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Sustainability and Capacity Estimation of Photovoltaic Based Distribution Network Integrated with Demand Response Using arithmetic optimizer algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2024年第1期49卷 197-226页

作者： Chowdhury, Anirban Roy, Ranjit Mandal, Kamal Krishna Jadavpur Univ Dept Power Engn Block LBPlot 8 Sect 3 Kolkata 700106 India SRM Univ Fac Engn & Technol Dept Elect & Elect Engn Sonepat 131029 Haryana India

Distribution networks (DNs) are becoming congested due to the rapid rise in the number of consumers, resulting in an energy crisis, voltage stability issues, and unforeseen power outages. Also, the harmful effects of emissions from fossil fuel-based power plants, limited fossil fuel reserves, etc. are making network operators integrate distributed generators (DGs) based on renewable energy sources into the DNs. This paper proposes an incentive-based demand response (DR) program for PV-integrated DNs in which a price-demand elasticity matrix relates the load demand and the electricity prices. The hourly solar irradiance for different seasons is modeled using geographical equations for the location in Jammu and Kashmir, India. An objective function based on technical, economic, and environmental benefits, averaged over the entire day, and subjected to operational constraints, has been developed, which is maximized using the arithmetic optimizer algorithm. Nine different scenarios considering different combinations of incentives and demand-electricity price elasticities are tested on the IEEE 69 bus test DN. This work also estimates the optimal number of PV modules required to be installed based on solar irradiance modeling using geographical data for the location, uncertainties, and seasonal changes. The maximum percentage improvement in the overall day-averaged objective of IEEE 69 bus DN is 30.82%. The percentage reduction in the day-averaged active power loss corresponding to the best test case is 5.54%, considering the combined effects of PV and DR.

关键词： arithmetic optimizer algorithm Demand response Distributed generator Distribution network Probability distribution function Renewable energy sources

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Having Deep Investigation on Predicting Unconfined Compressive Strength by Decision Tree in Hybrid and Individual Approaches

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第7期15卷 127-139页

作者： Zhang, Qingqing Wang, Lei Gu, Hongmei Hebei Inst Commun Informat Technol & Cultural Management Inst Shijiazhuang 051430 Hebei Peoples R China

In the field of geotechnical engineering Rocks' unconfined compressive strength (UCS) is an important variable that plays a significant part in civil engineering projects like foundation design, mining, and tunneling. These projects' stability and safety depend on how accurately UCS predicts the future. In this study, machine learning (ML) techniques are applied to forecast UCS for soil-stabilizer combinations. This study aims to build complex and highly accurate predictive models using the robust Decision Tree (DT) as a primary ML tool. These models show relationships between UCS considering a variety of intrinsic soil properties, including dispersion, plasticity, linear particle size shrinkage, and the kind of and number of stabilizing additives. Furthermore, this paper integrates two meta-heuristic algorithms: the Population-based vortex search algorithm (PVS) and the arithmetic optimizer algorithm (AOA) to enhance the precision of models. These algorithms work in tandem to bolster the accuracy of predictive models. This study has subjected models to rigorous validation by analyzing UCS samples from different soil types, drawing from historical stabilization test results. This study unveils three noteworthy models: DTAO, DTPB, and an independent DT model. Each model provides invaluable insights that support the meticulous projection of UCS for soil-stabilizer blends. Notably, the DTAO model stands out with exceptional performance metrics. With an R-2 value of 0.998 and an impressively low RMSE of 1.242, it showcases precision and reliability. These findings not only underscore the accuracy of the DTAO model but also emphasize its effectiveness in predicting soil stabilization outcomes.

关键词： Unconfined compressive strength machine learning decision tree population-based vortex search algorithm arithmetic optimizer algorithm

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