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Regulation and Control of Flexible Resources in Resilient and Sustainable Power Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 2812

Special Issue Editors

College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: power and energy system operation and control; vehicle-to-grid; virtual energy storage and demand response; intelligent control of industrial loads; renewable energy; energy internet
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Guest Editor
Institute of Knowledge Technology, Complutense University of Madrid, 28040 Madrid, Spain
Interests: intelligent control (neuro and fuzzy control, evolutive optimization); modelling and simulation; autonomous vehicles: AGV; wind turbines
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Guest Editor
School of Electronics, Electrical Engineering and Computer Science (EEECS), Queen’s University, Belfast BT9 5AH, UK
Interests: power systems; stability; renewable energy; energy storage; electric vehicles; Internet of Things (IoT); signal processing; asset management
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Guest Editor
Intelligent Clean Energy Unit, Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg
Interests: optimal power flow; deep reinforcement learning for power and energy system operation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Large-scale renewable energy has emerged in both centralized and distributed infrastructure to augment conventional generation and limit the impact of fossil fuels on climate change while preserving natural fuel resources. Renewable energy investment and integration, including energy storage and intelligent loading, is one critical facet of progressive power system evolution, which is defining transitional paths to sustainability. However, the stochastic and intermittent availability of renewable energy has meant that the regulation and control of sustainable power systems is especially challenging. The traditional “generation follows consumption” approach is complex, and it is particularly difficult to manage and realize one system hosting distributed units of low-inertia and renewable resources. In terms of centralized and conventional large-scale generation, the flexible control of steam turbines and hydro turbines is achievable via day- and hour-ahead dispatch forecasting and unit commitment scheduling. However, flexible dispatch, regulation and control of renewable generation in different scenarios such as extreme heat (desert conditions), offshore, and islands, introduce much complexity. However, in terms of loading and consumption, virtual power plant and demand response management offers important strategies for flexible regulation and control. By using new and superior information and communication technologies (ICT) and Internet of Things (IoT) developments, energy policies and grid codes, and dynamic electricity pricing and market conditions, technical and financial incentives are available to resolve many of the problems posed by large-scale renewable energy and to improve availability, flexibility and sustainability for future power systems.

This Special Issue focuses on the regulation and control of resilient and sustainable power systems in terms of generation and consumption. Topics for this Special Issue include, but are not limited to, the following innovations and findings:

  1. Flexible regulation and control of steam and hydro turbines;
  2. Regulation and control of renewable energy in challenging scenarios, including desert, offshore, and island at transmission and distribution levels;
  3. Regulation and control of virtual power plant of typical regions;
  4. Regulation and control of demand response, including residential, commercial and industrial loading and system interconnection;
  5. ICT and IoT, energy policies and grid codes, and electricity market pricing and trading mechanisms;
  6. Impacts of renewable generation on dispatch and unit commitment;
  7. The role of energy storage in control and regulation.

You may choose our Joint Special Issue in Energies.

Dr. Bowen Zhou
Prof. Dr. Matilde Santos
Dr. Timothy Littler
Dr. Jun Cao
Guest Editors

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. Sustainability 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 2400 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

  • resilient power systems
  • sustainable power systems
  • regulation and control
  • flexible resources
  • virtual power plant
  • renewable energy

Published Papers (2 papers)

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22 pages, 11519 KiB  
Article
Material Tradeoff of Rotor Architecture for Lightweight Low-Loss Cost-Effective Sustainable Electric Drivetrains
by Ahmed Selema
Sustainability 2023, 15(19), 14413; https://doi.org/10.3390/su151914413 - 1 Oct 2023
Cited by 1 | Viewed by 1286
Abstract
The art of the successful design of high-speed electrical machines comes with many challenges in the mass, size, reliability, and energy efficiency. Material engineering of electrical machines has been identified as a key solution for higher power dense electric drivetrains. One of the [...] Read more.
The art of the successful design of high-speed electrical machines comes with many challenges in the mass, size, reliability, and energy efficiency. Material engineering of electrical machines has been identified as a key solution for higher power dense electric drivetrains. One of the main challenges at high speed is the eddy-current losses in the active electromagnetic parts, especially magnetic materials and permanent magnets (PMs). This study is devoted to the selection of PM rotor materials using multidisciplinary design optimization for a high-speed electric drivetrain. Beside AC loss minimization, more disciplines are considered, such as the minimization of weight, and cost. Different laminations are investigated with different magnetic properties as well as cost. Additionally, different PMs are optimized considering low-cost ferrite and high-coercivity permanent magnets (HCPMs). Moreover, the optimal materials are identified which have the best balance between loss, weight, cost, ripples. Finally, different rotor designs are prototyped, assembled, and tested using the same stator configuration. Also, the best rotor design is selected, and the electromagnetic performance is measured and compared with conventional designs. The optimal design results in 8% extra torque with at least 20% weight reduction. Full article
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24 pages, 3974 KiB  
Article
XGBoost–SFS and Double Nested Stacking Ensemble Model for Photovoltaic Power Forecasting under Variable Weather Conditions
by Bowen Zhou, Xinyu Chen, Guangdi Li, Peng Gu, Jing Huang and Bo Yang
Sustainability 2023, 15(17), 13146; https://doi.org/10.3390/su151713146 - 1 Sep 2023
Cited by 4 | Viewed by 1017
Abstract
Sustainability can achieve a balance among economic prosperity, social equity, and environmental protection to ensure the sustainable development and happiness of current and future generations; photovoltaic (PV) power, as a clean, renewable energy, is closely related to sustainability providing a reliable energy supply [...] Read more.
Sustainability can achieve a balance among economic prosperity, social equity, and environmental protection to ensure the sustainable development and happiness of current and future generations; photovoltaic (PV) power, as a clean, renewable energy, is closely related to sustainability providing a reliable energy supply for sustainable development. To solve the problem with the difficulty of PV power forecasting due to its strong intermittency and volatility, which is influenced by complex and ever-changing natural environmental factors, this paper proposes a PV power forecasting method based on eXtreme gradient boosting (XGBoost)–sequential forward selection (SFS) and a double nested stacking (DNS) ensemble model to improve the stability and accuracy of forecasts. First, this paper analyzes a variety of relevant features affecting PV power forecasting and the correlation between these features and then constructs two features: global horizontal irradiance (GHI) and similar day power. Next, a total of 16 types of PV feature data, such as temperature, azimuth, ground pressure, and PV power data, are preprocessed and the optimal combination of features is selected by establishing an XGBoost–SFS to build a multidimensional climate feature dataset. Then, this paper proposes a DNS ensemble model to improve the stacking forecasting model. Based on the gradient boosting decision tree (GBDT), XGBoost, and support vector regression (SVR), a base stacking ensemble model is set, and a new stacking ensemble model is constructed again with the metamodel of the already constructed stacking ensemble model in order to make the model more robust and reliable. Finally, PV power station data from 2019 are used as an example for validation, and the results show that the forecasting method proposed in this paper can effectively integrate multiple environmental factors affecting PV power forecasting and better model the nonlinear relationships between PV power forecasting and relevant features. This is more applicable in the case of complex and variable environmental climates that have higher forecasting accuracy requirements. Full article
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