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首页> 外文期刊>Renewable Power Generation, IET >Robust expansion co-planning of electricity and natural gas infrastructures for multi energy-hub systems with high penetration of renewable energy sources
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Robust expansion co-planning of electricity and natural gas infrastructures for multi energy-hub systems with high penetration of renewable energy sources

机译：针对多能源枢纽系统的电力和天然气基础设施进行稳健的扩展共计规划，可再生能源的渗透率很高

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摘要

High penetration of renewable energy sources will cause crucial challenges for future energy systems. This study presents a three-level model for adaptive robust expansion co-planning of electricity and natural gas infrastructures in multi-energy-hub networks, which is robust against uncertainties of maximum production of wind generation and gas-fired power plants as well as estimated load levels. The proposed min-max-min model is formulated as a mixed integer linear programming problem. The first level minimises the investment cost of electricity and natural gas infrastructures, the worst possible case is determined through the second level, and the third level minimises the overall operation cost under that condition. To solve this model, the final minimisation problem is replaced by its Karush-Kuhn-Tucker conditions and a two-level problem is determined. Finally, by using the column and constraint generation algorithm the original problem is decomposed to master and sub-problems and the optimal solution is derived iteratively. The proposed robust expansion co-planning model is tested on modified Garver's 6-hub, modified IEEE RTS 24-hub, and modified IEEE 118-hub test systems and numerical results show its effectiveness to cope with uncertainties with regard to control conservativeness of the plan.
机译：可再生能源的高渗透率将对未来的能源系统造成重大挑战。这项研究提出了一个三级模型，用于多能源枢纽网络中的电力和天然气基础设施的自适应鲁棒扩展协同规划，该模型对于风力发电和燃气发电厂的最大产量不确定性以及估计负载水平。提出的最小-最大-最小模型被公式化为混合整数线性规划问题。第一级降低了电力和天然气基础设施的投资成本，第二级确定了最坏的情况，而第三级降低了该条件下的总体运营成本。为了解决该模型，将最终的最小化问题替换为其Karush-Kuhn-Tucker条件，并确定了一个两层问题。最后，通过使用列和约束生成算法，将原始问题分解为主要问题和子问题，并迭代得出最优解。在改进的Garver的6集线器，改进的IEEE RTS 24集线器和改进的IEEE 118集线器测试系统上测试了建议的鲁棒扩展共计划模型，数值结果表明，该模型可有效应对计划的控制保守性方面的不确定性。

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