Thunuguntla Vinod Kumar, Maineni Vijayasanthi, Injeti Satish Kumar, Kumar Polamarasetty P, Nuvvula Ramakrishna S S, Dhanamjayulu C, Rahaman Mostafizur, Khan Baseem
Department of Electrical and Electronics Engineering, Vignan's LARA Institute of Technology & Science, Guntur District, Vadlamudi, 522213, Andhra Pradesh, India.
Electrical and Electronics Engineering Department, CMR College of Engineering and Technology, Kandlakoya, 501401, Telangana, India.
Sci Rep. 2024 Nov 29;14(1):29688. doi: 10.1038/s41598-024-79519-4.
The growing concerns regarding the depletion of fossil fuels, CO emissions, and the effects of climate change prompt the usage of plug-in electric vehicles (PHEVs) all over the world in a big way. The increased electrical demand brought on by the charging of electric vehicles puts a burden on the distribution network parameters like energy loss, voltage profile and thermal limits. Recently, renewable energy-based distribution generation (RDGs) units are firmly integrated with the transmission and distribution system networks to lower the carbon footprint generated due to conventional thermal power plants. In addition, Battery Energy Storage Systems (BESS) are used to enhance grid operation and lessen the consequences of the high intermittency nature of RDGs power. In this work, two charging methods of PHEVs are considered: charging electric vehicles at home during night-time and charging electric vehicles at public fast charging stations (PFCS). The uncertain nature of arrival time and trip distance of PHEVs are addressed using probability density functions (PDFs). The 33-bus test system consists of commercial, industrial and residential buses is taken to implement the proposed methodology. In this work, 500 PHEVs are taken into consideration. The aforementioned charging methods produce a 24-h electric demand for PHEVs, which is then placed on the corresponding distribution system buses. The effect of PHEVs on technical distribution system metrics, including voltage profile and energy loss, is investigated. To improve the above metrics, optimal planning of inverter-based non-dispatchable PV units and dispatchable PV-BESS units in the distribution network by the inclusion of PHEVs electric load demand is addressed. The Pareto-based meta-heuristic multi-objective chaotic velocity-based butterfly optimization method (MOCVBOA) is chosen for optimization of desired objectives. The results of the MOCVBOA optimization algorithm are compared with those of the other optimization algorithms, NSGA-II & MOBOA, frequently described in the literature to assess its effectiveness.
对化石燃料枯竭、一氧化碳排放以及气候变化影响的日益担忧，促使世界各地大力使用插电式电动汽车（PHEV）。电动汽车充电带来的电力需求增加，给诸如能量损耗、电压分布和热极限等配电网参数带来了负担。近来，基于可再生能源的分布式发电（RDG）单元与输配电系统网络紧密集成，以降低传统火力发电厂产生的碳足迹。此外，电池储能系统（BESS）用于改善电网运行，并减轻RDG电力高间歇性的影响。在这项工作中，考虑了PHEV的两种充电方式：夜间在家充电和在公共快速充电站（PFCS）充电。使用概率密度函数（PDF）来处理PHEV到达时间和行驶距离的不确定性。采用由商业、工业和住宅母线组成的33节点测试系统来实施所提出的方法。在这项工作中，考虑了500辆PHEV。上述充电方式产生了PHEV的24小时电力需求，然后将其施加到相应的配电系统母线上。研究了PHEV对包括电压分布和能量损耗在内的配电系统技术指标的影响。为了改善上述指标，通过纳入PHEV的电力负荷需求，对配电网中基于逆变器的不可调度光伏单元和可调度光伏 - BESS单元进行了优化规划。选择基于帕累托的元启发式多目标混沌速度蝴蝶优化方法（MOCVBOA）来优化期望目标。将MOCVBOA优化算法的结果与文献中经常描述的其他优化算法NSGA - II和MOBOA的结果进行比较，以评估其有效性。
