Probabilistic energy flow and risk assessment of Electricity–Gas systems considering the thermodynamic process

A Deep-Learning-Based Optimal Energy Flow Method for Reliability Assessment of Integrated Energy Systems

“…s is the system state. P s is the possibility of the state s, which is calculated by (9). s IES is the set of the possible system states in period time t, which is determined by MCS as (8).…”

“…Compared with the traditional power system, bidirectional energy interactions and physical characteristics should be studied to evaluate the reliability level [6,7]. Residual gas [8] and the dynamic transmission process [9] are incorporated into the reliability assessment process. Some byproducts of gas utilization, including smoke and steam, are considered to refine the fluid model [10].…”

A non-intrusive probabilistic multi-energy flow calculation method and its application in operation risk analysis of integrated energy systems

“…The most representative probabilistic multi-energy flow calculation method is the Monte-Carlo simulation (MCS), which generates numerous random samples and conducts a DMEF calculation for each sample to derive the overall probability distribution of energy flows [15,16]. However, the accuracy of MCS method is highly dependent on the Temperature of the end node of pipeline ‖ ⋅ ‖ 0 L0-norm number of samples, which may result in unacceptable computational costs for a large IES with large numbers of random variables.…”