Abstract:

Most of the typical distribution network programmings with distributed new energy utilize passive， conservative programming method to access distributed new energy， which indeed secure the safe of distribution network， but cannot reflect the output characteristics of distributed new energy and cause unnecessary investment on distribution network construction. To solve this problem， this paper proposed active power distribution network programming method based on random chance constrained programming. Firstly， the hard constraint conditions in active power distribution network programming were transformed into soft ones with higher confidence level. Meanwhile， three independent objective functions including the investment cost reflecting the economic benefit， the power loss and the voltage deviation degree reflecting the distribution network power supply security were set to form the multiple-objective active power distribution network planning model based on random chance constrained programming. Then， the model was solved to obtain non-inferior solution Pareto frontier by the improved NSGA-2 （non-dominated sorting genetic algorithm2） combined with the quantum method. On this basis， the TOPSIS （technique for order preference by similarity to ideal solution） was used to sort the non-inferior solution， in order to obtain the optimal solution. Finally， a distribution network with 57 nodes was used as example to verify the feasibility and availability of the proposed method.

Key words: random chance constraint, distributed generation, time sequential characteristics, active power distribution network planning, improved NSGA-2