Search Results (242)

Recent advances in prefabricated construction have enabled modular systems offering structural performance, rapid assembly, and design flexibility. Textile Ceramic Technology (TCT) integrates ceramic elements within a stainless-steel mesh, creating versatile architectural envelopes for façades, roofs, and pavements. This study investigates the integration of silicon photovoltaic (PV) modules into TCT to develop an industrialized Building-Integrated Photovoltaics (BIPV) system that maintains energy efficiency and visual coherence. Three full-scale solar brick prototypes are presented, detailing design objectives, experimental results, and conclusions. The first prototype demonstrated the feasibility of scaling small silicon PV units with good efficiency but limited aesthetic integration. The second embedded PV cells within ceramic bricks, improving aesthetics while maintaining electrical performance. Durability tests—including humidity, temperature cycling, wind, and hail impact—confirmed system stability, though structural reinforcement is needed for impact resistance. The third prototype outlines future work focusing on modularity and industrial scalability. Results confirm the technical viability of silicon PV integration in TCT, enabling active façades that generate renewable energy without compromising architectural freedom or aesthetics. This research advances industrialized, sustainable building envelopes that reduce environmental impact through distributed energy generation. Full article

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

Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity. Full article

Renewable energy sources are being progressively incorporated into modern power grids to increase sustainability, stability, and resilience. To ensure that residential, commercial, and industrial customers have a dependable and efficient power supply, the transmission system must deliver electricity to end-users via the distribution network. To improve the performance of the distribution system, this study employs distributed generator (DG) units and focuses on determining their optimal placement, sizing, and power factor. A novel metaheuristic algorithm, referred to as Rüppell’s fox optimizer (RFO), is proposed to address this optimization problem under various scenarios. In the first scenario, where the DG operates at unity power factor, it is modeled as a photovoltaic system. In the second and third scenarios, the DG is modeled as a wind turbine system with fixed and optimal power factors, respectively. The performance of the proposed RFO algorithm is benchmarked against five well-known metaheuristic techniques to validate its effectiveness and competitiveness. Simulations are conducted on the IEEE 33-bus and IEEE 69-bus radial distribution test systems to demonstrate the applicability and robustness of the proposed approach. Full article

Renewable energy sources (RES) are the foundation of the ongoing energy transition in Poland and worldwide. However, increased use of RES has brought several challenges, as most of these sources are dependent on weather conditions. The instability and lack of control over electricity production lead to both overloads and power shortages in transmission and distribution networks. A significant advantage of biogas plants over sources such as photovoltaics or wind turbines is their ability to control electricity generation and align it with actual demand. Biogas produced during fermentation can be temporarily stored in a biogas tank above the digester and later used in an enlarged CHP unit to generate electricity and heat during peak demand periods. While demand-driven biogas plants operate similarly to traditional installations, their development requires navigating regulatory and administrative procedures, particularly those related to the grid connection of the generated electricity. In Poland, it has only recently become possible to obtain grid connection conditions for such installations, following the adoption of the Act of 28 July 2023, which amended the Energy Law and certain other acts. However, the biogas sector still faces challenges, particularly the need for effective incentive mechanisms and the removal of regulatory and economic barriers, especially given its estimated potential of up to 7.4 GW. Full article

Sustainable Development Reserves are organized units in the Amazon that are essential for the proper use and sustainable management of the region’s natural resources and for the livelihoods and economy of the local communities. This study aims to provide a climatic characterization of the Madeira River Sustainable Development Reserve (MSDR), offering scientific support to efforts to assess the feasibility of implementing adaptation measures to increase the resilience of isolated Amazon communities in the face of extreme climate events. Significant statistical analyses based on time series of observational and reanalysis climate data were employed to obtain a detailed diagnosis of local climate variability. The results show that monthly mean two-meter temperatures vary from 26.5 °C in February, the coolest month, to 28 °C in August, the warmest month. Monthly precipitation averages approximately 250 mm during the rainy season, from December until May. July and August are the driest months, August and September are the warmest months, and September and October are the months with the lowest river level. Cold spells were identified in July, and warm spells were identified between July and September, making this period critical for public health. Heavy precipitation events detected by the R80, Rx1day, and Rx5days indices show an increasing trend in frequency and intensity in recent years. The analyses indicated that the MSDR has no potential for wind-energy generation; however, photovoltaic energy production is viable throughout the year. Regarding the two major commercial crops and their resilience to thermal stress, the region presents suitable conditions for açaí palm cultivation, but Brazil nut production may be adversely affected by extreme drought and heat events. The results of this study may support research on adaptation strategies that includethe preservation of local traditions and natural resources to ensure sustainable development. Full article

This study analyzes the feasibility of energy mixes composed of different shares of various types of power generation units, including photovoltaic (PV) and wind farms, hydropower, fossil fuel-based plants, and nuclear power. The analysis uses the concept of Energy Return on Investment (EROI), which is considered the most reliable indicator for comparing different technologies as it measures the energy required rather than monetary costs needed to build and operate each technology. Literature-based EROI values for individual generation technologies were used, along with the minimum EROI thresholds for the entire energy mix that are necessary to sustain developed societies and a high quality of life. The results show that, depending on the assumed minimum EROI value, which ranges from 10 to 30, the maximum share of intermittent renewable energy sources (IRESs), such as PV and wind farms, in the system cannot exceed 90% or 60%, respectively. It is important to emphasize that this EROI-based analysis does not account for power grid stability, which currently can only be maintained by the inertia of large synchronous generators. Therefore, the scenario with a 90% IRES share should be regarded as purely theoretical. Full article

With the increasing proportion of grid-connected capacity of new energy units, such as wind power and photovoltaics, accurately constructing simulation models of these units is of great significance to the study of new power systems. However, the actual control strategies and parameters of many new energy units are often unavailable due to factors like outdated equipment or commercial confidentiality. This unavailability creates modeling challenges that compromise accuracy, ultimately affecting grid-connected power generation performance. Aiming at the problem of accurate modeling of fault ride-through control of new energy turbine “black box” controllers, this paper proposes an accurate identification method of fault ride-through control characteristics of doubly fed wind turbines based on hardware-in-the-loop testing. Firstly, according to the domestic and international new energy turbine fault ride-through standards, the fault ride-through segmentation control characteristics are summarized, and a general structured model for fault ride-through segmentation control of doubly fed wind turbines is constructed; Secondly, based on the measured hardware-in-the-loop data of the doubly fed wind turbine black box controller, the method of data segmentation preprocessing and structured model identification of the doubly fed wind turbine is proposed by utilizing statistical modal features and genetic Newton’s algorithm, and a set of generalized software simulation platforms for parameter identification is developed by combining Matlab and BPA; lastly, using the measured data of the doubly fed wind turbine in the black box and the software platform, the validity and accuracy of the proposed parameter identification method and software are tested in the simulation. Finally, the effectiveness and accuracy of the proposed parameter identification method and software are simulated and tested by using the measured data of black box doubly fed wind turbine and the software platform. The results show that the method proposed in this paper has higher recognition accuracy and stronger robustness, and the recognition error is reduced by 2.89% compared with the traditional method, which is of high value for engineering applications. Full article

Distributed renewable energy sources with significant output fluctuations can negatively impact the power grid stability when it is connected to the power grid. Therefore, it is necessary to develop a capacity configuration method that improves the output stability of highly uncertain energy sources such as wind and photovoltaic (PV) power by integrating pumped storage units. In response, this study proposes a capacity configuration method for a cascade small hydropower-pumped storage–wind–PV complementary system. The method utilizes the regulation capacity of cascade small hydropower plants and pumped storage units, in conjunction with the fluctuating characteristics of local distributed wind and PV, to perform power and energy time-series matching and determine the optimal capacity allocation for each type of renewable energy. Furthermore, an optimization and scheduling model for the cascade small hydropower-pumped storage–wind–PV complementary system is constructed to verify the effectiveness of the configuration under multiple scenarios. The results demonstrate that the proposed method reduces system energy deviation, improves the stability of power output and generation efficiency, and enhances the operational stability and economic performance of the system. Full article

As a decentralized energy management paradigm, micro-energy grid (MEG) clusters enable synergistic operation of heterogeneous distributed energy assets, particularly through multi-energy vector coupling mechanisms that enhance distributed energy resource (DER) utilization efficiency in next-generation power networks. While individual MEGs demonstrate limited capability in responding to upper-grid demands using surplus energy after fulfilling local supply/demand balance, coordinated cluster operation significantly enhances system-wide flexibility. This paper proposes a two-stage robust optimization model that systematically addresses both the synergistic complementarity of multi-MEG systems and renewable energy uncertainty. First, the basic operation structure of MEG, including distributed generation, cogeneration units, and other devices, is established, and the operation mode of the MEG cluster responding to host utility grid flexibly is proposed. Then, aiming to reduce operation expenses, an optimal self-scheduling plan is generated by establishing a MEG scheduling optimization model; on this basis, the flexibility response capability of the MEG is measured. Finally, to tackle the uncertainty issue of wind and photovoltaic power generation, the two-stage robust theory is employed, and the scheduling optimization model of MEG cluster flexibility response to the host utility grid is constructed. A southern MEG cluster is chosen for simulation to test the model and method’s effectiveness. Results indicate that the MEG cluster’s flexible response mechanism can utilize individual MEGs’ excess power generation to meet the host utility grid’s dispatching needs, thereby significantly lowering the host utility grid’s dispatching costs. Full article

The increasing penetration of renewable energy sources presents significant challenges for power system stability and operation. Accurately assessing renewable energy absorption capacity is essential to ensuring grid reliability while maximizing renewable integration. This paper proposes a security-constrained sequential production simulation (SPS) framework, which incorporates grid voltage and frequency support constraints to provide a more realistic evaluation of renewable energy absorption capability. Additionally, hierarchical clustering (HC) based on dynamic time warping (DTW) and min-max linkage is employed for temporal aggregation (TA), significantly reducing computational complexity while preserving key system characteristics. A case study on the IEEE 39-bus system, integrating wind and photovoltaic generation alongside high-voltage direct current (HVDC) transmission, demonstrates the effectiveness of the proposed approach. The results show that the security-constrained SPS successfully prevents overvoltage and frequency deviations by bringing additional conventional units online. The study also highlights that increasing grid demand, both locally and through HVDC export, enhances renewable energy absorption, though adequate grid support remains crucial. Full article

Under the “dual-carbon” strategic framework, the installed capacity of renewable energy sources has continuously increased, while that of conventional generation units has progressively decreased. This structural shift significantly diminishes the operational flexibility of power generation systems and intensifies grid imbalances caused by renewable energy volatility. To address these challenges, this study proposes a carbon-aware multi-timescale virtual power plant (VPP) scheduling framework with coordinated multi-energy integration, which operates through two sequential phases: day-ahead scheduling and intraday rolling optimization. In the day-ahead phase, demand response mechanisms are implemented to activate load-side regulation capabilities, coupled with information gap decision theory (IGDT) to quantify renewable energy uncertainties, thereby establishing optimal baseline schedules. During the intraday phase, rolling horizon optimization is executed based on updated short-term forecasts of renewable energy generation and load demand to determine final dispatch decisions. Numerical simulations demonstrate that the proposed framework achieves a 3.76% reduction in photovoltaic output fluctuations and 3.91% mitigation of wind power variability while maintaining economically viable scheduling costs. Specifically, the intraday optimization phase yields a 1.70% carbon emission reduction and a 7.72% decrease in power exchange costs, albeit with a 3.09% increase in operational costs attributable to power deviation penalties. Full article

For a high proportion of new energy with access to the grid, the typical random volatility of wind power and photovoltaic output greatly increases the peak load of the grid; in addition, the problem of wind and solar abandonment needs to be solved. This paper proposes the use of electric vehicle charging stations as new peak load resources to participate in grid dispatching. First, according to the actual operation and regulation characteristics of the load of EV charging stations, a refined regulation model enabling charging stations to participate in grid peak load regulation is established; then, combined with the deep peak load regulation model of hydropower units, in order to minimize system abandonment and minimize operating costs, a hierarchical optimization model for the joint peak load regulation of charging stations and hydropower deep regulation is established; finally, taking the actual power grid system as an example, a deep reinforcement learning algorithm is used to solve and analyze the problem, and the effectiveness of the scheme is verified. This study provides valuable insights into the coordinated optimization of electric vehicle charging stations and hydro–wind–solar systems for seamless integration into grid peak-shaving services. Full article

This paper addresses critical challenges in renewable energy research, particularly under the difficult operational conditions caused by the military conflict in Ukraine. Despite significant infrastructure loss due to the armed conflict (13% of solar and 70% of wind power), Ukraine maintains a commitment to reach 27% renewable energy in final consumption by 2030. However, the wartime conditions present unique challenges to scientific research, with laboratories vulnerable to missile strikes and frequently requiring evacuation. This paper introduces innovative portable laboratory stands designed for comprehensive analysis and monitoring of photovoltaic (PV) module parameters. These portable platforms, integrating advanced microcontrollers, sensors, and data-processing units, enable effective real-time monitoring and parameter estimation of PV modules, thereby enhancing their operational efficiency and reliability. Two distinct portable laboratory setups were developed and are detailed: the first focuses on real-time voltage and current measurements, while the second, termed the photovoltaic module parameter scanner (SPFEM), emphasizes data collection, remote data transmission, and database integration for subsequent analysis. This research provides essential tools for ensuring continuity in scientific activities and practical training for students and researchers amidst the ongoing security threats. The presented systems significantly contribute to optimizing the performance of PV systems in Ukraine and underscore the necessity for continuous adaptation and technological advancement in renewable energy infrastructure. Full article

We propose a coordinated control strategy for off-grid 10 kV wind–solar–hydrogen energy storage DC microgrid systems based on hybrid energy storage and controllable loads to improve their stability and accommodation level. First, mathematical models of each unit are established based on the operating characteristics of wind turbines, photovoltaic (PV) units, alkaline electrolyzers, fuel cells, and lithium batteries. Second, on the side of the electro-hydrogen hybrid energy storage DC/DC converter, the traditional dual-loop control is improved by proposing a control scheme combining an extended state observer with adaptive backstepping control (ESO-adaptive backstepping). On the load demand side, an electric spring incorporating adaptive fuzzy control (AFC) is introduced to adjust and compensate for the voltage. Finally, an actual case analysis is conducted using data from the Ningbo Cixi hydrogen–electric coupling DC microgrid demonstration project. The results demonstrate that the control method proposed in this study significantly outperforms the traditional double closed-loop control method. Specifically, the proposed method reduces the bus voltage fluctuation range in the presence of load disturbances by 24.07% and decreases the stabilization time by 56.92%. Additionally, the efficiency of the hydrogen fuel cell is enhanced by 31.88%. This control method can be applied to 10 kV DC microgrid systems with distributed energy resources. It aims to reduce the fluctuation amplitude of the DC bus voltage and enhance the system’s ability to withstand transient impact events. Full article