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Sustainable coastal energy development: Integrated modeling of renewable energy sources for optimal economic and environmental performance

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ENERGY 2025年 316卷

作者： Chen, Qiuju Yin, Xiaomin Qujing Normal Univ Sch Econ & Management Qujing 655011 Yunnan Peoples R China Zhejiang Shuren Univ Sch Marxism Hangzhou 310015 Zhejiang Peoples R China

This study presents an integrated modeling framework for the design and optimization of a sustainable energy hub tailored to coastal urban areas, using a case study. The model addresses critical energy demands-electricity, heating, cooling, and water supply-while integrating renewable energy sources (RESs) such as wind turbines, photovoltaic thermal (PV/T) systems, and microturbines. A novel two-level optimization approach is adopted, combining "design" and "environmental design" levels to evaluate both technical performance and environmental impacts. The water cycle algorithm (WCA) is employed to enhance optimization efficiency, ensuring precise utilization of RESs while minimizing costs and emissions. Results demonstrate the system's capability to dynamically adapt to environmental conditions, such as wind speed and solar radiation, optimizing energy production across seasons. The optimized energy hub achieves a total energy generation of 14810 kW for electricity, 25210 kW for heating, and 53748 kW for cooling, with an overall 28 % reduction in operational costs compared to traditional setups. Detailed economic analysis highlights the PV/T system's significant role, constituting nearly 60 % of the initial investment while ensuring sustainable and efficient operation. Moreover, seasonal wind energy potential contributes substantially to electricity supply during peak consumption periods. This work underscores the feasibility and benefits of integrating RESs in energy hubs for coastal regions. By providing a robust framework for energy planning, it offers a blueprint for sustainable urban development with significant implications for reducing carbon emissions and improving resource efficiency.

关键词： Energy hub optimization Integrated modeling Renewable energy sources Environmental design water cycle algorithm

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A hybrid CFD and machine learning study of energy performance of photovoltaic systems with a porous collector: Model development and validation

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CASE STUDIES IN THERMAL ENGINEERING 2025年 69卷

作者： Wang, Yinling Yu, Lei Ali, Mazhar Khan, Imran Ali Maqsood, Tahir Gao, Haining Wang, Qi Guo, Xiaolei Huanghuai Univ Int Joint Lab New Energy Digitalizat Technol Henan Zhumadian 463000 Henan Peoples R China COMSATS Univ Islamabad Dept Comp Sci Abbottabad 22060 Pakistan

This study investigates the predictive modeling of temperature (T(K)) using a dataset of over 128,000 data points characterized by x, y, and z coordinates as inputs. The case study considered here is a photovoltaic system with porous collector for enhancing the efficiency of solar system. Computational modeling was carried out via CFD (Computational Fluid Dynamics), and the temperature distribution was determined which was later used in machine learning (ML) evaluation. Indeed, a hybrid model was developed combining CFD and ML for the first time to predict temperature distribution versus special coordinates in a photovoltaic thermal system. Three advanced machine learning models, i.e., Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and Histogram-based Gradient Boosting (HGB) were applied to analyze and predict T in system. A systematic preprocessing pipeline was developed to enhance model performance, including outlier detection and feature normalization. Hyperparameter optimization process in this study uses the water cycle algorithm (WCA), a metaheuristic method inspired by natural processes. Among the models, XGB emerged as the best performer, revealing a total R2 of 0.99823, a Root Mean Square Error (RMSE) of 0.06596, and a Mean Absolute Error (MAE) of 0.04442. These results demonstrated the capability of machine learning to accurately capture complex relationships within structured datasets.

关键词： CFD Hybrid modeling Solar energy Extreme gradient boosting Histogram-based gradient boosting water cycle algorithm

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Assessing stability in renewable microgrid using a novel-optimized controller for PV-battery based micro grid with OPAL-RT based real-time validation

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RESULTS IN ENGINEERING 2025年 26卷

作者： Patra, Prakash Chandra Panigrahi, Pratap Kumar Satpathy, Anshuman Nayak, Niranjan GIET Univ Dept Elect & Elect Engn Gunupur Odisha India ITER Siksha O Anusandhan Deemed Univ Dept Elect & Elect Engn Bhubaneswar Odisha India

This study introduces the Modified water cycle algorithm (MWCA), designed to enhance controller operation within a Micro Grid consisting of Multiple Distributed Generators (DGs). The fluctuating nature of renewablebased DGs poses stability challenges to micro grid operation. The MWCA, incorporating a sinusoidal chaotic map, addresses nonlinearities within the micro grid, aiming to boost stability margins and transient profiles amidst diverse uncertainties. Validation is conducted using a multi-DG-based micro grid system, integrating two photovoltaic (PV) systems with supporting storage (battery). Grid synchronization of DGs relies on second-order phase-locked loop-based feedback controllers. MWCA optimizes grid-side converter parameters, enhancing power regulation at the Point of Common Coupling (PCC). PV-VSC controller gain values are fine tuned to minimize the generation losses as well as maintaining stability inside the desired region of micro grid operation. Performance evaluations are conducted under various uncertainties to justify MWCA's efficacy in terms of stability. Calculations are carried out using MATLAB scripts and OPAL-RT based hardware-in-loop platforms. How ever, the study underscores the MWCA's potential in mitigating challenges associated with DG-based micro grid operation simultaneously improving stability and performance metrics. The proposed MWCA improves stability margin as compared to conventional PSO and WCA techniques. The major contribution in this work is to study a Modified water cycle algorithm (MWCA) to optimize the PV-battery micro grid controllers, enhancing stability and performance under various uncertainties. Real-time validation using OPAL-RT shows improved stability margins, faster fault mitigation, and reduced power oscillations compared to conventional methods, proving MWCA's effectiveness.

关键词： Solar photo voltaic Duty-cycle Feedback controller Voltage control water cycle algorithm Irradiance variation Partial shading

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Metaheuristic-driven enhancement of categorical boosting algorithm for flood-prone areas mapping

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2025年 136卷

作者： Razavi-Termeh, Seyed Vahid Pourzangbar, Ali Sadeghi-Niaraki, Abolghasem Franca, Mario J. Choi, Soo-Mi Sejong Univ XR Res Ctr Dept Comp Sci & Engn & Convergence Engn Intelligen Seoul South Korea Karlsruhe Inst Technol KIT Inst Water & Environm IWU Karlsruhe Germany

Managing and controlling costly natural hazards such as floods has been a fundamental and essential issue for decision-makers and planners from the past to the present. Artificial intelligence (AI) has recently proven promising to improve disaster management. There is growing interest in using AI to predict and identify floodprone areas. However, creating accurate flood susceptibility maps with AI remains a significant challenge. Therefore, the present work endeavors to cope with this gap and produce the most efficient flood susceptibility maps employing Categorical Boosting (CatBoost) algorithms and three system-based metaheuristic methods, including Augmented Artificial Ecosystem Optimization (AAEO), Germinal Center Optimization (GCO), and water Circle algorithm (WCA). We selected Jahrom County, Iran, to develop machine learning-based models as our case study. We used 13 flood conditioning geophysical factors as input parameters and flood occurrence (binary classification), derived from satellite imagery, as the output. Our results show that CatBoost-AAEO performs better in flood susceptibility mapping than the other combined models, CatBoost-WCA, CatBoostGCO, and the basic CatBoost model, which are mentioned in descending order of performance. The partial Dependence Plots (PDP) approach is used to interpret the results of the developed algorithms, highlighting that low slope, low elevation, minimal vegetation cover, flat curvature, and proximity to rivers significantly impact the performance of ML models to predict flood occurrence. The findings of this research can help planners manage and prevent floods and avoid development in sensitive areas to reduce financial losses caused by floods.

关键词： Flood susceptibility mapping Categorical boosting Augmented artificial ecosystem optimization Germinal center optimization water cycle algorithm Metaheuristic algorithms

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Improvement of Voltage and Frequency Regulation in the Combined LFC and AVR of a Two-Area Interconnected Power System

Improvement of Voltage and Frequency Regulation in the Combi...

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2024 Electric Power and Renewable Energy Conference

作者： Kalyan, Ch. Naga Sai Joshi, Navneet Bajaj, Mohit Vasireddy Venkatadri Inst Technol EEE Dept Guntur 522508 Andhra Pradesh India Graph Era Hill Univ Dept Allied Sci Bhimtal Uttarakhand India Graph Era Deemed Univ Dept Elect Engn Dehra Dun 248002 Uttarakhand India Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman Jordan Graph Era Hill Univ Dehra Dun 248002 Uttarakhand India

ISBN: (纸本)9789819779239;9789819779215;9789819779208

The combined load frequency control (CLFC) and automatic voltage regulation (AVR) (CLFC-AVR) analysis is performed in this paper to regulate the frequency and terminal voltage. The CLFC-AVR investigation is performed on the multi-area multi-fuel system and carried the analysis for the perturbation in step load (SLP) of 10% in area 1. The fractional order proportional-integral-derivative (FOPID) controller based on the optimization of the water cycle algorithm (WCA) is enforced, and its performance is compared with other regulators in the literature to exhibit its dominance. However, the effect of AVR on the dynamic behavior of the generation units in the LFC loop is showcased in this work. Further, a high-voltage direct current (HVDC) line is enacted as the tie-line instead of the alternating current line to improve the system's performance.

关键词： CLFC-AVR analysis FOPID controller 10%SLP HVDC line water cycle algorithm

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A Novel Hybrid MPPT Technique for a PV System Operated Under Partial Shading Conditions with Three Phase Interleaved Boost Converter

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2025年 1-19页

作者： Krishnaram, K. Rajakumaran, M. Senthilkumar, S. Mangaiyarkarasi, S. P. Raj, R. Gandhi EGS Pillay Engn Coll Dept Elect & Elect Engn Nagapattinam Tamil Nadu India SASTRA Deemed Univ Sch Comp Thanjavur Tamilnadu India EGS Pillay Engn Coll Dept ECE Nagapattinam Tamil Nadu India Univ Coll Engn Dept Elect & Elect Engn Panruti Campus Panruti 607106 Tamilnadu India Anna Univ Univ Coll Engn Dept Elect & Elect Engn BIT Campus Tiruchirappalli 620024 Tamil Nadu India

When PV arrays are operated under constant climatic conditions, the MPPT techniques allow the PV system to generate maximum output power. During partial shading or complex partial shading conditions, conventional MPPTs and MPPT-based optimization are alone not enough to find the global peak, as several peaks occur. A hybrid MPPT can solve the aforementioned issues, but oscillations around GMPP, tracking efficiency, and convergence time need to be addressed. To solve this, a hybrid MPPT that combines a water cycle algorithm and an incremental conductance algorithm (WCO-INC) has been developed for a PV system that is operated for standalone applications. The proposed work uses a three-phase interleaved boost converter as an interface between the photovoltaic array and the load. The developed PV system was tested under rapidly changing irradiance and temperature using the MATLAB/Simulink tool. The proposed work provides better tracking efficiency, reduced oscillations, and quick convergence time when compared with WCA, INC, PO, and WCO-PO MPPT techniques. The WCO-INC technique has better tracking efficiency (99.76%) and convergence time (0.90 ms) with 1217.35 W as maximum power generated under constant irradiance, and in complex partial shaded conditions (CPSC), it has 98.61% tracking efficiency, and the convergence time is 1.40 ms with 586.56 W as maximum power generated. Whereas in the INC algorithm, tracking efficiency is 78.58% in CPSC, 2.5 ms as convergence time with 467.40 W as maximum power generated.

关键词： Hybrid MPPT water cycle algorithm Incremental conductance MPPT Interleaved boost converter WCO-INC PV system Standalone applications

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Synergizing machine learning and experimental analysis to predict post-heating compressive strength in waste concrete

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STRUCTURAL CONCRETE 2025年

作者： Mahmoud, Alaa A. El-Sayed, Alaa A. Aboraya, Ayman M. Fathy, Islam N. Zygouris, Nikos Sadollah, Ali Agwa, Ibrahim Saad Tayeh, Bassam A. Asteris, Panagiotis G. Fayoum Univ Fac Engn Civil Engn Dept Faiyum Egypt October High Inst Engn & Technol Construct & Bldg Engn Dept Giza Egypt Higher Inst Engn Culture & Sci Construct & Bldg Engn Dept Giza Egypt Sch Pedag & Technol Educ Computat Mech Lab Athens 15122 Greece Univ Sci & Culture Dept Mech Engn Tehran Iran Suez Univ Fac Technol & Educ Civil & Architectural Construct Dept Suez Egypt Islamic Univ Gaza Fac Engn Civil Engn Dept Gaza Palestine

In the current study, the impact of utilizing granite and marble construction waste powders as replacements (1%-9%) for cement on concrete compressive strength was investigated. In the second stage of the experimental program, combined mixtures were designed to evaluate their response to high temperatures using various machine learning (ML) techniques. Models employing water cycle algorithm (WCA) and genetic algorithms (GA) were developed based on 288 experimental results, featuring input variables such as temperature, exposure time, waste powder type, and cement replacement ratio, with residual compressive strength (RCS) as the sole output. Artificial neural networks (ANN), fuzzy logic (FL), and multiple linear regression (MLR) models were also developed for comparison. Optimal performance, with a 22% increase in compressive strength at 28 days, was observed by replacing 9% of cement with waste granite powder (WGP). At high temperatures, the best performance occurred with 9% WGP + 5% waste marble powder (WMP), resulting in a 59.6% increase in RCS value after exposure to 800 degrees C for 2 h. The predictive WCA model outperformed GA and MLR, closely aligning with ANN and FL models, with a mean absolute error of 3.96 kg/cm(2). Additionally, nonlinear prediction equations of RCS with high regression values were successfully developed using WCA and GA. Furthermore, sensitivity analyses were conducted using the weights of the hidden layers of the idealized neural networks and revealed that the RCS value exhibits high sensitivity to temperature variations. Exposure time had the second-highest impact on RCS value, followed by the WGP ratio, and then the WMP ratio.

关键词： artificial neural networks elevated temperature fuzzy logic genetic algorithms residual compressive strength waste granite powder waste marble powder water cycle algorithm

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water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures

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COMPUTERS & STRUCTURES 2015年 149卷 1-16页

作者： Sadollah, Ali Eskandar, Hadi Bahreininejad, Ardeshir Kim, Joong Hoon Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea Semnan Univ Fac Engn Semnan Iran Inst Teknol Brunei Fac Engn Bandar Seri Begawan Brunei

This paper presents the applications of the mine blast algorithm (MBA) and the water cycle algorithm (WCA), in addition to an improved version of MBA for weight minimization of truss structures including discrete sizing variables. The MBA mimics the explosion of landmines, while the WCA is inspired by the observation of water cycle process. An improved version of MBA (IMBA), is also presented. The efficiency of the three optimization algorithms is tested using classical benchmark discrete truss design problems. Optimization results show that MBA, IMBA, and WCA offer a good degree of competitiveness against other state-of-the-art metaheuristic techniques. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Mine blast algorithm water cycle algorithm Truss structures Discrete variables Sizing optimization

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A water cycle-based error minimization technique in predicting the bearing capacity of shallow foundation

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 5期38卷 3993-4006页

作者： Moayedi, Hossein Mosavi, Amirhosein Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Civil Engn Da Nang 550000 Vietnam Ton Duc Thang Univ Environm Qual Atmospher Sci & Climate Change Res Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Environm & Labour Safety Ho Chi Minh City Vietnam

Selecting the appropriate training technique is a significant step in utilizing intelligent approaches. It becomes even more important when it comes to critical problems like analyzing the bearing capacity of foundations. This study investigates the feasibility of a capable metaheuristic algorithm, called the water cycle algorithm (WCA), for training a multi-layer perceptron (MLP). The WCA-MLP is applied to a large finite element dataset to predict the settlement. The results of this model are compared with electromagnetic field optimization (EFO) and shuffled complex evolution (SCE) benchmarks. With reference to the obtained Pearson correlation factors (larger than 0.88 in all stages), all employed models are suitable for the mentioned objective. Moreover, it was observed that the training error of the WCA was 5.84 and 3.89% smaller than the EFO and SCE, respectively. Likewise, the accuracy of the WCA-MLP was 1.85 and 2.04% larger in the testing phase. Also, a predictive equation is finally elicited for practical applications in compatible circumstances.

关键词： Bearing capacity Settlement measurement Artificial neural network water cycle algorithm Machine learning Artificial intelligence

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New Modified water cycle Optimized Fuzzy PI Controller for Improved Stability of Photovoltaic-Based Distributed Generation Towards Microgrid Integration 1st

New Modified Water Cycle Optimized Fuzzy PI Controller for I...

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1st International Conference on Innovation in Electrical Power Engineering, Communication, and Computing Technology (IEPCCT)

作者： Adam, M. Mohamed Hamad Hannoon, Naeem M. S. Dhar, Snehamoy Univ Teknol MARA Shah Alam Malaysia Siksha O Anusandhan Deemed Univ Bhubaneswar India

ISBN: (纸本)9789811523052;9789811523045

In this paper, a new modified water cycle (WC)-based optimization is incorporated for independent distributed generation controllers' (IDGCs') gain parameters tuning. Photovoltaic (PV)-based distributed generation (DG) is considered for active distribution network/microgrid tied operation, where the IDGC feedback path is designed with fuzzy Proportional-Integral (PI) scheme. The PI controller is linear and hence unable to address operational uncertainties (e.g. variation in irradiation profile, partial shading, symmetrical/asymmetrical faults, load variations, etc.) for a PV-based DG incorporation. The fuzzy rule-based adaptive gains are also limited towards unbounded uncertainties (grid connected to autonomous mode operation). Thus, to improve the system stability with effective feedback gain parameters' selection, the modified WC algorithm is designed in this paper. The proposed WC scheme is included with a sinusoidal chaos map to address the nonlinearities. The performance of the proposed IDGC feedback path is evidenced under various grid operational contingencies. The efficacy measure of proposed scheme is recorded in terms of dynamic oscillation damping. This performance validation is implemented in MATLAB Script environment.

关键词： Photovoltaic Independent distributed generation controller water cycle algorithm Fuzzy PI control

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