Multi-Time-Scale Robust Optimization Strategy for Integrated Energy System Considering the Refinement of Hydrogen Energy Use
Hu Junjie1, Tong Yuxuan1, Liu Xuetao1, Wang Jianxiao2, Xu Yanhui1
1. State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power University Beijing 102206 China; 2. National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing 100871 China
Abstract：The local flexibility of the high percentage of new energy power system is seriously insufficient, and it is urgent to establish a new energy structure system that is clean, efficient and flexible. Hydrogen energy as a secondary energy source with diverse and efficient conversion forms. Refined modeling of hydrogen energy utilization is a key issue to study the flexibility value of electric-hydrogen coupled units. At the same time, the energy system has uncertainty and fluctuating variability on multiple time scales, and the existing studies are too adventurous or conservative in considering the uncertainty at the day-ahead stage. In response to the above problems, a robust optimization strategy is proposed for the multi-timescale distribution of the integrated energy system taking into account the refined hydrogen energy utilization. Firstly, the two-stage operation process of P2H is considered, and the refinement modeling of hydrogen energy use process and equipment is carried out by taking into account the operating characteristics of electrolyzer, hydrogen fuel cell and other equipment. The operating states of PEM are divided into shutdown, cold standby, overload, variable load and low load states, and a mixed integer linear mathematical model of the electrolyzer with variable load start-stop characteristics is established, taking into account the loss of hydrogen output during the cold start of PEM. In order to improve the operational flexibility of the cogeneration unit, the adjustable thermoelectric ratio of CHP and HFC is considered to decouple the thermoelectric linkage, and an adjustable thermoelectric ratio heat model of the cogeneration unit is established. Secondly, to reduce the power fluctuation caused by the deviation of wind power and multi-energy load forecasts in the day-ahead and intra-day, a two-stage optimization model of day-ahead scheduling and intra-day rolling is established. In the day-ahead stage, a data-driven distribution robust optimization model is established, and the probability distribution is constrained by the composite norm to adjust the conservativeness of the model; in the intra-day stage, the differences in the time scales of flexibility regulation of multi-energy flows are considered, and the impact of power fluctuations is reduced by rolling optimization on multiple time scales. In the case simulation, five scenarios are set up for comparative analysis in the day-ahead phase, and the proposed multi-timescale model is compared with day-ahead programming (DA-P) in the intra-day phase, and the conservativeness of the data-driven DRO model is investigated, leading to the following conclusions: (1) The intermediate energy ladder losses are avoided after refining the two-stage operation process of P2H. The efficiency and flexibility of hydrogen energy utilization are fully exploited, and the comprehensive energy utilization of IES is significantly improved. (2) The proposed PEM mixed integer linear model and adjustable cogeneration model can adjust the equipment output in real time according to the load, which promotes the wind power consumption and improves the operating economy. (3) The data-driven DRO model proposed in the day-ahead stage fully takes into account the uncertainty of the energy system based on historical data samples, and its conservativeness is influenced by the number of reduction scenarios and sample size. It has a better ability to resist the fluctuation of uncertainty forecast error in the intra-day correction phase. (4) The intra-day phase takes into account the differences in the forecast characteristics of different energy sources, and smoothes out power fluctuations by regulating different energy coupling devices on a sub-time scale, effectively reducing wind power volatility and operating costs.
胡俊杰, 童宇轩, 刘雪涛, 王剑晓, 徐衍会. 计及精细化氢能利用的综合能源系统多时间尺度鲁棒优化策略[J]. 电工技术学报, 2024, 39(5): 1419-1435.
Hu Junjie, Tong Yuxuan, Liu Xuetao, Wang Jianxiao, Xu Yanhui. Multi-Time-Scale Robust Optimization Strategy for Integrated Energy System Considering the Refinement of Hydrogen Energy Use. Transactions of China Electrotechnical Society, 2024, 39(5): 1419-1435.
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2024, Vol. 39 
