A data driven multi-state model for distribution system flexible planning utilizing hierarchical parallel computing

With the development of smart grid and electricity market, the uncertainty for power flow is greatly aggravated, and thus leads to a great challenge on the traditional expansion methods for distribution systems to satisfy the future demands. In this paper, a data-driven multi-state distribution system expansion planning (DSEP) model is explored. Innovatively, amplitude values and profiles of uncertain factors in distribution systems are considered separately. Based on the massive historical measurement data, kernel density estimation and adaptive clustering are utilized to aggregate the typical amplitudes and profiles of time-varying variables respectively without prior knowledge. Consolidating all the uncertain factors, a multi-state model is established which extends DSEP into the deterministic initial planning and the probabilistic re-planning. The minimization of the overall planning cost is considered as the objective, which takes the initial planning costs and the expected costs of the initial plans being adapted to other future states into account. In this way, the flexibility of DSEP can be greatly enhanced and extra investments caused by frequent adjustments of plans are reduced. To avoid the rapid growth of CPU time due to multi-state model utilization, an integrated differential evolution and cross entropy algorithm implemented on a three-hierarchy parallel platform is proposed. The feasibilities of the proposed data-driven multi-state DSEP model and the parallel integrated solution method are demonstrated by numerical studies on a realistic 61-bus distribution system.