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Planning, Operation, and Energy Efficiency of Sustainable Electric Power Systems
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Planning, Operation, and Energy Efficiency of Sustainable Electric Power Systems

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2584

Special Issue Editor

Dr. Boyu Qin

E-Mail Website
Guest Editor
1. School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China
2. School of Energy and Electrical Engineering, Qinghai University, Xining 810016, China
Interests: power system planning; power system stability; integrated energy system; energy storage technology; low-carbon energy technolog
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In response to escalating environmental degradation and climate change, accelerating the development of a sustainable power system dominated by renewable energy has become a crucial path for global low-carbon transformation. However, the high randomness and volatility of large-scale renewable energy integration pose significant challenges to the economic and stable operation of power systems. This Special Issue explores innovative approaches to the planning, operation, and energy efficiency of sustainable electric power systems, aiming to enhance clean energy utilization efficiency and facilitate the low-carbon transformation of power systems.

Current research on the planning and operation of power systems faces numerous technical hurdles. The variations in renewable energy exhibit seasonal fluctuations and short-term uncertainties, with significant differences in resource distribution across regions. Traditional methods for planning flexible adjustment resources are limited in their adaptability to multiple scenarios, and the potential for flexible resource response has not been fully exploited. Further research is needed in planning and operating flexible resources across multiple time scales, and in achieving coordinated complementarity of resources across different regions. Additionally, it is essential to explore and optimize the flexible resource regulation characteristics of generation, grid, load, and storage to support the flexible adjustment needs of multiple scenarios.

Original research articles and reviews are welcome in this Special Issue.

I look forward to receiving your contributions.

Dr. Boyu Qin
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 250 words) can be sent to the Editorial Office for assessment.

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

  • sustainable power systems
  • power system planning
  • economic operation
  • energy efficiency
  • multi-time-scale planning
  • multi-regional complementarity
  • multiple flexible resources
  • low-carbon transition

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Published Papers (4 papers)

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Research

36 pages, 42696 KB  
Article
Bayesian Optimisation-Based Solar Power Forecasting Model and Its Analysis of Interpretability
by Qianqian Zheng, Yushuai Zhang, Zhenyu Wang, Xinru Lei, Jianxin Guo, Feng Wang and Rui Zhu
Sustainability 2026, 18(9), 4568; https://doi.org/10.3390/su18094568 - 6 May 2026
Viewed by 270
Abstract
Accurate solar power forecasting is a key technology for efficient operation of photovoltaic (PV) power plants and safe grid dispatch. Under the “dual carbon” goals and the increasing share of renewable energy connected to the grid, ultra-short-term power forecasting is important for improving [...] Read more.
Accurate solar power forecasting is a key technology for efficient operation of photovoltaic (PV) power plants and safe grid dispatch. Under the “dual carbon” goals and the increasing share of renewable energy connected to the grid, ultra-short-term power forecasting is important for improving dispatch decisions and supporting system operation. To address the ultra-short-term forecasting task at two PV sites, this study develops an end-to-end framework that integrates machine learning, Bayesian optimisation, and SHAP-based interpretability. First, correlation analysis was performed on the datasets from the two sites to provide a foundation for subsequent model development. Next, seven forecasting models, including CatBoost, NGBoost, Random Forest (RF), AdaBoost, ARIMA, CNN-LSTM, and LSTM, were developed and uniformly optimised using Bayesian optimisation. Under a unified framework of data partitioning, optimisation budget, and evaluation metrics, the predictive performance of all models at the two sites was systematically assessed. The results show that the optimal model varies across sites: at Site 1, LSTM delivered the best performance, with test-set R2, MSE, RMSE, and MAE values of 0.972, 17.610, 4.196, and 2.267, respectively; at Site 2, CatBoost achieved the best results, with corresponding values of 0.994, 0.385, 0.621, and 0.249, respectively. These findings highlight pronounced site-specific differences in model performance, indicating that different modeling approaches exhibit distinct adaptability under varying data characteristics and operational conditions. Further error analysis and SHAP interpretation indicate that solar irradiation and key meteorological variables are the main drivers of power output, and their effects are nonlinear, confirming the model’s ability to capture complex nonlinear relationships in PV power forecasting. Finally, a graphical user interface (GUI) tool was developed to support site selection, real-time forecasting, and parameter input, providing a practical and convenient solution for PV plant operation and grid dispatch. Full article
(This article belongs to the Special Issue Planning, Operation, and Energy Efficiency of Sustainable Electric Power Systems)
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22 pages, 3386 KB  
Article
UAV Visual Localization via Multimodal Fusion and Multi-Scale Attention Enhancement
by Yiheng Wang, Yushuai Zhang, Zhenyu Wang, Jianxin Guo, Feng Wang, Rui Zhu and Dejing Lin
Sustainability 2026, 18(9), 4277; https://doi.org/10.3390/su18094277 - 25 Apr 2026
Viewed by 1115
Abstract
For power-grid applications such as transmission corridor inspection, substation asset inspection, and post-disaster emergency repair, reliable UAV self-localization under GNSS-degraded or GNSS-denied conditions is critical to ensuring operational safety and accurate defect geotagging. Due to substantial discrepancies in viewpoint, scale, and geometric structure [...] Read more.
For power-grid applications such as transmission corridor inspection, substation asset inspection, and post-disaster emergency repair, reliable UAV self-localization under GNSS-degraded or GNSS-denied conditions is critical to ensuring operational safety and accurate defect geotagging. Due to substantial discrepancies in viewpoint, scale, and geometric structure between oblique UAV images and nadir satellite images, conventional RGB-based cross-view retrieval methods often suffer from unstable alignment and insufficient geometric modeling, particularly in scenarios with repetitive textures and partial overlap. To address these challenges, we propose a cross-view visual geo-localization model that integrates RGBD multimodal inputs with multi-scale attention enhancement. Specifically, MiDaS is used to estimate relative depth from UAV imagery, which is concatenated with RGB to form a four-channel input, while satellite images are padded with an additional zero channel to maintain dimensional consistency. A shared-weight ViTAdapter is adopted to learn joint semantic–geometric representations, and a lightweight Efficient Multi-scale Attention (EMA) module is adopted on spatial feature maps to strengthen multi-scale spatial consistency. In addition, an IoU-weighted InfoNCE loss is employed to accommodate partial matching during training, thereby improving the robustness of feature alignment. Experiments on the GTA-UAV dataset under the cross-area protocol show stable performance across both retrieval and localization metrics. Specifically, Recall@1, Recall@5, and Recall@10 reach 18.12%, 38.83%, and 49.47%, respectively; AP is 28.01 and SDM@3 is 0.53; meanwhile, the top-1 geodesic distance error Dis@1 is 1052.73 m. These results indicate that explicit geometric priors combined with multi-scale spatial enhancement can effectively improve cross-view feature alignment, leading to enhanced robustness and accuracy for localization in challenging power inspection scenarios. Full article
(This article belongs to the Special Issue Planning, Operation, and Energy Efficiency of Sustainable Electric Power Systems)
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28 pages, 8931 KB  
Article
Optimized Machine Learning Model and Interpretability Analysis of the Tree-Structured Parzen Estimator for Wind Power Forecasting
by Xinru Lei, Yushuai Zhang, Yunqiang Wang, Zhenyu Wang, Jianxin Guo, Feng Wang and Rui Zhu
Sustainability 2026, 18(8), 3760; https://doi.org/10.3390/su18083760 - 10 Apr 2026
Viewed by 302
Abstract
Accurate wind power forecasting is essential for efficient wind farm operation and reliable grid dispatch. This study proposes a site-adaptive forecasting framework that integrates machine learning, Tree-structured Parzen Estimator (TPE)-based Bayesian hyperparameter optimization, and SHapley Additive exPlanations (SHAP) for interpretability. Using real-world meteorological [...] Read more.
Accurate wind power forecasting is essential for efficient wind farm operation and reliable grid dispatch. This study proposes a site-adaptive forecasting framework that integrates machine learning, Tree-structured Parzen Estimator (TPE)-based Bayesian hyperparameter optimization, and SHapley Additive exPlanations (SHAP) for interpretability. Using real-world meteorological and power generation data from two wind farms, we first perform joint-distribution feature analysis to characterize statistical relationships between key inputs and power output, supporting model development and interpretation. TPE optimization is then applied to six benchmark models (CatBoost, Extra Trees, GBM, LightGBM, TabNet, and XGBoost). The optimized Extra Trees model achieves the best performance at Site 1 (R2 = 0.965, RMSE = 3.872 kW, MAE = 2.333 kW), whereas the optimized XGBoost model performs best at Site 2 (R2 = 0.921, RMSE = 3.049 kW, MAE = 1.382 kW), demonstrating the effectiveness of TPE tuning and the strong predictive capability of tree-ensemble learners. SHAP analysis further reveals heterogeneous drivers across sites: Site 1 benefits from synergistic wind-speed contributions across multiple heights, while Site 2 is primarily governed by hub-height wind speed. Overall, the proposed framework achieves both high accuracy and robust interpretability for multi-site wind power forecasting. Full article
(This article belongs to the Special Issue Planning, Operation, and Energy Efficiency of Sustainable Electric Power Systems)
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24 pages, 4907 KB  
Article
Multi-Time-Scale Energy Storage Stochastic Planning for Power Systems During Typhoon
by Shidong Hong, Boyu Qin, Peicheng Chen, Weike Song, Yiwei Su, Zhe Wu and Tong Ma
Sustainability 2026, 18(5), 2416; https://doi.org/10.3390/su18052416 - 2 Mar 2026
Cited by 1 | Viewed by 494
Abstract
The high penetration of renewable energy is becoming an important feature of new power systems. However, the power grid is facing greater threats of failures with the increasing frequency of extreme weather, making it necessary to enhance the resilience of power systems. In [...] Read more.
The high penetration of renewable energy is becoming an important feature of new power systems. However, the power grid is facing greater threats of failures with the increasing frequency of extreme weather, making it necessary to enhance the resilience of power systems. In this paper, a multi-time-scale energy storage planning system is proposed for power system resilience improvement. Firstly, the characteristics of multi-time-scale energy storage are analyzed, and models of battery energy storage and hydrogen energy storage are established. Secondly, based on an analysis of random extreme weather scenarios, a bi-level stochastic programming model for multi-energy storage aimed at enhancing the resilience of power systems is constructed. Finally, based on the modified IEEE-24 node system, the model solution and example analysis are carried out, and the optimal configuration scheme for multi-energy storage is obtained. The results show that multi-energy storage is able to adjust more flexibly and effectively improve the resilience of the power system. Compared with the configurations of short-term and long-term energy storage systems, adopting multi-timescale energy storage reduces the total cost by 22.77% and 14.08%, respectively, and improves resilience by 4.33% and 0.67%, respectively. Full article
(This article belongs to the Special Issue Planning, Operation, and Energy Efficiency of Sustainable Electric Power Systems)
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