Search

Although the global market for wind energy is growing rapidly, leading-edge erosion is a critical issue hindering the development of wind power. The impact force of a droplet colliding with flat surfaces has been investigated in previous studies. However, the impact force exerted on curved surfaces, such as that experienced by eroded wind turbine blades, is not well understood. This study discusses the relationship between the impact force generated on a solid surface by a water droplet and the radius of curvature of the impacting surface. The impact force by a droplet was measured using a force sensor mounted on semi-cylindrical caps with different radii of curvature. The measurement results showed that the impact force decreased as the radius of curvature decreased. A computational fluid dynamics model solving incompressible flows showed that, unlike the case of a curved surface, the initial momentum of the droplet was mostly transferred to the flat surface. This resulted in a high impulse for an impact with a flat surface. The falling droplet was blocked by the surface, and the lateral jet was accelerated sideward. This acceleration was moderate for curved surfaces. When colliding with a flat surface, a higher impact force was generated owing to the wider area of the excited surface pressure compared with that of the curved surface. Finally, the relationship between the peak of the impact force and the surface curvature was derived, suggesting that the force peak is inversely proportional to the curvature. [ABSTRACT FROM AUTHOR]

With increasing environmental concerns and energy crisis, a variety of renewable energy sources (RES) are being increasingly utilised worldwide. However, the integration of RES such as wind power and photovoltaics in large-scale can lead to increased load fluctuations, which can undermine the overall environmental benefits and pose risks to the secure and stable operation of the power system. To mitigate this challenge, a two-stage electricity production scheduling is developed incorporating energy storage system (ESS) and dynamic emission modelling (DEM). In the first stage, a multi-objective mixed integer programming model schedules the production of RES, increasing penetration rate and system stability. In the second stage, a data-driven dynamic emission model is developed to optimise the load allocation of thermal power unit to reduce the carbon emissions. Furthermore, a flexible operating reserve strategy is proposed to handle the uncertainty resulting from the intermittent character of RES. Experimental results demonstrate that the proposed method effectively schedules the production of RES thereby alleviating the contradiction between high RES utilisation and stable system operation. Compared to the benchmark model, the proposed method can reduce the carbon emissions and total cost of the system by 20.34% and 10.65%, respectively. [ABSTRACT FROM AUTHOR]

The inherent unpredictability and volatility of renewable energy generation contribute to insufficient prediction accuracy, resulting in substantial operational discrepancies. Consequently, frequent dispatching adjustments of conventional generator sets are necessary. The imperative of mitigating the frequent activation of generator sets and curbing the associated costs stands as a paramount challenge in the seamless integration of wind energy within the extant power system. To address this issue, the present study incorporates the uncertainty associated with renewable energy into the optimization of hybrid power dispatch, aiming to reconcile system reliability with economic optimization. This investigation introduces the wind power prediction error histogram, employing discrete coordinate data to fit a curve. The curve serves to enhance the calculation strategy of optimal bandwidth in non-parametric Kernel Density Estimation. To further characterize uncertainty, a random sampling method is employed to convert uncertain wind power variables into deterministic values. Additionally, the scenario reduction method is utilized to mitigate computational demands. Subsequently, based on quantified uncertainty information, a hybrid variable spinning reserve capacity power dispatching model strategy is formulated and simulated using the CPLEX solver. Experimental results demonstrate that the proposed method diminishes load shedding and enhances power system stability. [ABSTRACT FROM AUTHOR]

The design of wind turbines has been continually evolving to enhance aerodynamic efficiency, which is crucial for improving energy conversion and reducing operational costs. One promising technique is the application of the Co-Flow Jet (CFJ) method, which utilizes simultaneous blowing and suction to augment aerofoil performance. Despite its potential benefits, the relationship between aerofoil thickness and the resulting improvements in lift and drag coefficients has not been thoroughly explored, especially for the National Renewable Energy Laboratory (NREL) aerofoils commonly used in wind turbine applications. This study utilizes computational fluid dynamics (CFD) simulations in order to investigate the aerodynamic efficiency improvements of the NREL S826, NREL S825, and NREL S818 aerofoils through the application of the CFJ technique. Various configurations with different blowing (B) and suction (S) configurations were tested, including 0.08B-0.7S, 0.08B-0.8S, 0.1B-0.7S, and 0.2B-0.7S configurations. The results demonstrate that the S826-0.2B-0.7S, S825-0.2B-0.7S, and S818-0.08B-0.7S configurations yield the most significant enhancements at a common momentum coefficient (Cm) of 0.08. Specifically, there were increases in lift coefficients by about 51.1%, 66.38%, and 109%, and improvements in lift-to-drag ratios by about 11.5%, 14.38%, and 146.18% for the S826-0.2B-0.7S, S825-0.2B-0.7S and S818- 0.08B-0.7S configurations, respectively. [ABSTRACT FROM AUTHOR]

The Global Climate Risk Index ranks Pakistan as the fifth most vulnerable nation in the world because of its climate conditions. Further, 28% of Pakistan's total greenhouse gas (GHG) emissions are contributed by the transport sector. Renewable energy resources based on solar photovoltaics and wind turbines, along with hydrogen as a clean energy storage medium, are thought to be the most prominent and promising means of reducing these emissions from the transportation sector. This study aims to conduct a techno-economic viability assessment for an off-grid PV-wind-hydrogen storage-based EV charging station in order to determine the optimal configuration that can satisfy the necessary load demand. For the purpose of the simulation and optimization process, HOMER Pro software is used. Moreover, this research work has performed a comparative analysis among four cities of Pakistan to determine the influence of geographical and climate factors on energy generation (MWh), size (MW), and cost parameters (i.e., NPC and COE) of the charging station. For the purpose of in-depth analysis, two cases are considered in which load demand is kept the same and the designed system for Hyderabad city is considered as a "base system". In case 1, the objective is to determine the variation in annual renewable energy generation keeping the same system size in all four cities. In case 2, the variation in energy generation is minimized near the base value to determine the impact on size and cost parameters. The results revealed that the lowest NPC and COE occur in Peshawar, followed by Quetta, Lahore, and Hyderabad. For Hyderabad, when the outcomes of cases 1 and 2 were compared, its NPC and COE stayed the same due to the base case, with values of 21 M$ and 0.567 $/kWh, respectively. Whereas, in Peshawar, the value of NPC dropped from 18 M$ to 14.6 M$ while the value of COE decreased from 0.485 $/kWh to 0.393 $/kWh. The research findings provide the possible implications of distinct geographical locations and climate conditions upon renewable energy generation and its further influence on system size and economic parameters. The outcomes of this research will be insightful for policymakers and stakeholders while planning and designing an off-grid EV charging station infrastructure. • Techno-economic feasibility analysis to determine the optimal size and cost parameters of EV charging station. • A comparative analysis of EV charging stations among four climatically distinct cities. • The potential of hydrogen as a sole energy storage medium. • HOMER Pro software is utilized for modelling, simulation, and optimization. • The lowest NPC and COE are $14.6 M and 0.393 $/kWh respectively. [ABSTRACT FROM AUTHOR]

This work investigates the optimal design and operation of an alkaline water electrolysis plant fed by renewable electricity. At the basis of this investigation lies a mathematical model that is developed to capture essential mechanisms regarding gas purity and temperature constraints. This model is optimized for maximized hydrogen production given varying power inputs and design variations. Resulting operational profiles are then weighted with realistic wind power data, allowing for a novel framework in which mechanistic models of different scales are combined to predict annual hydrogen production and online-factors. The highest online-factors are achieved with independent power supplies, which, for degraded plants, results in the largest annual production increase of all design variations (up to 9%). Designs facilitating flexible pressure operation show the largest improvement potential for novel plants, increasing the production rate by up to 4.5%. [Display omitted] • Development of modeling and optimization framework for alkaline electrolysis plant. • Investigation of BoP design on load range and utilization of wind power. • HTO related load limit reduced by flexible pressure or individual power supplies. • Flexible pressure operation increases production most (4.5%) for novel plant. • Individual power supplies increase production most (9%) for degraded plant. [ABSTRACT FROM AUTHOR]

A new damping nonlinear grey multivariate seasonal forecasting power model DAFGM (1, N, α, γ) is proposed to solve the problem of small sample forecasting with seasonal, nonlinear. and uncertain system behavior characteristic sequence. Firstly, the seasonal moving filter is used to eliminate the seasonal characteristics of the original series. Then, according to the principle of "new information priority," the damping accumulation coefficient is introduced, the unknown factors which are difficult to collect are simulated by introducing dummy variables, and a new seasonal forecasting model is constructed. Finally, the model is used to forecast the quarterly wind power generation in China. The results show that the model has good practicability and effectiveness. [ABSTRACT FROM AUTHOR]

The increasing demand for electrical energy, the limitation of fossil fuel resources, and the aggravation of environmental pollution have made it inevitable to use clean energy sources, such as wind energy. In the meantime, the doubly fed induction generators are commonly used due to their capability of controlling the reactive power with no need for capacitor banks. Furthermore, increasing the capacity of wind farms has made the high-voltage direct current (HVDC) transmission network in the form of multiple terminals a preferred option for transferring the power of these sources over long distances. In this situation, the interaction of transmission system controllers with other controllers and devices has become a primary concern of the network. Research has shown that power systems that undergo stress exhibit complex dynamic behaviors that increase the possibility of interaction between system components and can endanger the stability of the power system. The present study attempted to investigate the problem of interaction in multi-terminal HVDC transmission systems, and the problem of torsional interaction in wind farms connected to these transmission systems. The linear modal analysis was used to investigate the interaction, and the analytical results were validated using MATLAB/Simulink time-domain simulation. In addition, due to the change of system parameters over time, this paper comprehensively investigated the system damping over different system parameters, and a structured scheme was employed to obtain an optimal range of control coefficients, ensuring the stable operation of the system. [ABSTRACT FROM AUTHOR]

Specialized sensors in wireless networks monitor the environment with minimal power, which is typically provided by batteries but it can also be powered with renewable sources using smart materials. One such solution is piezoelectric wind energy harvesting to power these sensors. However, the low output power of piezoelectric materials is still a concern. This research aims to improve piezoelectric wind energy harvesting by modifying the geometry of the bluff body. A square-shaped bluff body is used as a reference, and geometric changes are made by attaching square-shaped extensions. The study creates a mathematical model of a cantilever beam system with a piezoelectric material at fix end and a bluff body at the free end, using a multi-physics approach that combines electromechanical and aerodynamic models. The study uses MATLAB Simulink to solve the differential equations and verify the results with experiments. A parametric analysis examines the impact of external resistance, wind speed, and bluff body shape on circuit metrics. The study found that at a wind speed of 6 m/s and an optimal load resistance of 200 kΩ, the system generated 4.984 mW of power. This result demonstrates the potential of wind energy harvesting to power various distributed sensors for a wide range of applications. [ABSTRACT FROM AUTHOR]

Wind energy is vital for transitioning to renewable sources, reducing reliance on fossil fuels, and mitigating climate change. Harnessing wind power provides sustainable solutions to meet global energy demands. The systematic review provides valuable insights into recent advancements in blade design for wind turbines, crucial for optimizing efficiency, and sustainability in wind energy systems. The study emphasized advancements in Horizontal Axis Wind Turbines, concentrating on efficiency enhancements with numerical simulations and optimization strategies. Additionally, it explored developments in Vertical Axis Wind Turbine technology, notably hybrid rotor systems, and primarily utilized wind turbine airfoils over the past 25 years. Many researchers focused on optimizing aerodynamic systems using advanced numerical simulations, experiments, and optimization techniques, stressing the significance of achieving optimal lift-to-drag ratios. Furthermore, factors like bend-twist coupling and stall characteristics to prolong turbine lifespan and maximize power extraction were investigated. The interdisciplinary nature of wind energy research emphasizes collaborative efforts to address challenges and foster innovation, driving progress toward sustainable development goals. This review also supports current issues in the sustainable development goals (SDGs). [ABSTRACT FROM AUTHOR]

Annual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free‐stream wind conditions. Physics‐based estimates of wind farm power production typically rely on low‐fidelity engineering wake models that approximate the steady‐state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition's expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed‐form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient‐based wind farm layout optimization, in nondimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. Although the FLOWERS approach is found to predict the exact value of AEP with some error relative to the reference model (within 14% on average), it dramatically reduces computation time by an order of magnitude, produces a qualitatively similar design space at relatively low resolution, and yields comparable optimal layouts. This significant speed improvement is critical in layout optimization applications, where determining an optimal layout in an efficient manner is more important than precise AEP prediction. [ABSTRACT FROM AUTHOR]

Maximising the power production of wind farms is vital to meet the growing demand for wind energy and reduce its cost. Wake effects, resulting from the aerodynamic interactions between turbines in a wind farm, significantly impact farm efficiency, leading to substantial annual power losses. Wake steering, an influential control strategy, involves mitigating wake effects by strategically yaw misaligning upstream turbines to deflect their wakes. Conventional wake steering approaches typically rely on physics‐based analytical wake models with their parameters often calibrated using higher fidelity data. However, these approaches determine a fixed set of parameters prior to conducting wake steering, neglecting each parameter's dependency on yaw misalignment (i.e. the optimisation variables) exhibited throughout the optimisation process, potentially affecting its accuracy. To address this limitation, this paper introduces a novel data‐driven parameter tuning approach that integrates higher fidelity power measurements using Gaussian processes to continuously adapt parameters in lower fidelity wake models based on the current farm's yaw configuration. The effectiveness of the proposed approach is demonstrated on a 5×5$$ 5\times 5 $$ wind farm and a layout corresponding to the Horns Rev wind farm, where various wind directions are investigated. The results reveal that the approach can enable a lower fidelity model to capture more complex physics, thereby improving its accuracy in wake steering optimisation, while maintaining robustness and computational efficiency. This method holds promise for real‐time control applications and can be extended to other control strategies and closed‐loop frameworks. [ABSTRACT FROM AUTHOR]

This paper investigates a novel control strategy that enables hybrid excitation permanent magnet synchronous generator (HPMSG) to track the optimal extracted power of the modern wind turbine type (NASA-NSF). The proposed control mathematical model is based on two cases of variable speed—Maximum Power Point Tracking (MPPT) and variable speed—Constant Power Point Tracking (CPPT). The later one is specified for wind gust and higher than rated wind speed withstanding operation. The HPMSG generator quantitative performance characteristics are presented and validated through simulation for both steady and dynamics states. Simulation results prove the capability of the generator to operate correctly under load and speed variation over both MPPT and CPPT. The output voltage stays, in both cases, within the much lower limits that imposed by maximum values. [ABSTRACT FROM AUTHOR]

Incorporating renewable energy sources (RESs) introduces a notable amount of uncertainty in the optimal planning and operation of electrical power grids. Under these circumstances, this paper proposes the application of a recently introduced metaheuristic optimization technique to solve the stochastic optimal power flow (OPF) problem involving wind and solar power sources. The self-adaptive bonobo optimizer (SaBO) is used to minimize three distinct objective functions: (i) Total generation cost (TGC) minimization, including both thermal and wind/solar generation costs, (ii) Power loss minimization, (iii) Combined generation cost and emissions effect minimization. The costs associated with the stochastic generation of wind and solar power included direct costs, reserves and penalty costs from the overestimation and underestimation of available wind and solar power, respectively. The performance of the proposed algorithm is evaluated on two power systems: the modified IEEE 30-bus and the Algerian DZA 114-bus test systems. To demonstrate the efficacy of the SaBO, the obtained results have been compared with those obtained from the Kepler optimization algorithm (KOA) and other recently published optimizers under the same case studies and constraints. The comparative results clearly show the superiority of the SaBO algorithm over all other well-known optimization algorithms provided in the literature for solving the OPF problem. This is evidenced by minimizing total generation costs of 781.2363 $/h for the modified IEEE 30-bus and 16,706.1630 $/h for the Algerian DZA-114-bus system. Furthermore, the integration of RES led to a notable 2.33% and 11.67% reduction in total generation cost for the IEEE 30-bus and Algerian DZA 114-bus systems, respectively, compared to their initial configurations without RESs. The promising findings highlight the powerful of the optimizer to solve non-linear and complex optimization problems in power systems. [ABSTRACT FROM AUTHOR]

Considering the physical characteristics of wind turbine wing icing, icing synthesis rate, and icing type, we selected the icing type and surface roughness of ice-coated blades as sensitive parameters. The focus of our research was on the equivalent particle roughness height correction model, and we numerically analyzed the two icing processes (frost ice and clear ice) on wind turbine blade surfaces by combining FENSAP-ICE and FLUENT analysis tools. We predicted the ice type on blade surfaces using a multi-time step method and analyzed how variations in icing shape and ice surface roughness affect the aerodynamic performance of blades during frost ice formation or clear ice formation. Our results indicate that differences in blade surface roughness and heat flux lead to disparities in both ice formation rate and shape between frost ice and clear ice. Clear ice has a greater impact on aerodynamics compared to frost ice, while frost ice is significantly influenced by the roughness of its icy surface. These findings can serve as valuable references for wind power operators and manufacturers seeking solutions to issues related to blade surface icing under extremely cold conditions. [ABSTRACT FROM AUTHOR]

Wind farm power curves are particularly relevant in different applications. A better understanding of wind farm power curves under different atmospheric regimes is sought. In the present work, the atmospheric stability regime was identified by the vertical gradient of the potential temperature. Vertical profile of the wind velocity presented a clearly different pattern during the stable and unstable regimes. Analysis was performed considering 5 years of data from three wind farms, each corresponding to different mesoscale wind. Power curves are presented as a function of atmospheric stability. For each wind farm, we utilized wind velocity and temperature data measurements of a correspondent near the tower location. Clearly different patterns of wind farm power curves were observed as a function of the stability regime, with clear differences in the dependence of mesoscale wind regimes. For the same mean velocity, wind power production is greater for higher values of turbulence intensity TI. [ABSTRACT FROM AUTHOR]

Access to energy is critical for promoting economic growth and alleviating poverty. Cost-effective and dependable energy services are essential for modernising agriculture, expanding trade, saving lives, growing industries and empowering women. Renewable energy resources, which include wind, are employed worldwide to augment energy needs. Apart from generating electricity, it has also been harnessed for pumping water, milling grains, and irrigation in agriculture. Farming remains one of the significant economic activities in Ghana, and for farmers to stay in business all year, they must irrigate their farmlands. Through its Irrigation Development Authority, the Government of Ghana promotes irrigation among farmers. Irrigation in Ghana is mainly done by fossil fuel-operated pumps or manual labour, with little or no irrigation powered by renewable sources. Though the Ministry of Energy and Food and Agriculture promoted the Poldaw Windpump for irrigation and communal water supply in 2004, wind-powered irrigation is non-existent in Ghana. This paper reviews wind irrigation development in Ghana. It highlights the piloting of the Poldaw Windpump in Ghana and the present situation in the country. [ABSTRACT FROM AUTHOR]

In this work, a linear quadratic regulator controller is utilized to optimize the gains of the machine-side converter and grid-side converter controllers. The DC-link voltage and the rotational speed of the doubly-fed induction generator are optimally regulated by these controls. Additionally, the small-signal stability of the proposed system has been evaluated using the linearized model of the system. The small-signal stability analysis of the designed system has been accomplished through the application of the eigenvalues and participation factors technique. The suggested controller has been tested in a variety of operating conditions under small disturbances. MATLAB/SIMULINK simulation simulations have been used to evaluate the efficacy of the suggested optimal control system. Additionally, the generator-angular speed and DC-link voltage dynamic performances demonstrate the efficacy of the proposed controller. Finally, the robustness of the proposed controller has also been tested and validated using OPAL-RT technology under large perturbations at stator terminal voltage. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel robust nonlinear optimal second-order sliding mode controller using the homotopy perturbation method (RNOSOSMC-HPM) to maximize wind power capture and minimize the mechanical stress on the drive train. To design the nonlinear optimal controller, the homotopy perturbation method (HPM) is applied to compute the approximate solution of the partial differential Hamilton-Jacobi-Bellman (HJB) equation. Next, the nonlinear optimal controller is combined with a second-order sliding mode controller to create robustness and eliminate chattering. The RNOSOSMC-HPM controller can provide safe wind turbine operation under uncertainties and create a good trade-off between maximizing the wind power captured and attenuating the mechanical loads by minimizing the control input. To investigate the effectiveness of the presented the RNOSOSMC-HPM controller, we compare the results of the proposed method with some existing control schemes in two different scenarios. The results indicate that the RNOSOSMC-HPM controller furnishes desired responses. [ABSTRACT FROM AUTHOR]

The main goal of the power system is to congregate the power demand within the power grid while maintaining economical operation, system security and minimal environmental impact. Due to the increasing demand for electrical energy, many problems have arisen with the power systems. These problems include excessive load, uneven system performance, unsatisfactory voltage profile, and an increase in network power losses. To address these issues, more generation sources and improved transmission capacity are required. In order to meet increasing electricity demand, it is more efficient to integrate a sufficient number of smaller generation units. Utilities and consumers can get the significant benefit from installation of distributed generation (DG), which reduces power losses, progress voltage profile, increases power quality and reliability, delays system updates, supports local reactive power, standby generation and peak limiting. This article aims to enrich the performance of the entire network through the best possible placement and penetration of wind energy and solar photovoltaic (PV) dispersed generation. [ABSTRACT FROM AUTHOR]

The stable operation of the power grid requires accurate predictions of wind power generation and the stabilization of its fluctuations through the integration of other energy sources. An increasing number of deep learning methods are now being employed in the field. However, due to the instability of wind power data, existing methods struggle to uncover deep spatiotemporal dependencies. We propose a novel method named SD-STGNN (Series Decomposition and Spatio-Temporal Graph Neural Network). SD-STGNN first decomposes unstable wind power data into seasonal and trend components. For the seasonal data reflecting short-term fluctuation patterns, we employ a Gated Temporal Convolutional Network and Graph Convolutional Network to capture spatiotemporal relationships. Additionally, for trend data reflecting long-term fluctuation patterns, we introduce a Temporal-Feature Enhancement module, utilizing Multi-Layer Perceptrons to extract deep information along both temporal and feature dimensions. Extensive experiments were conducted on the SDWPF public dataset. Compared to existing state-of-the-art baseline methods, our proposed SD-STGNN model achieves a notable average reduction in Mean Absolute Error by approximately 6.26%, in Root Mean Squared Error by 7.55%, and in Mean Absolute Percentage Error by 2.65%. Additionally, there is an average improvement of about 4.74% in the coefficient of determination. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Regarding the relationship between wind power generation and meteorological factors, previous studies tend to focus on the correlation between them, but correlation does not imply causation. In this context, we propose to use a combination of time-lagged convergent cross mapping (CCM) and graph network theory to investigate the causal relationship between wind power generation and meteorological factors. The effectiveness of time-lagged CCM is demonstrated by applying it to three scenarios of strong linear correlation, weak linear correlation and no linear correlation, respectively, and comparing it with Standard CCM and time-lagged Pearson correlation coefficient (PCC). Time-lagged CCM can accurately identify the causal relationship between wind power generation and meteorological factors and quantitatively assess the variables' causal intensity. Further, combined with graph network theory, we constructed a causal pattern diagram. From it, we can find that the causal relationship and intensity between wind power generation and meteorological factors also change dynamically throughout the year, following a specific temporal causal chain law. This finding is conducive to establishing a more accurate wind power prediction model, especially in the model's feature selection and feature relationship analysis. [ABSTRACT FROM AUTHOR]

The present study focuses on the harris hawks optimizer. harris hawks optimization (HHO) is introduced based on population and nature patterns. The HHO algorithm imitates harris hawks attacking behavior and includes two phases called exploration and exploitation, which can be modeled with three strategies, 1) discovering the prey, 2) surprising attack, and 3) prey attack. The main purpose of using this type of algorithm is to optimally solve the short-term hydro-thermal self-scheduling (STHTSS) problem with wind power(WP), photovoltaic (PV), small hydro (SH) and pumped hydro storage (PHS) powr plants while considering uncertainties such as energy prices, ancillary services prices, etc, in the energy market. It will be shown how energy generation companies can use this algorithm and other algorithms and innovative methods that will be introduced in the future to achieve profit maximum with careful scheduling. It is worth mentioning that in this study, the effect of the presence and absence of two important factors, namely valve load cost (VLC) effect and prohibited operating zones (POZs) (with linear modeling) that can affect the profit of units (power plants) has been pointed out. Finally, as shown in this study, several tests perfomed on the IEEE118-bus system validate the precision and credibility of the harris hawks optimization algorithm. [ABSTRACT FROM AUTHOR]

Ethiopia possesses abundant wind resources that have the potential to revolutionize its energy sector by providing reliable and sustainable electricity through wind power. Despite the presence of a few operational wind farms, the country is facing challenges in generating sustainable electricity. The slow progress in wind power development raises concerns, as the impact of declining power generation from existing wind farms on economic development and return on investment remains insufficiently examined. The research paper aims to examine the status, challenges, and opportunities in developing, deploying, and sustaining wind power generation. This was accomplished through qualitative and quantitative analysis using 11 years of power generation data from operational wind farms and field research. Data sources include Ethiopian Electric Power, wind power operation reports, official government reports, field observations and discussions with wind power operators. The study reveals that challenges such as intermittent wind resources, high initial investment costs, inadequate transmission infrastructure, limited skilled workforce, and environmental concerns hinder the realization of wind energy's full potential. The article provides evidence-based recommendations for policymakers and the wider stakeholders to address the challenges and maximize benefits of wind energy in Ethiopia. This contributes to understanding the current situation and necessary actions to harness the country's abundant wind power potential. [ABSTRACT FROM AUTHOR]

Flow‐induced vibrations (FIVs) serve as the fundamental principle of non‐rotary wind energy harvesting. However, nanogenerators relying on a single FIV effect remain constrained by insufficient breeze energy conversion efficiency. In this paper, we propose a novel galloping‐flutter coupled nanogenerator (GFNG) that leverages the synergistic interaction between these two aerodynamic phenomena, to achieve high performance across broad wind speed bandwidth. A galloping‐flutter coupled mechanism (GFM) is implemented using a multifunctional flexible beam that integrates a galloping piezoelectric energy harvester (GPEH) and a fluttering triboelectric nanogenerator (FTENG). Through meticulous optimization, it significantly enhances the average electrical output of the FTENG by up to six times at low wind speeds below 6 m s−1, by intensifying the triboelectric contact behavior through galloping‐induced beam oscillations. The GFNG demonstrates a maximum average power of 6.3 mW across wind speeds from 1.4 to 10 m s−1, along with a remarkable power density of 7.1 W m−2 of the enhanced FTENG at 10 m s−1, enabling the lighting of 508 LEDs and stable power supply for wireless sensor nodes (WSNs). This study offers new insights into designing high‐performance aerodynamics‐driven nanogenerators by harnessing multiple FIV synergistic effects, broadening the potential for intelligent wind energy applications. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper gives a detailed study for the design and implementation of an energy management system (EMS) for a hybrid renewable microgrid system using real‐time software. Microgrids, with their ability to integrate renewable energy sources, face challenges in maintaining stability and reliability. The implemented EMS aimed to maximize the renewable energy sources utilization, including PV and wind power, in conjunction with a battery energy storage system. The objectives of this research included the implementation of an EMS that ensures a reliable and stable operation between the microgrid system and the main grid including the control of charge and discharge of the battery using Typhoon Hardware‐in‐the‐Loop (HIL) software. The simulation results and case studies demonstrated the effectiveness and performance of the developed EMS in managing a hybrid renewable microgrid system. The results also demonstrated that the time of charging was maximized by utilizing a higher power. By doing so, the battery was fully charged in a shorter timeframe. The battery state of charge (SOC) was maintained between the fixed values (20% and 100%) as stated by the algorithm. [ABSTRACT FROM AUTHOR]

Bringing floating offshore wind turbines (FOWTs) to a real industrial maturity and reducing the levelized cost of floating wind energy are key to significantly increasing the penetration of renewables in the energy mix of Mediterranean countries, especially if in combination with suitable energy storage systems, such as those involving green hydrogen production. The present study analyses techno‐economic aspects of some of the technologies related to FOWTs and hydrogen production by means of offshore‐generated energy, aiming to evaluate the potential of a floating wind farm integrated with a power‐to‐gas energy storage system in a specific installation site near the Sardinian shores. In comparison to the pioneering studies to date, a more detailed computational model is used, able to account for several critical factors like a better description of metocean conditions, constraints on grid capacity, and a state‐of‐the‐art model to define the farm layout. Concerning hydrogen production, a comparison between the statistical approach, which is commonly used in the field, and a fully time‐dependent method is performed. Proposed results obtained with the statistic and the time‐dependent approach show values ranging between 3.79 and 5.47€/kg, respectively. These outcomes are thought to provide an interesting comparison between different fidelity approaches and realistic reference values for the levelized cost of hydrogen by floating wind in the Mediterranean Sea. [ABSTRACT FROM AUTHOR]

Eight scenarios where high efficiency reversible solid oxide cells (rSOC) are combined with an offshore wind farm are identified. Thanks to the PyPSA power system modelling tool combined with a sensitivity study, optimized rSOC system capacities, hydrogen storage capacities, and subsea cable connection capacities are investigated under various combinations of rSOC system capital cost, prices paid for hydrogen, and electricity prices, which give indications on the most profitable scenario for offshore hydrogen production from a 600 MW wind farm situated 60 km from shore. Low electricity prices (yearly average 45 £/MWh) combined with mild fluctuations (standard deviation 6 or 13 £/MWh) call for dedicated hydrogen production when the hydrogen price exceeds 4 £/kg. High electricity prices (yearly average 118 or 204 £/MWh), combined with extreme fluctuations (standard deviation between 73 and 110 £/MWh), make a reversible system economically profitable. The amount of hydrogen which is recommended to be reconverted into electricity depends on the price paid for hydrogen. Comparison of the optimized cases to the default case of a wind farm without hydrogen production improved profit by at least 3% and up to 908%. Comparison to the default case of dedicated hydrogen production, showed that in the case of low hydrogen prices, an unprofitable scenario can be made profitable, and improvement of profit in the case of a profitable default case starts at 4% and reaches numbers as high as 324%. [ABSTRACT FROM AUTHOR]

This article presents a novel methodology to maximize wind farm power generation by integrating graph neural networks (GNN), supervised learning, and reinforcement learning techniques. First, the article introduces a graph‐based representation of the wind farm, capturing wind turbines as vertices and the inter‐turbine wake interactions as edges. The construction of this graph representation integrates the Jensen wake model, which includes insights derived from prior knowledge of wind farm aerodynamics. Subsequently, a detailed description of the GNN model's architecture, incorporating a message passing mechanism, is outlined. This GNN model is trained initially with supervised learning using a dataset of optimal pitch angles generated from the analytical results derived from Jensen wake model. Moreover, to improve the GNN model's accuracy and adaptability, reinforcement learning techniques are employed. The GNN model interacts with a high‐fidelity wind farm simulation environment, receiving feedback in the form of rewards derived from the wind farm's actual power output. Through a policy gradient approach, the GNN parameters undergo iterative updates, enabling the model to learn and adapt to dynamic wind conditions and intricate turbine interactions. The effectiveness and advantages of the proposed methodology are demonstrated through comprehensive case studies across various wind farm layouts. [ABSTRACT FROM AUTHOR]

Wind energy being a free source of energy is becoming popular over the past decades and is being studied extensively. Integration of wind turbines is now being expanded to urban and offshore settings in contrast to the conventional wind farms in relatively open areas. The direct installation of wind turbines poses a potential risk, as it may result in financial losses in scenarios characterized by inadequate wind resource availability. Therefore, wind energy availability analysis in such urban environments is a necessity. This research paper presents an in‐depth investigation conducted to predict the exploitable wind energy at four distinct locations within Nottingham, United Kingdom. Subsequently, the most suitable location, Clifton Campus at Nottingham Trent University, is identified where a comprehensive comparative analysis of power generation from eleven different wind turbine models is performed. The findings derived from this analysis suggest that the QR6 wind turbine emerges as the optimal choice for subsequent experimental investigations to be conducted in partnership with Nottingham Trent University. Furthermore, this study explores the selection of an appropriate probability density function for assessing wind potential considering seven different distributions namely, Gamma, Weibull, Rayleigh, Log‐normal, Genextreme, Gumbel, and Normal. Ultimately, the Weibull probability distribution is selected, and various methodologies are employed to estimate its parameters, which are then ranked using statistical assessments. [ABSTRACT FROM AUTHOR]

Integration of large‐scale wind farms (WFs) into the grid has to meet the critical constraints set in the national grid code. Wind farm operators (WFOs) are inclined to comply with these constraints and avoid heavy penalty costs for violating such regulations. However, this may result in reduced power sent to the grid. Moreover, the addition of new rules to account for the increased penetration of WFs brings challenges to the profitability of the WFs. A battery energy storage system (BESS), if sized optimally, can be a reliable method to fulfill the grid code requirements without sacrificing profit. This paper provides a techno‐economic model to find the optimal rated capacity and power for a BESS in WFs. This optimization model takes the absolute production and delta production constraints into account. Two approaches are studied for integrating these constraints into the grid code. It is shown that the flexible strategy financially outperforms the strict addition of the new rules. This will be useful, especially to attract investments in wind energy projects despite the abovementioned limitations in the grid code. All the modeling and analysis are done for a potential offshore wind power plant (OWPP) in Turkey. Simulation results show the effectiveness of the optimal BESS in increasing the amount of energy delivered to the grid and improving the profitability of the OWPP. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind speed is a prerequisite for the safe and accurate operation of wind power generation, however, WRF models typically do not produce sufficiently accurate wind speed predictions. This study proposed a Seasonal and Temporal Correlation - Deep Forest (STC-DF) model for offshore wind speed prediction. Different from traditional methods, the STC-DF model takes the advantages of the deep forest algorithm to automatically learn complex feature interactions without manual feature engineering. The model is designed to capture the seasonal and temporal characteristics of wind speed variations. To test the effectiveness of the proposed method, we applied the trained STC-DF model to an offshore wind farm in Hainan Province, China. Seven days of data from each season were selected for testing. The results show that the STC-DF model can effectively reduce the error caused by WRF forecast. The error index of the corrected wind speed reduced more than 40%, the accuracy of wind speed forecast increased 15%. And the method passed the multi-model comparison test and robustness experiment. These research results show that the STC-DF model has strong versatility and good correction ability, and is suitable for wind speed forecasting in different regions, which is a feasible method to improve the reliability of offshore wind power generation. [ABSTRACT FROM AUTHOR]

The integration of super large‐scale offshore wind clusters into the onshore power grid brings challenges for the flexibility of the transmission network. Therefore, this paper proposes a two‐stage collaborative planning of points of common coupling siting and transmission network expansion for super large‐scale offshore wind clusters, based on a multi‐voltage‐level stratified integration mode which enables offshore wind power to transmit to larger regions for accommodation. At first, the transmission network flexibility indexes are established. Then, a two‐stage collaborative planning model for points of common couplings siting and transmission network expansion is proposed, which comprises a collaborative planning model and an optimal power flow operation model. The proposed model reduces wind curtailment and load shedding by optimizing the annual total cost and flexibility transmission capability of the network. Finally, the effectiveness of the proposed method is verified using a modified IEEE 30‐bus system and a 65‐bus system. [ABSTRACT FROM AUTHOR]

Integrating renewable energy, particularly wind generation, into power systems brings significant uncertainty and intermittency, challenging generation companies (GenCos) to maintain economic operations. This paper proposes a strategy for a wind‐based GenCo equipped with a gas turbine and a power‐to‐gas (PtG) system to balance wind variability. The gas turbine offsets power shortfalls by consuming natural gas, while the PtG system absorbs excess wind energy, converting it to natural gas and injecting it into the natural gas network as a form of storage. The GenCo operates in day‐ahead electricity and natural gas markets and the real‐time electricity market, addressing uncertainties from wind output, energy prices, and natural gas access. A tri‐level adaptive robust optimization model is introduced for the GenCo's short‐term operational scheduling. At the first level, decisions related to the GenCo's participation in the day‐ahead electricity and natural gas markets are made. The second level deals with determining the worst‐case realization of the uncertainties, constrained by the budget of uncertainty and the limited range of variations of uncertain variables. The third level sets the operational strategy on the actual day, considering power and gas balance, as well as constraints for the wind farm, gas turbine, and PtG unit. A column & constraint generation (C&CG) algorithm is used to solve the model. Numerical results demonstrate the model's effectiveness in various case studies, showing that the GenCo can manage wind uncertainties, benefit from arbitrage opportunities in electricity and gas markets, and improve economic outcomes. This approach supports the broader integration of renewable energy into power systems. [ABSTRACT FROM AUTHOR]

With the rapid development of wind power, the power performance and mechanical load characteristics of wind turbine are simultaneously considered and focused. Normally, wind turbine senses the incoming flow characteristics through the nacelle mounted anemometer, due to the inability to perceive the characteristics of wind speed in advance, the control strategy makes the wind turbine itself to be at a passive state during the operation process. In this paper, a wind turbine mechanical load optimization control strategy based on an accurate wind speed estimator with time series Broad Learning System Method (BLSM) is designed, simulated and also verified. Firstly, the basic control theory of the BLSM and also a mechanical load optimization controller is designed. Then the OpenFAST is used to conduct a full‐life cycle simulation comparison study on mechanical load characteristics of wind turbine before and after the implementation of the optimization control strategy. Finally, a field empirical mechanical load test is performed on the wind turbine, which is configured with BLSM mechanical load optimization control technology. The findings indicate that the implementation of this control strategy can significantly mitigate the ultimate and fatigue loads of wind turbines, particularly the fatigue loads of tower base tilt and roll bending moments, with a reduction rate of approximately 6.2% and 4.3%, respectively. [ABSTRACT FROM AUTHOR]

With the increase of wind power generation, the safety and economy of power system operations are greatly influenced by the intermittency and fluctuation of wind power. To take the advantage of the complementary characteristics between different energy storage devices, a Hybrid Energy Storage System (HESS) consisting of Battery Energy Storage System (BESS) and Flywheel Energy Storage System (FESS) can alleviate the uncertainty of wind power. This article has proposed a coordinated control strategy through group consensus algorithm based on Model Predictive Control (MPC) for Hybrid Energy Storage Array (HESA) to smooth wind power fluctuations. To allocate power commands to the FESS and BESS, the fluctuation of wind power output is extracted with different frequency domain characteristics as instructions by Empirical Mode Decomposition (EMD) technology. Moreover, a group consensus algorithm based on MPC is proposed to complete the adaptive power allocation of energy storage units. Eventually, the actual wind farm data is used for the simulation to verify the effect of control strategy proposed in this paper. It can be seen that the developed group consensus algorithm based on MPC can cope with different frequency power commands, avoid overcharging and discharging of energy storage media, and smooth wind power effectively. [ABSTRACT FROM AUTHOR]

Given the unpredictable and intermittent nature of wind energy, precise forecasting of wind power is crucial for ensuring the safe and stable operation of power systems. To reduce the influence of noise data on the robustness of wind power prediction, a wind power prediction method is proposed that leverages an enhanced multi‐objective sand cat swarm algorithm (MO‐SCSO) and a self‐paced long short‐term memory network (spLSTM). First, the actual wind power data is processed into time series as input and output. Then, the progressive advantage of self‐paced learning is used to effectively solve the instability caused by noisy data during long short‐term memory network (LSTM) training. Following this, the improved MO‐SCSO is employed to iteratively optimize the hyperparameters of spLSTM. Ultimately, a combined MO‐SCSO‐spLSTM model is constructed for wind power prediction. This model is validated with the data of onshore wind farms in Austria and offshore wind farms in Denmark. The experimental results show that compared with the traditional LSTM prediction method, the proposed method has better prediction accuracy and robustness. Specifically, in the onshore and offshore wind power prediction experiments, the proposed method reduces the minimum MAE by 5.44% and 4.96%, respectively, and reduces the MAE range by 4.45% and 17.21%, respectively, which could be conducive to the safe and stable operation of power system. [ABSTRACT FROM AUTHOR]

The integration of triboelectric nanogenerator (TENG) with electromagnetic generator (EMG) holds promise for the efficient harvesting of broadband wind energy, yet the challenge of coupling these two systems into one high‐durability unit has persisted. Herein, a novel magnetic attraction coupling principle is proposed to enable seamless switching between TENG unit and EMG unit across varying rotation speeds without the need for additional mechanical components, thereby enhancing the energy harvesting efficiency of the magnetotropic hybrid generator (MHG). The rotating magnetic field induces the Si‐Mn steel plates to operate in a contact‐separation mode within a compact space, producing a 270 V open‐circuit voltage output, while the excess magnetic flux is captured by coils to generate an induced current of 5 mA. Most importantly, the overall structure is free from mechanical wear, maintaining consistent performance after 130 000 cycles. The harvested wind energy by MHG is utilized to power human infrared sensors, flame sensors, and Bluetooth hygrometers, providing a long‐term, stable power supply for forest fire early warning systems. This study pioneers a novel and robust mechanism that effectively combines TENG and EMG using magnetotropic property, opening a new avenue for efficiently harvesting wide‐band mechanical energy. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind power is crucial for the efficient operation and risk management of wind farms. This paper introduces a deep learning model for wind power prediction that integrates an Attention mechanism with a convolutional neural network (CNN) and a gated recurrent unit (GRU) neural network. Addressing the randomness, intermittency, volatility and uncertainty of wind speed, we first apply swarm decomposition (SWD) to preprocess the original wind power data into subsequences. Subsequently, the CNN extracts spatial features, and the GRU identifies temporal correlations. The Attention mechanism enhances feature significance, further optimizing prediction accuracy. Complex error sequences generated by the CNN–GRU–Attention (CGA) model are corrected using the autoregressive integrated moving average (ARIMA). We evaluated the model's performance using three wind power datasets against 16 other models, employing six evaluation indices (MSE, RMSE, MAPE, Theil's U , TIC and SPL) and the Diebold–Mariano (DM) test and model confidence set (MCS) for model testing. Our results demonstrate the proposed model's superior accuracy and efficiency in predicting wind power. [ABSTRACT FROM AUTHOR]

As research into wave energy converters progresses and new developers enter the field, there arises a growing requirement for a standardized modelling approach. This article presents a novel design and dynamic emulation for a hybrid solar-wind-wave energy converter (SWWEC) which is the combination of three very well-known renewable energies: solar, wind and wave energy. Photovoltaic (PV) panels and vertical axis wind turbine (VAWT) are installed on top of the floating WEC that harness the energies from the sun and wind respectively. The SWWEC is designed with a point absorber capture system. An electrical motor is used to dynamically emulate the performance of the SWWEC under real world conditions to drive the DC generator. The present paper shows the importance and necessity of the required control schemes for the proper control of generator side converters which is present in the offshore marine substation and the most required grid connected onshore converters. The better switching signal generation for the converter control and generated harmonics elimination techniques are also presented in the paper. Outcomes of the present study are discussed and verified. [ABSTRACT FROM AUTHOR]

A conventional vortex-induced vibration (VIV)-based energy harvester is typically restricted to capturing wind energy from a very limited range of wind directions, making it inefficient in varying wind conditions. This Letter proposes a tri-section beam configuration for VIV-based piezoelectric energy harvester to enable harnessing wind energy from varying incident angle with different vibration modes being triggered. The finite element analysis investigates the tri-section beam harvester's mode shapes and natural frequencies. A wind tunnel experiment is conducted for a comparative study of the energy output performance of the harvesters with straight and tri-section beams. The findings show that the proposed harvester with the tri-section beam can efficiently capture wind energy from a much wider range of incident angles, as opposed to the specific limited directions of its counterpart with the straight beam. The proposed harvester can also widen the lock-in speed range with a higher bending mode being triggered and achieve the optimal output power of 1.388 mW when the proposed harvester works in the second mode at a higher natural frequency, superior to that of its counterpart (0.386 mW) that can only work in the first mode. The proposed configuration sheds light on developing multi-directional and multi-modal VIV-based energy harvesters adapted to wind conditions in natural environments. [ABSTRACT FROM AUTHOR]

The wind energy requirements for high temporal and spatial observations, unavailable in many countries, are met by reanalysed wind data, which are increasingly used in wind power assessments. However, these data also include unrealistic features, making their validation compulsory. The validation analysis of ERA5 and MERRA-2 wind data, two of the most successful wind reanalyses, with observations near Morocco's coast, highlighted the existence of bias in both datasets, and other shortcomings. The study was conducted at eight selected sites in the period 2011-2020 using linear and probability density function based statistical tests. The local ERA5-BA and MERRA-2 BA velocity fields were created using Bias Adjustment (BA) Techniques. Only part of the observed and modelled datasets (training period) were used to identify the transfer function. The BA fields were verified against observations during the remaining period (verification period). They outperformed their respective non-adjusted fields and also the WFDE5 (the global bias-corrected ERA5 wind field) in the wind potential assessments, represented by capacity factor and low-wind day. At every location, the relative errors in the latter's estimates using ERA5-BA were lowered to less than 10 %, and the same was true for MERRA-2-BA. Thus, an observational dataset of reduced length can help overcome some reanalysed wind data limitations for wind power estimations. Additionally, this methodology can be applied to the correction of each reanalysis update. [ABSTRACT FROM AUTHOR]

In order to solve the problem of voltage fluctuation caused by the grid integration of wind power cluster, a multitime scale reactive power and voltage optimal regulation method based on model predictive control (MPC) is proposed in this paper. In the day‐ahead stage, the reduction of network active power loss is mainly achieved through the regulation of discrete reactive power compensation devices and generator units, focusing on the economic operation of the power system. In the intraday stage, considering the rapid response characteristics of continuous reactive power compensation devices, the optimal regulation aiming to minimize voltage control deviation is carried out under 15‐ and 5‐min time scale based on the MPC algorithm with the adjustment of continuous reactive power compensation devices. In the feedback correction stage, a fast calculation method considering reactive power partitioning is adopted instead of solving the 5‐min optimization model for 288 periods, avoiding excessive adjustment of reactive compensation devices. While effectively improving voltage fluctuations caused by wind power prediction deviation, it also alleviated the computational and communication burden on the system. Finally, the effectiveness of the proposed regulation method in this paper is verified with the improved IEEE‐39 test case. [ABSTRACT FROM AUTHOR]

Managers attempt to minimize spatial use conflicts in siting of offshore wind developments, but they must rely on available data to balance biological, commercial, and recreational needs. Marine spatial planning products are only as good as the data they are built upon and fishing data present major challenges due to their confidentiality and the difficulty in isolating true fishing activity. We present a methodology to increase the spatiotemporal resolution of fishing effort and exposure estimates for Southern New England scallop fishing activity using random decision forests to perform supervised classification on AIS data, with fallback to lower resolution datasets for vessels without AIS coverage. Final predictive accuracy of the tuned random forest AIS model was 97.9%, offering improvements of 24.7, 48.6, and 50% over VTR fishing footprints, and AIS and VMS speed cutoff methods, respectively, to predict whether vessel locations correspond to fishing activity. Comparison of the AIS model with VMS and VTR fallback to the VTR fishing footprints data product demonstrated that the increased precision of the AIS point data delineated as fishing dramatically changed how fishing effort, and therefore exposure in the form of fishery landings values, is distributed spatially in Southern New England wind energy areas. This is due to how the probability of fishing is distributed across location data points in the various products, which has implications for marine spatial planning and mitigation decision-making. Therefore, multiple data products should be considered when evaluating management options, as exposure estimates may differ depending on what inputs are used. The higher resolution AIS product may offer enhanced value in understanding exposure and impacts to individual vessels, especially once wind farms are under construction or operational. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose a novel dual-level multisource information fusion approach for handling open multicriteria decision-making (MCDM) issues related to accurately determining the weights of criteria while considering the relative importance of expert opinions, handling heterogeneous information, and selecting an appropriate MCDM ranking method. The proposed approach includes two levels. The first integrates the "analytic hierarchy process (AHP)—full consistency method (FUCOM)", which comprises two phases. In the first phase, criteria weights are assigned using AHP, and FUCOM is employed to correct inconsistencies. In the second phase, the weights are computed for decision-makers. Triangular fuzzy numbers are employed due to their ability to encapsulate all the utilized heterogeneous data types. At the second level of the approach, three well-known distance-based methods, including "technique for order preference by similarity to ideal solution", "visekriterijumska optimizacija i kompromisno resenje", and "multiattributive border approximation area comparison", are fused to process a homogeneous decision matrix and provide comprehensive and robust rankings for alternatives. To develop our novel approach, a renewable energy source for Pakistan's electricity generation is adopted as a case study. The decision matrix contains four alternatives (i.e., hydropower, biomass energy, solar energy, and wind energy) and six main evaluation criteria (i.e., economic, quality of energy, social, political, environmental, and technical), with several subevaluation criteria under each criterion. The findings of the proposed approach were as follows: A2 was in the first rank with a score of 1.7889, and A4 was in the last rank with a score of 0.1199. The rest of the alternatives were distributed between them. The paper's implications include the advancement of decision-making methods, enhancement of decision-making outcomes, and addressing heterogeneous information to highlight the relative importance of experts. [ABSTRACT FROM AUTHOR]

Background: Social acceptance of energy infrastructure projects affects public support for the energy transition and is essential for the transition's sustainability and success. Despite extensive research focusing on the social acceptance of renewable energy, and on the acceptance of onshore wind power in particular, energy system models have largely prioritized techno-economic aspects. This focus has created a gap between model results and decision-makers' needs. In this study, we offer recommendations for integrating disamenity costs and equality considerations—two critical social aspects related to onshore wind power—into energy system optimization. To achieve this, we use a spatially distributed model from a climate-neutral Germany and explore various implementations and trade-offs of these two social aspects. Results: We identified effective linear formulations for both disamenity costs and equality considerations as model extensions, notably outperforming quadratic alternatives, which exhibit longer solution times (+ 50–115%). Our findings reveal that the endogenous consideration of disamenity costs in the optimization approach can significantly reduce the human population's exposure to wind turbines, decreasing the average disamenity per turbine by 53%. Drawing on notions of welfare economics, we employ two different approaches for integrating equality into the optimization process, permitting the modulation of the degree of equality within spatial distributions in energy system models. The trade-offs of the two social aspects compared to the cost-optimal reference are moderate, resulting in a 2–3% increase in system costs. Conclusions: Disamenity costs emerge as a predominant factor in the distribution of onshore wind power in energy system optimization models. However, existing plans for onshore wind power distribution in Germany underscore equality as the driving factor. The inclusion of social aspects in energy system models facilitates the identification of socially superior wind turbine locations. Neglecting disamenity costs and equality considerations leads to an overestimation of the practical solution space for decision-makers and, consequently, the resulting energy system designs. [ABSTRACT FROM AUTHOR]