Abstract: With the construction and development of new energy power systems, the unit commitment is facing multiple uncertain challenges of multi-regions and new energy with different characteristics. Chance constraint is one of the most representative models considering stochastic factors. The unit commitment model for multi-area systems with different stochastic characteristics of new energy uses joint chance constraint model of multiple stochastic variables, and the accurate solution requires the convolution operation of multiple probability distribution functions, which is computationally complex. Therefore, existing methods often ignore the differences in the distribution of new energy and simply deal with them according to the same probability distribution, leading to a large deviation between the scheduling results and the actual situation. In this paper, an improved solution method based on simplified joint chance constraint is proposed. In the joint chance constraint, inequality forms with linear combination of multiple stochastic variables are transformed into inequality forms with single stochastic variable by introducing auxiliary variables. This approach avoids complex convolution operation and preserves the difference of distinct probability distribution characteristics of new energy. By optimizing the model to obtain the minimum probability point (multi-dimensional p-efficient point) that satisfies the confidence level, it achieves the conversion from joint chance constraint to deterministic constraint of single stochastic variable. The complex integral operation of multiple probability distribution functions is converted into a conventional integer programming problem. Finally, the superiority of the proposed method is verified through the 3-regional 117-node and 196-node system test cases in terms of spinning reserve allocation, curtailment rate of new energy power running cost and calculation time.