Smart grid energy management system for industrial applications

Abstract

Energy is one of the top operating expenses in industries. Following the increased adoption of smart grids in recent years, industries can leverage on its capabilities to design effective energy management schemes for competitive advantage. This paper addresses the challenge of energy management in industries by incorporating the aspects of a smart grid in designing an energy management system (EMS) where demand side management (DSM) is utilized to enable users control their energy usage and minimize costs. A forecasting model for electricity prices and demand is developed using Long Short Term Memory (LSTM) - Recurrent Neural Network (RNN). The predicted prices are used in load scheduling to realize potential energy cost savings. The nonpriority loads are scheduled to leverage on low electricity prices during off peak times. The effectiveness of the designed energy management strategy is tested using an IEEE 30 bus system. A suitable operation schedule with committed units for each hour is given for one sample day. Using the test system with 20 loads yielded an annual energy cost saving of $2,961,169.20 and a payback period (PBP) of 4.39 years. Quantifying both the energy and non-energy benefits of investing in an EMS justifies its high investment cost. Long term use of an industrial EMS is likely to yield huge energy and cost savings.