Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (4,313)

Search Parameters:
Keywords = integrated renewable energy systems

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
30 pages, 2504 KiB  
Article
Battery Energy Storage Systems: Energy Market Review, Challenges, and Opportunities in Frequency Control Ancillary Services
by Gian Garttan, Sanath Alahakoon, Kianoush Emami and Shantha Gamini Jayasinghe
Energies 2025, 18(15), 4174; https://doi.org/10.3390/en18154174 - 6 Aug 2025
Abstract
Battery energy storage systems (BESS) are considered a good energy source to maintain supply and demand, mitigate intermittency, and ensure grid stability. The primary contribution of this paper is to provide a comprehensive overview of global energy markets and a critical analysis of [...] Read more.
Battery energy storage systems (BESS) are considered a good energy source to maintain supply and demand, mitigate intermittency, and ensure grid stability. The primary contribution of this paper is to provide a comprehensive overview of global energy markets and a critical analysis of BESS’ participation in frequency control ancillary service (FCAS) markets. This review synthesises the current state of knowledge on the evolution of the energy market and the role of battery energy storage systems in providing grid stability, particularly frequency control services, with a focus on their integration into evolving high-renewable-energy-source (RES) market structures. Specifically, solar PV and wind energy are emerging as the main drivers of RES expansion, accounting for approximately 61% of the global market share. A BESS offers greater flexibility in storage capacity, scalability and rapid response capabilities, making it an effective solution to address emerging security risks of the system. Moreover, a BESS is able to provide active power support through power smoothing when coupled with solar photovoltaic (PV) and wind generation. In this paper, we provide an overview of the current status of energy markets, the contribution of battery storage systems to grid stability and flexibility, as well as the challenges that BESS face in evolving electricity markets. Full article
25 pages, 3220 KiB  
Article
Distributed Energy Management for Ship-Integrated Energy System Under Marine Environmental Risk Field
by Yuxin Zhang, Yang Xiao and Tieshan Li
Energies 2025, 18(15), 4163; https://doi.org/10.3390/en18154163 - 6 Aug 2025
Abstract
To reduce carbon emissions in the shipping industry, the energy management problem of the ship-integrated energy system (S-IES) is analyzed in this paper. Firstly, a marine environmental risk field model is constructed to quantify the degree of hazard when designing the sailing route. [...] Read more.
To reduce carbon emissions in the shipping industry, the energy management problem of the ship-integrated energy system (S-IES) is analyzed in this paper. Firstly, a marine environmental risk field model is constructed to quantify the degree of hazard when designing the sailing route. Meanwhile, an energy management model that considers both economic and environmental benefits is developed to enhance the penetration rate of renewable resources. Subsequently, a distributed energy management algorithm based on finite-time consensus theory is proposed to ensure a rapid and accurate response to load demand. Moreover, a mathematical analysis is provided to demonstrate the algorithm’s effectiveness. Finally, the sea area between Singapore Port (Singapore) and Penang Port (Malaysia) is chosen as the simulation environment. The experimental results demonstrate the effectiveness of energy management for the S-IES. Full article
Show Figures

Figure 1

30 pages, 3996 KiB  
Article
Incentive-Compatible Mechanism Design for Medium- and Long-Term/Spot Market Coordination in High-Penetration Renewable Energy Systems
by Sicong Wang, Weiqing Wang, Sizhe Yan and Qiuying Li
Processes 2025, 13(8), 2478; https://doi.org/10.3390/pr13082478 - 6 Aug 2025
Abstract
In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems [...] Read more.
In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems with high renewable energy penetration. A three-stage joint operation framework is proposed. First, a medium- and long-term trading game model is established, considering multiple energy types to optimize the benefits of market participants. Second, machine learning algorithms are employed to predict renewable energy output, and a contract decomposition mechanism is developed to ensure a smooth transition from medium- and long-term contracts to real-time market operations. Finally, a day-ahead market-clearing strategy and an incentive-compatible settlement mechanism, incorporating the constraints from contract decomposition, are proposed to link the two markets effectively. Simulation results demonstrate that the proposed mechanism effectively enhances resource allocation and stabilizes market operations, leading to significant revenue improvements across various generation units and increased renewable energy utilization. Specifically, thermal power units achieve a 19.12% increase in revenue, while wind and photovoltaic units show more substantial gains of 38.76% and 47.52%, respectively. Concurrently, the mechanism drives a 10.61% increase in renewable energy absorption capacity and yields a 13.47% improvement in Tradable Green Certificate (TGC) utilization efficiency, confirming its overall effectiveness. This research shows that coordinated optimization between medium- and long-term/spot markets, combined with a well-designed settlement mechanism, significantly strengthens the market competitiveness of renewable energy, providing theoretical support for the market-based operation of the new power system. Full article
(This article belongs to the Section Energy Systems)
Show Figures

Figure 1

23 pages, 3337 KiB  
Article
Imbalance Charge Reduction in the Italian Intra-Day Market Using Short-Term Forecasting of Photovoltaic Generation
by Cristina Ventura, Giuseppe Marco Tina and Santi Agatino Rizzo
Energies 2025, 18(15), 4161; https://doi.org/10.3390/en18154161 - 5 Aug 2025
Abstract
In the Italian intra-day electricity market (MI-XBID), where energy positions can be adjusted up to one hour before delivery, imbalance charges due to forecast errors from non-programmable renewable sources represent a critical issue. This work focuses on photovoltaic (PV) systems, whose production variability [...] Read more.
In the Italian intra-day electricity market (MI-XBID), where energy positions can be adjusted up to one hour before delivery, imbalance charges due to forecast errors from non-programmable renewable sources represent a critical issue. This work focuses on photovoltaic (PV) systems, whose production variability makes them particularly sensitive to forecast accuracy. To address these challenges, a comprehensive methodology for assessing and mitigating imbalance penalties by integrating a short-term PV forecasting model with a battery energy storage system is proposed. Unlike conventional approaches that focus exclusively on improving statistical accuracy, this study emphasizes the economic and regulatory impact of forecast errors under the current Italian imbalance settlement framework. A hybrid physical-artificial neural network is developed to forecast PV power one hour in advance, combining historical production data and clear-sky irradiance estimates. The resulting imbalances are analyzed using regulatory tolerance thresholds. Simulation results show that, by adopting a control strategy aimed at maintaining the battery’s state of charge around 50%, imbalance penalties can be completely eliminated using a storage system sized for just over 2 equivalent hours of storage capacity. The methodology provides a practical tool for market participants to quantify the benefits of storage integration and can be generalized to other electricity markets where tolerance bands for imbalances are applied. Full article
(This article belongs to the Special Issue Advanced Forecasting Methods for Sustainable Power Grid: 2nd Edition)
Show Figures

Figure 1

23 pages, 782 KiB  
Article
From Local Actions to Global Impact: Overcoming Hurdles and Showcasing Sustainability Achievements in the Implementation of SDG12
by John N. Hahladakis
Sustainability 2025, 17(15), 7106; https://doi.org/10.3390/su17157106 - 5 Aug 2025
Abstract
This study examines the progress, challenges, and successes in implementing Sustainable Development Goal 12 (SDG12), focusing on responsible consumption and production, using Qatar as a case study. The State has integrated Sustainable Consumption and Production (SCP) into national policies, established coordination mechanisms, and [...] Read more.
This study examines the progress, challenges, and successes in implementing Sustainable Development Goal 12 (SDG12), focusing on responsible consumption and production, using Qatar as a case study. The State has integrated Sustainable Consumption and Production (SCP) into national policies, established coordination mechanisms, and implemented action plans aligned with SDG12 targets. Achievements include renewable energy adoption, waste management reforms, and sustainable public procurement, though challenges persist in rationalizing fossil fuel subsidies, addressing data gaps, and enhancing corporate sustainability reporting. Efforts to reduce food loss and waste through redistribution programs highlight the country’s resilience, despite logistical obstacles. The nation has also advanced hazardous waste management, environmental awareness, and sustainable tourism policies, though gaps in data systems and policy coherence remain. Qatar’s approach provides a valuable local-to-global example of balancing resource-dependent economies with sustainability goals. Its strategies and lessons offer potential adaptability for other nations, especially those facing similar challenges in achieving SDG12. By strengthening data systems, enhancing policy integration, and fostering regional and international cooperation, Qatar’s efforts underscore the importance of aligning economic growth with environmental stewardship, serving as a blueprint for global sustainability initiatives. Full article
Show Figures

Graphical abstract

22 pages, 3601 KiB  
Article
Support-Vector-Regression-Based Intelligent Control Strategy for DFIG Wind Turbine Systems
by Farhat Nasim, Shahida Khatoon, Ibraheem Nasiruddin, Mohammad Shahid, Shabana Urooj and Basel Bilal
Machines 2025, 13(8), 687; https://doi.org/10.3390/machines13080687 - 5 Aug 2025
Abstract
Achieving sustainable energy goals requires efficient integration of renewables like wind energy. Doubly fed induction generator (DFIG)-based wind turbine systems (WTSs) operate efficiently across a range of speeds, making them well-suited for modern renewable energy systems. However, sudden wind speed variations can cause [...] Read more.
Achieving sustainable energy goals requires efficient integration of renewables like wind energy. Doubly fed induction generator (DFIG)-based wind turbine systems (WTSs) operate efficiently across a range of speeds, making them well-suited for modern renewable energy systems. However, sudden wind speed variations can cause power oscillations, rotor speed fluctuations, and voltage instability. Traditional proportional–integral (PI) controllers struggle with such nonlinear, rapidly changing scenarios. A control approach utilizing support vector regression (SVR) is proposed for the DFIG wind turbine system. The SVR controller manages both active and reactive power by simultaneously controlling the rotor- and grid-side converters (RSC and GSC). Simulations under a sudden wind speed variation from 10 to 12 m per second show the SVR approach reduces settling time significantly (up to 70.3%), suppresses oscillations in rotor speed, torque, and power output, and maintains over 97% DC-link voltage stability. These improvements enhance power quality, reliability, and system performance, demonstrating the SVR controller’s superiority over conventional PI methods for variable-speed wind energy systems. Full article
(This article belongs to the Special Issue Modelling, Design and Optimization of Wind Turbines)
Show Figures

Figure 1

51 pages, 4099 KiB  
Review
Artificial Intelligence and Digital Twin Technologies for Intelligent Lithium-Ion Battery Management Systems: A Comprehensive Review of State Estimation, Lifecycle Optimization, and Cloud-Edge Integration
by Seyed Saeed Madani, Yasmin Shabeer, Michael Fowler, Satyam Panchal, Hicham Chaoui, Saad Mekhilef, Shi Xue Dou and Khay See
Batteries 2025, 11(8), 298; https://doi.org/10.3390/batteries11080298 - 5 Aug 2025
Abstract
The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery [...] Read more.
The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article
Show Figures

Figure 1

14 pages, 1536 KiB  
Article
Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving
by Peiyu Chen, Wenqing Cui, Jingan Shang, Bin Xu, Chao Li and Danyang Lun
Appl. Sci. 2025, 15(15), 8656; https://doi.org/10.3390/app15158656 (registering DOI) - 5 Aug 2025
Abstract
In order to achieve the goals of carbon neutrality, large-scale storage of renewable energy sources has been integrated into the power grid. Under these circumstances, the power grid faces the challenge of peak shaving. Therefore, this paper proposes a coordinated variable-power control strategy [...] Read more.
In order to achieve the goals of carbon neutrality, large-scale storage of renewable energy sources has been integrated into the power grid. Under these circumstances, the power grid faces the challenge of peak shaving. Therefore, this paper proposes a coordinated variable-power control strategy for multiple battery energy storage stations (BESSs), improving the performance of peak shaving. Firstly, the strategy involves constructing an optimization model incorporating load forecasting, capacity constraints, and security indices to design a coordination mechanism tracking the target load band with the equivalent power. Secondly, it establishes a quantitative evaluation system using metrics such as peak–valley difference and load standard deviation. Comparison based on typical daily cases shows that, compared with the constant power strategy, the coordinated variable-power control strategy has a more obvious and comprehensive improvement in overall peak-shaving effects. Furthermore, it employs a “dynamic dispatch of multiple BESS” mode, effectively mitigating the risks and flexibility issues associated with single BESSs. This strategy provides a reliable new approach for large-scale energy storage to participate in high-precision peaking. Full article
Show Figures

Figure 1

36 pages, 5151 KiB  
Article
Flexibility Resource Planning and Stability Optimization Methods for Power Systems with High Penetration of Renewable Energy
by Haiteng Han, Xiangchen Jiang, Yang Cao, Xuanyao Luo, Sheng Liu and Bei Yang
Energies 2025, 18(15), 4139; https://doi.org/10.3390/en18154139 - 4 Aug 2025
Abstract
With the accelerating global transition toward sustainable energy systems, power grids with a high share of renewable energy face increasing challenges due to volatility and uncertainty, necessitating advanced flexibility resource planning and stability optimization strategies. This paper presents a comprehensive distribution network planning [...] Read more.
With the accelerating global transition toward sustainable energy systems, power grids with a high share of renewable energy face increasing challenges due to volatility and uncertainty, necessitating advanced flexibility resource planning and stability optimization strategies. This paper presents a comprehensive distribution network planning framework that coordinates and integrates multiple types of flexibility resources through joint optimization and network reconfiguration to enhance system adaptability and operational resilience. A novel virtual network coupling modeling approach is proposed to address topological constraints during network reconfiguration, ensuring radial operation while allowing rapid topology adjustments to isolate faults and restore power supply. Furthermore, to mitigate the uncertainty and fault risks associated with extreme weather events, a CVaR-based risk quantification framework is incorporated into a bi-level optimization model, effectively balancing investment costs and operational risks under uncertainty. In this model, the upper-level planning stage optimizes the siting and sizing of flexibility resources, while the lower-level operational stage coordinates real-time dispatch strategies through demand response, energy storage operation, and dynamic network reconfiguration. Finally, a hybrid SA-PSO algorithm combined with conic programming is employed to enhance computational efficiency while ensuring high solution quality for practical system scales. Case study analyses demonstrate that, compared to single-resource configurations, the proposed coordinated planning of multiple flexibility resources can significantly reduce the total system cost and markedly improve system resilience under fault conditions. Full article
(This article belongs to the Special Issue Analysis and Control of Power System Stability)
16 pages, 3086 KiB  
Article
Design and Optimization Strategy of a Net-Zero City Based on a Small Modular Reactor and Renewable Energy
by Jungin Choi and Junhee Hong
Energies 2025, 18(15), 4128; https://doi.org/10.3390/en18154128 - 4 Aug 2025
Viewed by 13
Abstract
This study proposes the SMR Smart Net-Zero City (SSNC) framework—a scalable model for achieving carbon neutrality by integrating Small Modular Reactors (SMRs), renewable energy sources, and sector coupling within a microgrid architecture. As deploying renewables alone would require economically and technically impractical energy [...] Read more.
This study proposes the SMR Smart Net-Zero City (SSNC) framework—a scalable model for achieving carbon neutrality by integrating Small Modular Reactors (SMRs), renewable energy sources, and sector coupling within a microgrid architecture. As deploying renewables alone would require economically and technically impractical energy storage systems, SMRs provide a reliable and flexible baseload power source. Sector coupling systems—such as hydrogen production and heat generation—enhance grid stability by absorbing surplus energy and supporting the decarbonization of non-electric sectors. The core contribution of this study lies in its real-time data emulation framework, which overcomes a critical limitation in the current energy landscape: the absence of operational data for future technologies such as SMRs and their coupled hydrogen production systems. As these technologies are still in the pre-commercial stage, direct physical integration and validation are not yet feasible. To address this, the researchers leveraged real-time data from an existing commercial microgrid, specifically focusing on the import of grid electricity during energy shortfalls and export during solar surpluses. These patterns were repurposed to simulate the real-time operational behavior of future SMRs (ProxySMR) and sector coupling loads. This physically grounded simulation approach enables high-fidelity approximation of unavailable technologies and introduces a novel methodology to characterize their dynamic response within operational contexts. A key element of the SSNC control logic is a day–night strategy: maximum SMR output and minimal hydrogen production at night, and minimal SMR output with maximum hydrogen production during the day—balancing supply and demand while maintaining high SMR utilization for economic efficiency. The SSNC testbed was validated through a seven-day continuous operation in Busan, demonstrating stable performance and approximately 75% SMR utilization, thereby supporting the feasibility of this proxy-based method. Importantly, to the best of our knowledge, this study represents the first publicly reported attempt to emulate the real-time dynamics of a net-zero city concept based on not-yet-commercial SMRs and sector coupling systems using live operational data. This simulation-based framework offers a forward-looking, data-driven pathway to inform the development and control of next-generation carbon-neutral energy systems. Full article
(This article belongs to the Section B4: Nuclear Energy)
Show Figures

Figure 1

26 pages, 4116 KiB  
Article
Robust Optimal Operation of Smart Microgrid Considering Source–Load Uncertainty
by Zejian Qiu, Zhuowen Zhu, Lili Yu, Zhanyuan Han, Weitao Shao, Kuan Zhang and Yinfeng Ma
Processes 2025, 13(8), 2458; https://doi.org/10.3390/pr13082458 - 4 Aug 2025
Viewed by 47
Abstract
The uncertainties arising from high renewable energy penetration on both the generation and demand sides pose significant challenges to distribution network security. Smart microgrids are considered an effective way to solve this problem. Existing studies exhibit limitations in prediction accuracy, Alternating Current (AC) [...] Read more.
The uncertainties arising from high renewable energy penetration on both the generation and demand sides pose significant challenges to distribution network security. Smart microgrids are considered an effective way to solve this problem. Existing studies exhibit limitations in prediction accuracy, Alternating Current (AC) power flow modeling, and integration with optimization frameworks. This paper proposes a closed-loop technical framework combining high-confidence interval prediction, second-order cone convex relaxation, and robust optimization to facilitate renewable energy integration in distribution networks via smart microgrid technology. First, a hybrid prediction model integrating Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and Quantile Regression (QR) is designed to extract multi-frequency characteristics of time-series data, generating adaptive prediction intervals that accommodate individualized decision-making preferences. Second, a second-order cone relaxation method transforms the AC power flow optimization problem into a mixed-integer second-order cone programming (MISOCP) model. Finally, a robust optimization method considering source–load uncertainties is developed. Case studies demonstrate that the proposed approach reduces prediction errors by 21.15%, decreases node voltage fluctuations by 16.71%, and reduces voltage deviation at maximum offset nodes by 17.36%. This framework significantly mitigates voltage violation risks in distribution networks with large-scale grid-connected photovoltaic systems. Full article
(This article belongs to the Special Issue Applications of Smart Microgrids in Renewable Energy Development)
Show Figures

Figure 1

18 pages, 1317 KiB  
Article
A Stackelberg Game for Co-Optimization of Distribution System Operator Revenue and Virtual Power Plant Costs with Integrated Data Center Flexibility
by Qi Li, Shihao Liu, Bokang Zou, Yulong Jin, Yi Ge, Yan Li, Qirui Chen, Xinye Du, Feng Li and Chenyi Zheng
Energies 2025, 18(15), 4123; https://doi.org/10.3390/en18154123 - 3 Aug 2025
Viewed by 208
Abstract
The increasing penetration of distributed renewable energy and the emergence of large-scale, flexible loads such as data centers pose significant challenges to the economic and secure operation of distribution systems. Traditional static pricing mechanisms are often inadequate, leading to inefficient resource dispatch and [...] Read more.
The increasing penetration of distributed renewable energy and the emergence of large-scale, flexible loads such as data centers pose significant challenges to the economic and secure operation of distribution systems. Traditional static pricing mechanisms are often inadequate, leading to inefficient resource dispatch and curtailment of renewable generation. To address these issues, this paper proposes a hierarchical pricing and dispatch framework modeled as a tri-level Stackelberg game that coordinates interactions among an upstream grid, a distribution system operator (DSO), and multiple virtual power plants (VPPs). At the upper level, the DSO acts as the leader, formulating dynamic time-varying purchase and sale prices to maximize its revenue based on upstream grid conditions. In response, at the lower level, each VPP acts as a follower, optimally scheduling its portfolio of distributed energy resources—including microturbines, energy storage, and interruptible loads—to minimize its operating costs under the announced tariffs. A key innovation is the integration of a schedulable data center within one VPP, which responds to a specially designed wind-linked incentive tariff by shifting computational workloads to periods of high renewable availability. The resulting high-dimensional bilevel optimization problem is solved using a Kriging-based surrogate methodology to ensure computational tractability. Simulation results verify that, compared to a static-pricing baseline, the proposed strategy increases DSO revenue by 18.9% and reduces total VPP operating costs by over 28%, demonstrating a robust framework for enhancing system-wide economic and operational efficiency. Full article
Show Figures

Figure 1

23 pages, 1146 KiB  
Review
A Review of Optimization Scheduling for Active Distribution Networks with High-Penetration Distributed Generation Access
by Kewei Wang, Yonghong Huang, Yanbo Liu, Tao Huang and Shijia Zang
Energies 2025, 18(15), 4119; https://doi.org/10.3390/en18154119 - 3 Aug 2025
Viewed by 239
Abstract
The high-proportion integration of renewable energy sources, represented by wind power and photovoltaics, into active distribution networks (ADNs) can effectively alleviate the pressure associated with advancing China’s dual-carbon goals. However, the high uncertainty in renewable energy output leads to increased system voltage fluctuations [...] Read more.
The high-proportion integration of renewable energy sources, represented by wind power and photovoltaics, into active distribution networks (ADNs) can effectively alleviate the pressure associated with advancing China’s dual-carbon goals. However, the high uncertainty in renewable energy output leads to increased system voltage fluctuations and localized voltage violations, posing safety challenges. Consequently, research on optimal dispatch for ADNs with a high penetration of renewable energy has become a current focal point. This paper provides a comprehensive review of research in this domain over the past decade. Initially, it analyzes the voltage impact patterns and control principles in distribution networks under varying levels of renewable energy penetration. Subsequently, it introduces optimization dispatch models for ADNs that focus on three key objectives: safety, economy, and low carbon emissions. Furthermore, addressing the challenge of solving non-convex and nonlinear models, the paper highlights model reformulation strategies such as semidefinite relaxation, second-order cone relaxation, and convex inner approximation methods, along with summarizing relevant intelligent solution algorithms. Additionally, in response to the high uncertainty of renewable energy output, it reviews stochastic optimization dispatch strategies for ADNs, encompassing single-stage, two-stage, and multi-stage approaches. Meanwhile, given the promising prospects of large-scale deep reinforcement learning models in the power sector, their applications in ADN optimization dispatch are also reviewed. Finally, the paper outlines potential future research directions for ADN optimization dispatch. Full article
Show Figures

Figure 1

17 pages, 5214 KiB  
Article
Geothermal–Peltier Hybrid System for Air Cooling and Water Recovery
by Michele Spagnolo, Paolo Maria Congedo, Alessandro Buscemi, Gianluca Falcicchia Ferrara, Marina Bonomolo and Cristina Baglivo
Energies 2025, 18(15), 4115; https://doi.org/10.3390/en18154115 - 3 Aug 2025
Viewed by 134
Abstract
This study proposes a new air treatment system that integrates dehumidification, cooling, and water recovery using a Horizontal Air–Ground Heat Exchanger (HAGHE) combined with Peltier cells. The airflow generated by a fan flows through an HAGHE until it meets a septum on which [...] Read more.
This study proposes a new air treatment system that integrates dehumidification, cooling, and water recovery using a Horizontal Air–Ground Heat Exchanger (HAGHE) combined with Peltier cells. The airflow generated by a fan flows through an HAGHE until it meets a septum on which Peltier cells are placed, and then separates into two distinct streams that lap the two surfaces of the Peltier cells: one stream passes through the cold surfaces, undergoing both sensible and latent cooling with dehumidification; the other stream passes through the hot surfaces, increasing its temperature. The two treated air streams may then pass through a mixing chamber, where they are combined in the appropriate proportions to achieve the desired air supply conditions and ensure thermal comfort in the indoor environment. A Computational Fluid Dynamics (CFD) analysis was carried out to simulate the thermal interaction between the HAGHE and the surrounding soil. The simulation focused on a system installed under the subtropical climate conditions of Nairobi, Africa. The simulation results demonstrate that the HAGHE system is capable of reducing the air temperature by several degrees under typical summer conditions, with enhanced performance observed when the soil is moist. Condensation phenomena were triggered when the relative humidity of the inlet air exceeded 60%, contributing additional cooling through latent heat extraction. The proposed HAGHE–Peltier system can be easily powered by renewable energy sources and configured for stand-alone operation, making it particularly suitable for off-grid applications. Full article
(This article belongs to the Section A: Sustainable Energy)
Show Figures

Figure 1

24 pages, 997 KiB  
Article
A Spatiotemporal Deep Learning Framework for Joint Load and Renewable Energy Forecasting in Stability-Constrained Power Systems
by Min Cheng, Jiawei Yu, Mingkang Wu, Yihua Zhu, Yayao Zhang and Yuanfu Zhu
Information 2025, 16(8), 662; https://doi.org/10.3390/info16080662 - 3 Aug 2025
Viewed by 187
Abstract
With the increasing uncertainty introduced by the large-scale integration of renewable energy sources, traditional power dispatching methods face significant challenges, including severe frequency fluctuations, substantial forecasting deviations, and the difficulty of balancing economic efficiency with system stability. To address these issues, a deep [...] Read more.
With the increasing uncertainty introduced by the large-scale integration of renewable energy sources, traditional power dispatching methods face significant challenges, including severe frequency fluctuations, substantial forecasting deviations, and the difficulty of balancing economic efficiency with system stability. To address these issues, a deep learning-based dispatching framework is proposed, which integrates spatiotemporal feature extraction with a stability-aware mechanism. A joint forecasting model is constructed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to handle multi-source inputs, while a reinforcement learning-based stability-aware scheduler is developed to manage dynamic system responses. In addition, an uncertainty modeling mechanism combining Dropout and Bayesian networks is incorporated to enhance dispatch robustness. Experiments conducted on real-world power grid and renewable generation datasets demonstrate that the proposed forecasting module achieves approximately a 2.1% improvement in accuracy compared with Autoformer and reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 18.1% and 14.1%, respectively, compared with traditional LSTM models. The achieved Mean Absolute Percentage Error (MAPE) of 5.82% outperforms all baseline models. In terms of scheduling performance, the proposed method reduces the total operating cost by 5.8% relative to Autoformer, decreases the frequency deviation from 0.158 Hz to 0.129 Hz, and increases the Critical Clearing Time (CCT) to 2.74 s, significantly enhancing dynamic system stability. Ablation studies reveal that removing the uncertainty modeling module increases the frequency deviation to 0.153 Hz and raises operational costs by approximately 6.9%, confirming the critical role of this module in maintaining robustness. Furthermore, under diverse load profiles and meteorological disturbances, the proposed method maintains stable forecasting accuracy and scheduling policy outputs, demonstrating strong generalization capabilities. Overall, the proposed approach achieves a well-balanced performance in terms of forecasting precision, system stability, and economic efficiency in power grids with high renewable energy penetration, indicating substantial potential for practical deployment and further research. Full article
(This article belongs to the Special Issue Real-World Applications of Machine Learning Techniques)
Show Figures

Figure 1

Back to TopTop