A novel cluster-specific analysis framework for demand-side management and net metering using smart meter data

Demand-side management (DSM) and net metering (NM) plays a critical role in managing the energy imbalance by supervising the customer behavioural patterns. The availability of fine-grained energy consumption information from the smart meter empowers the energy policy-makers to design realistic DSM and NM plans with a detailed analysis of usage patterns, dynamics, and characteristics. This paper presents a novel cluster-specific analysis framework to examine trends in residential electricity consumption to understand their potential for participating in energy efficiency, demand response, and net metering programs. In this process, the proposed framework utilises the Hierarchical clustering on energy usage patterns of residential consumers of Kolkata, India to obtain the representative profiles. Further, six different load shape characteristics are proposed to semantically analyse the clustered profiles for understanding the underlying diversity in consumption behaviour. Moreover, a novel statistical approach is incorporated into the proposed framework to evaluate the electricity consumption of every customer based on the number of peaks in a day, solar size required, and consumption during solar generation hours. The findings emphasise the critical nature of examining consumption habits in order to execute rational and productive decisions for the residential sector The scalability experiment demonstrates the effectiveness of the proposed framework. The cluster-specific analysis thus recommends power utilities in categorising the groups to participate in the demand-side management, net metering, and hybrid programs based on their respective potential. This would also help utilities in better planning for future electricity needs with an increase in renewable energy sources.