Smart-grids has a promising future as they are embedded with an information layer that allows communication between its various components so they can better respond to quick changes in energy demand or urgent situations. Additionally, the carbon-free technologies like renewable energy are getting more and more attention as they can help combat climate change, but many of them, e.g., wind and water power, have not reached their full potential yet. This is mainly because currently, the mathematical models underlying the operations of power production are approximately 30 years old and are generally incompatible with the current realities. Additionally, the increasing uncertainty of parameters such as future precipitation levels and pricing are among the many challenges to optimizing production and profit.
The demand response (DR) programs are designed to encourage end-users to make short-term reductions in their energy demands in response to a price signal from the hourly electricity market, or a trigger initiated by the grid operator. DR programs learn from the consumption patterns of energy consumers in order to balance the demand and supply see-saw. It gives consumers an opportunity to take part in grid operations by reducing or shifting their electricity usage patterns during peak consumption periods and emergencies in response to an hourly pricing scheme. The smart consumers are also offered financial incentives. In Incentive based programs, the consumers are offered fixed or time-varying financial benefits in response to the reduction in their electricity consumption during peak times and contingencies. This activity constitutes of the following two use-cases fueled by several models.
Based on consumption data, we have built several scientific models to estimate consumption demands
Various pricing schemes have been employed for billing purposes by the distribution companies to make an energy management scheme more efficient. We support various pricing schemes that can be fused with our demand forecasting methods, this includes RTP, ToU pricing scheme, CPP, day ahead pricing (DAP), etc. In RTP scheme, consumer is informed about the pricing rates at hourly basis as the rates may change hourly. In ToU pricing scheme a consumer is charged least during off-peak, less during mid-peak and more during peak periods.
