Dynamic Scheduling, Optimisation and Control of Futures Smart Grids)

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14 pages, 3577 KiB

Open AccessArticle

Efficient Unbalanced Three-Phase Network Modelling for Optimal PV Inverter Control

by Chi-Thang Phan-Tan and Martin Hill

Energies 2020, 13(11), 3011; https://doi.org/10.3390/en13113011 - 11 Jun 2020

Cited by 2 | Viewed by 2048

Abstract

High penetration levels of renewable energy generation in the distribution network require voltage regulation to avoid excessive voltage at generating nodes. To effectively control the network and optimize network hosting capacity, the distribution system operator must have an efficient model for power flow analysis. This paper presents the formulas and steps to express the power flow analysis equations of an unbalanced 3-phase network in matrix form suited to programmed solutions. A benchmark MATLAB/Simulink network with unbalanced distribution lines, photovoltaic inverters, and loads is built to verify the matrix model. To demonstrate the application of the model, the control of reverse energy flow from the photovoltaic inverters to keep the voltage in the network below the regulated level is simulated. Two decentralized control algorithms are applied in the network, including an on/off and a multi-objective constrained optimization controller. The detailed construction of the optimization problem for the 3-phase network in matrix form, which is consistent with the power flow calculation, is described. Simulation with the control methods over a day shows that the total active power of the on/off and optimized controllers deliver 41.92% and 99.39% of the available solar power, respectively, while maintaining the network node voltages within limits. Full article

(This article belongs to the Special Issue Dynamic Scheduling, Optimisation and Control of Futures Smart Grids)

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17 pages, 1148 KiB

Open AccessArticle

Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

by Pascal A. Schirmer, Iosif Mporas and Akbar Sheikh-Akbari

Energies 2020, 13(9), 2148; https://doi.org/10.3390/en13092148 - 01 May 2020

Cited by 14 | Viewed by 2957

Abstract

A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets. Full article

(This article belongs to the Special Issue Dynamic Scheduling, Optimisation and Control of Futures Smart Grids)

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17 pages, 3921 KiB

Open AccessArticle

Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging

by Adnane Houbbadi, Rochdi Trigui, Serge Pelissier, Eduardo Redondo-Iglesias and Tanguy Bouton

Energies 2019, 12(14), 2727; https://doi.org/10.3390/en12142727 - 17 Jul 2019

Cited by 56 | Viewed by 5092

Abstract

Electro-mobility is increasing significantly in the urban public transport and continues to face important challenges. Electric bus fleets require high performance and extended longevity of lithium-ion battery at highly variable temperature and in different operating conditions. On the other hand, bus operators are more concerned about reducing operation and maintenance costs, which affects the battery aging cost and represents a significant economic parameter for the deployment of electric bus fleets. This paper introduces a methodological approach to manage overnight charging of an electric bus fleet. This approach identifies an optimal charging strategy that minimizes the battery aging cost (the cost of replacing the battery spread over the battery lifetime). The optimization constraints are related to the bus operating conditions, the electric vehicle supply equipment, and the power grid. The optimization evaluates the fitness function through the coupled modeling of electro-thermal and aging properties of lithium-ion batteries. Simulation results indicate a significant reduction in the battery capacity loss over 10 years of operation for the optimal charging strategy compared to three typical charging strategies. Full article

(This article belongs to the Special Issue Dynamic Scheduling, Optimisation and Control of Futures Smart Grids)

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