计及电转气的区域综合能源系统日前优化调度

A Day-Ahead Optimal Scheduling of Regional Integrated Energy System Considering Power to Gas

  • 摘要: 为了提高区域综合能源系统的经济性以及可再生能源的消纳能力,提出含电转气(power-to-gas,P2G)的调度优化模型。首先将电转气分为2个阶段运行,在电解水产生氢气环节加入储氢罐作为氢燃料电池的燃料来源,通过氢燃料电池实现氢能向电能、热能的转化,之后将剩余氢气输入到甲烷反应器中,减少将氢气全部直接甲烷化所产生的能量损耗。其次燃气轮机采用变效率运行模式,通过灵活调节燃气轮机的供电、供热效率,使热电出力更为经济合理。基于此,以由系统购电成本、购气成本、弃风成本以及环境成本构成的日运行成本最小为经济目标,构建含电转气的区域综合能源系统日前优化调度模型。最后利用基于空间距离的混沌粒子群算法求解,并通过算例仿真表明所提调度模型可有效促进多级能源合理高效利用,提高可再生能源消纳能力与系统运行经济性。

     

    Abstract: To enhance the economy of regional integrated energy system and improve the accommodation ability of renewable energy sources, a scheduling optimization model containing power-to-gas (abbr. P2G) was proposed. Firstly, the P2G was divided into two operating stages: in the link of water electrolysis the produced hydrogen was added into hydrogen storage tank as the fuel source of hydrogen fuel cell, and the energy conversion of hydrogen energy into electric energy and thermal energy was implemented by hydrogen fuel cell, in the next link the residual hydrogen was input into methanator to reduce the energy loss due to directly methanating all hydrogen. Secondly, the gas turbine units adopted the variable efficiency operation mode, by means of flexibly regulating the efficiency of power supply and thermal supply of gas turbine units the output of heat and electricity became more economic and reasonable. On this basis, taking the minimum daily operating cost composed of system electricity purchasing cost, gas purchasing cost, wind curtailment cost and environmental cost as economic objective, a day-ahead optimization scheduling model for of regional integrated energy system containing P2G was constructed. Finally, this problem was solved by chaotic particle swarm optimization algorithm based on spatial distance. Simulation results show that the proposed scheduling model can effectively promote the reasonable and high-efficient utilization of multi-class energy sources and improve the accommodation ability of renewable energy sources and the economy of system operation.

     

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