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Probabilistic multi-energy flow (PMEF) analysis is the fundamental of planning and operation of integrated energy system (IES) considering uncertainties. The injection of hydrogen will make the PMEF calculation more complex and can deeply affect the distribution of energy flows. This paper studies an efficient PMEF calculation method which can obtain the probabilistic information of electricity-gas IES with hydrogen injection. Firstly, the steady-state model of electricity-gas IES with hydrogen injection is established, in which the gas network with hydrogen injection is formulated in compact matrix form. Secondly, the Jacobian matrix for the steady-state model of electricity-gas IES with hydrogen injection is derived. Thirdly, the cumulant method and Nataf transformation are applied to calculate the PMEF considering the correlations. Finally, case studies are carried out on a coupled IES consists of modified IEEE 33-bus electricity system and 11-node gas system. The results demonstrate that the proposed PMEF method can reduce computational time greatly while keep high computational accuracy when compared to the Monte Carlo simulation method.

The replacement of hydrogen with conventional fossil fuel has been viewed as a feasible route to the ZERO emission transportation. Nowadays, the water electrolysis method has become mature and efficient enough to produce large-scale of hydrogen, and the condensation and reliquefaction technologies also greatly reduce the hydrogen loss via pipeline transmission, which both motivate the applications of hydrogen as a fuel. In this study, a combined power-hydrogen network is planned to supply the power demand of residential blocks and the charging/fueling demand for electrical vehicles and hydrogen vehicles, respectively. However, the joint scheduling of power-hydrogen network is challenging due to the non-linearity and non-convexity inherited from the hydraulic dynamics of hydrogen transmission. To resolve this problem, a convex-concave based sequential convex approximation method is proposed. A revised 37-bus system is used to demonstrate the validity of proposed method.

To describe the impact of uncertainties, such as fluctuation of bus loads and intermittent behavior of renewable generations, on the available load supply capability (ALSC) of distribution system accurately and comprehensively, this paper defines a series of meaningful indices for the probabilistic evaluation of ALSC. An efficient simulation method, Latin hypercube sampling-based Monte Carlo simulation (LHS-MCS), combined with step-varied repeated power flow method is proposed to compute the defined indices. Compared with simple random sampling-based Monte Carlo simulation (SRS-MCS), LHS-MCS is found to be more suitable for the probabilistic evaluation of ALSC. It can achieve more accurate and stable ALSC indices under relatively small sample sizes. The calculation speed of LHS-MCS is comparable with that of SRS-MCS under the same sample sizes, and the required CPU time of LHS-MCS is far less than SRS-MCS under the same calculation accuracy. Case studies carried out on the modified Baran & Wu 33-bus and the modified IEEE 123-bus distribution systems verify the feasibility of the defined indices and high performance of the proposed method.