Demand response based congestion management of power system with uncertain renewable resources

Abstract

In this paper, the combined approach of rescheduling of generators and demand-side management using various demand response programme (DRP) for congestion management is analysed. The uncertainties of wind and solar output power are modelled by Rayleigh and Beta distribution function. These uncertainties result in a large number of scenarios which increases the computational burden. The k-means clustering algorithm is applied to reduce the number of scenarios. This system is analysed by using Monte Carlo simulation. The impact of various DRPs on technical and economic characteristics of the load profile is investigated. The priority of DRPs for the reduction in rescheduling cost and a bill is derived. The result has shown that all DRPs are not profitable for minimising rescheduling cost compared to without DRPs. The best preferred DRP for congestion management is determined among all DRPs. The proposed approach is modelled in GAMS environment and solved by using CONOPT solver.