Quasi-Monte Carlo Method for Probabilistic Power Flow Considering Uncertainty of Heat Loads

The rapid development of the modern energy systems enhances coupling of electric and heat loads, while thermoelectric coupling devices greatly increase the flexibility of power system operation, satisfying the multi-energy demand of the users more efficiently. However, the stochastic nature of heat loads poses acute threats to the operation of the power systems, which requires a highly accurate probability power flow method to support the decision-making. On the other hand, huge computational burden seriously impairs the computational efficiency of traditional Monte Carlo simulation (MCS) methods, limiting the application in theoretical research and engineering practice. This shows that the existing analysis methods can be further improved. This paper presents a quasi-Monte Carlo method for probabilistic power flow, which takes the uncertainty of heat loads into consideration. In order to reduce the computation burden, the proposed approach exploits quasi-Monte Carlo methods in the sampling procedure. The comprehensive numerical experiments demonstrate that the probabilistic characteristic of heat loads causes huge influences on the of power system operation, and therefore cannot be ignored. The superior efficiency of the algorithm in this paper has also been verified in the case studies.