Big Data Solutions for Extracting Load Flexibility Potential and Assessing Benefits

Abstract

Large datasets generated by smart meters are more and more frequent in electricity consumption. This paper explores and compares a couple of big data solutions to handle massive volumes of data and extract valuable insights to improve retailers’ business and consumers’ benefits. There are many smart meter data applications, but one of the most recent applications with smart meter data is related to the flexibility potential of the commercial buildings that could be traded and assessment of the benefits. There is a variety of Demand Response (DR) programs that can be implemented to electricity residential or commercial consumers. However, the successful implementation of DR programs depends on the characteristics of electricity consumers and their consumption behavior that can be found out using big data solutions such as complex libraries that free the computer’s memory by splitting the large datasets into chunks and use the concept of lazy computation and memory mapping. Thus, we propose a calculation method for evaluating the flexibilities and benefits of the commercial buildings’ owners. Reference smart meter generic data for 16 types of commercial buildings in the U.S.A. is processed to create relevant simulations, identify flexibility potential using previous studies and calculate the gains related to such DR services.