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<p>The use of intelligent parking lot (IPL) systems which combine renewable energy sources (RES), hydrogen storage systems (HSS), and electric vehicles (EVs) are coming up as a key engineering solution to curb grid price volatility under Demand Response Programs (DRPs). The study describes a multi-objective scheduling model based on an algorithm known as the Modified Honey Badger Algorithm (MHBA) which has been developed to work in uncertain environments. The practical engineering worth of the work, which is that it can utilise photovoltaic (PV), wind, HSS and EV resources with realistic commercial hardware specification, thus, making it instantly deployable in a modern parking system. The methodology suggested converges very fast with an average runtime of 3.29 s, and can be applied to real-time operation. Analysis of experimental validation on a hybrid testbed shows that the average cost savings is about 4% and savings in cost variability can be up to 47.01% compared to conventional methods, but all of the conditions of a real equipment can be achieved. These findings substantiate that the MHBA-based framework can provide a commercially viable solution to uncertainty-resistant cost-optimum energy management of next-generation IPL facilities.</p>

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Advanced electric vehicle parking lot architecture integrating energy regulation, hydrogen storage technologies, and consumer demand management by modified honey badger algorithm

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Halkawt Abdalqadir kh ahmed

摘要

The use of intelligent parking lot (IPL) systems which combine renewable energy sources (RES), hydrogen storage systems (HSS), and electric vehicles (EVs) are coming up as a key engineering solution to curb grid price volatility under Demand Response Programs (DRPs). The study describes a multi-objective scheduling model based on an algorithm known as the Modified Honey Badger Algorithm (MHBA) which has been developed to work in uncertain environments. The practical engineering worth of the work, which is that it can utilise photovoltaic (PV), wind, HSS and EV resources with realistic commercial hardware specification, thus, making it instantly deployable in a modern parking system. The methodology suggested converges very fast with an average runtime of 3.29 s, and can be applied to real-time operation. Analysis of experimental validation on a hybrid testbed shows that the average cost savings is about 4% and savings in cost variability can be up to 47.01% compared to conventional methods, but all of the conditions of a real equipment can be achieved. These findings substantiate that the MHBA-based framework can provide a commercially viable solution to uncertainty-resistant cost-optimum energy management of next-generation IPL facilities.