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Sustainable Management and Design of Renewable Power Systems
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Sustainable Management and Design of Renewable Power Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (1 February 2025) | Viewed by 5541

Special Issue Editor

Prof. Dr. Hansang Lee

E-Mail Website1 Website2
Guest Editor
Department of Electrical Engineering, Semyung University, Jecheon, Republic of Korea
Interests: power system stability; renewable generation; numerical analysis; energy storage; electric railway system

Special Issue Information

Dear Colleagues,

In order to achieve carbon neutrality, there is no doubt that classic fossil-fuel-based power generation should be replaced with renewable energy sources that do not emit greenhouse gases. However, apart from the unique advantages of green electricity production, the grid connection of the large capacity of renewable power sources creates various problems from the perspective of grid control, operation, and planning. These problems are mainly due to the uncontrollability of the output, which is called intermittency or variability. This might seriously threaten the stability, reliability, and safety of the power grid, which are considered the gold standard for conventional power grid operation, and can cause great confusion in economic dispatch in the short term, and optimal investment planning in the long term in terms of economics.

Each of the issues related to stability, reliability, safety, and economics is often presented in terms of inertia, voltage restoration, flexibility resources, and hosting capacity. For the contingency analysis, the replacement of classical generators by converter-based renewable power sources would reduce the inertia of the grid, making it difficult to maintain frequency against fault. In addition, the self-protection operation of converters during faults might disconnect renewable sources from the grid, causing a shortage of resources for frequency and voltage restoration after the fault clearing. Even under steady-state conditions, forecast errors in the future output of renewable sources, which should be dependent on forecasting, require supply flexibility from controllable sources to ensure supply reliability. Finally, more economic and systematic planning should be carried out regarding the expansion of grid facilities to accommodate renewable power sources.

Research areas may include (but are not limited to) the following:

  • Renewable generation forecasting;
  • Virtual inertia;
  • Grid-forming inverter;
  • Energy storage application;
  • Dynamic voltage restoration;
  • Renewable generation monitoring system;
  • Hosting capacity expansion of the grid;
  • Renewable generation curtailment;
  • Sustainable energy mix;
  • LCOE (levelized cost of energy) of renewable generation;
  • Flexibility of the grid;
  • Sector coupling;
  • Load flexibility (DR, plus DR, V2G, etc.).

We look forward to your participation and contributions.

Prof. Dr. Hansang Lee
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. 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

  • renewable energy
  • sustainable generation
  • grid inertia with renewable energy
  • frequency regulation against RE output fluctuation
  • energy storage system

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Published Papers (3 papers)

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Research

15 pages, 1372 KiB  
Article
A Deep Learning-Based Solar Power Generation Forecasting Method Applicable to Multiple Sites
by Seon Young Jang, Byung Tae Oh and Eunsung Oh
Sustainability 2024, 16(12), 5240; https://doi.org/10.3390/su16125240 - 20 Jun 2024
Cited by 1 | Viewed by 1594
Abstract
This paper addresses the challenge of accurately forecasting solar power generation (SPG) across multiple sites using a single common model. The proposed deep learning-based model is designed to predict SPG for various locations by leveraging a comprehensive dataset from multiple sites in the [...] Read more.
This paper addresses the challenge of accurately forecasting solar power generation (SPG) across multiple sites using a single common model. The proposed deep learning-based model is designed to predict SPG for various locations by leveraging a comprehensive dataset from multiple sites in the Republic of Korea. By incorporating common meteorological elements such as temperature, humidity, and cloud cover into its framework, the model uniquely identifies site-specific features to enhance the forecasting accuracy. The key innovation of this model is the integration of a classifier module within the common model framework, enabling it to adapt and predict SPG for both known and unknown sites based on site similarities. This approach allows for the extraction and utilization of site-specific characteristics from shared meteorological data, significantly improving the model’s adaptability and generalization across diverse environmental conditions. The evaluation results demonstrate that the model maintains high performance levels across different SPG sites with minimal performance degradation compared to site-specific models. Notably, the model shows robust forecasting capabilities, even in the absence of target SPG data, highlighting its potential to enhance operational efficiency and support the integration of renewable energy into the power grid, thereby contributing to the global transition towards sustainable energy sources. Full article
(This article belongs to the Special Issue Sustainable Management and Design of Renewable Power Systems)
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20 pages, 8064 KiB  
Article
Smart Reserve Planning Using Machine Learning Methods in Power Systems with Renewable Energy Sources
by Serdal Atiç and Ercan Izgi
Sustainability 2024, 16(12), 5193; https://doi.org/10.3390/su16125193 - 18 Jun 2024
Cited by 1 | Viewed by 1195
Abstract
Estimation of the power obtained from intermittent renewable energy sources (IRESs) is an important issue for the integration of these power plants into the power system. In this study, the expected power not served (EPNS) formula, a reliability criterion for power systems, is [...] Read more.
Estimation of the power obtained from intermittent renewable energy sources (IRESs) is an important issue for the integration of these power plants into the power system. In this study, the expected power not served (EPNS) formula, a reliability criterion for power systems, is developed with a new method that takes into consideration the power generated from IRESs and the consumed power (CP) estimation errors. In the proposed method, CP, generated wind power (GWP), and generated solar power (GSP) predictions made with machine learning methods are included in the EPNS formulation. The most accurate prediction results were obtained with the Multi Layer Perceptron (MLP), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN) algorithms used for prediction, and these results were compared. Using different forecasting methods, the relation between forecast accuracy, reserve requirement, and total cost was examined. Reliability, smart reserve planning (SRP), and total cost analysis for power systems were carried out with the CNN algorithm, which provides the most successful prediction result among the prediction algorithms used. The effect of increasing the limit EPNS value allowed by the power system operator, that is, reducing the system reliability, on the reserve requirement and total cost has been revealed. This study provides a useful proposal for the integration of IRESs, such as solar and wind power plants, into power systems. Full article
(This article belongs to the Special Issue Sustainable Management and Design of Renewable Power Systems)
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15 pages, 4079 KiB  
Article
A Wind Power Scenario Generation Method Based on Copula Functions and Forecast Errors
by Jaehyun Yoo, Yongju Son, Myungseok Yoon and Sungyun Choi
Sustainability 2023, 15(23), 16536; https://doi.org/10.3390/su152316536 - 4 Dec 2023
Cited by 3 | Viewed by 1994
Abstract
The scenario of renewable energy generation significantly affects the probabilistic distribution system analysis. To reflect the probabilistic characteristics of actual data, this paper proposed a scenario generation method that can reflect the spatiotemporal characteristics of wind power generation and the probabilistic characteristics of [...] Read more.
The scenario of renewable energy generation significantly affects the probabilistic distribution system analysis. To reflect the probabilistic characteristics of actual data, this paper proposed a scenario generation method that can reflect the spatiotemporal characteristics of wind power generation and the probabilistic characteristics of forecast errors. The scenario generation method consists of a process of sampling random numbers and a process of inverse sampling using the cumulative distribution function. In sampling random numbers, random numbers that mimic the spatiotemporal correlation of power generation were generated using the copula function. Furthermore, the cumulative distribution functions of forecast errors according to power generation bins were used, thereby reflecting the probabilistic characteristics of forecast errors. The wind power generation scenarios in Jeju Island, generated by the proposed method, were analyzed through various indices that can assess accuracy. As a result, it was confirmed that by using the proposed scenario generation method, scenarios similar to actual data can be generated, which in turn allows for preparation of situations with a high probability of occurrence within the distribution system. Full article
(This article belongs to the Special Issue Sustainable Management and Design of Renewable Power Systems)
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