Search Results (1,210)

Desalination is a vital solution to global water scarcity, yet its substantial energy demand persists as a major challenge. As the core energy-consuming components, pumps are fundamental to both membrane and thermal desalination processes. This review provides a comprehensive analysis of renewable energy source (RES)-driven pumping systems for desalination, focusing on the integration of solar photovoltaic and wind technologies. It examines the operational principles and efficiency of key pump types, such as high-pressure feed pumps for reverse osmosis, and underscores the critical role of energy recovery devices (ERDs) in minimizing net energy consumption. Furthermore, the paper highlights the importance of advanced control and energy management systems (EMS) in mitigating the intermittency of renewable sources. It details essential control strategies, including maximum power point tracking (MPPT), motor drive control, and supervisory EMS, that optimize the synergy between pumps, ERDs, and variable power inputs. By synthesizing current technologies and control methodologies, this review aims to identify pathways for designing more resilient, energy-efficient, and cost-effective desalination plants, supporting a sustainable water future. Full article

This study investigates the dispersion and condensation behavior of tritiated water vapor released into the atmosphere using moist air as a carrier, with an emphasis on safety optimization for nuclear power plant effluent discharge. A coupled heat and mass transfer model was developed and implemented in CFD simulations to analyze the evolution of temperature and relative humidity during the mixing of exhaust moist air with ambient air. The effects of key atmospheric and operational parameters—including the ambient wind speed, turbulence intensity, ambient temperature, relative humidity, and exhaust velocity—were systematically examined. The results indicate that the temperature difference between the exhaust gas and ambient air is the primary factor governing condensation risk. Low wind speeds and weak turbulence favor near-field humidity accumulation, while higher wind speeds and turbulence intensities enhance mixing and dilution, thereby reducing local humidity peaks but extending the downwind impact range. Increasing exhaust velocity strengthens plume rise and long-range transport due to enhanced momentum and latent heat release, mitigating accumulation near the chimney outlet. Furthermore, high ambient temperatures significantly increase the air’s moisture-holding capacity, allowing higher exhaust humidity without inducing condensation. Full article

The coal-fired Plomin Thermal Power Plant (Plomin TPP) in Croatia is located in the center of the east coast of the Istrian peninsula (northern Adriatic) and is considered the main source of historical air pollution in the region. Between 1970 and 2000, sulfur-rich coal from the local Raša coal mine was primarily used. In this study, a screening of content and fate of TPP-derived sulfur in soil around the power plant was made two decades after the S-rich coal was banned from use. Soil samples were collected at varying distances from the TPP in the prevailing wind direction (NE), along with a control sample taken more than 10 km away. The samples were analyzed for total sulfur, sulfate, organic sulfur (humic and fulvic), and the stable isotope composition of total sulfur (δ³⁴S). Additionally, coal and coal ash were analyzed for total sulfur, sulfate and δ³⁴S. Soil sampling along the prevailing wind direction from the Plomin TPP revealed markedly elevated sulfur content, with levels at 100 m downwind reaching up to 4 wt.%, which is over 100 times higher than the 0.04 wt.% measured at the control site located upwind. Sulfur content decreases sharply with increasing distance from the TPP, reflecting the deposition gradient along the prevailing wind path. Speciation analysis showed that over 95% of the sulfur in the soil is now present in organic form, mainly bound to humic acids. The δ³⁴S_VCDT values of the bulk coal used in the TPP ranged from −10.0 to −5.0‰. In most soil samples, the bulk δ³⁴S values were positive (+7.0 to +20.0‰). The values of sulfate in soil range from +1.0 to +5.5‰, while those in organic sulfur range from −3.5 to +6.0‰. This indicates that atmospheric deposition of ³⁴S-depleted fly ash and sulfate from coal are the most important sulfur sources, while some of the sulfur in the soil is also of marine origin. Finally, we showed that natural attenuation was a significant and efficient process within the sustainable management of the site historically contaminated by anthropogenic atmospheric sulfur deposition. Full article

In modern power systems, the implementation of load-frequency control (LFC) must reconcile continuous-time plant dynamics with discrete-time digital controllers operating under coarsely sampled communications. This paper develops a sampled-data $H_{\infty }$ framework for PI-type secondary LFC that explicitly accounts for aperiodic sampling and reduced inertia due to high wind penetration. Using a two-sided looped Lyapunov functional and free-matrix inequalities, sampling-interval-dependent linear matrix inequalities (LMIs) are derived for stability, $H_{\infty }$ performance and an exponential decay rate (EDR). The synthesis returns PI gains and the admissible maximum sampling period (MASP) via simple bisection. Numerical examples based on one-area, two-area, and three-area power systems demonstrate that the proposed stability conditions allow larger admissible sampling periods compared with existing approaches, while preserving satisfactory dynamic behaviour under different operating scenarios. Full article

It has become an urgent problem to deal with the uncertain influence caused by the high proportion of new energy connected to the grid and improve the consumption level of new energy in the background of the new power system. Based on the constantly updated predicted information of wind power, photovoltaic power, and load power, a multi-time-scale coordinated scheduling model for a multi-energy complementary power generation system integrated with a high proportion of new energy, including an electricity-to-hydrogen system, is proposed. The complex nonlinear factors in the operation cost of thermal power and pumped storage power generation were converted into a mixed integer linear model for solving the problem. The results show that the participation of the pumped storage units in the power grid dispatching can effectively alleviate the peak regulation and reserve pressure of the thermal power units. The electricity-to-hydrogen system has the advantages of fast power response and a wide adjustment range. Pumped storage plant, together with the electricity-to-hydrogen system, enhances the flexible adjustment ability of the power grid on the power side and the load side, respectively. The coordinated dispatch of the two can take into account the safety and economy of the power grid operation, maintain the power balance of the high-proportion new energy power generation system, and effectively reduce green power abandonment and improve the consumption level of clean energy. Full article

Alternative energies are essential for meeting the global demand for environmentally friendly energy, especially as the use of fossil fuels is being reduced. In recent years, largely due to diminishing fossil fuel reserves, Portugal has been actively promoting investment in renewable energies to reduce its reliance on energy imports and fossil fuels. However, despite the country’s high daily sunshine hours and utilization of wind and hydropower, energy production remains unstable due to climate variability. Climate instability leads to fluctuations in the energy supplied to the grid and can even partially withstand blackouts such as the one that occurred on 28 April 2025 on the Iberian Peninsula. To address this problem, energy storage systems are crucial to guarantee the stability of the supply during periods of low production or in situations such as the one mentioned above. This paper analyzes the feasibility of implementing an energy storage system to increase the profitability of a wind farm located in Alto Douro, Portugal. The study begins with a demand analysis, followed by simulations of the system’s performance in terms of profitability based on efficiency and power. Based on these assumptions, a modular lithium battery storage system with high efficiency and rapid charge/discharge capabilities was selected. This battery, with less autonomy but high capacity, is more profitable, since a 5% increase in efficiency results in high profits (€84,838) and curtailment (€70,962) using batteries with lower autonomy, i.e., 2 h (power rating of 5 MW combined with 10 MWh energy storage). Therefore, two scenarios (A and B) were considered, with one more optimistic (A) in which the priority is to discharge the batteries whenever possible. In the more realistic scenario (B), it is assumed that the batteries are fully charged before discharge. On the other hand, in the event of a blackout, it enables faster commissioning of the surrounding water installations, because solar and battery energy have no inertia, which facilitates the back start protocol. Full article

Accurate and reliable estimation of renewable energy generation is critical for modern power grid management, yet the inherent volatility and distinct physical drivers of multi-source renewables present significant modeling challenges. This paper proposes a unified deep learning framework for the concurrent estimation of power generation from solar, wind, and hydro sources. This methodology, termed nowcasting, utilizes real-time weather inputs to estimate immediate power generation. We introduce a hybrid spatio-temporal CNN-LSTM architecture that leverages a two-branch design to process both sequential weather data and static, plant-specific attributes in parallel. A key innovation of our approach is the use of a physics-informed Capacity Factor as the normalized target variable, which is customized for each energy source and notably employs a non-linear, S-shaped tanh-based power curve to model wind generation. To ensure high-fidelity spatial feature integration, a cKDTree algorithm was implemented to accurately match each power plant with its nearest corresponding weather data. To guarantee methodological rigor and prevent look-ahead bias, the model was trained and validated using a strict chronological data splitting strategy and was rigorously benchmarked against Linear Regression and XGBoost models. The framework demonstrated exceptional robustness on a large-scale dataset of over 1.5 million records spanning five European countries, achieving R-squared ($R^2$) values of 0.9967 for solar, 0.9993 for wind, and 0.9922 for hydro. While traditional ensemble models performed competitively on linear solar data, the proposed CNN-LSTM architecture demonstrated superior performance in capturing the complex, non-linear dynamics of wind energy, confirming its superiority in capturing intricate meteorological dependencies. This study validates the significant contribution of a spatio-temporal and physics-informed framework, establishing a foundational model for real-time energy assessment and enhanced grid sustainability. Full article

The supporting system of super-large cooling towers is crucial for the structural safety of nuclear power plants. The X-shaped reinforced concrete column has emerged as a promising solution due to its superior stability. However, the performance of the cast-in-place base joint, which is a key force-transfer component, requires thorough investigation. This study experimentally investigates the mechanical performance of the joints under ultimate vertical compressive and tensile loads. The loads represent gravity-dominated and extreme wind uplift scenarios, respectively. A comprehensive testing program monitored load–displacement responses, strain distributions, crack propagation, and failure modes. The compression specimen failed in a ductile flexural compression manner with plastic hinge formation above the column base. In contrast, the tension specimen exhibited a tension-controlled failure pattern. Crucially, the joint remained stable after column yielding in both loading scenarios. The result validates the “strong connection–weak member” design principle. The findings confirm that the proposed cast-in-place joint possesses excellent load-bearing capacity and ductility. Therefore, the study provides a reliable design basis for the supporting structures of super-large cooling towers. Full article

The Paris Agreement and related international climate frameworks aim to reduce global carbon intensity; however, carbon dioxide emissions from electricity generation remain high, motivating the development of coal–renewable coupling technologies to lower the carbon intensity of power production. Coal–renewable coupling refers to the technical integration of conventional coal-fired power systems with renewable energy sources such as wind and solar to form a synergistic and complementary energy supply system. At present, systematic reviews and comprehensive analyses of coal–renewable coupling technologies are still limited. Accordingly, this paper categorizes existing approaches into two pathways—deep flexible load regulation and co-firing-based emission reduction—and systematically reviews the current state of technological development, identifies key challenges, and discusses potential future directions. Deep flexible load regulation includes flexibility retrofitting of coal-fired units and the integration of energy storage modules, whereas co-firing-based emission reduction mainly involves the co-combustion of coal with zero-carbon fuels. The analysis focuses on large-scale coal-fired units, covering low-load stable combustion technologies, steam turbine retrofitting, and rapid start-up and shut-down strategies. For energy storage-assisted load regulation, both conventional options and emerging technologies such as molten salt and high-temperature solid particle thermal energy storage are examined. Zero-carbon fuels considered include biomass, ammonia, and hydrogen. Furthermore, the economic feasibility of the various technologies is evaluated, providing reference value for deep flexibility retrofitting and substantial emission reduction in large-scale coal-fired power plants. Full article

Under the dual targets of carbon peaking and carbon neutrality, virtual power plants (VPPs) are expected to coordinate distributed energy resources in distribution networks to ensure low-carbon operation. This paper introduces a distribution-level dynamic carbon emission factor (DCEF), derived from nodal carbon potentials on an IEEE 33-bus distribution network, and uses it as a time-varying carbon signal to guide VPP scheduling. A bi-objective ε-constraint mixed-integer linear programming model is formulated to minimise daily operating costs and CO₂ emissions, with a demand response and battery storage being dispatched under network constraints. Four seasonal typical working days are constructed from measured load data and wind/PV profiles, and three strategies are compared: pure economic dispatch, dispatch with a static average carbon factor, and dispatch with the proposed spatiotemporal DCEF. Our results show that the DCEF-based strategy reduces daily CO₂ emissions by up to about 8–9% in the typical summer day compared with economic dispatch, while in spring, autumn, and winter, it achieves smaller but measurable reductions in the order of 0.1–0.3% of daily emissions. Across all seasons, the average and peak carbon potential are noticeably lowered, and renewable energy utilisation is improved, with limited impacts on costs. These findings indicate that feeder-level DCEFs provide a practical extension of existing carbon-aware demand response frameworks for low-carbon VPP dispatch in distribution networks. Full article

Electrolytic aluminum is one of the most energy-intensive industrial processes and offers strong potential for demand-side flexibility and renewable energy integration. However, existing studies mainly focus on operational scheduling, while comprehensive planning frameworks at the industrial-park scale remain limited. This study proposes an optimal planning framework for electrolytic aluminum that co-optimizes renewable energy investments, waste heat recovery, and green power trading while capturing the temperature safety constraints of electrolytic cells. The electrolytic aluminum process is explicitly modeled with heat exchangers to enable combined cooling–heating–power supply for nearby users. A wind power priority subscription mechanism and green certificate compliance are incorporated to enhance practical applicability and support future decarbonization requirements. Moreover, a two-stage particle swarm-deterministic optimization scheme is developed to provide a tractable solution to the inherently nonconvex mixed-integer nonlinear model. Case studies based on a real plant in Xinjiang, China, demonstrate that the proposed framework can raise the green electricity aluminum share to 60.4%, reduce annual carbon emissions by 52.0%, and significantly increase total system profit compared with the benchmark configuration, highlighting its economic and sustainability benefits for industrial park development. Full article

The growing share of distributed energy resources in the power system increases the number of power quality issues. The variable nature of their generation contributes to voltage fluctuations. This paper proposes a method for compensating voltage fluctuations utilising reactive power generated by a doubly fed induction generator (DFIG). The proposed method was first evaluated using a simulation model developed in the Matlab Simulink R2025a environment and subsequently validated experimentally under laboratory conditions. The results obtained are highly satisfactory, with the compensation time in laboratory tests not exceeding 500 ms. Since DFIGs are used in approximately 50% of wind power plants and the implementation of the proposed approach does not require additional hardware—only modifications to the generator control software—the method appears highly promising. It offers the possibility of rapid deployment without incurring significant costs. Full article

This study establishes a multi-scale validation framework for Computational Fluid Dynamics (CFD) simulations of building-induced pollutant dispersion, integrating wind tunnel experiments, the CEDVAL benchmark dataset, and field measurements from a thermal power plant that serves as a proxy for nuclear facilities. The RNG k-ε and Large Eddy Simulation (LES) models were evaluated across these validation tiers. Results demonstrate that both models effectively capture key flow characteristics, with LES showing superior performance in predicting roof-level velocity and turbulence intensities. A systematic overestimation of rooftop and leeward concentrations was observed, though predictive accuracy improved with downwind distance (e.g., FAC2 > 0.5). The RNG k-ε model provided the best balance between accuracy and computational efficiency for engineering applications, while LES is recommended for high-fidelity near-field analysis. This work provides validated methodologies for environmental risk assessment in nuclear power planning and emission control strategy development. Full article

With the growing demand for energy and the limitations of fossil fuel resources, the utilization of renewable energy sources has become a vital and sustainable solution. However, identifying optimal locations for the development of these resources remains a major challenge in energy planning. Accurate spatial potential assessment can play a critical role in enhancing efficiency and reducing production costs. This study aims to present a scenario-based framework for assessing solar and wind energy potential in Pakistan. A total of 19 spatial criteria were used, categorized into evaluation and constraint factors. The full consistency method (FUCOM) was applied to weight the criteria, while the ordered weighted averaging (OWA) method was employed to model various potential scenarios. The results revealed that global horizontal irradiation (GHI) and proximity to transmission lines are the most significant factors for solar energy, whereas wind speed and wind power density are crucial for wind energy potential. Scenario analysis indicated that, under the AND scenario, the area with very high potential for solar and wind energy is 8005.72 km² and 968.98 km², respectively. These values increase to 63,607.52 km² and 16,288.32 km² under the OR scenario. The spatial agreement map for the simultaneous development of solar and wind energy showed an overlap of 461.42 km² in the AND scenario and 11,836 km² in the OR scenario. These findings highlight the importance of scenario-based decision-making approaches and accurate spatial evaluations in the development of multiple renewable energy plant sites under various investment and policy conditions. Moreover, the proposed framework can serve as a practical model for simulating and assessing renewable energy development potential in other regions of the world. Full article

Balancing (real-time) market price forecasting is a vital enabler for renewable integration, storage arbitrage, and risk-aware trading, yet the literature remains fragmented and underdeveloped. This review addresses these shortcomings by systematically categorizing and evaluating studies across prediction horizons, modeling paradigms, and data-engineering practices. We show that enriching forecasts with auxiliary features, such as day-ahead prices, net imbalance volumes, renewable forecast errors, and meteorological inputs, substantially reduces error relative to price-only baselines. Probabilistic frameworks, while invaluable for providing risk envelopes in bidding strategies, are still underexploited. Typical reported accuracy spans mean absolute percentage errors of approximately 3–10% for very short-term (1–6 h ahead) horizons, 10–20% for mid-term horizons (12–24 h ahead), and around 25% for longer horizons (24–36 h ahead), with spikes and rapid ramps driving most residual error. From this synthesis, we identify the following four critical research gaps: (1) inadequate modeling of price spikes and ramps, (2) limited innovation in pre- and post-processing techniques, (3) sparse adoption of profit-driven (revenue-aware) evaluation, and (4) weak segmentation of distinct temporal regimes. By mapping prevailing methodologies, benchmarking performance, and highlighting emerging paradigms, such as feedback-driven, risk-aware, feature-enriched pipelines, this review delineates the state of the art and proposes a research agenda focused on maximizing economic value. Full article