Rathod Asmita Ajay, S Balaji
School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Sci Rep. 2024 Aug 29;14(1):20127. doi: 10.1038/s41598-024-70663-5.
The off-grid Hybrid Renewable Energy Systems (HRES) demonstrate great potential to be sustainable and economically feasible options to meet the growing energy needs and counter the depletion of conventional energy sources. Therefore, it is crucial to optimize the size of HRES components to assess system cost and dependability. This paper presents the optimal sizing of HRES to provide a very cost-effective and efficient solution for supplying power to a rural region. This study develops a PV-Wind-Battery-DG system with an objective of 3E analysis which includes Energy, Economic, and Environmental CO emissions. Indispensable parameters like technical parameters (Loss of Power Supply Probability, Renewable factor, PV fraction, and Wind fraction) and social factor (Human Developing Index) are evaluated to show the proposed modified Harris Hawks Optimization (mHHO) algorithm's merits over the existing algorithms. To achieve the objectives, the proposed mHHO algorithm uses nine distinct operators to obtain simultaneous optimization. Furthermore, the performance of mHHO is evaluated by using the CEC 2019 test suite and the most optimal mHHO is chosen for sizing and 3E analysis of HRES. The findings demonstrate that the mHHO has achieved optimized values for Cost of Energy (COE), Net Present Cost (NPC), and Annualized System Cost (ASC) with the lowest values being 0.14130 $/kWh, 1,649,900$, and 1,16,090$/year respectively. The reduction in COE value using the proposed mHHO approach is 0.49% in comparison with most of the other MH-algorithms. Additionally, the system primarily relies on renewable sources, with diesel usage accounting for only 0.03% of power generation. Overall, this study effectively addresses the challenge of performing a 3E analysis with mHHO algorithm which exhibits excellent convergence and is capable of producing high-quality outcomes in the design of HRES. The mHHO algorithm attains optimal economic efficiency while simultaneously minimizing the impact on the environment and maintaining a high human development index.
离网混合可再生能源系统（HRES）展现出巨大潜力，有望成为可持续且经济可行的选择，以满足不断增长的能源需求并应对传统能源的枯竭。因此，优化HRES组件的规模对于评估系统成本和可靠性至关重要。本文介绍了HRES的优化规模，为农村地区供电提供了一种极具成本效益和高效的解决方案。本研究开发了一个光伏-风能-电池-柴油发电机系统，目标是进行包括能源、经济和环境碳排放的3E分析。评估了诸如技术参数（停电概率、可再生能源占比、光伏占比和风能占比）和社会因素（人类发展指数）等不可或缺的参数，以展示所提出的改进哈里斯鹰优化（mHHO）算法相对于现有算法的优点。为实现这些目标，所提出的mHHO算法使用九个不同的算子来实现同步优化。此外，通过使用CEC 2019测试套件评估mHHO的性能，并选择最优的mHHO进行HRES的规模确定和3E分析。研究结果表明，mHHO实现了能源成本（COE）、净现值成本（NPC）和年化系统成本（ASC）的优化值，最低值分别为0.14130美元/千瓦时、1,649,900美元和116,090美元/年。与大多数其他MH算法相比，使用所提出的mHHO方法使COE值降低了0.49%。此外，该系统主要依赖可再生能源，柴油使用量仅占发电量的0.03%。总体而言，本研究有效地解决了使用mHHO算法进行3E分析的挑战，该算法具有出色的收敛性，能够在HRES设计中产生高质量的结果。mHHO算法在实现最优经济效率的同时，最大限度地减少了对环境的影响，并保持了较高的人类发展指数。
