Energies

37 pages, 4383 KB

Open AccessArticle

Financial Drivers of Green Hydrogen Deployment: A Comparison Between Australia, Germany, and Brazil

by Roberto Ivo Da Rocha Lima Filho, Thereza Cristina Aquino, Lino Guimarães Marujo, Vinicius Botelho, Kalyne Brito and Pedro Senna

Energies 2026, 19(10), 2488; https://doi.org/10.3390/en19102488 - 21 May 2026

Viewed by 470

Abstract

The main challenge of hydrogen electrolysis lies in the high cost of hydrogen production. Achieving a decarbonized energy sector requires substantial investment to shift from carbon-intensive technologies to more sustainable alternatives. However, investment decisions in this context remain complex and uncertain. Currently, green [...] Read more.

The main challenge of hydrogen electrolysis lies in the high cost of hydrogen production. Achieving a decarbonized energy sector requires substantial investment to shift from carbon-intensive technologies to more sustainable alternatives. However, investment decisions in this context remain complex and uncertain. Currently, green hydrogen projects account for more than 500 initiatives worldwide and are expected to expand rapidly in the coming years. Evidence from feasibility studies suggests that green hydrogen produced from renewable energy is already technically viable and is approaching economic competitiveness. The current emphasis is on large-scale deployment and learning-by-doing processes to reduce electrolyzer costs and improve supply chain efficiency. This transition requires appropriate funding mechanisms, often involving significant public sector participation alongside private investment. This study analyzes the financing structures of green hydrogen projects in Germany, Australia, and Brazil using Principal Component Analysis (PCA) to identify the most relevant combinations of technical, economic, and financial variables. Unlike previous studies that address technical, economic, and financial dimensions in isolation, this study offers an integrated, empirically grounded analysis at the project level, combining cross-country comparison with a multivariate approach. The results indicate that project characteristics are strongly associated with capital intensity and financing structures, while cost variables such as levelized cost of hydrogen (LCOH) play a secondary role in explaining variation across projects. These findings suggest that financing arrangements—particularly those involving public support mechanisms—are closely associated with project configuration in this emerging sector. However, these results should be interpreted as patterns of statistical association rather than evidence of causal relationships. Overall, the analysis highlights the importance of coordinated financing strategies in supporting the development of green hydrogen and its potential contribution to emissions reduction in line with the Paris Agreement and the transition toward climate neutrality. Full article

(This article belongs to the Special Issue Advances in Green Hydrogen Energy Production)

► Show Figures

Figure 1

24 pages, 9037 KB

Open AccessArticle

Dynamic Programming-Based Model Predictive Control of Energy Management for a Novel Plug-In Hybrid Electric Vehicle

by Shunzhang Zou, Jun Zhang, Yunfeng Liu, Yu Yang, Yunshan Zhou, Jingyang Peng and Guolin Wang

Energies 2026, 19(10), 2487; https://doi.org/10.3390/en19102487 - 21 May 2026

Viewed by 328

Abstract

To address the conflict between real-time performance and global optimality in the energy management of dual-motor plug-in hybrid electric vehicles (PHEVs), this paper proposes a model predictive control (MPC) strategy based on dynamic programming (DP). Firstly, a radial basis function (RBF) neural network [...] Read more.

To address the conflict between real-time performance and global optimality in the energy management of dual-motor plug-in hybrid electric vehicles (PHEVs), this paper proposes a model predictive control (MPC) strategy based on dynamic programming (DP). Firstly, a radial basis function (RBF) neural network is employed to predict future driving conditions, providing preview information for the MPC. Subsequently, a DP-MPC cooperative architecture is constructed, which invokes DP to solve for local optimal solutions during the receding horizon optimization process and incorporates linear reference SOC trajectory planning to approximate the global optimum. Simulation results under the WLTC driving cycle demonstrate that the fuel consumption of the proposed strategy is 2.311 L/100 km, representing a 33.2% reduction in pure fuel consumption compared to the rule-based (RB) strategy, and a 16.3% reduction in equivalent fuel consumption (including electricity converted to fuel based on the engine’s generation efficiency), while achieving 96.31% of the fuel economy of the global optimal DP strategy. The study validates that this method significantly improves fuel economy while guaranteeing real-time performance. Full article

(This article belongs to the Special Issue Innovation in Energy Management Strategy for Hybrid Electric Vehicles)

► Show Figures

Figure 1

22 pages, 2009 KB

Open AccessArticle

A Study on the Optimization of Energy Storage Capacity for Ship Hybrid Energy Systems Based on a Two-Layer Optimization Model

by Huanbo Liu, Xiaoyan Xu, Yi Guo and Yuanhan Zhao

Energies 2026, 19(10), 2486; https://doi.org/10.3390/en19102486 - 21 May 2026

Viewed by 282

Abstract

In response to the dual pressures of energy consumption and environmental pollution faced by the global shipping industry, this paper proposes an optimization method for the energy storage capacity of a ship’s hybrid energy system based on a two-layer optimization model, aiming to [...] Read more.

In response to the dual pressures of energy consumption and environmental pollution faced by the global shipping industry, this paper proposes an optimization method for the energy storage capacity of a ship’s hybrid energy system based on a two-layer optimization model, aiming to enhance the energy utilization efficiency and operational stability of the system. A DNN-IPSO optimization framework integrating deep neural networks (DNN) and the improved particle swarm optimization algorithm (IPSO) was constructed, and combined with robust control strategies, it optimized the energy storage capacity configuration problem under complex dynamic conditions. The results show that the proposed method exhibits superior performance in terms of energy utilization efficiency, system dynamic response, and stability. The energy utilization efficiency of the system has been increased to 91.3%, the bus voltage fluctuation has been reduced to 3.98%, the load tracking error has been decreased to 17.6 kW, and the average convergence iteration times have been reduced to 71 times. The 17.6 kW load tracking error accounts for only 1.76% of the rated propulsion power of the 1 MW-level experimental platform, which is approximately 38% lower than that of the GA-PSO method. The experimental results on the real ship show that after using the DNN-IPSO optimization, the unit voyage energy consumption has been reduced to 41.7 kWh/km, the propulsion power stability coefficient has been increased to 0.956, the system transient recovery time has been shortened to 3.2 s, and the power reserve margin has been increased to 18.4%. The proposed method can effectively enhance the energy management capability, dynamic response performance, and operational stability of the ship’s hybrid energy system in the actual operating environment, providing reliable technical support for the engineering application of the integrated energy system of ships. Full article

(This article belongs to the Section B2: Clean Energy)

► Show Figures

Figure 1

32 pages, 1197 KB

Open AccessArticle

Cost-Optimal Decarbonization Pathways for Data Centers in Japan: A Bottom-Up Model Integrating Location, Energy Systems, and Carbon Pricing

by Jin Toyohara and Weisheng Zhou

Energies 2026, 19(10), 2485; https://doi.org/10.3390/en19102485 - 21 May 2026

Viewed by 343

Abstract

This study develops a bottom-up cost optimization model (DC-DECOM) to evaluate decarbonization pathways for Japan’s data center industry, targeting carbon neutrality of the information and communications technology (ICT) sector by 2040. The model represents Power Usage Effectiveness (PUE) as a dynamic function of [...] Read more.

This study develops a bottom-up cost optimization model (DC-DECOM) to evaluate decarbonization pathways for Japan’s data center industry, targeting carbon neutrality of the information and communications technology (ICT) sector by 2040. The model represents Power Usage Effectiveness (PUE) as a dynamic function of ambient temperature and cooling technology, and integrates technology selection, regional energy supply, and carbon pricing within a single cost-minimization framework. Three scenarios are compared: a reference case (REF), a centralized carbon-neutral scenario (C-CN) that restricts new capacity to metropolitan areas, and a regional decentralization scenario (R-CN) that allows for nationwide siting. Input parameters are calibrated against data from the International Energy Agency (IEA), the Uptime Institute, Japan’s Ministry of Internal Affairs and Communications (MIC) White Papers, and the Japan Science and Technology Agency (JST). The R-CN scenario achieves the 2040 net-zero target at 18–23% lower total system cost than C-CN. The cost gap decomposes into four channels (cooling-energy reduction ∼35%, lower regional renewable procurement cost ∼30%, lower carbon cost ∼25%, and lower siting-related cost ∼10%). Sensitivity analysis identifies the carbon-price trajectory and the hardware-efficiency improvement rate as the most influential parameters; the R-CN advantage remains positive across all

\pm 1 σ

parameter variations and across two combined-scenario stress tests. Full article

(This article belongs to the Special Issue Sustainable Energy Systems: Progress, Challenges and Prospects)

► Show Figures

Figure 1

17 pages, 292 KB

Open AccessArticle

Challenges for Managing Electromobility System—A Case Study from the Central European Region

by Aleksander Pabian and Barbara Pabian

Energies 2026, 19(10), 2484; https://doi.org/10.3390/en19102484 - 21 May 2026

Viewed by 322

Abstract

The use of electricity to power vehicles is currently seen as a key opportunity for climate protection and the development of global economies. In this context, the aim of this article is to identify and consider the advantages of electric vehicles and the [...] Read more.

The use of electricity to power vehicles is currently seen as a key opportunity for climate protection and the development of global economies. In this context, the aim of this article is to identify and consider the advantages of electric vehicles and the barriers to their development, as well as to present opportunities for leveraging knowledge from modern management to mitigate them. The study utilized desk research and qualitative methods. The results indicate that, despite significant consumer interest in electromobility in the European Union, the observed growth rate has been declining recently. This is due to a number of unfavorable administrative, technical, financial, and organizational conditions, which the researchers observed and present in this article. It turns out that eliminating these barriers is impossible without leveraging management knowledge, particularly in the areas of energy management and sustainable development. The article identifies specific solutions in the area of sustainable management, when implemented in practice, can contribute to increasing the efficiency of electricity use in transport, improving energy security, and protecting the natural environment. Full article

(This article belongs to the Special Issue AI Solutions for Energy Management: Smart Grids and EV Charging)

► Show Figures

Figure 1

24 pages, 2980 KB

Open AccessArticle

Optimal Capacity Allocation of Long- and Short-Term Energy Storage for Power Grids with High Penetration of Renewable Energy

by Lingguo Kong, Jinhao Wu and Xuekai Li

Energies 2026, 19(10), 2483; https://doi.org/10.3390/en19102483 - 21 May 2026

Viewed by 505

Abstract

The development of a new-type power system requires addressing the long-timescale imbalance between electricity supply and demand caused by the high penetration of wind and solar energy, which places higher demands on the secure and stable operation of power systems. Conventional single-type energy [...] Read more.

The development of a new-type power system requires addressing the long-timescale imbalance between electricity supply and demand caused by the high penetration of wind and solar energy, which places higher demands on the secure and stable operation of power systems. Conventional single-type energy storage cannot simultaneously satisfy short-term power regulation and medium- to long-term energy balancing requirements. Therefore, coordinated optimal allocation of multi-type energy storage is necessary. This study investigates the optimal capacity allocation of short- and long-duration energy storage in high-renewable-penetration power grids to improve renewable energy accommodation, enhance system flexibility, and optimize life-cycle cost. A mathematical model of a Multi-Type Energy Storage Coupled System (MTESCS) considering both power and energy balance is first established, together with a life-cycle economic model. Then, a source-load time-series reduction method based on Ward’s method is adopted to preserve the original temporal trends while reducing optimization complexity, and an optimal capacity allocation model is developed with the objective of minimizing system life-cycle cost. Finally, different storage configuration scenarios are constructed for comparative analyses under various renewable energy penetration levels. Results show that the proposed MTESCS can effectively improve renewable energy accommodation and economic performance, providing useful support for system design and engineering applications. Full article

► Show Figures

Figure 1

48 pages, 7391 KB

Open AccessArticle

Degradation-Aware Stochastic Scheduling of Multi-Stack Power-to-X Plants Under Joint Renewable and Electricity Price Uncertainty

by Ilyes Tegani, Hamza Afghoul, Salah S. Alharbi, Saleh S. Alharbi, Salem Tegani and Okba Kraa

Energies 2026, 19(10), 2482; https://doi.org/10.3390/en19102482 - 21 May 2026

Viewed by 267

Abstract

The day-ahead scheduling of multi-stack Power-to-X (PtX) plants must simultaneously cope with stack degradation under variable loading and with compound uncertainty in renewable generation and electricity prices. Existing scheduling frameworks address these two challenges in isolation, since degradation-aware models remain deterministic and stochastic [...] Read more.

The day-ahead scheduling of multi-stack Power-to-X (PtX) plants must simultaneously cope with stack degradation under variable loading and with compound uncertainty in renewable generation and electricity prices. Existing scheduling frameworks address these two challenges in isolation, since degradation-aware models remain deterministic and stochastic models treat the electrolyser as a constant-efficiency device. This work develops a degradation-aware two-stage stochastic mixed-integer linear programming (MILP) framework that closes this gap. First-stage binaries fix the commitment and startup decisions of every stack, while second-stage scenario-indexed variables capture the dispatch, the hydrogen output, the shortfall, and the load-dependent and start–stop cycling degradation cost monetised at the stack level through a piecewise linear epigraph. Joint wind price uncertainty is represented by a Gaussian copula fitted on empirical CDF marginals and reduced to twenty representative scenarios via k-medoids clustering. The framework is fully implemented in MATLAB R2024a with the Optimization Toolbox, using the built-in intlinprog and linprog solvers. On a 100 MW reference plant with ten heterogeneous PEM stacks, out-of-sample evaluation against four formal benchmarks demonstrates the lowest LCOH at EUR 24/kg, the highest demand reliability at 85.0%, the highest hydrogen delivery at 7.68 t/day, and up to 50% total cost reduction over deterministic baselines, with end-to-end runtime under two minutes on standard workstation hardware. Full article

(This article belongs to the Section F: Electrical Engineering)

► Show Figures

Figure 1

22 pages, 11301 KB

Open AccessArticle

Real-Time Sedimentation and Operational Technology Integration to Enhance Hydropower Operational Reliability: Case Study of the Chivor Hydropower Plant in Colombia

by Aldemar Leguizamon-Perilla, Johann A. Caballero, Leonardo Rojas, Francisco E. López-Cely, Nhora Cecilia Parra-Rodriguez, Laidi Morales-Cruz, César Nieto-Londoño, Wilber Silva-López and Rafael E. Vásquez

Energies 2026, 19(10), 2481; https://doi.org/10.3390/en19102481 - 21 May 2026

Viewed by 349

Abstract

This study addresses the critical challenge of sediment-driven degradation in aging hydropower infrastructure by implementing a novel Digital Operational Technology modernization framework at the AES Chivor Hydropower Plant in Colombia. While conventional sediment monitoring relies on sporadic manual campaigns, this research introduces a [...] Read more.

This study addresses the critical challenge of sediment-driven degradation in aging hydropower infrastructure by implementing a novel Digital Operational Technology modernization framework at the AES Chivor Hydropower Plant in Colombia. While conventional sediment monitoring relies on sporadic manual campaigns, this research introduces a continuous, real-time sensing architecture that integrates hybrid acoustic–optical sensors, covering a range of 10 to 6000 mg/L, directly into the plant’s SCADA (Supervisory Control and Data Acquisition) system. The novelty of this approach lies in the seamless coupling of high-frequency physical data (15 min sampling) with an Operational Decision Support Module, enabling adaptive turbine management. Statistical validation against laboratory gravimetric standards yielded a robust correlation of 0.93, confirming the system’s precision in quantifying suspended sediment concentrations. By identifying critical fine particle fractions in real time, the proposed model enables a precision-based maintenance strategy that significantly reduces unscheduled production downtime and mitigates accelerated wear in Pelton turbines. These findings provide a scalable benchmark for extending the operational life of large-scale hydropower facilities facing advanced sedimentation risks through digital transformation. Full article

(This article belongs to the Special Issue Energy Security and Energy Transition: Towards Sustainable Energy Systems—Second Edition)

► Show Figures

Figure 1

19 pages, 2748 KB

Open AccessArticle

Particle Swarm Optimization-Based Neural Network Control of PEM Fuel Cell Air Supply System

by Yunlong Wang, Cunliang Ye, Yan Liu, Kai Li and Bin Liu

Energies 2026, 19(10), 2480; https://doi.org/10.3390/en19102480 - 21 May 2026

Viewed by 239

Abstract

To boost the net power output of proton exchange membrane (PEM) fuel cell systems under variable operating conditions, this study proposes an adaptive neural network (NN) control strategy that integrates parameter optimization. The air supply subsystem is the primary focus, as its performance [...] Read more.

To boost the net power output of proton exchange membrane (PEM) fuel cell systems under variable operating conditions, this study proposes an adaptive neural network (NN) control strategy that integrates parameter optimization. The air supply subsystem is the primary focus, as its performance is crucial to the overall net power. First, a comprehensive model of the air supply subsystem is developed, along with a detailed analysis of cathode pressure, oxygen excess ratio (OER), and net power output. Then, a two-dimensional particle swarm optimization (TDPSO) algorithm is used to optimize the reference signals for cathode pressure and OER, thereby maximizing net power. By applying input–output linearization techniques, the originally coupled nonlinear multi-input multi-output (MIMO) system is decoupled and transformed into a canonical form. Based on this transformation, an adaptive NN controller is designed to regulate the pressure valve and compressor. A series of hardware-in-loop (HIL) tests confirm that the proposed control strategy effectively optimizes net power across diverse operating scenarios. Quantitative results show that the proposed method achieves a net power output of 28.6 kW to 42.1 kW over the tested current range of 100–300 A. Meanwhile, the comparisons show that the proposed controller achieves OER tracking with root mean square error (RMSE) of 0.1221 and cathode pressure with RMSE of 0.0033. In comparison, the fuzzy logic controller (FLC) achieves OER with RMSE of 0.1453 and pressure with RMSE of 0.0044, while proportional–integral–derivative (PID) controller achieves OER with RMSE of 0.2133 and pressure with RMSE of 0.0109. Full article

(This article belongs to the Special Issue Advanced Studies for PEM Fuel Cells in Hydrogen-Fueled Vehicles: 2nd Edition)

► Show Figures

Figure 1

34 pages, 6148 KB

Open AccessArticle

A Bi-Level MIQP + SAC Framework for Short-Term Optimal Scheduling of a Hydro–PV–Battery Energy Storage System

by Haoyan Zhang, Jing Qian, Haocheng He and Danning Tian

Energies 2026, 19(10), 2479; https://doi.org/10.3390/en19102479 - 21 May 2026

Viewed by 317

Abstract

With the increasing integration of photovoltaic (PV) generation, short-term scheduling of hydro–PV–battery energy storage systems (HPBS) faces growing challenges due to the stochastic variability of PV output, the temporal coupling of hydropower operation, and the accumulation of deviations during the real-time execution of [...] Read more.

With the increasing integration of photovoltaic (PV) generation, short-term scheduling of hydro–PV–battery energy storage systems (HPBS) faces growing challenges due to the stochastic variability of PV output, the temporal coupling of hydropower operation, and the accumulation of deviations during the real-time execution of day-ahead schedules. This paper proposes a bi-level coordinated scheduling framework that integrates day-ahead mixed-integer quadratic programming (MIQP) with intraday Soft Actor–Critic (SAC)-based correction. In the upper layer, MIQP generates a 24 h baseline schedule subject to unit output limits, mutually exclusive charging/discharging logic, and operational constraints. In the lower layer, SAC performs bounded real-time residual correction for hydropower and battery storage around the MIQP baseline, while a deviation-triggered replanning mechanism forms a closed-loop process of planning, execution, correction, and replanning. Comparative experiments under the tested setting show that SAC achieves better overall performance than Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Proximal Policy Optimization (PPO). Typical-day evaluations under dry-, normal-, and wet-season conditions show that, in the selected case studies, the proposed MIQP + SAC framework achieves better performance than standalone MIQP and MIQP-Replan, which refers to a deviation-triggered MIQP re-optimization strategy, in load tracking, PV curtailment reduction, and hydro-storage coordination. These results indicate the effectiveness of the proposed framework for short-term HPBS scheduling under representative operating conditions. Full article

► Show Figures

Figure 1

24 pages, 3075 KB

Open AccessReview

Low-Carbon and Zero-Carbon Marine Power Systems: Key Technologies and Development Prospects of Energy Materials

by Xiaojing Sui, Wenjie Dai, Bochen Jiang and Yanhua Lei

Energies 2026, 19(10), 2478; https://doi.org/10.3390/en19102478 - 21 May 2026

Viewed by 456

Abstract

As the core pillar of international trade, the global shipping industry has seen its carbon and pollutant emissions become a key challenge in global environmental governance. Statistics indicate that ship carbon emissions account for 3% of the world’s total anthropogenic CO₂ emissions, [...] Read more.

As the core pillar of international trade, the global shipping industry has seen its carbon and pollutant emissions become a key challenge in global environmental governance. Statistics indicate that ship carbon emissions account for 3% of the world’s total anthropogenic CO₂ emissions, while contributing 20% of global NO_x and 12% of SO₂ emissions, posing a serious threat to coastal ecosystems and public health. In response to the International Maritime Organization (IMO) “Net Zero Framework” and national green shipping policies, the transformation of ship power systems toward low-carbon and zero-carbon operation has become an inevitable trend. This paper systematically reviews the research progress and application status of green energy materials for ships, focusing on the working principles, technical characteristics, and engineering application cases of solar photovoltaic (PV) materials, wind energy utilization technologies, fuel cell materials, and alternative clean energy fuels (e.g., liquefied natural gas (LNG), methanol, and hydrogen energy). It also discusses the integration mode and optimization strategy of multi-energy hybrid power systems. The research findings show that solar photovoltaic technology has achieved large-scale application in coastal ships; hydrogen fuel cells are suitable for long-range ocean navigation scenarios due to their high energy density; LNG and methanol have become the current mainstream alternative fuels, relying on mature infrastructure; and hybrid energy systems can significantly improve power supply reliability and emission reduction efficiency through multi-energy complementarity. Finally, aiming at the existing bottlenecks (e.g., cost, energy storage, and safety) of various technologies, future development directions are proposed. This study provides a reference for the technological breakthrough and engineering practice of green energy power systems for ships and contributes to the realization of the “carbon neutrality” goal in the global shipping industry. Full article

(This article belongs to the Special Issue Sustainable Energy Systems: Progress, Challenges and Prospects)

► Show Figures

Figure 1

13 pages, 2195 KB

Open AccessArticle

Influence of FDM Processing Parameters on the AC Breakdown Strength of Oil-Immersed PLA Insulation

by Józef Roehrich, Piotr Pająk and Dominik Guzik

Energies 2026, 19(10), 2477; https://doi.org/10.3390/en19102477 - 21 May 2026

Viewed by 402

Abstract

This study presents an experimental investigation of 3D-printed poly(lactic acid) samples (PLA) subjected to high-voltage AC stress. Although additive manufacturing is gaining importance in electrical engineering, studies on FDM-printed materials have concentrated mainly on mechanical behaviour. Their dielectric strength under oil-immersed high-voltage stress—a [...] Read more.

This study presents an experimental investigation of 3D-printed poly(lactic acid) samples (PLA) subjected to high-voltage AC stress. Although additive manufacturing is gaining importance in electrical engineering, studies on FDM-printed materials have concentrated mainly on mechanical behaviour. Their dielectric strength under oil-immersed high-voltage stress—a critical aspect for insulation applications—has not been systematically investigated. Additive manufacturing is increasingly considered for auxiliary insulating components in oil-immersed high-voltage equipment; however, process-induced voids and interlayer interfaces can intensify the local electric field and reduce dielectric strength. This work evaluates the AC breakdown strength of 3D-printed PLA specimens under oil immersion using the standard AC electrical strength test method for solid insulating materials. Two parameter sets were investigated: extrusion temperature (190–240 °C ) at a constant nozzle diameter, and nozzle diameter (

0.3

–

0.6 mm

) at a constant extrusion temperature of

T_{e} = 200 ° C

. Breakdown data were analysed using the standard two-parameter Weibull approach typically used in the statistical evaluation of electrical insulation breakdown strength, with the results additionally expressed in terms of the

B_{10}

,

B_{50}

, and

B_{90}

percentiles. The experimental observations were interpreted using simplified electric-field simulations representing inter-bead and interlayer voids. The results indicate that, for a given material, there exists an optimal extrusion temperature that yields the highest electrical breakdown performance. Full article

(This article belongs to the Special Issue Testing, Monitoring and Diagnostic of High Voltage Equipment, 3rd Edition)

► Show Figures

Figure 1

24 pages, 3251 KB

Open AccessArticle

Coordinated Low-Voltage Ride-Through Control of a Flywheel-Assisted Permanent-Magnet Direct-Drive Wind Power System Under Asymmetrical Grid Faults

by Dahai Guo, Guangchen Liu, Jianwei Zhang, Guizhen Tian, Sufang Wen, Zicheng He and Yan Wang

Energies 2026, 19(10), 2476; https://doi.org/10.3390/en19102476 - 21 May 2026

Viewed by 341

Abstract

To address fault-period DC-link overvoltage, the reduction in grid-side active-power regulation margin caused by reactive-current-priority operation, and the double-frequency current fluctuation induced by negative-sequence components under asymmetrical grid faults in a flywheel-assisted permanent-magnet direct-drive wind power system, this paper proposes a coordinated low-voltage [...] Read more.

To address fault-period DC-link overvoltage, the reduction in grid-side active-power regulation margin caused by reactive-current-priority operation, and the double-frequency current fluctuation induced by negative-sequence components under asymmetrical grid faults in a flywheel-assisted permanent-magnet direct-drive wind power system, this paper proposes a coordinated low-voltage ride-through (LVRT) strategy based on DC-link-voltage-threshold partitioning. According to the DC-link voltage level, the operating process is divided into a normal regulation region, a grid-side saturation region, and a flywheel activation region, thereby enabling coordinated regulation between grid-side reactive-current support and flywheel-side active-power absorption. To improve transient smoothness, an anti-windup mechanism together with a bumpless transfer scheme is incorporated into the coordinated control process to suppress integrator saturation and mitigate mode-transition disturbances. In addition, a grid-side proportional–integral–vector resonant controller (PI-VRC) is introduced to improve the suppression of double-frequency current fluctuation under asymmetrical faults and enhance converter capacity utilization. Simulation results show that the proposed strategy can effectively restrain fault-period DC-link voltage rise, improve three-phase current symmetry and grid power quality, and strengthen transient reactive-power support, thereby enhancing the asymmetrical-fault LVRT capability of the system. Full article

(This article belongs to the Special Issue Advanced Control and Performance Optimization in Power Electronic Systems)

► Show Figures

Figure 1

20 pages, 1336 KB

Open AccessArticle

Opportunities and Challenges for China–Japan Cooperation Regarding Renewable Hydrogen: A 3E Perspective

by Ze Ran and Weisheng Zhou

Energies 2026, 19(10), 2475; https://doi.org/10.3390/en19102475 - 21 May 2026

Viewed by 484

Abstract

China is the world’s largest producer of hydrogen, and it has the potential to export renewable hydrogen and its derivatives. Japan has set ambitious targets for developing a hydrogen-based society but is facing cost challenges. There is strong potential for China and Japan [...] Read more.

China is the world’s largest producer of hydrogen, and it has the potential to export renewable hydrogen and its derivatives. Japan has set ambitious targets for developing a hydrogen-based society but is facing cost challenges. There is strong potential for China and Japan to cooperate regarding renewable hydrogen across the value chain. This study evaluates the cooperation opportunities from the 3E perspective (energy security, economics, and the environment). It estimates the renewable hydrogen production potential in both countries, as well as the economics and greenhouse gas (GHG) emissions associated with the production and export of renewable hydrogen from China to Japan using proton exchange membrane (PEM) technology. The renewable hydrogen production potential in China is estimated to be 12.00 Mt/year by 2035 in the base case of this study, providing a strong foundation for exports to Japan. The levelized cost of hydrogen (LCOH) using PEM technology and onshore wind is estimated at 4.27 USD/kg H₂ in China and 11.01 USD/kg H₂ in Japan for projects built in 2025. Even after accounting for liquefaction costs in China, transport costs from China to Japan (Chifeng—Dalian—Kobe) and regasification costs in Japan, renewable hydrogen produced in China remains more cost-effective than that produced in Japan. In terms of GHG emissions, when renewable hydrogen is produced using wind power, and wind power is also used for liquefaction and other electricity-consuming processes, the total emissions within the case study boundary amount to 2.24 kg CO₂-eq/kg H₂, below Japan’s low-carbon hydrogen threshold of 3.4 CO₂-eq/kg H₂. This study also discusses the challenges which are critical to facilitating cooperation, particularly in regards to coordinating standards and certification systems between the two countries. Full article

(This article belongs to the Special Issue Sustainable Energy Systems: Progress, Challenges and Prospects)

► Show Figures

Figure 1

17 pages, 6990 KB

Open AccessArticle

Distributed De-Icing Approach for Overhead Ground Wires Based on AC Power Supply with Thermodynamic Validation

by Yongliang Yi, Xiaofu Xiong, Changli Yu, Junyu Zhu and Jingang Wang

Energies 2026, 19(10), 2474; https://doi.org/10.3390/en19102474 - 21 May 2026

Cited by 1 | Viewed by 270

Abstract

The accumulation of ice on power lines severely affects the safety of power systems. Conventional ice melting methods suffer from poor flexibility and adaptability, accompanied by high power consumption. As a novel technical approach, distributed ice melting deploys modular and movable ice melting [...] Read more.

The accumulation of ice on power lines severely affects the safety of power systems. Conventional ice melting methods suffer from poor flexibility and adaptability, accompanied by high power consumption. As a novel technical approach, distributed ice melting deploys modular and movable ice melting units at key sections of overhead ground wires, which generate heat on site according to the actual icing conditions of icing segments, and imposes high requirements on the miniaturization of ice melting equipment as well as the regulation strategy of ice melting current. This study proposes a distributed ice melting method for overhead ground wires based on AC power supply, which can adjust the current in accordance with the specific demands of wire protection and ice melting for different line sections. The feasibility and effectiveness of the proposed method are verified through thermodynamic simulations and experimental tests. The de-icing method injects power–frequency AC into the overhead ground wire through a Scott transformer combined with a series capacitor reactive power compensation structure, enabling on-demand regulation by adjusting capacitor switching strategies and transformer operating modes. This approach balances efficiency and flexibility. Based on a reactive power compensation capacity current control strategy and thermodynamic analysis, an electro-thermal-fluid field coupling simulation model for the experimental ground wire was developed. The current regulation strategies for different environmental and operating conditions were calculated and validated. The simulation results show that, under different conditions, the adjustable current effective values of the de-icing system in this model range from 101 to 380 A (line maintenance current), 304 to 622 A (critical de-icing current), and 661 to 1121 A (maximum de-icing current). Field tests demonstrate that this method can stably achieve AC de-icing and current control. For the experimental JLB40-150 model ground wire, adjusting the injected current to 350 A enables safe operation under line maintenance conditions, with a limit not exceeding 400 A. This paper provides a more efficient, flexible, controllable, and widely applicable method for the de-icing of overhead ground wires. Full article

(This article belongs to the Special Issue Electrical Equipment State Measurement and Intelligent Calculation: 2nd Edition)

► Show Figures

Figure 1

26 pages, 4600 KB

Open AccessArticle

Integrated Multi-Scale Spectral Framework for Tropical Cyclone Dynamics: Implications for Offshore Wind Energy Resilience in the Atlantic Caribbean Basin

by Mario Eduardo Carbonó dela Rosa, Adalberto Ospino-Castro, Carlos Robles-Algarín, Diego Restrepo-Leal and Victor Olivero-Ortiz

Energies 2026, 19(10), 2473; https://doi.org/10.3390/en19102473 - 21 May 2026

Viewed by 387

Abstract

The development of offshore wind energy in tropical cyclone-prone regions requires analytical frameworks that capture non-stationary climate dynamics. This study presents a multi-scale spectral approach to characterize Atlantic tropical cyclone variability and assess implications for offshore wind resilience in the Caribbean Basin. The [...] Read more.

The development of offshore wind energy in tropical cyclone-prone regions requires analytical frameworks that capture non-stationary climate dynamics. This study presents a multi-scale spectral approach to characterize Atlantic tropical cyclone variability and assess implications for offshore wind resilience in the Caribbean Basin. The methodology integrates Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) to resolve temporal variability in sea surface temperature, cyclone frequency, and intensity, complemented by two-dimensional kernel density estimation (KDE) and non-stationarity analysis. Using NOAA and National Hurricane Center datasets, results identify dominant periodicities at annual and ENSO (2–7 year) scales, a post-1995 spectral energy shift associated with the positive AMO phase, and a thermodynamically consistent energy corridor along 12–16° N. A statistically significant change point in 1987 (Pettitt test, p < 0.05) is detected, although spatial displacement is not significant. An integrated Wind Risk Index highlights the central-western Caribbean as a high-exposure zone overlapping offshore wind development areas. Exceedance analysis shows that 39.8% of observations surpass 25 m/s, 6.0% exceed 50 m/s, and 1.3% approach 70 m/s, indicating relevant design considerations. These findings support the need for non-stationary, multi-scale approaches in offshore wind risk assessment under tropical cyclone influence. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

► Show Figures

Figure 1

33 pages, 2587 KB

Open AccessArticle

A Study on Emission Reduction Strategies for Freight Trucks in the Context of China’s Carbon Neutrality Objectives

by Peihong Chen, Qi Chen, Ruitian Yao and Zhaoxia Kang

Energies 2026, 19(10), 2472; https://doi.org/10.3390/en19102472 - 21 May 2026

Viewed by 341

Abstract

Road freight contributes over half of China’s transport carbon emissions, making its decarbonization critical for carbon neutrality. This study combines total cost of ownership (TCO) and life cycle assessment (LCA) to analyze the economic efficiency and carbon emission effects of diesel, electric, and [...] Read more.

Road freight contributes over half of China’s transport carbon emissions, making its decarbonization critical for carbon neutrality. This study combines total cost of ownership (TCO) and life cycle assessment (LCA) to analyze the economic efficiency and carbon emission effects of diesel, electric, and hydrogen fuel cell trucks. Combined with the LSTM neural network and vehicle ownership model, this study predicts the fleet emission reduction potential from 2020 to 2050. The results show that all new energy trucks can achieve TCO parity with diesel trucks before 2050, and electrification shows better economic competitiveness than hydrogen fuel cell technology across all vehicle types in the Chinese context. Fuel cell trucks powered via solar-powered water electrolysis exhibit the lowest carbon intensity, and grid decarbonization can significantly improve the emission reduction effects of electric and fuel cell trucks. Freight fleet carbon emissions are expected to peak around 2030. In an ideal scenario, emission reductions of 19.5%, 41.9%, and 82.9% can be achieved by 2030, 2040, and 2050, respectively. Heavy-duty trucks are the main emission contributors (47–58%) and the main target of emission reduction strategies. Short-term reduction depends on fuel economy, while long-term reduction prioritizes new energy substitution. Policy recommendations include promoting alternative fuel trucks, upgrading emission standards, and adopting differential taxation. Full article

(This article belongs to the Section B: Energy and Environment)

► Show Figures

Figure 1

23 pages, 4001 KB

Open AccessArticle

Data-Driven Tailpipe Emission Prediction for Heavy-Duty Diesel Engines During B7–B20 Fuel Transition

by Anna Borucka, Mariusz Klimas, Jerzy Merkisz and Adam Sordyl

Energies 2026, 19(10), 2471; https://doi.org/10.3390/en19102471 - 21 May 2026

Cited by 1 | Viewed by 399

Abstract

The use of biodiesel blends in heavy-duty diesel engines changes the relationship between engine operating conditions, fuel properties, and exhaust emissions, which may limit the reliability of data-driven emission models trained under a single fuel condition. This study investigates the cross-fuel transferability of [...] Read more.

The use of biodiesel blends in heavy-duty diesel engines changes the relationship between engine operating conditions, fuel properties, and exhaust emissions, which may limit the reliability of data-driven emission models trained under a single fuel condition. This study investigates the cross-fuel transferability of virtual emission sensors for a heavy-duty diesel engine operating on B7 and B20 fuel blends. The analysis was carried out for three target signals: nitrogen oxides concentration, hydrocarbon concentration, and dry carbon dioxide concentration, using data from the World Harmonized Transient Cycle (WHTC) and World Harmonized Stationary Cycle (WHSC) tests. A structured modelling workflow was developed, including signal time alignment, construction of baseline, dynamic, and memory-based features, feature selection, and separate evaluation scenarios: within-domain, cross-cycle, and cross-fuel transfer. Three tree-based regression algorithms were compared: Random Forest (RF), Histogram-Based Gradient Boosting (HGB), and Extreme Gradient Boosting (XGBoost). XGBoost achieved the best predictive performance in the source domain and was selected as the reference model. The results showed that a change in cycle characteristics led to a significant decrease in predictive performance, whereas the transition from B7/WHTC to B20/WHTC resulted in a clearly smaller drop in the evaluation metrics. The relationship between engine operating signals and emission response remained partially transferable across fuels. The highest stability was observed for carbon dioxide, intermediate stability for nitrogen oxides, and the lowest stability for hydrocarbons. The findings support the development of robust data-driven virtual sensing methods for emission monitoring and calibration of heavy-duty diesel engines operating with biodiesel blends. Full article

(This article belongs to the Section I2: Energy and Combustion Science)

► Show Figures

Figure 1

26 pages, 1065 KB

Open AccessArticle

Urban Circular Economy and Energy Efficiency Improvement: Evidence from China’s “Zero-Waste City” Pilot Program

by Rui Li and Jiajun Xu

Energies 2026, 19(10), 2470; https://doi.org/10.3390/en19102470 - 21 May 2026

Viewed by 443

Abstract

The circular economy offers a key pathway to achieve the joint improvement of resource conservation and carbon reduction, yet its causal effect on urban energy efficiency remains insufficiently examined. This paper takes China’s Zero-Waste City (ZWC) policy as a quasi-natural experiment and uses [...] Read more.

The circular economy offers a key pathway to achieve the joint improvement of resource conservation and carbon reduction, yet its causal effect on urban energy efficiency remains insufficiently examined. This paper takes China’s Zero-Waste City (ZWC) policy as a quasi-natural experiment and uses panel data from prefecture-level cities between 2006 and 2023. By applying staggered difference-in-differences and double machine learning methods, we evaluate the effect of urban circular economy transformation on energy efficiency. The results reveal four main findings: (1) The ZWC policy significantly improves energy efficiency in pilot cities. (2) The policy operates through three mechanisms: resource circulation, structural optimization, and innovation compensation. (3) Policy effects are stronger in environmentally regulated cities, large cities, and regions with higher artificial intelligence development. (4) The policy also generates broader benefits beyond energy savings, including coordinated fiscal, economic, and environmental gains. Overall, this paper highlights the spillover benefits of the circular economy from waste reduction to energy conservation and provides policy implications for coordinating waste management and energy transition at the urban level. Full article

(This article belongs to the Special Issue Circular Economy Mechanisms for Improving Energy Efficiency)

► Show Figures

Figure 1

16 pages, 8786 KB

Open AccessArticle

The Use of a Supercapacitor as a Supplementary Storage of Electricity in a Trolleybus

by Piotr Hołyszko, Dariusz Zieliński, Joanna Rymarz, Andrzej Niewczas and Ewa Dębicka

Energies 2026, 19(10), 2469; https://doi.org/10.3390/en19102469 - 21 May 2026

Viewed by 281

Abstract

The article presents the concept of using supercapacitor as an energy storage in a trolleybus in order to ensure the continuity of power supply to on-board trolleybus devices during the passage through isolated sections of the overhead contact line. A mathematical model of [...] Read more.

The article presents the concept of using supercapacitor as an energy storage in a trolleybus in order to ensure the continuity of power supply to on-board trolleybus devices during the passage through isolated sections of the overhead contact line. A mathematical model of the on-board power supply system and an example of the power limit calculation have been described. The required capacity of the supercapacitor has been determined. A series of simulation studies were conducted, which made it possible to analyze and evaluate the potential capabilities and limitations of the proposed methods for maintaining the operation of auxiliary equipment. The results of simulation studies showed that the proposed model can be effectively used under typical trolleybus traction conditions. The use of a supercapacitor can ensure an uninterrupted power supply to auxiliary equipment across the entire range of operating speeds and power requirements of the trolleybus. Full article

(This article belongs to the Collection "Electric Vehicles" Section: Review Papers)

► Show Figures

Figure 1

28 pages, 1208 KB

Open AccessArticle

Resilience-Driven Overload Protection Framework for Mitigating Cascading Failures in Power Systems

by Gourab Schmidt-Banerjee, Christian Hachmann and Martin Braun

Energies 2026, 19(10), 2468; https://doi.org/10.3390/en19102468 - 21 May 2026

Viewed by 260

Abstract

Multiple-fault events can initiate overload propagation and cascading outages, resulting in severe load loss and reduced system resilience. Therefore, beyond conventional protection concepts based on the (n − 1) criterion, there is also a need to address multiple-fault events to minimize loss of [...] Read more.

Multiple-fault events can initiate overload propagation and cascading outages, resulting in severe load loss and reduced system resilience. Therefore, beyond conventional protection concepts based on the (n − 1) criterion, there is also a need to address multiple-fault events to minimize loss of load. This paper presents an optimized overload tripping scheme to mitigate cascading outages in high-voltage grids under multiple-fault conditions, where selected line switches or circuit breakers are opened in a controlled manner to isolate limited grid sections, minimize interrupted load, and prevent further overload propagation. The method combines inverse definite minimum time relay modeling with a heuristic graph-search algorithm implemented in pandapower to identify feasible switching actions that minimize load loss while preventing overload propagation. The approach is demonstrated on SimBench high-voltage urban and mixed benchmark grids under double-line fault scenarios. In the urban grid, the proposed scheme reduces the maximum load loss from 34.0% to 2.4%, while in the mixed grid, the reduction is from 50.3% to 5.2%. A SAIFI-inspired resilience proxy is introduced to quantify the reduction in customer/load interruptions, showing a resilience improvement factor of about 3.6 for cascading scenarios. In addition, thermal inertia analysis indicates that corrective switching must be completed within approximately 5 min to remain within line-temperature limits. The analysis is based on quasi-steady-state power-flow and relay simulations; transient stability effects are outside the scope of this study. The results demonstrate that the optimized overload tripping scheme is a promising adaptive protection strategy for improving grid resilience under severe contingency conditions. Full article

(This article belongs to the Section F1: Electrical Power System)

► Show Figures

Figure 1

19 pages, 1447 KB

Open AccessArticle

Robust MILP Optimization of Renewable Power Plants: The Role of BESS Sizing in Uncertainty Mitigation

by Tommaso Dieci, Corrado Maria Caminiti, Matteo Spiller and Marco Merlo

Energies 2026, 19(10), 2467; https://doi.org/10.3390/en19102467 - 21 May 2026

Viewed by 348

Abstract

The reduction of carbon dioxide related to the energy sector is one of the greatest challenges of this century. To ensure a proper transition towards a sustainable electric power system, innovative solutions are fundamental for the efficient integration of renewable energy sources. Hybrid [...] Read more.

The reduction of carbon dioxide related to the energy sector is one of the greatest challenges of this century. To ensure a proper transition towards a sustainable electric power system, innovative solutions are fundamental for the efficient integration of renewable energy sources. Hybrid Renewable Energy Systems (HRES) play a crucial role in this scenario; they can ensure a stable and reliable electricity supply thanks to the combination of different renewable technologies, particularly thanks to the integration of storage systems. However, the optimal sizing process of such systems is a complex challenge due to the multiple uncertainties that can be present, involving demand fluctuations and electricity zonal price variations. The aim of this work was to develop a Mixed-Integer Linear Programming (MILP) optimization approach for the robust sizing of a HRES under multiple sources of uncertainty. The developed hybrid model consists of a wind farm, a photovoltaic (PV) plant, a Battery Energy Storage System (BESS), and an industrial load with the entire infrastructure for connection to the national power grid. Additionally, the model includes the capability to manage the over-generation of renewable resources through curtailment mechanisms. The objective of the sizing tool is to minimize the Net Present Cost (NPC) of the plant, while ensuring the reliability of the system. The developed tool can represent a useful assistant for the evaluation of different possible configurations, helping the decision-making process during the design of a HRES. The results will show the best trade-off between economic and reliability aspects, highlighting the impact that the uncertainty has on the optimal size of the plant. In particular, the best configuration analyzed is able to reduce the NPC of more than 50% compared to a plant with a single renewable source. Full article

(This article belongs to the Special Issue Advances in Battery Modelling, Applications, and Technology)

► Show Figures

Figure 1

19 pages, 7951 KB

Open AccessArticle

Secondary Voltage Drops in Dry-Type Transformers Caused by Coupled Magnetic Flux Effects of Voltage Unbalance and Harmonics in Isolated Offshore Power Systems

by Byung Chul Sung and Seongil Kim

Energies 2026, 19(10), 2466; https://doi.org/10.3390/en19102466 - 21 May 2026

Viewed by 225

Abstract

This paper investigates abnormal secondary voltage drops in dry-type transformers operating in isolated offshore power systems. While conventional analyses primarily attribute voltage deviations to load conditions and transformer impedance, this study shows that noticeable voltage drops can also occur under no-load conditions due [...] Read more.

This paper investigates abnormal secondary voltage drops in dry-type transformers operating in isolated offshore power systems. While conventional analyses primarily attribute voltage deviations to load conditions and transformer impedance, this study shows that noticeable voltage drops can also occur under no-load conditions due to the combined effects of voltage unbalance, harmonic distortion, and residual magnetic flux. A comprehensive approach integrating on-site measurements, PSCAD simulations, and laboratory experiments is employed to systematically analyze this phenomenon. The results indicate a coupled electromagnetic effect in which source-side voltage imperfections induce asymmetric core flux distribution, which is associated with reduced secondary voltage. In addition, a relationship between synchronous generator winding pitch and harmonic voltage distortion is observed, suggesting its influence on power quality in isolated grids. Simulation results show that the interaction of these factors can lead to a secondary voltage drop of approximately 4–6 V under no-load conditions, even in the absence of transformer defects. Finally, mitigation strategies based on voltage balancing and harmonic reduction are experimentally validated, restoring the secondary voltage to 1.002 pu. These findings provide practical insights for improving voltage stability and power quality in offshore and other isolated power systems. Full article

(This article belongs to the Section F: Electrical Engineering)

► Show Figures

Figure 1

22 pages, 3599 KB

Open AccessArticle

Coordinated Short-Term Scheduling and Control Integration in Microgrids Using Dual-Layer Deep Reinforcement Learning

by Kamelia Norouzi, Hao Xu and Wenxin Liu

Energies 2026, 19(10), 2465; https://doi.org/10.3390/en19102465 - 21 May 2026

Viewed by 254

Abstract

To optimize microgrid performance, both short-term and real-time operational objectives must be addressed, typically through energy scheduling and power control. Short-term scheduling maximizes the benefits of battery energy storage systems (BESS) by leveraging forecasted load and renewable generation conditions. However, real-time adjustments are [...] Read more.

To optimize microgrid performance, both short-term and real-time operational objectives must be addressed, typically through energy scheduling and power control. Short-term scheduling maximizes the benefits of battery energy storage systems (BESS) by leveraging forecasted load and renewable generation conditions. However, real-time adjustments are required to account for prediction errors, as neglecting these can lead to a loss of short-term optimality and visibility in overall system performance. The challenge of coordinating scheduling with control remains underexplored due to the limited timescale difference and decoupling of optimization and control. To address this problem, a bi-level deep reinforcement learning (DRL) method is presented. The upper-level DRL optimizes short-term generation and charging/discharging schedules for synchronous generators (SGs) and BESS, respectively. The lower-level DRL implements the schedules while maintaining stability and optimality. The two DRL levels learn together in a dynamic environment to ensure the short-term and real-time operational objectives are well coordinated. Simulation results demonstrate the effectiveness of the proposed algorithm. The effectiveness of the proposed algorithm is demonstrated on the studied microgrid system. Extension to larger and more complex systems is considered as future work. Full article

(This article belongs to the Special Issue Advanced Control and Operation of Distributed Energy Resources in Modern Power Systems)

► Show Figures

Figure 1

23 pages, 2120 KB

Open AccessArticle

Wind Potential Assessment of Polokwane, South Africa, Using Statistical Models for Wind Power Density Estimation

by Ngwarai Shambira and Patrick Mukumba

Energies 2026, 19(10), 2464; https://doi.org/10.3390/en19102464 - 21 May 2026

Viewed by 288

Abstract

This study evaluates the wind energy potential of Polokwane, South Africa, using statistical distribution models to estimate wind power density (WPD) and assess turbine performance under low-wind inland conditions. Hourly wind speed and direction data (2015–2024) measured at a 10 m height above [...] Read more.

This study evaluates the wind energy potential of Polokwane, South Africa, using statistical distribution models to estimate wind power density (WPD) and assess turbine performance under low-wind inland conditions. Hourly wind speed and direction data (2015–2024) measured at a 10 m height above ground level (AGL) were analysed to characterise wind behaviour and assess energy availability. Four probability distributions, namely generalised logistic (GLD), generalised extreme value (GEVD), Gumbel (GD), and Weibull (WD), were fitted using the maximum likelihood (ML) method. Model performance was evaluated using Kolmogorov–Smirnov (KS), Anderson–Darling (AD), and Chi-square

(χ^{2})

tests, while wind power density accuracy was assessed using wind power density error (WPDE). The results showed that Polokwane is characterised by low wind speeds, with an overall mean wind speed of 2.72 m/s at 10 m AGL, reaching a low of 3.88 m/s at a hub height of 125 m. The GEVD model produced the most accurate wind power density estimate of 32.37 W/m², classifying the site within the poor wind resource category. Wind direction analysis revealed a dominant northeast sector with seasonal shifts toward the south. Wind turbine performance analysis showed improved energy generation at higher hub heights, with the Gamesa G136-4.5 MW turbine identified as the most suitable option for the site, achieving the highest net annual energy production (AEP) of 10.82 GWh/yr and the highest net capacity factor (CF) of 27.44%. These results indicate that the Polokwane site is suitable for low-to-moderate wind energy applications and small-scale distributed wind generation rather than large-scale commercial wind farm development. Full article

(This article belongs to the Special Issue Integration of Power Generation and Wind Energy)

► Show Figures

Figure 1

25 pages, 3344 KB

Open AccessArticle

Buckley–Leverett Solution for Two-Phase Displacement in a Composite Porous–Cavernous–Porous System

by Fang-Fang Chen, Xu-Jian Jiang, Ting Yan, Xiao-Ping Ma, Zhen-Yu Zhang, Ming-Jie Li and Zhao-Qin Huang

Energies 2026, 19(10), 2463; https://doi.org/10.3390/en19102463 - 20 May 2026

Cited by 1 | Viewed by 381

Abstract

Fluid flow in fractured-vuggy carbonate reservoirs is characterized by extreme multiscale heterogeneity, where the coexistence of tight matrix rock and macroscopic cave challenges traditional Darcy-based continuum models. This paper presents a semi-analytical solution for two-phase immiscible displacement in a one-dimensional composite porous–cavernous–porous (PCP) [...] Read more.

Fluid flow in fractured-vuggy carbonate reservoirs is characterized by extreme multiscale heterogeneity, where the coexistence of tight matrix rock and macroscopic cave challenges traditional Darcy-based continuum models. This paper presents a semi-analytical solution for two-phase immiscible displacement in a one-dimensional composite porous–cavernous–porous (PCP) system. The main feature of the model is that the cave region is treated separately from the porous domains: classical Darcy flow is used in the surrounding matrix, whereas an idealized free-flow representation is introduced for open caves based on a simplified one-dimensional treatment of the cave momentum balance. To elucidate the impact of distinct flow regimes on displacement dynamics, three physical models are compared for the cave region: (1) an open-cave model represented by a simplified free-flow formulation; (2) a filled-cave non-Darcy model governed by the Forchheimer equation using the Ergun correlation; and (3) a creeping-flow model governed by Darcy’s law. A piecewise semi-analytical solution procedure is established to enforce flux continuity, characterize interfacial state remapping, and determine the downstream front under global water-balance closure. The results show that both cave geometry and internal cave-flow mechanism critically control water-front advancement. While the open-cave model exhibits piston-like displacement behavior with high local displacement efficiency but stronger preferential flow, the Forchheimer model shows that inertial resistance can modify the saturation profile and delay breakthrough relative to the Darcy prediction. The proposed framework provides an idealized theoretical reference for benchmarking numerical simulators and for interpreting waterflooding behavior in complex vuggy reservoirs under one-dimensional, incompressible, gravity-free, and capillarity-free conditions. Full article

(This article belongs to the Special Issue New Advances in Oil, Gas and Geothermal Reservoirs—3rd Edition)

► Show Figures

Figure 1

20 pages, 4531 KB

Open AccessArticle

Techno-Economic Assessment of Electrochemical CO₂ Reduction to Ethylene: A Cu₁₀–Sn Catalyst Case Study and Performance Targets

by Kuquan Xiao, Ping Zhou and Xiqiang Zhao

Energies 2026, 19(10), 2462; https://doi.org/10.3390/en19102462 - 20 May 2026

Cited by 1 | Viewed by 616 | Correction

Abstract

Electrocatalytic CO₂ reduction reaction (CO₂RR) to ethylene (C₂H₄) has emerged as a promising approach for converting CO₂ into valuable chemicals while utilizing renewable electricity. To facilitate the commercialization of this technology, a process-level techno-economic assessment [...] Read more.

Electrocatalytic CO₂ reduction reaction (CO₂RR) to ethylene (C₂H₄) has emerged as a promising approach for converting CO₂ into valuable chemicals while utilizing renewable electricity. To facilitate the commercialization of this technology, a process-level techno-economic assessment (TEA) is constructed for a plant producing 100 tons/day of C₂H₄ from coal-power flue gas CO₂ using a membrane electrode assembly (MEA) electrolyzer and downstream gas separations. The model integrates (i) flue gas CO₂ capture by chemical absorption, (ii) CO₂RR to C₂H₄ with H₂ as the only co-product, and (iii) cathode off-gas separation by pressure swing adsorption (PSA) plus anode off-gas CO₂ recovery and recycle. A Cu₁₀–Sn catalyst measured in an H-cell is projected to MEA operation by scaling current density by 10×, yielding a “Case Study in This Article” scenario of j = 246 mA·cm⁻² and FE(C₂H₄) = 48.74%. Under this scenario, the total cost is 592.61 thousand USD/day (5926 USD/ton), dominated by electricity (39.8%). Scenario analysis shows that the total cost can decrease to 76,755.0 USD/day (767.6 USD/ton) under a future-outlook case with improved electrolyzer performance and low-cost power, enabling a net profit of 19,945.0 USD/day at an ethylene selling price of 967 USD/ton. Sensitivity analysis identifies FE(C₂H₄), full-cell voltage, and electricity price as the most influential variables. The results translate laboratory catalyst metrics into industrial cost drivers and clarify quantitative performance targets for commercialization. Full article

(This article belongs to the Section B: Energy and Environment)

► Show Figures

Figure 1

35 pages, 1637 KB

Open AccessArticle

Optimizing High-Resolution CSP–PV Hybrid Power Plant Configurations for Morocco: A Techno-Economic Study

by Nicholas Chandler, Daniel Marshal, Melisa Klein, Anna Heimsath, Christof Wittwer, Werner Platzer and Gregor Bern

Energies 2026, 19(10), 2461; https://doi.org/10.3390/en19102461 - 20 May 2026

Cited by 1 | Viewed by 443

Abstract

Hybridizing concentrating solar power (CSP) with photovoltaics (PV) offers a pathway to combine low-cost daytime generation with dispatchable nighttime supply. This study compares two CSP–PV hybridization concepts for Midelt, Morocco, under a common tender-style design framework: (i) a co-located configuration in which PV [...] Read more.

Hybridizing concentrating solar power (CSP) with photovoltaics (PV) offers a pathway to combine low-cost daytime generation with dispatchable nighttime supply. This study compares two CSP–PV hybridization concepts for Midelt, Morocco, under a common tender-style design framework: (i) a co-located configuration in which PV and CSP interact at the grid level and (ii) an EH-integrated configuration in which an electric heater (EH) uses PV electricity to heat molten salt in a topping cycle. The main contribution of this study lies in the two-stage optimization workflow, in which leading candidates are selectively re-simulated at higher temporal resolution. This workflow is applied to a common design framework that compares EH-integrated and co-located concepts while considering multiple PV technologies and a broad set of interdependent sizing variables. A surrogate-assisted genetic algorithm evaluates more than 200,000 candidate designs across PV technology, inverter size, TES capacity, EH capacity, and battery energy storage system (BESS) size. The optimization minimizes the levelized cost of energy (LCOE) subject to a

200 {MW}_{el}

export limit, a CAPEX ceiling, and a nighttime-delivery constraint of

{CF}_{night} \geq 39 %

. Candidate designs are screened at 600 s and selectively re-simulated at 120 s, showing that temporal refinement affects not only KPI values but also candidate feasibility, final ranking, and preferred component sizing. The lowest-LCOE solution is the EH-integrated bifacial configuration, achieving 64.5% overall capacity factor,

{CF}_{night} = 39.1 %

, less than 0.1% curtailment, a specific CAPEX of $4698/kW, and an LCOE of 7.29 ¢/kWh. Pareto-front and parameter-trend analyses further show that stricter nighttime-delivery targets shift the dominant sizing levers and define a neighborhood of near-optimal solutions rather than a single fixed design. Full article

(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)

► Show Figures

Figure 1

18 pages, 5306 KB

Open AccessArticle

Particle Swarm-Based Active Power Command Correction Virtual Synchronous Generator Control for Inverters with Current Limiting Capability and Enhanced Transient Stability

by Qiang Wang, Min Shi, Hao Lv, Fei-Fei Zhang, Yan Gao, Chen-Miao Lv, Xiao-Qi Yin and Juan Yan

Energies 2026, 19(10), 2460; https://doi.org/10.3390/en19102460 - 20 May 2026

Viewed by 398

Abstract

When a fault occurs in the power grid to which the Virtual Synchronous Generator (VSG) is connected, it leads to overcurrent phenomena, which threatens the safety of the inverter and easily results in device damage. Although existing direct current limiting unit (CLU) control [...] Read more.

When a fault occurs in the power grid to which the Virtual Synchronous Generator (VSG) is connected, it leads to overcurrent phenomena, which threatens the safety of the inverter and easily results in device damage. Although existing direct current limiting unit (CLU) control strategies can restrict the fault current, the input active power command far exceeds the power output, causing the virtual rotor to continuously accelerate. This leads to power angle divergence and a subsequent loss of synchronization. To address the conflict between direct current-limiting control and system transient stability, this paper proposes a control strategy based on the Particle Swarm Optimization (PSO) algorithm to modify the active power command, building upon existing direct current-limiting VSG control. During grid faults, the output current is constrained to its maximum value, leading to a reduction in the system’s output power. By leveraging the PSO algorithm, the proposed strategy decreases the active power command to minimize the power mismatch between the command and the output. This maximizes the system’s transient stability by minimizing the rotor acceleration torque and effectively suppressing excessive power angle deviation. Meanwhile, the active power command reduction is introduced as a penalty term to maximize the active power output capability during the fault period. Simulation results demonstrate that, compared to VSG with only direct current-limiting control, the proposed strategy significantly enhances the transient stability and transmission efficiency of the VSG under long-term fault conditions across various grid voltage sag scenarios. Furthermore, it ensures a seamless transition from the fault state to normal operation during short-term faults. Full article

(This article belongs to the Special Issue Advanced Control Methods for Power Electronics, Energy Storage, Photovoltaics, and Microgrids)

► Show Figures

Figure 1

35 pages, 6936 KB

Open AccessArticle

Role-Aware Hierarchical Primary Frequency Regulation of Heterogeneous Source–Grid–Load Energy Storage System

by Hongwei Deng, Jun He, Penghui Yan, Xiaoyu Nie, Yifan Lv and Shuyi Wang

Energies 2026, 19(10), 2459; https://doi.org/10.3390/en19102459 - 20 May 2026

Viewed by 307

Abstract

With high renewable penetration, primary frequency regulation (PFR) in low-inertia power systems faces a critical challenge of balancing frequency security with sustainable energy storage system (ESS) utilization. Existing ESS-based PFR studies mainly focus on single-sided resource support or homogeneous modeling, which cannot address [...] Read more.

With high renewable penetration, primary frequency regulation (PFR) in low-inertia power systems faces a critical challenge of balancing frequency security with sustainable energy storage system (ESS) utilization. Existing ESS-based PFR studies mainly focus on single-sided resource support or homogeneous modeling, which cannot address the cross-side coordination demands arising from heterogeneous ESS on the source, grid, and load sides. This paper extends ESS-based PFR to a synergistic scenario involving heterogeneous three-sided ESS. A unified modeling framework is established, explicitly incorporating differences in capacity, functional roles, participation priority, and security boundaries. Based on this, a hierarchical decoupling control structure is proposed, separating system-level frequency regulation from side-level energy coordination. The upper level uses a low-dimensional equivalent representation to reduce the optimization burden from modeling numerous ESS units, while the lower level achieves complementary advantages and orderly task allocation among the three sides through differentiated coordination. Simulations show that the method maintains system frequency performance while achieving rational PFR responsibility allocation across source-, grid-, and load-side ESS, effectively leveraging multi-sided heterogeneous ESS for synergistic regulation, and verifying the hierarchical decoupling framework as an effective approach for coordinating multi-side energy storage. Full article

(This article belongs to the Section F1: Electrical Power System)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Energies, Volume 19, Issue 10 (May-2 2026) – 240 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI