Renewable Energy Generation Research Articles

Enhanced solar-powered H2 production from water splitting and sustainable wastewater treatment using interface-engineered ZnIn2S4/TiO2/Ti3C2 S-scheme heterocatalyst

Developing efficient photocatalysts that can degrade persistent antibiotics in water bodies and generate clean H2 through water splitting is crucial for environmental and energy sustainability. This study introduces an advanced hierarchical ZnIn2S4/TiO2/Ti3C2 (ZIS/TO/TC) heterocatalyst for the efficient removal of amoxicillin (AXC) from wastewater and the production of H2 through water reduction process under simulated solar illumination. The ZIS/TO/TC hybrids were fabricated via an in situ hydrothermal process, resulting in a hierarchical 3D structure that capitalizes on the complementary properties of each component. The design of the S-scheme heterojunction significantly enhances charge separation and transfer efficiency, offering improvements over traditional type-II or Z-scheme mechanisms. Notably, the optimized ZIS10/TO/TC hybrid catalyst demonstrated remarkable activity, achieving 100 % AXC degradation and excellent mineralization within 90 min, along with an H2 production rate of 4104 μmol h−1 g−1, significantly outperforming the individual components and exceeding previously reported efficiencies. The outstanding performance of the ZIS10/TO/TC hybrid is attributed to its enhanced light absorption properties, synergistic interactions among the ZIS, TO, and TC MXene, and the efficient S-scheme charge transfer mechanism, which facilitates effective charge separation and transfer while preserving the strong reducing and oxidizing capabilities of the components. Additionally, the ZIS10/TO/TC hybrid exhibited excellent stability over multiple photocatalytic tests, emphasizing its potential for practical applications. Therefore, this research demonstrates an advanced and highly efficient hybrid photocatalyst that offers a promising and sustainable solution for both integrated water treatment and renewable energy generation.

ADPA Optimization for Real-Time Energy Management Using Deep Learning

The current generation of renewable energy remains insufficient to meet the demands of users within the network, leading to the necessity of curtailing flexible loads and underscoring the urgent need for optimized microgrid energy management. In this study, the deep learning-based Adaptive Dynamic Programming Algorithm (ADPA) was introduced to integrate real-time pricing into the optimization of demand-side energy management for microgrids. This approach not only achieved a dynamic balance between supply and demand, along with peak shaving and valley filling, but it also enhanced the rationality of energy management strategies, thereby ensuring stable microgrid operation. Simulations of the Real-Time Electricity Price (REP) management model under demand-side response conditions validated the effectiveness and feasibility of this approach in microgrid energy management. Based on the deep neural network model, optimization of the objective function was achieved with merely 54 epochs, suggesting a highly efficient computational process. Furthermore, the integration of microgrid energy management with the REP conformed to the distributed multi-source power supply microgrid energy management and scheduling and improved the efficiency of clean energy utilization significantly, supporting the implementation of national policies aimed at the development of a sustainable power grid.

A holistic analysis of environmental impacts and improvement pathways for the Brazilian electric sector based on long-term planning and life cycle assessment

Brazil presents a high share of renewable electricity generation, mainly due to its favorable hydroelectric potential. However, reports estimate that demand will grow substantially in the long term, requiring a significant diversification of the electricity mix. In this context, this paper analyzes how the environmental impacts of electricity generation tend to change over the horizon 2025–2050 in Brazil and identifies improvement opportunities accounting for environmental and economic factors. To do so, the Open Source Energy Modeling System (OSeMOSYS) is applied to estimate the composition of the electricity mix in 2050. Then, life cycle assessment (LCA) is used to evaluate the associated environmental impacts across ten categories. Lastly, hybrid weighted ɛ-constraint multi-objective optimization (MOO) is employed to evaluate which sources can be used to minimize climate change and the levelized cost of electricity while ensuring that other categories are reasonably restrained. The results indicate that the environmental performance of the Brazilian electricity matrix tends to deteriorate substantially, both when analyzing overall impacts and impacts per kWh. Moreover, the proposed MOO approach suggests that some sources, mainly hydropower, onshore wind, and centralized PV present an all-around economic/environmental performance and might be favorable for expanding the power system.

Development of Anemometer Based on Inertial Sensor.

The current article elucidates a study centered on the development of an anemometer leveraging an inertial sensor for wind speed measurement in the northeast region of Brazil, focusing on renewable energy generation. The study encompassed a series of experiments aimed at calibrating the anemometer, analyzing the noise generated by the inertial sensor, and scrutinizing the data acquired during wind speed measurement. The calibration process unfolded in three stages: initial noise analysis, subsequent inertial data analysis, and the derivation of calibration curves. The first two stages involved experiments conducted at an average sampling rate of 10 Hz. Simultaneously, the third stage incorporated data collected over a 1 h duration while maintaining the same sampling rate. The outcomes underscore the suitability of the anemometer based on an inertial sensor for wind energy systems and diverse applications. While the wind readings from the prototype exhibit considerable fluctuations, a three-length moving average filter is applied to the prototype's output to mitigate these fluctuations. The calibration surface was established using observational data, and the resultant surface is detailed. Data analysis assumes paramount significance in wind speed measurement, and the K-NN algorithm demonstrated superior efficacy in estimating the correspondence between measured and control data.

Biogas production and techno‐economic feasibility studies of setting up household biogas technology in Africa: A critical review

AbstractThis critical review examines the potential of household biogas technology in Africa, focusing on biogas production and techno‐economic feasibility studies. The review highlights the benefits of biogas technology, including renewable energy generation, waste management, and improved public health. A significant portion of Africa's rural population, approximately 60%, grapples with limited access to reliable power sources. The study analyzes various biogas production systems, including anaerobic digesters and biomass gasifiers, and evaluates their economic viability in different African contexts. The review identifies key factors influencing the adoption of household biogas technology, including policy and regulatory frameworks, financing mechanisms, and public awareness. Biogas production stands out as one of the promising renewable energy sources. However, it also discusses the challenges and limitations of implementing this technology on a continental scale. The study analyzes various biogas production systems, including anaerobic digesters and biomass gasifiers, and evaluates their economic viability in different African contexts. The review identifies key factors influencing the adoption of household biogas technology, including policy and regulatory frameworks, financing mechanisms, and public awareness. The study concludes by recommending strategies for scaling up biogas technology in Africa, including capacity building, technology transfer, and innovative financing models. The review aims to provide a comprehensive resource for researchers, policymakers, and practitioners working towards sustainable energy solutions in Africa.

Two-stage multi-objective distributionally robust operation optimization and benefits equalization of an off-grid type electric-hydrogen-ammonia-methanol coupling system

As the proportion of renewable energy has increased significantly, electric-hydrogen coupling technology has emerged as a pivotal strategy for optimizing the utilization of renewable energy resources. In particular, within the power and chemical sectors, the preparation of ammonia and methanol using electric-hydrogen coupling technology has emerged as the two principal avenues for the utilization of hydrogen energy. However, as a nascent technology, the preparation of ammonia and methanol by electric-hydrogen coupling is confronted with a multitude of challenges, including the considerable uncertainty surrounding the generation of renewable energy, the dearth of economic benefits, and the need to ensure a fair allocation of benefits among the various stakeholders. To enhance the risk resistance of the electric-hydrogen-ammonia-methanol coupling system (EHAMCS), improve the economic benefits, ensure the environmentality and stability of the system, and realize the fair distribution of benefits among the energy participants, this paper constructs a multi-objective distributionally robust optimization (DRO) and multi-dimensional benefits equilibrium model for the EHAMCS and performs a numerical analysis with an off-grid type EHAMCS as an example. The results demonstrate that: 1) The cooperative operation of the EHAMCS offers notable economic advantages. When compared with only pursuing economic benefits, under multi-objective optimization, the economic benefits of the coupling system can be reduced by 1.49 %, but the carbon emission can be reduced by 10.06 %, and the instability is reduced by 0.02, which can create derivative benefits. 2) The two-stage DRO model is an effective means of addressing the impact of system operation uncertainty and the affine strategy enables the rapid adjustment of the day-ahead dispatching plan, thereby balancing the power deviation. 3) The multi-dimensional factor benefit balancing scheme is more conducive to the participation of clean energy and downstream ammonia and methanol energy subsystems in cooperative operation and promotes the consumption of clean energy while improving the energy conversion value of the EHAMCS to increase economic benefits. 4) EHAMCS operators should adjust their operational strategies in time with market and policy signals, and select historical samples reasonably, so as to realize the balance of the DRO model in terms of economy, robustness, and practicality. The effectiveness of the model and the superiority of the EHAMCS are thus verified.

Automated monitoring of natural parameters of renewable energy production systems

The paper discusses the urgent issues of the need to automate the monitoring of natural parameters of renewable energy production systems. Monitoring involves the collec- tion and analysis of various parameters of the natural environment that affect the efficiency and environmental friendliness of systems powered by renewable energy. Based on analytical materials and practical work related to techno-economic and ecological studies concerning the assessment of water reserves in indi- vidual wells, the study focused on the development of technologies and technical means for monitoring. It was found that, in practice, hydrogeological research on groundwaters use outdated methods and technical means for measuring and storing data. A local automated system for monitoring well parameters was developed and constructed. It ensures the operation of the hydrothermal heat pump system at the experimental site of the Institute of Renewable Energy of the National Academy of Sciences of Ukraine. This system was tested in our research. The benefits of this study are revealing the need to create an intelligent monitoring system with the possibility of automated decision-making regarding the management of renewable energy generation systems, depending on changes in the am- bient parameters. A network automated system for monitoring natural parameters was developed, consisting of several wireless sensor subsystems that communicate with each other via wireless connection and transmit information to the gateway. The gateway, in turn, is connected to the computer with a graphical interface for interaction with the system. The results of work indicate that the proposed structures for automated water monitoring are simple and easy to implement. Consequently, the study revealed that one of the prospects is the development of automated monitoring systems with intelligent features, capable of making optimal individual decisions regard- ing the management of renewable energy generation systems under variable environmental parameters. As a result, the data obtained from this study have significant scientific and practical implications for advancing the design methodology of geothermal heat pump wells, focusing on long-term forecasts and assessments of their ecological and economic efficiency.

A study into the correlation between single array-hull configurations and wave spectrum for floating solar photovoltaic systems

Floating photovoltaic (FPV) systems offer a viable renewable energy solution due to easy installation and cost-effectiveness compared to other renewable energy generation methods. On the other hand, land-based solar photovoltaics face challenges such as space scarcity and environmental impacts. Shifting to nearshore locations unlocks vast ocean space potential, though waves expose significant challenges to FPV systems. Several novel FPV system designs are proposed, inspired by high-speed vessel multihulls, including catamaran, trimaran, quadrimaran, and pentamaran configurations, as floating supports for solar panels. Simulations were conducted to determine Response Amplitude Operators (RAOs) under various irregular wave spectrum conditions in a free-floating initial state. The FPV motion problem was solved using linear potential-flow theory with the Boundary Element Method (BEM) with Green-Function approach. Superposition of wave spectral energy and motion RAOs was used to obtain spectral structural responses. Motion in heave, roll, and pitch modes was evaluated across wave spectrum types. Results show that adding hulls reduces the significant amplitude response in all motion modes. In summary, valuable insights into floater designs and the hydrodynamic evaluation of FPV systems are presented.

Renewable Energy Generation Efficiency of Asian Economies: An Application of Dynamic Data Envelopment Analysis

Renewable Energy Generation Efficiency of Asian Economies: An Application of Dynamic Data Envelopment Analysis

Numerical simulation of seasonal underground hydrogen storage: Role of the initial gas amount on the round-trip hydrogen recovery efficiency

Subterranean structures such as aquifers and depleted gas reservoirs (DGRs) offer a scalable, high-pressure, secure, cost-efficient, and ecologically friendly means of hydrogen (H2) storage. Underground H2 storage (UHS) has emerged as a potential solution to alleviate the imbalance between the fluctuating renewable energy generation and the demand for a constant energy supply. Quantifying the recovery efficiency is of paramount importance in the effort toward large-scale UHS. In this numerical simulation study, we employed a high-resolution grid for the discretization of a synthetic (but realistic) heterogeneous anticline intended as a H2 storage facility, and we assessed the H2 recovery efficiency, and the purity of produced gas associated with UHS in natural reservoirs involving different pre-existing gases and their quantities. All of these studies included an initial phase of cushion gas injection, followed by 4 cycles of H2 storage operations composed of injection-idle-withdrawal periods. The simulation results indicated that the H2 round-trip recovery efficiency RH increased (a) with an increasing number of storage cycles, routinely exceeding 90% and providing evidence of a self-enhancement or self-optimization H2 recovery mechanism and (b) with an increasing amount of pre-existing gas in the storage zone prior to the H2 injections — at the end of the 4-cycle test, the highest RH is associated with a DGR with a pre-existing gas consisting of residual CH4 and additional injected N2, and the lowest RH corresponds to an aquifer with no cushion gas. Conversely, the H2 mass fraction of the produced gas FHQ (a) increased with an increasing number of storage cycles, but (b) decreased rapidly with an increasing amount of pre-existing gas. The presence of a pre-existing gas inevitably leads to severe contamination of the produced H2, which never exceeds 40% in the produced gas, can be as low as 4% in early cycles, and cannot be used as a H2 fuel without gas separation. Aquifer storage without a cushion gas may exhibit the lowest H2 recovery (about 75% in the long run), but yields practically pure H2 that does not require further processing before use. A positive conclusion is that, under the conditions of this study, total H2 losses (including H2 escaping into the caprock, and inaccessible H2 dissolved in the aqueous phase of the formation or remaining in the gas storage zone) are limited and manageable, indicating the technical feasibility of geologic H2 storage.

Experimental Infrastructure Design for Energy-Independent Car Park Building Based on Parametric Photovoltaic Facade System

The purpose of this study is to develop a new architectural model that responds to environmental pollution. The subject of this study is infrastructure buildings related to automobiles, which cause environmental pollution. Parking facilities accommodate several vehicles, necessitating the design of large-scale parking infrastructure. In this study, the parametric design of an energy-independent building was developed targeting the facade of a large-scale parking facility. As basic research for the development of the parametric design, a parking building was planned toward the optimization of parking space. Based on this basic research, a kinetic photovoltaic facade was developed to achieve optimal renewable energy generation from the perspective of eco-friendly architectural design. Energy simulation using building information modeling (BIM) on the kinetic photovoltaic system developed in this study over a period of one year resulted in the generation of a total of 692,386 kWh·year−1. The novelty of this study is the development of a kinetic photovoltaic facade that is oriented according to the optimal tilt angle every month, focusing on the infrastructure. The significance of the kinetic photovoltaic system lies in the fact that it not only maximizes the efficiency of renewable energy generation but also presents a new architectural design model.

The Practical Impact of Price-Based Demand-Side Management for Occupants of an Office Building Connected to a Renewable Energy Microgrid

This paper examined the practical impact of price-based demand-side management (DSM) for occupants of an office building connected to a renewable energy microgrid. There has been an absence of studies that have analyzed occupant reactions, in terms of perceived practicality, regarding the implementation of DSM in conjunction with factors including renewable energy generation, load shifting and energy costs. An increased understanding of the practicality of DSM will support future improvements in building energy efficiency and sustainability. Ten occupants of the National Build Energy Retrofit Test-bed (NBERT) were selected as a case study. The electricity consumption pattern of the NBERT occupants was derived over a period of two years. Five unique DSM schedules were developed for each of the NBERT occupants, and a survey was carried out to investigate the practicality of these DSM schedules. A real-time electricity pricing tariff, electricity CO2 intensity, three feed-in tariffs and two microgrid control methods were evaluated to assess the practicality of each DSM schedule on the ten NBERT occupants. The results showed that the incorporation of onsite renewable energy generation with price-based DSM had a positive impact (r = 0.69–0.75) on occupant practicality. Onsite renewable energy generation was able to offset the demand for expensive electricity from the grid during peak hours, which aligned with the occupants’ typical work schedules. However, the introduction of a feed-in tariff with a renewable energy microgrid made price-based DSM less practical (r = 0.15–0.64).

PV Based Microgrid for Remote Area Electrification in Nigeria: A Systematic Review of Concepts and Extant Strategies

Centralized power generation, transmission and distribution system operations in Nigeria can no longer deliver affordable and reliable electricity to remote households, both on and off the national grid. Therefore, a balanced approach, through renewable energy generation is the best option that would potentially lead accelerated journey to full electrification in the country. Renewable energy has become a panacea to energy problems worldwide because it is clean, environmentally friendly and ultimately cheaper. Due to the massive capacity of Nigeria to generate electricity from its numerous green energy resources, it has become crystal clear that access to sustainable and reliable energy in Nigeria is required for proper economic and social development. This work is based on an extensive review of recent experiences of more than 120 papers covering mostly the off-grid PV sector in the past 15years. The review included: scientific journals, conference papers, (PhD) theses, books and scientific reports from governments/NGOs and publications from energy institutions. A comprehensive review of relevant concepts and current microgrids optimization strategies applied to sizing, siting, load estimation and energy management has been conducted. Future research directions and potential of PV based microgrids for rural electrification have equally been specified.

The real-time shadow detection of the PV module by computer vision based on histogram matching and gamma transformation method

Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance.

A Wide-Range TCSC Based ADN in Mountainous Areas Considering Hydropower-Photovoltaic-ESS Complementarity.

Due to the radial network structures, small cross-sectional lines, and light loads characteristic of existing AC distribution networks in mountainous areas, the development of active distribution networks (ADNs) in these regions has revealed significant issues with integrating distributed generation (DGs) and consuming renewable energy. Focusing on this issue, this paper proposes a wide-range thyristor-controlled series compensation (TCSC)-based ADN and presents a deep reinforcement learning (DRL)-based optimal operation strategy. This strategy takes into account the complementarity of hydropower, photovoltaic (PV) systems, and energy storage systems (ESSs) to enhance the capacity for consuming renewable energy. In the proposed ADN, a wide-range TCSC connects the sub-networks where PV and hydropower systems are located, with ESSs configured for each renewable energy generation. The designed wide-range TCSC allows for power reversal and improves power delivery efficiency, providing conditions for the optimization operation. The optimal operation issue is formulated as a Markov decision process (MDP) with continuous action space and solved using the twin delayed deep deterministic policy gradient (TD3) algorithm. The optimal objective is to maximize the consumption of renewable energy sources (RESs) and minimize line losses by coordinating the charging/discharging of ESSs with the operation mode of the TCSC. The simulation results demonstrate the effectiveness of the proposed method.

A sustainable floating dome biogas plants integrated with semi-transparent photovoltaic thermal collectors: Construction, production and economic analysis

The design and construction of a self-sustained solar photo-voltaic active thermal heating floating dome biogas plant (35 m3) offers an alternative solution for round year biogas production, managing food and agricultural waste, reducing greenhouse gas emissions and minimize pollution. Moreover, this proposed model addresses the challenge of achieving the desired operating temperature (35 ± 2 °C) specially during the cold climatic conditions, which often leads to low gas production and high hydraulic retention time in anaerobic digestion. The integration of solar modules with the biogas digester enables the utilization of thermal energy for slurry heating and electricity for force mode of operation, ultimately enhancing overall energy efficiency. However, to assess the performance of the system, proximate and ultimate analyses as well as a cost-benefit analysis were carried out, considering revenue, and costs associated with construction, operation, and maintenance. The plant produces a significant amount of gas (9–12 m3) and hot water (∼2000 L) replacing approximately one LPG cylinder/2 day as well as organic fertilizer. The economic analysis shows a positive net present value (19.5 lakhs), an internal rate of return (20%), and short discounted payback period (5.7 years). This highlights the economic feasibility of the proposed model and emphasizes the importance of considering economic factors in the design and implementation of sustainable energy solutions. Furthermore, this approach demonstrates the potential for sustainable development and the transition to cleaner and more efficient energy systems by integrating renewable energy generation, waste management, and cost reductions.

Insights about the use wood waste from the neotropical Amazonian for the generation of renewable energy

The abundance and availability of wood waste in the Amazon region pose a great challenge in terms of their use, such as to generate clean and renewable energy. The use of blends of the most different species constitutes a challenge in view of the energetic characteristics that are unknown for the replacement of fossil fuels. Thus, the objective of this study was to investigate the energy potential of wood waste from Amazonian species (Dinizia excelsa and from Manilkara elata, commonly used as lumber) as a way to support the generation of clean and sustainable energy. The wood waste of two Amazonian species were collected during the mechanical processing of logs; the sawdust of Eucalyptus spp. was collected in a biomass company. The contents of total extractives, total lignin, ash, moisture, bulk density, calorific value and energy density were investigated in different blends of residual biomass. The Dinizia excelsa species stood out for presenting higher values of extractives, total lignin, calorific value and energy density. The waste of Amazonian species showed satisfactory characteristics for energy use, which encourages the use of this biomass from wood processing. In addition, forest biomass waste enable the production of various compacted solid fuels, such as pellets and briquettes.

Coordination of Renewable Energy Integration and Peak Shaving through Evolutionary Game Theory

This paper presents a novel approach to optimizing the coordination between renewable energy generation enterprises and power grid companies using evolutionary game theory. The research focuses on resolving conflicts and distributing benefits between these key stakeholders in the context of large-scale renewable energy integration. A theoretical model based on replicator dynamics is developed to simulate and analyze the evolutionary stable strategies of power generation enterprises and grid companies with particular emphasis on peak shaving services and electricity bidding. These simulations are based on theoretical models and do not incorporate real-world data directly, but they aim to replicate scenarios that reflect realistic behaviors within the electricity market. The model is validated through dynamic simulation under various scenarios, demonstrating that the final strategic choices of both thermal power and renewable energy enterprises tend to evolve towards either high-price or low-price bidding strategies, significantly influenced by initial system parameters. Additionally, this study explores how the introduction of peak shaving compensation affects the coordination process and stability of renewable energy integration, providing insights into improving grid efficiency and enhancing renewable energy adoption. Although the results are simulation-based, they are designed to offer practical recommendations for grid management and policy development, particularly for the integration of renewable energies such as wind power in competitive electricity markets. The findings suggest that effective government regulation, alongside well-designed compensation mechanisms, can help establish a balanced interest distribution between stakeholders. By offering a clear framework for analyzing the dynamics of renewable energy integration, this work provides valuable policy recommendations to promote cooperation and stability in electricity markets. This study contributes to the understanding of the complex interactions in the electricity market and offers practical solutions for enhancing the integration of renewable energy into the grid.

Commercial EV fleet smart charging for cost reduction and renewables integration: A case study in Germany

The transition to electric mobility reduces transportation carbon emissions but presents challenges in integrating growing electricity demand with renewable electricity generation and power systems. Smart charging emerges as a key technology to minimize operational costs and support higher utilization of green electricity for electric vehicles (EVs). Market analysis indicates a trend towards dynamic electricity tariffs, incentivizing EV charging at times favourable to the electricity grid, yet existing literature fails to address smart charging technology in sync with times of lower electricity prices and higher renewables integration. This work examines different smart charging strategies for a commercial EV fleet in Germany, considering a Real-Time Pricing (RTP) tariff indexed to day-ahead market prices and renewable electricity integrations. HOMER Grid is used to model these smart charging strategies and generate new EV load profiles. Direct and indirect CO2 emissions and renewable electricity usage ratios are calculated based on detailed electricity grid and self consumption data. The resulting Levelized Cost of Energy (LCOE) is minimized, by adjusting solar PV and battery capacities. The adoption of an RTP tariff decreases the LCOE from €0.530 to €0.314 per kWh. A smart charging strategy designed to reduce peak demand and schedule charging at lower price times further reduces the LCOE to €0.283 per kWh. The lowest LCOE of €0.281 per kWh is achieved with 34.4 kWp of solar PV capacity and no batteries. The carbon impact is minimized with larger solar PV and battery capacities. The performed work indicates a favourable framework for EV charging integrators to maximize cost savings and renewable energy integration for an EV fleet charging use case.

Cumulative collision risk and population-level consequences of industrial wind-power plant development for two vulture species: A quantitative warning

Prioritizing renewable energy generation over the conservation of natural habitats and species on a large spatial scale, leads to the paradox of impacting biodiversity to mitigate climate change. In this study, we aim at quantifying the long-term demographic impact of the excess mortality caused by collisions with wind turbines on the populations of two vulture species of conservation concern. Using long-term monitoring data and Integrated Population Models (IPMs), we quantified demographic parameters and projected population trends under various wind power development scenarios. Our findings indicate that even under our most optimistic scenarios, annual collision mortality could reach up to 30 % of the current Cinereous vulture population and 7 % of the Griffon vulture population. Without further wind power expansion, both vulture populations are predicted to remain stable or increase over the next 20 years. However, the addition of 85 wind turbines is likely to drive the Cinereous vulture to local extinction within 18 years and significantly slow the growth of the Griffon vulture population. Scenarios involving larger numbers of turbines could result in the extinction of both species within two to five years for Cinereous vultures and up to 20 years for Griffon vultures, depending on space use intensity. Our results underscore the vulnerability of long-lived species to excess mortality and highlight the need for comprehensive Environmental Impact Assessments (EIAs) that incorporate population dynamics analyses. Effective conservation strategies must include rigorous pre- and post-construction monitoring, the availability of monitoring data, and cumulative impact assessments that consider the entire foraging range of these species. Additionally, strategic planning to avoid critical vulture habitats and implementing mitigation measures in buffer zones are essential. This study emphasizes the necessity of integrating biodiversity considerations into renewable energy planning to balance the goals of energy production and wildlife conservation.