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Energies
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Design, Planning and Evaluation of Flexible Power Systems
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Design, Planning and Evaluation of Flexible Power Systems

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

Deadline for manuscript submissions: closed (10 December 2022) | Viewed by 16935

Special Issue Editors

Dr. Ameena Al Sumaiti

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science, Advanced Power and Energy Center, Khalifa University, Abu Dhabi, United Arab Emirates
Interests: energy management; data analytics; energy policy and economics
Dr. Mousa Marzband

grade E-Mail Website
Guest Editor
Physics and Electrical Engineering, Department of Mathematics, Newcastle, United Northumbria University, London E1 7HT, UK
Interests: smart grids; scheduling; active distribution networks; power systems; energy economics; renewable energy integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Power systems undergo transitions into smart renewable era. With renewable energy penetration into grid, challenges exist in attaining balance between variable supply and demand. Power supply is variable due to renewable power generation uncertainty. So, energy management is essential to ensure energy efficient power system operation. Power system flexibility is new term introduced to account for both aspects of management and control at supply and demand sides and the link between them. With power market players and their diverse goals and renewable energy infrastructure expansion, more research is mandated to enhance sustainability in energy management system and attain energy autonomy in lucrative market. This issue targets research on power system flexibility by proposing solutions for power system management covering its optimal and efficient design, planning, control and operation. The call also targets assessing data modelling impact on power system uncertainty and solutions

Dr. Ameena Al Sumaiti
Dr. Mousa Marzband
Guest Editors

Manuscript Submission Information

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Keywords

  • power system flexibility
  • energy management
  • demand flexibility
  • renewable energy
  • data analytics of renewable energy and demand
  • design, control, planning and operation of power systems and renewable energy
  • energy autonomy
  • smart charging/discharging control
  • sustainability zero emissions

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Published Papers (5 papers)

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Research

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19 pages, 4574 KiB  
Article
Performance Enhancement of an Islanded Microgrid with the Support of Electrical Vehicle and STATCOM Systems
by Omar Makram Kamel, Ahmed A. Zaki Diab, Mohamed Metwally Mahmoud, Ameena Saad Al-Sumaiti and Hamdy M. Sultan
Energies 2023, 16(4), 1577; https://doi.org/10.3390/en16041577 - 4 Feb 2023
Cited by 30 | Viewed by 1699
Abstract
Modern electrical power systems now require the spread of microgrids (MG), where they would be operating in either islanded mode or grid-connected mode. An inherent mismatch between loads and sources is introduced by changeable high renewable share in an islanded MG system with [...] Read more.
Modern electrical power systems now require the spread of microgrids (MG), where they would be operating in either islanded mode or grid-connected mode. An inherent mismatch between loads and sources is introduced by changeable high renewable share in an islanded MG system with stochastic load demands. The system frequency is directly impacted by this mismatch, which can be alleviated by incorporating cutting-edge energy storage technologies and FACTS tools. The investigated islanded MG system components are wind farm, solar PV, Electric vehicles (EVs), loads, DSTATCOM, and diesel power generator. An aggregated EVs model is connected to the MG during uncertain periods of the generation of renewable energy (PV and wind) to support the performance of MGs. The ability to support ancillary services from the EVs is checked. DSTATCOM is used to provide voltage stability for the MG during congestion situations. The MG is studied in three scenarios: the first scenario MG without EVs and DSTATCOM, the second scenario MG without DSTATCOM, and the third scenario MG with all components. These scenarios are addressed to show the role of EVs and DSTATCOM, and the results in the third scenario are the best. The system voltage and frequency profile is the best in the last scenario and is entirely satisfactory and under the range of the IEEE standard. The obtained results show that both EVs and DSTATCOM are important units for improving the stability of modern power grids. The Matlab/Simulink program is considered for checking and validating the dynamic performance of the proposed configuration. Full article
(This article belongs to the Special Issue Design, Planning and Evaluation of Flexible Power Systems)
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14 pages, 3379 KiB  
Article
Multi-Feature Data Fusion-Based Load Forecasting of Electric Vehicle Charging Stations Using a Deep Learning Model
by Prince Aduama, Zhibo Zhang and Ameena S. Al-Sumaiti
Energies 2023, 16(3), 1309; https://doi.org/10.3390/en16031309 - 26 Jan 2023
Cited by 23 | Viewed by 3325
Abstract
We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on observations of historical weather (wind speed, temperature, and humidity) data [...] Read more.
We propose a forecasting technique based on multi-feature data fusion to enhance the accuracy of an electric vehicle (EV) charging station load forecasting deep-learning model. The proposed method uses multi-feature inputs based on observations of historical weather (wind speed, temperature, and humidity) data as multiple inputs to a Long Short-Term Memory (LSTM) model to achieve a robust prediction of charging loads. Weather conditions are significant influencers of the behavior of EV drivers and their driving patterns. These behavioral and driving patterns affect the charging patterns of the drivers. Rather than one prediction (step, model, or variables) made by conventional LSTM models, three charging load (energy demand) predictions of EVs were made depending on different multi-feature inputs. Data fusion was used to combine and optimize the different charging load prediction results. The performance of the final implemented model was evaluated by the mean absolute prediction error of the forecast. The implemented model had a prediction error of 3.29%. This prediction error was lower than initial prediction results by the LSTM model. The numerical results indicate an improvement in the performance of the EV load forecast, indicating that the proposed model could be used to optimize and improve EV load forecasts for electric vehicle charging stations to meet the energy requirements of EVs. Full article
(This article belongs to the Special Issue Design, Planning and Evaluation of Flexible Power Systems)
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17 pages, 3551 KiB  
Article
Optimal Loss of Load Expectation for Generation Expansion Planning Considering Fuel Unavailability
by Radhanon Diewvilai and Kulyos Audomvongseree
Energies 2022, 15(21), 7854; https://doi.org/10.3390/en15217854 - 23 Oct 2022
Cited by 6 | Viewed by 2691
Abstract
In generation expansion planning, reliability level is the key criterion to ensure enough generation above peak demand in case there are any generation outages. This reliability criterion must be appropriately optimized to provide a reliable generation system with a minimum generation cost. Currently, [...] Read more.
In generation expansion planning, reliability level is the key criterion to ensure enough generation above peak demand in case there are any generation outages. This reliability criterion must be appropriately optimized to provide a reliable generation system with a minimum generation cost. Currently, a method to determine an optimal reliability criterion is mainly focused on reserve margin, an accustomed criterion used by several generation utilities. However, Loss of Load Expectation (LOLE) is a more suitable reliability criterion for a generation system with a high proportion of renewable energy since it considers both the probabilistic characteristics of the generation system and the entire load’s profile. Moreover, it is also correlated with the reserve margin. Considering the current fuel supply situation, a probabilistic model based on Bayes’ Theorem is also proposed to incorporate fuel supply unavailability into the probabilistic criterion. This paper proposes a method for determining the optimal LOLE along with a model that incorporates fuel supply unavailability into consideration. This method is tested with Thailand’s Power Development Plan 2018 revision 1 to demonstrate numerical examples. It is found that the optimal LOLE of the test system is 0.7 day/year, or shifted to 0.55 day/year in the case of considering the fuel supply unavailability. Full article
(This article belongs to the Special Issue Design, Planning and Evaluation of Flexible Power Systems)
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20 pages, 4818 KiB  
Article
Techno-Economic Analysis of Hybrid Renewable Energy Systems Designed for Electric Vehicle Charging: A Case Study from the United Arab Emirates
by Alya AlHammadi, Nasser Al-Saif, Ameena Saad Al-Sumaiti, Mousa Marzband, Tareefa Alsumaiti and Ehsan Heydarian-Forushani
Energies 2022, 15(18), 6621; https://doi.org/10.3390/en15186621 - 10 Sep 2022
Cited by 22 | Viewed by 4767
Abstract
The United Arab Emirates is moving towards the use of renewable energy for many reasons, including the country’s high energy consumption, unstable oil prices, and increasing carbon dioxide emissions. The usage of electric vehicles can improve public health and reduce emissions that contribute [...] Read more.
The United Arab Emirates is moving towards the use of renewable energy for many reasons, including the country’s high energy consumption, unstable oil prices, and increasing carbon dioxide emissions. The usage of electric vehicles can improve public health and reduce emissions that contribute to climate change. Thus, the usage of renewable energy resources to meet the demands of electric vehicles is the major challenge influencing the development of an optimal smart system that can satisfy energy requirements, enhance sustainability and reduce negative environmental impacts. The objective of this study was to examine different configurations of hybrid renewable energy systems for electric vehicle charging in Abu Dhabi city, UAE. A comprehensive study was conducted to investigate previous electric vehicle charging approaches and formulate the problem accordingly. Subsequently, methods for acquiring data with respect to the energy input and load profiles were determined, and a techno-economic analysis was performed using Hybrid Optimization of Multiple Energy Resources (HOMER) software. The results demonstrated that the optimal electric vehicle charging model comprising solar photovoltaics, wind turbines, batteries and a distribution grid was superior to the other studied configurations from the technical, economic and environmental perspectives. An optimal model could produce excess electricity of 22,006 kWh/year with an energy cost of 0.06743 USD/kWh. Furthermore, the proposed battery–grid–solar photovoltaics–wind turbine system had the highest renewable penetration and thus reduced carbon dioxide emissions by 384 tons/year. The results also indicated that the carbon credits associated with this system could result in savings of 8786.8 USD/year. This study provides new guidelines and identifies the best indicators for electric vehicle charging systems that will positively influence the trend in carbon dioxide emissions and achieve sustainable electricity generation. This study also provides a valid financial assessment for investors looking to encourage the use of renewable energy. Full article
(This article belongs to the Special Issue Design, Planning and Evaluation of Flexible Power Systems)
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Review

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21 pages, 3545 KiB  
Review
Electric Vehicle Charging Infrastructure and Energy Resources: A Review
by Prince Aduama, Ameena S. Al-Sumaiti and Khalifa H. Al-Hosani
Energies 2023, 16(4), 1965; https://doi.org/10.3390/en16041965 - 16 Feb 2023
Cited by 7 | Viewed by 3321
Abstract
Recent motivation to cut greenhouse gas emissions to combat climate change has led to increasing transportation electrification. However, electric vehicle proliferation comes with a number of challenges such as battery capacities and the range anxiety of electric vehicles. In this paper, a review [...] Read more.
Recent motivation to cut greenhouse gas emissions to combat climate change has led to increasing transportation electrification. However, electric vehicle proliferation comes with a number of challenges such as battery capacities and the range anxiety of electric vehicles. In this paper, a review of the main components that affect electric vehicle adoption, which are charging infrastructure and energy resources, is presented. We discuss the categories of electric vehicle charging infrastructure, based on the location-of-charge and the charging technology. In addition, a review of the energy resources required for electric vehicles is also presented. The key features of these batteries are also discussed. Full article
(This article belongs to the Special Issue Design, Planning and Evaluation of Flexible Power Systems)
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