A conceptual possibilistic framework is proposed to manage the input uncertainties encountered in steady-state real power flow analysis. This study is devoted to building the robust input framework of the possibilistic load flow (POLF) algorithm under an uncertain environment. Three probability density functions are transformed consistently into the corresponding possibilistic representations. Under the possibilistic framework, a compromise between transformation consistency and human updating experience can be satisfied. The possibility distribution function (podf) derived yields both a mean interval and a reasonable spread interval. Compared with the results of probabilistic load flow (PLF) analysis, the spread interval of the compromise line flow podf can be derived to cover only the reasonable output region and its mean interval contains most of the significant probability information. Numerical simulations for three extreme line flows in the IEEE 25-bus system indicate the feasibility of the proposed approach.
All Science Journal Classification (ASJC) codes
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering