Forecasting Electricity Price Using Seasonal ARIMA model and Implementing RTP Based Tariff in Smart Grid

-A Smart Grid has a two-way digital communication system and it encourages customers to actively participate in different types of Demand Response (DR) programs. In the Smart Grid market, both the supplier and broker agent earn profit while distributing the electrical energy. They have to balance the supply and demand during the distribution of energy. They also participate in energy trading to earn more money. To minimize trading risks, forecasting of wholesale electricity prices is necessary. A Real Time Price (RTP) based power scheduling scheme can be implemented effectively in Smart Grid to match supply and demand. In this scheme, Home Energy Controllers (HEC) and Smart Plugs can be used to shift the operation of schedulable load from peak period to off-peak period. To shift the operation of schedulable load during off-peak period, electricity price should be available in advance. In order to have the electricity price in advance, accurate forecasting is needed. Demand and supply depends on so many factors such as weather condition, cloud cover, wind speed, day of the week and festivals. It is difficult to forecast energy prices in such uncertainty. In this work, the best fitted seasonal ARIMA (Auto Regressive Integrated Moving Average) model is identified and used to forecast the next week’s electricity price. This forecasted electricity price helps in deciding the next day’s load pattern and minimizing the trading risk. Algorithms for HEC and Smart Plug are presented in this work to identify the optimized time slots and to allow power to the schedulable appliances during those slots. Key-Words: Smart Grid, Demand Response, Real Time Price, Smart Plug, Home Energy Controller, ARIMA.