sustainability-logo

Journal Browser

Journal Browser

Perspectives and Challenges: New Energy Power Generation and Power System Sustainability

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

Deadline for manuscript submissions: 7 September 2024 | Viewed by 892

Special Issue Editors


E-Mail Website
Guest Editor
Department of Engineering and Applied Sciences, University of Bergamo, 24044 Dalmine, Italy
Interests: smart grid; power mix; hydrogen chain; district heating & cooling; thermal energy storage; energy system optimization
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Engineering and Applied Sciences, University of Bergamo, 24044 Dalmine, Italy
Interests: power mix; concentrate solar power; heliostat field; hydrogen chain; energy system optimization

Special Issue Information

Dear Colleagues,

The future of power generation is related to the ongoing energy transition from fossil-based systems toward a new regime featuring larger quantities of renewable energy. To facilitate this transition, many governments around the world are planning new green energy mix initiatives in order to reduce CO2 emissions.

This new power generation often challenges load balance and grid stability because of the variability, intermittency, and unpredictability of renewable energy production.

New energy power generation systems, such as renewable-based energy mixes, have proven to be the best ways of increasing the contribution of renewables and either strengthening or replacing traditional systems.

We are pleased to invite you to contribute to this Special Issue, which has a specific focus on the sustainability of the power generation system. We aim to fill the existing gap in the literature around the technical feasibility and sustainability of the proposed solution from different points of view.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Concentrated solar power;
  • Renewable-based district heating and cooling;
  • Energy storage and their application;
  • Energy mix;
  • Distributed generation system, smart grids, microgrids and hybrid-system;
  • Approaches and tools for modelling and simulation of energy systems;
  • Cogeneration/Trigeneration and energy management;
  • Combination and integration of several energy sources and storage solutions;
  • Energy harvesting and recovery.

We look forward to receiving your contributions.

Dr. Giovanni Brumana
Dr. Elisa Ghirardi
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

  • sustainability
  • power generation
  • energy storage
  • energy conversion
  • renewable energy
  • energy management
  • smart grids’ testing and modelling

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

24 pages, 8882 KiB  
Article
Short-Term Power-Generation Prediction of High Humidity Island Photovoltaic Power Station Based on a Deep Hybrid Model
by Jiahui Wang, Mingsheng Jia, Shishi Li, Kang Chen, Cheng Zhang, Xiuyu Song and Qianxi Zhang
Sustainability 2024, 16(7), 2853; https://doi.org/10.3390/su16072853 - 29 Mar 2024
Viewed by 661
Abstract
Precise prediction of the power generation of photovoltaic (PV) stations on the island contributes to efficiently utilizing and developing abundant solar energy resources along the coast. In this work, a hybrid short-term prediction model (ICMIC-POA-CNN-BIGRU) was proposed to study the output of a [...] Read more.
Precise prediction of the power generation of photovoltaic (PV) stations on the island contributes to efficiently utilizing and developing abundant solar energy resources along the coast. In this work, a hybrid short-term prediction model (ICMIC-POA-CNN-BIGRU) was proposed to study the output of a fishing–solar complementary PV station with high humidity on the island. ICMIC chaotic mapping was used to optimize the initial position of the pelican optimization algorithm (POA) population, enhancing the global search ability. Then, ICMIC-POA performed hyperparameter debugging and L2-regularization coefficient optimization on CNN-BIGRU (convolutional neural network and bidirectional gated recurrent unit). The L2-regularization technique optimized the loss curve and over-fitting problem in the CNN-BIGRU training process. To compare the prediction effect with the other five models, three typical days (sunny, cloudy, and rainy) were selected to establish the model, and six evaluation indexes were used to evaluate the prediction performance. The results show that the model proposed in this work shows stronger robustness and generalization ability. K-fold cross-validation verified the prediction effects of three models established by different datasets for three consecutive days and five consecutive days. Compared with the CNN-BIGRU model, the RMSE values of the newly proposed model were reduced by 64.08%, 46.14%, 57.59%, 60.61%, and 34.04%, respectively, in sunny, cloudy, rainy, continuous prediction 3 days, and 5 days. The average value of the determination coefficient R2 of the 20 experiments was 0.98372 on sunny days, 0.97589 on cloudy days, and 0.98735 on rainy days. Full article
Show Figures

Figure 1

Back to TopTop