Affiliation:
1. IBM Research Africa, Kenya
2. University of Pittsburgh, USA
3. Microsoft Research, India
4. University of Massachusetts Amherst, USA
Abstract
Understanding the energy usage of a community is crucial for policymaking, energy planning, and achieving sustainable development. The advent of the smart grid has made is feasible to gather fine-grain energy usage data at large-scales, providing us with new opportunities to understand demand patterns at different spatial and temporal scales. In this paper, we conduct a large-scale empirical study of energy usage of 14,849 residential and commercial energy consumers from a small city in the United States. We conduct a wide ranging analysis of energy usage at multiple granularities—citywide, transformer-level, and individual home levels. In doing so, we demonstrate how city-wide smart meter datasets can answer a variety of questions on energy consumption, such as the impact of weather on energy usage. For example, we show that extreme weather events significantly increase energy usage, e.g., by 36% and 11.5% on hot summer and cold winter days, respectively. As another example, we show 19.2% of transformers in the grid get overloaded during peak load periods. Finally, we evaluate the impact of incorporating varying amounts of energy storage within the distribution grid and the impact such deployments will have on the peak demand patterns seen by the grid as well as the ability to reduce overloads seen by distribution transformers during peak periods.
Publisher
Association for Computing Machinery (ACM)
Reference57 articles.
1. [n. d.]. Transformer Overcurrent Protection. https://www.electricityforum.com/td/utility-transformers/transformer-overcurrent-protection. (Accessed on 08/04/2023). [n. d.]. Transformer Overcurrent Protection. https://www.electricityforum.com/td/utility-transformers/transformer-overcurrent-protection. (Accessed on 08/04/2023).
2. [n. d.]. U.S. Energy Information Administration (EIA). https://www.eia.gov/. (Accessed on 09/04/2020). [n. d.]. U.S. Energy Information Administration (EIA). https://www.eia.gov/. (Accessed on 09/04/2020).
3. IEEE Recommended Practice for Protection and Coordination of Industrial and Commercial Power Systems
4. A Albert and R Rajagopal . 2013. Smart meter driven segmentation: What your consumption says about you . IEEE Transactions on Power Systems( 2013 ). A Albert and R Rajagopal. 2013. Smart meter driven segmentation: What your consumption says about you. IEEE Transactions on Power Systems(2013).
5. Smart Meter Driven Segmentation: What Your Consumption Says About You;Albert A.;IEEE Transactions on Power Systems,2013