A data analytics system for adaptive demand response in smart grids

点击翻译按钮获取中文摘要

The load demand of the residential sector in Canada constitutes a third of the country's electricity consumption according to Natural Resources Canada. With thousands of houses being built every year, the growing demand has heightened the burden on electric grids. To address this concern, utility companies strive to find ways to engage consumers in demand response (DR) programs to reduce load demand. However, DR programs face multiple research challenges in the residential sector. First, existing DR programs do not explore the issue of reducing peak load while taking into account the consumer energy consumption behavior. Second, DR schemes require accurate prediction of energy spikes at the electric feeder level (e.g., neighborhood) to be effective. Third, targeting individual appliance/device at residential homes requires a prioritization and interruption cost mechanisms to ensure proper and fair monetary compensation for consumers. To address the above challenges, in this research project we propose to work with Synergy North, Thunder Bay, Canada, to study, design, and develop an intelligent adaptive data-driven DR system. Synergy North is the power distribution company responsible for delivering electricity to 56,000 homes and businesses in the cities of Thunder Bay and Kenora in Northwestern Ontario. The outcome of this research project can lead to breakthroughs in the design and development of next-generation energy DR for the residential sector. The project benefits Canada's economy and the environment in different aspects. Canadian residential consumers adopting the proposed DR solution will be able to save in their electricity bill by engaging in energy curtailment schemes and receive compensation. Canadian utilities will be able to reduce the cost of energy during peak hours and minimize their dependency on standby generators or fossil fuel plants; thus, helping Canada's environment by decreasing greenhouse gas emissions.