Agent Base Modelling and Simulation of Domestic Electricity Load Profiles for effective Demand side Management

Abstract

In Pakistan, domestic electricity consumption accounts for approximately 40% of the electricity sale share, stressing requirement of forecasting this consumption sector load profiles for establishing demand side management policies ever more important. It has become crucial for power generating companies to profile their clients' electricity consumption profiles in recent times. This process would allow them to optimize their grids by providing only enough power to serve their clients keeping in view the demand and thus avoid wastages and unnecessary strain on whole system particularly transmission system hence resultantly enables to bridge the gap between electrical supply and demand efficiently. Moreover, this step helps consider efficient electricity generation from renewable sources, like solar and wind, which require a power storage device. The best way for these companies to achieve this goal is by developing load profiling models that simulate consumers’ domestic power consumption under different load conditions and factors effecting these. Efficient electricity infrastructure based on accurately monitored load profiles of consumer will help implement smart grid infrastructure and demand side management solutions in a developing country like Pakistan in near future. This work undertakes to develop a model which forecast household electricity consumption profiles using coherent technique i.e. agent-based modelling. It considers a population having 300 households and then uses the Any-Logic software to model their behaviour according to their activities investigated through variables and functions. Multi layered hierarchal scheme incorporating agents and their characteristics subject to actions taken in inter-related environment based on their habits, nature, occupancy profiles and patterns is considered. This work discusses the processes involved in Agent based modelling and simulation environment which reflects characteristics in respect of the modelled entities to simulate actual operating conditions of the appliances for generating a high resolution household energy demand profile. Results show that the agent-based simulated total electricity consumption in kWh for a given set of households in a month approximates real time validated data with difference of 2.4%. Hence, this approach proves viable for the purpose of obtaining this vital information i.e. electricity load profiles through observing daily, weekly, yearly patterns of the electrical appliance consumption by households.