Implementation of a Smart Power Scheduling Method to Reduce Peak Demand and Cost of Energy in a Smart Grid

Abstract

Results and Discussion: Simulation and experimental work are carried out in different cases. Best fitted seasonal ARIMA model using past six month energy price data (obtained from Indian Energy Exchange) is identified which is very helpful to decide the next day s or next week s load pattern for HEC. It is also useful to manage the trading risk of the supplier and broker agent. Similar strategy is applied to energy price data obtained from European Power Exchange and best fitted seasonal ARIMA model is also obtained for it. At the end of this work, the comparison between simulation results and experimental results are presented to show the effectiveness of the optimization method adopted. newlineConclusion: In the experimental work done here, different types of cases are considered and these results are compared with the simulations results. For the data obtained from Indian Energy Exchange, the best fitted model is ARIMA (1, 0, 2) (1, 1, 1)24 (for six month s data between 1st December 2013 and 31st May 2014). The best fitted ARIMA model obtained from energy price data of European Power Exchange of duration between 1st October 2015 and 31st May 2016 is ARIMA (2, 1, 13) (2, 0, 1)24. If more numbers of schedulable appliances are used, PAR can be reduced further. It is presented in the experimental work that 9.4% money is saved by adopting the optimization of energy cost and PAR. Considering this saving payback period of the hardware used in this work is four years. The strategy presented in this work can also be applied to the commercial and industrial sector. Shifting of operation of schedulable appliances towards off peak period (during the night) results in the rise of the usage of non conventional energy sources like wind power which also results in a reduction in CO2 emission. In the majority of cases, this shifting forces power plant to run at full capacity which also results in a reduction in CO2 emission.