Optimal Modeling on Equipment Investment Planning of Wind Power-Hydrogen Energy Storage and Coal Chemical Pluripotent Coupling System
Yuan Tiejiang1, 4, Li Guojun1, Zhang Zengqiang2, Zhang Long3, Cai Gaolei1, Mei Shengwei1, 4
1. School of Electrical Engineering Xinjiang University Urumqi 830047 China; 2. The National Network of Xinjiang Electrical Power Economy and Technology Research Institute Urumqi 830013 China; 3. The National Network of Xinjiang Electric Power Research Institute Urumqi 830011 China; 4. State Key Laboratory of Power System Tsinghua University Beijing 100084 China
Abstract：Organically combining the wind power and coal chemical via the hydrogen energy storage system is one of the effective measures to increase the consumption capacity of wind farm and reduce energy consumption and emission of hydrogen production. A highly absorptive and low carbon integrated architecture design of wind power-hydrogen energy storage and coal chemical pluripotent coupling system was put forward. The optimized mathematical model of pluripotent coupling system was established, to minimize the one off investment, where the fully consumption of wind power and stable operation of coupling system were taken as constraint conditions. The above model was solved by genetic algorithm (GA). At last, taken a wind farm and coal enterprises in Xinjiang as examples, the validity of the proposed integrated architecture and its optimal mathematical model were verified. The changing rules of system investment costs were analyzed under the different wind power grid scales and wind power non-electric consumption scales according to prescriptive application scenarios.
袁铁江, 李国军, 张增强, 张 龙, 蔡高雷, 梅生伟. 风电—氢储能与煤化工多能耦合系统设备投资规划优化建模[J]. 电工技术学报, 2016, 31(14): 21-30.
Yuan Tiejiang, Li Guojun, Zhang Zengqiang, Zhang Long, Cai Gaolei, Mei Shengwei. Optimal Modeling on Equipment Investment Planning of Wind Power-Hydrogen Energy Storage and Coal Chemical Pluripotent Coupling System. Transactions of China Electrotechnical Society, 2016, 31(14): 21-30.
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2016, Vol. 31 
