计及气网约束的区域综合能源系统惯性资源配置研究

Research on inertial resource configuration of the regional integrated energy system considering gas network constraints

多能耦合的区域综合能源系统具有多能转换灵活性,尤其是其气热系统惯性资源具有功率波动抵御能力,可以支撑可再生能源不确定性造成的电力系统功率缺额。在充分考虑天然气网络约束的情况下,提出了区域综合能源系统惯性资源配置策略。首先,将天然气系统对功率波动的抵御能力以及对需求变化的延时响应纳入天然气系统的惯性特性,提出了区域综合能源系统气惯性概念和响应特性。由于气惯性的局部利用会影响天然气网络的整体参数变化,基于气网约束条件,对天然气网络的动态变化进行了深入的分析。然后,为了合理配置电力系统故障后区域综合能源系统中气热惯性资源,建立了计及气网约束的区域综合能源系统惯性资源配置模型,以明确气热惯性提供功率支撑的优势,并验证区域综合能源系统惯性资源配置过程中计及气网约束的重要性。最后,算例结果验证了气惯性对外界功率波动的抵御能力,计及气网约束的区域综合能源系统资源配置方法可提升经济性预估的准确性。

The multi-energy coupling regional integrated energy system(RIES)has the flexibility of multi-energy conversion.The gas-thermal inertia system resources of have the ability to resist power fluctuations,which can support power shortages caused by the uncertainty of renewable energy.With full considerations of gas network constraints,an inertial resource configuration strategy based on RIES inertia resources is proposed.Firstly,the concept and response characteristics of gas inertia are proposed,by incorporating the resistance of the natural gas system to power fluctuations and the delayed response to demand changes into the inertia characteristics of the natural gas system.Since the partially use of gas inertia will affect the parameters of the whole natural gas network,this paper specifically analyses the dynamic changes of the gas network based on the gas network constraint condition.Then,in order to reasonably allocate the gas-thermal inertia resources in the regional integrated energy system after power system failure,an inertial resource configuration model of RIES considering gas-thermal inertia is established to clarify the advantages of gas-thermal inertia in providing power support and to verify the importance of taking into account the gas network constraints in the process of inertia resource allocation of the regional integrated energy system.Finally,the studied case verifies the resistance characteristics of gas inertia to external power fluctuations,and the improved prediction accuracy on resource configuration and economy analysis of considering network constraints.