A novel load management algorithm for EMU by implementing demand side management techniques using ANN

Abstract

This paper proposes a novel smart load management algorithm for Energy Management Unit (EMU) in smart grid environment. The algorithm implements Demand Side Management (DSM) Techniques such as peak clipping, load priority Techniques using Artificial Neural Network (ANN) to integrate both Conventional and Non-Conventional sources of energy. The current challenge of the world is to manage the energy resources optimally with load and supply management. The algorithm presented in this paper proposes the energy management at domestic load centre by managing loads with an effective integration of energy sources available. EMU is an existing system which allows the domestic consumer to monitor energy consumption and control the loading with greater ease. The algorithm proposed, enhances the effectiveness of the EMU for better balancing the supply and load by using DSM Techniques. The algorithm proposed, introduces Threshold Power limit (PTh) for the hour based on load priority assignment. Objective function for Threshold Power is formulated and solved by using ANN based prediction model. Based on predicted Threshold Power (PTh) for the hour, the loads are transferred between Conventional and Non-Conventional energy sources. Evaluation of power consumption and CO2 emissions caused, before and after applying the algorithm is presented which shows the effectiveness of the algorithm.