Qamar Nouman, Alqahtani Mohammed, Rehan Muhammad, Ahmed Ijaz, Khalid Muhammad
Electrical Engineering Department, University of Engineering and Technology, Punjab, Pakistan.
Department of Industrial Engineering, King Khalid University, Abha, Saudi Arabia.
PLoS One. 2025 Jun 9;20(6):e0323491. doi: 10.1371/journal.pone.0323491. eCollection 2025.
The residential energy hub (REH) effectively satisfies power demands, but the incorporation of renewable energy sources (RES) and the increasing use of plug-in hybrid electric vehicles (PHEVs), with their unpredictable nature, complicates its optimal functionality and challenges the accurate modeling and optimization of REH. This work proposed a stochastic model for REH using mixed integer linear programming (MILP) to optimally handle the associated uncertainties of RES and PEHVs, which was then solved using GAMS software. Four case studies with varying conditions were conducted to verify the performance of the proposed scheme, and the results indicate that the approach is superior in optimally handling the system's associated limitations. These limitations include the intermittency and variability of RES and the uncertainties associated with PHEVs, such as arrival time, travel distance, and departure time. Additionally, this work introduces a smart charging mechanism that charges and discharges PHEVs economically, both in terms of cost and reliability. The results indicate that incorporating a smart charging mechanism decreases the total operating cost of smart REH by 2.59% while maintaining the comfort level of the consumer and increasing the reliability of the overall system. Finally, smart REH adopts a demand response program (DRP), which further reduces the operational cost by 3.7%. Furthermore, the proposed approach demonstrates a significant reduction in operating costs and an improvement in the reliability of the smart REH.
住宅能源枢纽（REH）有效地满足了电力需求，但可再生能源（RES）的并入以及插电式混合动力汽车（PHEV）使用量的增加，因其具有不可预测性，使其最优功能变得复杂，并对REH的精确建模和优化提出了挑战。这项工作提出了一种用于REH的随机模型，该模型使用混合整数线性规划（MILP）来最优地处理RES和PEHV的相关不确定性，然后使用GAMS软件进行求解。进行了四个不同条件的案例研究以验证所提方案的性能，结果表明该方法在最优处理系统相关限制方面具有优势。这些限制包括RES的间歇性和波动性以及与PHEV相关的不确定性，如到达时间、行驶距离和出发时间。此外，这项工作引入了一种智能充电机制，该机制在成本和可靠性方面都能经济地对PHEV进行充电和放电。结果表明，纳入智能充电机制可使智能REH的总运营成本降低2.59%，同时保持消费者的舒适度并提高整个系统的可靠性。最后，智能REH采用了需求响应计划（DRP），这进一步将运营成本降低了3.7%。此外，所提方法显著降低了运营成本并提高了智能REH的可靠性。
