In an energy grid, energy consumers are engaged with various types of energy supplying, trading, and storage components such as substations, batteries, windfarms and solar panels, micro-turbines, as well as demand response bids, to meet their respective demands and minimize the cost of energy commitment. To do so, the grid operator must determine how much energy each type of the resources should commit over a time frame, given the prices of soliciting different types of resources and the capacities and the physical characteristics of them.
This solution is built upon Cortana Intelligence Suite and external open-source tools, and it computes the optimal energy unit commitments from various types of energy resources. This solution demonstrates the ability of Cortana Intelligence Suite to accommodating external tools, to solve parallelized numerical optimization problems over an Azure Batch of Azure Virtual Machines.
Today, most facilities operate reactively to problems in tank levels. This often leads to spills, emergency shutdowns, expensive remediation costs, regulatory issues, costly repairs and fines. Tank level forecasting helps manage and abate these and other problems.