Facing the growing pressure of climate change and environmental protection, integrated energy systems (IESs), which comprise different energy sources, have become promising candidates for future energy systems. However, the capacity configuration of each source remains challenging due to the various couplings, randomness of renewables and numerical optimization difficulty. In this paper, a hierarchical optimization framework is proposed to determine the component capacities of trigeneration IESs, i.e., systems involving combined cooling, heating and power (CCHP) generation. The potential variation in the demand and renewable resource availability are considered stochastic factors and captured as scenarios generated according to a probability function. In the first level, with the component capacities and scenarios defined, a mixed-integer linear programming (MILP) problem is formulated to minimize the total system cost. Then, in the second level, the Monte Carlo method is applied to calculate the expectation by feeding different scenarios into the MILP and sampling the minimal costs. Finally, as the second level returns the expected value of the system cost considering the given component capacities, a genetic algorithm is adopted in the third level to search the optimal component capacities. Compared to the conventional deterministic optimization method, the proposed stochastic optimization method reduces the annual operational cost while allowing a wider operational range. In addition, it is revealed that the inclusion of heat storage and grid connections yields notable benefits in terms of IES cost reduction.

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中文翻译：

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基于层次遗传算法和混合整数线性规划的三代综合能源系统配置随机优化

面对气候变化和环境保护日益增长的压力，包含不同能源的综合能源系统（IES）已成为未来能源系统的有希望的候选者。但是，由于各种耦合，可再生能源的随机性和数值优化的难度，每个能源的容量配置仍然具有挑战性。在本文中，提出了一个层次优化框架来确定三代IES的组件容量，即涉及制冷，制热和发电（CCHP）组合发电的系统。需求和可再生资源可用性的潜在变化被认为是随机因素，并被捕获为根据概率函数生成的方案。在第一级，定义了组件容量和方案后，制定了混合整数线性规划（MILP）问题，以最大程度地降低总系统成本。然后，在第二级中，通过将不同的方案输入到MILP中并采样最小成本，应用蒙特卡洛方法来计算期望值。最后，当第二级返回考虑给定组件容量的系统成本的期望值时，第三级采用遗传算法搜索最佳组件容量。与传统的确定性优化方法相比，所提出的随机优化方法减少了年度运营成本，同时允许更大的运营范围。此外，还发现，在IES成本降低方面，将热量存储和电网连接包括在内可产生显着的收益。

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