AI Summary & Key Takeaways
AI InsightHere the most important part is modeling the probability ofload, which can say the load ’s behavior is following that most ofthe time in the planning period.
📌 3 Key Takeaways
Abstract
The presence of demand response programs (DRPs) in the distribution systems would certainly affect the power system problems such as capacitor studies. Some ofthese problems refer to economical and other ones are based on technical limits. Capacitor placement and responsive load installation are challenge that express as the base of distribution system problems which are considered in this paper. In this paper, in order to maximize the loss reduction benefit and voltage profile improvement simultaneously, considering difi'erent percentages ofpeak loads and electricity prices have been studied. Also, probabilistic load model at each load state is assumed in this paper instead ofutilizing time-series based models. Moreover, optimum numbers of capacitors and optimum load reduction by DRP should be used in each load state in order to optimizing the problem. Different scenarios including, individual or simultaneous placing of capacitors and DRPs at each load state have been established and interrogated in depth. Therefore, nondominated genetic algorithm version 11 (NSGA-II) has been used to solve the problems under study. NSGA-H is usedfor typical network by considering possibilities. The effectiveness ofthe proposed scheduling approach verified on IEEE 33-bus distribution test system in the planning period. Here the most important part is modeling the probability ofload, which can say the load ’s behavior is following that most ofthe time in the planning period.