Multi-Processing Data Analysis for the Residential Load Flexibility in Smart Cities

Residential consumption is gaining an increasing focus regarding assessing their flexibility and load control. Flexibility potential of smart cities becomes significant as the uncertainties of the power system operation are increasing with the integration of a large volume of Renewable Energy Sources (RES), numerous charging stations of Electric Vehicles (EV), and unexpected climate change. Thus, the load flexibility of residential buildings may contribute to the balance of the power system, flatten the daily load curves, and decrease the energy acquisition costs. The usage of load flexibility also depends on the availability of Demand Response (DR) programs, aggregation of flexible resources, and requirements regarding the implementation of DR programs. In this paper, to extract valuable insights, we propose a multi-processing data analytics framework identifying clusters of residential consumers and the flexibility of the load targeting various DR programs. For this purpose, we will use large and various datasets and multi-processing data facilities. In addition, other varieties of datasets could be correlated: such as ISO affiliation, DR programs, cost of the infrastructure requirements to implement DR, assess and value the flexibility. However, flexibility transactions and DR specific program implementation are complex aspects that also involve new legislative rules and pose policy challenges that will initiate business models and strategies.