Maximizing Renewable Energy Use with Decentralized Residential Demand Response

Due to steady 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. To ensure sustainability and reliability of electricity provision in the growing cities, a significant increase in energy generated from renewable sources (e.g., wind, solar) is required. However, renewable energy supply is much more variable and intermittent than traditional supply, as it depends on changing weather conditions. In order to optimize residential energy usage, demand response (DR) techniques are being investigated to shift device usage to the periods of low demand. Currently most DR approaches focus on traditional DR goals, e.g., reducing usage at peak times and increasing it at off-peak times. More flexible and adaptive techniques are needed that can not only meet traditional DR requirements, but enable just-in-time use of renewable energy, rather than requiring its curtailment or using expensive and inefficient storage options. This paper proposes the use of decentralized learning-based multi-agent residential DR to enable more efficient integration of renewable energy sources in the smart grid, in the presence of increased demand caused by high electric vehicle penetration. We evaluate the approach using real household usage data obtained from Irish smart meter trials and data on wind-generated energy from the Irish grid operator. We discuss advantages of the proposed decentralized approach and show that it is able to respond to multiple variable windgeneration patterns by shifting up to 35% of the overall energy usage to the periods of high wind availability.