Increasing the implementation of distributed generation and introducing multi-carrier energy systems highlight the need for energy hub systems. The energy hub is a new idea implemented in multi-carrier energy systems, sending, receiving, and storing different energy types. Therefore, the present paper proposes an improved energy hub consisting of different types of renewable energy-based DG units considering electricity and heating storage systems, which models the system’s operation and planning aspects. Furthermore, optimal planning and scheduling of multi-carrier energy hub system is modeled considering the stochastic behavior of wind and photovoltaic units. The operation section’s main challenge is determining the optimal interaction between different resources for supplying other loads in the system. The presented model is solved using a robust method based on a Quantum Particle Swarm Optimization (QPSO) approach to minimize the energy hub system’s total cost. The minimization of fuel consumption and pollutant emissions due to implementing the residential energy hub’s thermal storage system is evaluated. Simulation results show that the amount of consumed natural gas reduces by 48% after using CHP units produced heat to supply heating and cooling loads. After installing CHP and thermal storages in the energy hub system, the amount of CO₂ has reduced by about 904 tons during a year. It can be concluded that the produced power of CHP is at the highest, which is equal to 61%, as it can generate electricity at all times during the day. Moreover, to evaluate the efficiency of the proposed methodology, the Genetic Algorithm (GA) and PSO algorithm are also implemented for optimization of the mentioned energy hub system. The performance of the mentioned algorithms is compared with each other, and the results depicted that the QPSO algorithm is the best and the convergence speed and global search ability of the QPSO algorithm are significantly better than PSO and GA algorithms The obtained numerical results verify the efficiency of the proposed method in the optimal scheduling and planning of the energy hub system in the presence of stochastic renewable energy systems.

增加分布式发电的实施并引入多载波能源系统凸显了对能源枢纽系统的需求。能源枢纽是在多载波能源系统中实现的新概念，可以发送，接收和存储不同的能源类型。因此，本文提出了一种改进的能源枢纽，其中考虑了电力和热能存储系统，由不同类型的基于可再生能源的DG单元组成，该系统对系统的运行和规划方面进行了建模。此外，考虑到风能和光伏装置的随机行为，对多载波能源枢纽系统的最佳计划和调度进行建模。操作部分的主要挑战是确定不同资源之间的最佳交互，以提供系统中的其他负载。使用基于量子粒子群优化（QPSO）方法的鲁棒方法对提出的模型进行求解，以最大程度地减少能源枢纽系统的总成本。评估了由于实施住宅能源枢纽的储热系统而导致的燃料消耗和污染物排放的最小化。仿真结果表明，使用CHP装置产生的热量提供供热和制冷负荷后，天然气消耗量减少了48％。在能源枢纽系统中安装CHP和蓄热器后，CO的量 仿真结果表明，使用CHP装置产生的热量提供供热和制冷负荷后，天然气消耗量减少了48％。在能源枢纽系统中安装CHP和蓄热器后，CO的量 仿真结果表明，使用CHP装置产生的热量提供供热和制冷负荷后，天然气消耗量减少了48％。在能源枢纽系统中安装CHP和蓄热器后，CO的量₂一年减少了约904吨。可以得出结论，CHP的发电功率最高，等于61％，因为它可以在一天中的所有时间发电。此外，为了评估所提出方法的效率，还采用了遗传算法（GA）和PSO算法来优化所提到的能源枢纽系统。将上述算法的性能进行了比较，结果表明QPSO算法是最佳算法，QPSO算法的收敛速度和全局搜索能力明显优于PSO算法和GA算法。所得数值结果验证了算法的有效性。随机可再生能源系统存在时，该方法在能源枢纽系统的最佳调度和计划中的应用。