In an energy grid, energy consumers are engaged with various types of energy supplying, trading and storage components such as substations, batteries, wind farms, solar panels, micro-turbines and demand response bids to meet their respective demands and minimise the cost of energy commitment. To achieve this, the grid operator must determine how much energy each type of resource should commit over a given time frame, given the prices of soliciting different types of resources and the capacities and physical characteristics of each of them.
This solution is built on 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 accommodate external tools in order to solve parallelised numerical optimisation 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.