Demand Side Energy Management System Using ANN Based Linear Programming Approach

This paper presents an optimization method to the demand side Energy Management System (EMS) of a given consumer (e.g. an industrial compound or university campus) with respect to hourly electricity prices. This paper considers a cluster of interconnected price responsive demands in an Academic Campus. The demands can be supplied through the main grid and stochastic Distributed Energy Resources (DERs), such as wind and solar power sources. In addition, the cluster of demands owns an energy storage facility. The proposed EMS has ability that each consumer can employ their own strategy to regulate the present load and prices in the power distribution system. To solve this EMS problem and optimization algorithm based on Linear Programming (LP) approach has been implemented. In addition to LP algorithm an Artificial Neural Network was applied to predict the future power consumption of the cluster of price responsive demands. The objective of the proposed method is to maximize the utilization of the cluster of demands when it is subjected to a set of constrains. This LP algorithm allows the cluster of demand to buy, store and sell energy at suitable times to adjust the hourly load level. To evaluate the performance of the proposed algorithm an IEEE 14 bus system was considered. The results shows that the cluster of demands of energy management system using the proposed approach increasing the efficiency and minimizing the losses than the existing methods.