My research interests are associated with United Nation’s Sustainable Development Goal 7 Affordable and Clean Energy (SDG7). Energy is the dominant contributor to climate change, accounting for around 60 percent of total global greenhouse gas emissions. As described in SDG7, electricity is central to nearly every challenge and opportunity the world faces today, hence meeting the sustainability goals will require collective efforts from the utilities, the government and consumers of electricity altogether.
In this research work, we are exploring demand side management of electricity, majorly focusing on residential Demand Response (DR). The energy seesaw of load balancing is a roller coaster ride for the utilities and consumers. Any disruption in this balance may lead the utilities to either import costly power or not meet the demand and schedule the blackouts.
DR is a supplementary mechanism to fine tune this balance as it enables consumers to collectively play an active role in the operation of the electric grid by reducing or shifting their electricity usage during peak periods in response to time-based tariff or other forms of financial incentive.
Due to steadily increasing urbanization, the electrical grid is facing significant changes in the supply of resources as well as changes in the type, scale, and patterns of residential user demand that makes deploying residential demand response quite exigent. Moreover, International Renewable Energy Agency(IRENA) estimated that more than one billion people are living off the grid. Efforts towards integrating sustainable energy sources like Distributed Renewable Energy (DER), e.g. Solar rooftops or small wind turbines, strengthens the need of more adequate infrastructure to keep the balance between the supply, demand and storage.
The aim of this project is to extend the functions of Multi-agent Stochastic Simulation (MASS) platform to address the DR by integrating new agent for storage and supply systems. We plan to use quantitative methods to estimate the stochastic energy demand from individual households and scale the model to community level. In a broader context, we aim to build models to predict the likelihood of adoption of DR systems in residential sector.
We will combine the collation of secondary data, with primary data acquired through participatory design focus groups. ‘Proof of concept’ study has been performed for residences in the UK, now, we intend to identify focus groups for this project in developing countries, primarily in India, as many households lack basic electrical appliances and also face frequent blackouts due to inefficient grid supply. Once complete, this new comprehensive prototype will be deployed to study the potential impacts of DR technologies in different communities of varying socioeconomic levels.
My twitter handle is @reenasayani