An efficient framework for exploiting operational flexibility of load energy hubs in risk management of integrated electricity-gas systems

Faced with increasing operational uncertainties, e.g. wind power variation, the risk management of integrated electricity-gas systems (IEGSs) has become extremely vital, which puts forward a higher requirement for flexible resources. On the demand side of IEGSs, local energy hubs (LEHs) composed of different energy devices can provide flexible resources for system risk management through multi-energy substitution. However, it can be difficult to exploit the operational flexibility of massive LEHs in IEGS operation owing to two major issues, namely 1) privacy concerns of individuals; 2) computation efficiency requirements. To address this, an efficient framework based on flexible regions is innovatively proposed for exploiting LEH flexibility in the risk management of IEGSs. The flexible region is estimated to characterize the adjustable range of energy inputs to LEHs considering the operational requirements of internal energy devices. On this basis, essential flexibility-related information of LEHs is directly utilized for system risk management, which can preserve individual privacy and avoid time-consuming iteration. Firstly, a generalized method based on Minkowski Summation is proposed to efficiently determine the flexible region of LEH, which contains time-independent and temporal-related parts. The two regions are formulated separately considering the multi-energy substitution of LEH and the charging/discharging process of electric storage. The flexible region is then mathematically formulated as a set of linearized equations, which can be directly incorporated into the system scheduling model. On this basis, a risk-based two-stage optimization model is developed for the coordinate dispatch of IEGSs and LEHs considering wind power uncertainties. Case studies demonstrate that the exploitation of LEH flexibility can effectively mitigate operational risk levels of IEGSs and reduce system costs. Besides, the proposed model has the advantage of high computation efficiency compared to the existing iteration-based method.