The energy hub is a powerful conceptualization of how to acquire, convert, and distribute energy resources in the smart city. However, uncertainties such as intermittent renewable energy injection present challenges to energy hub optimization. This paper solves the optimal energy flow of adjacent energy hubs to minimize the energy costs by utilizing the flexibility of energy resources in a smart city with uncertain renewable generation. It innovatively models the power and gas flows between hubs using chance constraints, thus permitting the temporary overloading acceptable on real energy networks. This novelty not only ensures system security but also helps reduce or defer network investment. By restricting the probability of chance constraints over a specific level, the energy hub optimization is formulated as a multiperiod stochastic problem with the total generation cost as the objective. Cornish-Fisher expansion is utilized to incorporate the chance constraints into the optimization, which transforms the stochastic problem into a deterministic problem. The interior-point method is then applied to resolve the developed model. The proposed chance-constrained optimization is demonstrated on a three-hub system and results extensively illustrate the impact of chance constraints on power and gas flows. This work can benefit energy hub operators by maximizing renewable energy penetration at the lowest cost in a smart city.

中文翻译：

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智慧城市中多能源枢纽系统的机会约束优化


能源中心是关于如何在智慧城市中获取、转换和分配能源资源的强大概念化。然而，间歇性可再生能源注入等不确定性给能源中心优化带来了挑战。本文利用可再生能源发电不确定的智慧城市中能源资源的灵活性，解决了相邻能源中心的最优能源流，以最小化能源成本。它使用机会约束创新地对枢纽之间的电力和天然气流动进行建模，从而允许真实能源网络上可接受的临时过载。这种新颖性不仅保证了系统安全，而且有助于减少或推迟网络投资。通过将机会约束的概率限制在特定水平上，能源中心优化被表述为以总发电成本为目标的多周期随机问题。 Cornish-Fisher展开用于将机会约束纳入优化中，将随机问题转化为确定性问题。然后应用内点法来求解所开发的模型。所提出的机会约束优化在三轮毂系统上进行了演示，结果广泛说明了机会约束对电力和燃气流量的影响。这项工作可以在智慧城市中以最低的成本最大限度地提高可再生能源的渗透率，从而使能源中心运营商受益。