Adaptive distributed demand side management with weighted dimension reduction for energy resource management in smart grid

Abstract

Demand side management (DSM) consists of planning, executing, and controlling activities to reduce electricity consumption. By using demand response (DR), customers help reduce demand during peak times. Consumer participation in DSM depends on his behaviour. Consumer behaviour is determined by factors such as lifestyle, electricity price, electricity consumption tariff, contract type etc. Thus, factors affecting the consumer's behaviour should be considered in order to determine more accurately the amount of participation in DSM programs. This article presents a model for the optimal scheduling of distributed energy resources by taking into account factors related to consumer behaviour. To reduce the volume of the DR data while maintaining the main features, distributed principal component analysis (D_PCA) was used to reduce the volume of DR data. Also, by integrating this method with the accelerated hybrid alternating direction method of multipliers (AHADMM) algorithm, an adapted and accelerated method is achieved to realize the reliability and cyber security of the system. The case study was conducted on the IEEE 118 bus power system at different levels of demand, which verified proposed metaalgorithm improved at least between 4 to 13 iterations of energy resource convergence speed compared to similar methods and while DR is also intended.