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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 28494

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Special Issue Editors

Prof. Dr. Don Lee

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Guest Editor

School of Engineering and Computer Science, University of the Pacific, Stockton, CA 95211, USA
Interests: energy efficiency; sustainable precision agriculture; autonomous unmanned vehicle systems; digitally transformative techniques and controls

Prof. Dr. Eklas Hossain

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Guest Editor

Electrical and Computer Engineering, Boise State University, Boise, ID 83725, USA
Interests: energy systems; smart grids; machine learning; big data analytics; power electronics; advanced control system
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Special Issue Information

Dear Colleagues,

Energy is one of the most important never-ending needs that human beings have. Due to the exponential growth in power consumption and energy demand in the era of technology-driven societies, the necessity for more stable and efficient power system with zero-emission models is in desperate need. However, the complexity of such power systems has pushed the traditional models to the limit. Existing and emerging solutions are becoming difficult to implement due to the growing complications pertaining to on- and off-grid situations. Interests regarding the modelling of robust and resilient electric power and energy systems, with optimization scheme for existing resource utilization, and efficient control strategies, are burgeoning among researchers to meet the increasing demand of the energy. Applications to cars, storage devices, or robots are also welcomed.

The purpose of this Special Issue is to solve these problems together, collect collaborative research in various spectra, and contribute to the research globally in relation to power generation, transmission, or distribution systems modelling, optimization, and control. The bottom line of this Special Issue is to establish more reliable, sustainable, and intelligent power system and energy models to solve a complex power system. The research in this Special Issue thus includes, but is not limited to:

Energy demand, uncertainties, challenges, and stability issues
Novel energy and power system models
Dynamic and steady-state responses in power systems
Quality and stable energy based on modelling and control methodologies
Generation, transmission, distribution, and energy storage
On-/off-grids with clean Renewable energy sources and reduction in gas emissions
Modelling of various control strategies for robust and resilient grid systems
Power system automation, optimization, artificial intelligence, or sustainability
Alternative energy and power system solutions
Application to electric (hybrid) cars, robots, flying vehicles or their batteries

Prof. Dr. Don Lee
Prof. Dr. Eklas Hossain
Guest Editors

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Keywords

Power Systems
Energy Systems
Renewable Energy
Microgrid
Smart Grid
Modelling
Power Electronics
Control Systems
Automation
Steady-state and Dynamic Response
Grid Stability
Optimization
Energy Storage
Solar Photovoltaic
Wind Turbine
Geothermal Energy
Fuel Cell
Hydrogen
Biofuels
Sustainability
Grid Resilience and Reliability
Predictive Algorithms
Artificial Intelligence
Machine Learning
Efficiency
Micro-energy
Energy-efficient
Low-power
Self-powered Robots
Solid-state Battery
Data Analytics
Numerical Solutions
Security
Demand Response

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Published Papers (9 papers)

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Research

17 pages, 3750 KiB

Open AccessArticle

Optimal Observer-Based Power Imbalance Allocation for Frequency Regulation in Shipboard Microgrids

by Gianmario Rinaldi, Devika K. Baby and Prathyush P. Menon

Energies 2024, 17(7), 1703; https://doi.org/10.3390/en17071703 - 2 Apr 2024

Viewed by 985

Abstract

This paper proposes a two-level control strategy based on a super-twisting sliding-mode algorithm (STA) to optimally allocate power imbalances in shipboard microgrids (SMGs) while achieving frequency regulation. The strategy employs an STA observer to estimate the unknown power load demand imbalances in finite time. This estimate is then passed to an online high-level optimal control framework to periodically determine the optimal sequence of power reference values for each energy storage device (ESS), minimising the operational cost of the SMG. The online optimised power reference values are interpolated and passed to the low-level STA control strategy to control the output power of each ESS. The efficacy of the proposed methods is demonstrated through numerical simulations conducted on a prototypical model of an SMG equipped with two ESSs, namely batteries and fuel cells with associated hydrogen storage. Full article

(This article belongs to the Special Issue Modeling, Optimization, Control and Demand Response of Electric Power and Energy Systems)

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19 pages, 4493 KiB

Open AccessArticle

A Novel Analytical Formulation of SiC-MOSFET Losses to Size High-Efficiency Three-Phase Inverters

by Pedro Costa, Sónia Pinto and José Fernando Silva

Energies 2023, 16(2), 818; https://doi.org/10.3390/en16020818 - 11 Jan 2023

Cited by 7 | Viewed by 2525

Abstract

This paper presents a novel analytical loss formulation to predict the efficiency of three-phase inverters using silicon carbide (SiC) metal—oxide—semiconductor field-effect transistors (MOSFETs). The proposed analytical formulation accounts for the influence of the output current harmonic distortion on the conduction losses as well as the impact of the output parasitic capacitances and the deadtime on the switching losses. The losses are formulated in balanced conditions to select suitable SiC MOFETs for the desired target efficiency. To validate the proposed methodology, a 3-phase inverter is designed to present full load efficiency in excess of 99% when built using SiC MOSFETs antiparalleled with SiC Schottky diodes selected for the specified full load efficiency. Experimental assessment of the designed inverter efficiency is compared with the expected values from the proposed analytical formulation and shown to match or exceed the predicted results for loads ranging from 40% to 100% of full load. Full article

(This article belongs to the Special Issue Modeling, Optimization, Control and Demand Response of Electric Power and Energy Systems)

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17 pages, 505 KiB

Open AccessFeature PaperArticle

Bilateral Contracting and Price-Based Demand Response in Multi-Agent Electricity Markets: A Study on Time-of-Use Tariffs

by Hugo Algarvio and Fernando Lopes

Energies 2023, 16(2), 645; https://doi.org/10.3390/en16020645 - 5 Jan 2023

Cited by 4 | Viewed by 3081

Abstract

Electrical energy can be traded in liberalized organized markets or by negotiating private bilateral contracts. Competitive markets are central systems where market players can purchase and sell electrical energy. Bilateral contracting consists typically in a private negotiation of power over several months or years between two parties. Price-based demand response considers the active participation of consumers in electricity markets. Consumers adopt demand response programs when responding to market prices or tariffs, as they change over time. Those tariffs can be proposed by retailers by considering their load shape goals, influencing consumers to change their behavior. Consumers may adopt strategies from two different groups, namely by curtailing energy at times of high prices (e.g., peak and intermediate periods) and rescheduling energy away from those times to other times (shifting). This article considers bilateral contracting in electricity markets with demand response. It investigates how curtailment and shifting affect the energy quantity and energy cost of consumers that adopt a time-of-use tariff involving three block periods (i.e., base, intermediate and peak periods). The results indicate that consumers respond to changes in energy price according to their consumption flexibility, while retailers do not always change energy price in response to consumers’ efforts to change their consumption patterns. On average, by considering a 5% consumption reduction in the intermediate and peak periods by a consumer agent, a retailer agent reduces the energy price only by 1.5%. Full article

(This article belongs to the Special Issue Modeling, Optimization, Control and Demand Response of Electric Power and Energy Systems)

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18 pages, 5170 KiB

Open AccessArticle

An Evaluation of Co-Simulation for Modeling Coupled Natural Gas and Electricity Networks

by Brian Sergi and Kwabena Pambour

Energies 2022, 15(14), 5277; https://doi.org/10.3390/en15145277 - 21 Jul 2022

Cited by 4 | Viewed by 2063

Abstract

Reliance on natural gas for power generation has increased the coupling between gas and power networks. While this coupling can bring operational and economic benefits, it can also yield challenges, as the constraints in one system can impact the other. Co-simulation can capture the constraints and interactions between these systems, but so far, there has been limited comparison of co-simulation results to those of an integrated model. In this work, we develop a new co-simulation framework using the HELICS platform and the SAInt tool for modeling transient gas and AC optimal power flow. We evaluate this co-simulation framework against a fully integrated version of the SAInt power and gas simulators, thus providing a benchmarking of the co-simulation approach. We compare results across the two approaches for two test networks and a network representing the Belgian power and gas networks, testing both normal operating conditions and cases with compressor disruptions. In each of the cases tested, we find nearly identical results from the two approaches across various metrics of interest, such as nodal pressure, gas flow rates, and active power generation. This alignment suggests that co-simulation can yield comparable results to fully integrated models for modeling coupled gas and electricity networks. Full article

(This article belongs to the Special Issue Modeling, Optimization, Control and Demand Response of Electric Power and Energy Systems)

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20 pages, 6927 KiB

Open AccessArticle

Thermoeconomic Optimization of Steam Pressure of Heat Recovery Steam Generator in Combined Cycle Gas Turbine under Different Operation Strategies

by Zhen Wang and Liqiang Duan

Energies 2021, 14(16), 4991; https://doi.org/10.3390/en14164991 - 14 Aug 2021

Cited by 6 | Viewed by 2760

Abstract

The optimization of the steam parameters of the heat recovery steam generators (HRSG) of Combined Cycle Gas Turbines (CCGT) has become one of the important means to reduce the power generation cost of combined cycle units. Based on the structural theory of thermoeconomics, a thermoeconomic optimization model for a triple pressure reheat HRSG is established. Taking the minimization of the power generation cost of the combined cycle system as the optimization objective, an optimization algorithm based on three factors and six levels of orthogonal experimental samples to determine the optimal solution for the high, intermediate and low pressure steam pressures under different gas turbine (GT) operation strategies. The variation law and influencing factors of the system power generation cost with the steam pressure level under all operation strategies are analyzed. The research results show that the system power generation cost decreases as the GT load rate increases, T₄ plays a dominant role in the selection of the optimal pressure level for high pressure (HP) steam and, in order to obtain the optimum power generation cost, the IGV T3-650-F mode should be adopted to keep the T₄ at a high level under different GT load rates. Full article

(This article belongs to the Special Issue Modeling, Optimization, Control and Demand Response of Electric Power and Energy Systems)

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24 pages, 1268 KiB

Open AccessArticle

Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

by Seyed Mahdi Miraftabzadeh, Michela Longo, Federica Foiadelli, Marco Pasetti and Raul Igual

Energies 2021, 14(16), 4776; https://doi.org/10.3390/en14164776 - 6 Aug 2021

Cited by 57 | Viewed by 4499

Abstract

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models. Full article

(This article belongs to the Special Issue Modeling, Optimization, Control and Demand Response of Electric Power and Energy Systems)

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34 pages, 36443 KiB

Open AccessArticle

A New Decentralized Control Strategy of Microgrids in the Internet of Energy Paradigm

by Bilal Naji Alhasnawi, Basil H. Jasim, Bishoy E. Sedhom, Eklas Hossain and Josep M. Guerrero

Energies 2021, 14(8), 2183; https://doi.org/10.3390/en14082183 - 14 Apr 2021

Cited by 55 | Viewed by 3553

Abstract

The Energy Internet paradigm is the evolution of the Internet of Things concept in the power system. Microgrids (MGs), as the essential element in an Energy Internet, are expected to be controlled in a corporative and flexible manner. This paper proposes a novel decentralized robust control strategy for multi-agent systems (MASs) governed MGs in future Energy Internet. The proposed controller is based on a consensus algorithm applied with the connected distributed generators (DGs) in the MGs in the energy internet paradigm. The proposed controller’s objectives are the frequency/voltage regulation and proportional reactive/active power-sharing for the hybrid DGs connected MGs. A proposed two-level communication system is implemented to explain the data exchange between the MG system and the cloud server. The local communication level utilizes the transmission control protocol (TCP)/ internet protocol (IP) and the message queuing telemetry transport (MQTT) is used as the protocol for the global communication level. The proposed control strategy has been verified using a hypothetical hybrid DGs connected MG such as photovoltaic or wind turbines in MATLAB Simulink environment. Several scenarios based on the system load types are implemented using residential buildings and small commercial outlets. The simulation results have verified the feasibility and effectiveness of the introduced strategy for the MGs’ various operating conditions. Full article

(This article belongs to the Special Issue Modeling, Optimization, Control and Demand Response of Electric Power and Energy Systems)

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14 pages, 4468 KiB

Open AccessArticle

Regional Pole Placers of Power Systems under Random Failures/Repair Markov Jumps

by Farag Ali El-Sheikhi, Hisham M. Soliman, Razzaqul Ahshan and Eklas Hossain

Energies 2021, 14(7), 1989; https://doi.org/10.3390/en14071989 - 3 Apr 2021

Cited by 9 | Viewed by 1674

Abstract

This paper deals with a discrete-time stochastic control model design for random failure prone and maintenance in a single machine infinite bus (SMIB) system. This model includes the practical values of failure/repair rate of transmission lines and transformers. The probability matrix is, therefore, calculated accordingly. The model considers two extreme modes of operations: the most reliable mode and the least reliable contingency case. This allows the control design which stochastically stabilizes the system under jump Markov disturbances. For adequate transient response, the proposed state feedback power system stabilizer (PSS) achieves a desired settling time and damping ratio by placing the closed-loop poles in a desired region. The control target should also be satisfied for load variations in either mode of operation. A sufficient condition is developed to achieve the control objectives via solving a set of linear matrix inequalities (LMI). Using simulation, the performance of the designed controller is tested for the system that prone to random failure/maintenance under various loading conditions. Simulation results reveal that the closed-loop poles reside within the desired region satisfying the required settling time and damping ratio under the aforementioned disturbances. The contributions of the paper are summarized as follows: (1) modeling of transition probability matrix under Markov Jumps using practical data, (2) designing a controller by compelling the closed poles into the desired region to achieve adequate dynamic performance under different load varying conditions. Full article

(This article belongs to the Special Issue Modeling, Optimization, Control and Demand Response of Electric Power and Energy Systems)

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25 pages, 3107 KiB

Open AccessArticle

Modelling and Control of a Grid-Connected RES-Hydrogen Hybrid Microgrid

by Jonny Esteban Villa Londono, Andrea Mazza, Enrico Pons, Harm Lok and Ettore Bompard

Energies 2021, 14(6), 1540; https://doi.org/10.3390/en14061540 - 11 Mar 2021

Cited by 21 | Viewed by 4679

Abstract

This paper proposes a Hybrid Microgrid (HμG) model including distributed generation (DG) and a hydrogen-based storage system, controlled through a tailored control strategy. The HμG is composed of three DG units, two of them supplied by solar and wind sources, and the latter one based on the exploitation of theProton Exchange Membrane (PEM) technology. Furthermore, the system includes an alkaline electrolyser, which is used as a responsive load to balance the excess of Variable Renewable Energy Sources (VRES) production, and to produce the hydrogen that will be stored into the hydrogen tank and that will be used to supply the fuel cell in case of lack of generation. The main objectives of this work are to present a validated dynamic model for every component of the HμG and to provide a strategy to reduce as much as possible the power absorption from the grid by exploiting the VRES production. The alkaline electrolyser and PEM fuel cell models are validated through real measurements. The State of Charge (SoC) of the hydrogen tank is adjusted through an adaptive scheme. Furthermore, the designed supervisor power control allows reducing the power exchange and improving the system stability. Finally, a case, considering a summer load profile measured in an electrical substation of Politecnico di Torino, is presented. The results demonstrates the advantages of a hydrogen-based micro-grid, where the hydrogen is used as medium to store the energy produced by photovoltaic and wind systems, with the aim to improve the self-sufficiency of the system. Full article

(This article belongs to the Special Issue Modeling, Optimization, Control and Demand Response of Electric Power and Energy Systems)

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