Optimization of Energy Storage: Reducing the Peak Demand
Optimization of Energy Storage: Reducing the Peak Demand, Apply Mathematical Optimization and make Energy Storage reduce the peak load of any electricity grid. A Real World Case.
Why you should buy this course:
Because Electricity Grids suffer from high demand periods and so far the only solution has been to make very expensive investments to upgrade the grids.
You will learn how this problem is addressed through a Real-World complete study! We conduct excessive sensitivity analysis, write software and go through all necessary theoretical background.
What is the main idea of the course:
Peak demand refers to periods when the demand for electricity is at its highest.
Very high electricity demand is a big challenge because it puts much stress on electricity grids.
Energy storage is a new and promising technology that can offer a solution to this challenge.
Specifically, an Energy Storage unit is deployed close to the consumers that exhibit the peak demand.
Then, this unit is operated by the electricity grid operator according to Software.
This Software runs an optimization model.
This optimization model ensures that the Energy Storage unit will reduce the Peak Demand by storing excess electricity during low-demand periods and releasing it during peak times.
We write the software, analyse it, run it, and then conduct an excessive sensitivity analysis.
Employment prospects:
You can start your own company in the field of Energy Storage, or
work for a company related to Energy Storage.
You can google search and see how strong the career prospects in this area are.
Instructor support:
Contact the Instructor via Whatsapp, LinkedIn, e-mail, Private Messages here on Udemy, or Live Chat. All these capabilities are available on the website www energy-complete com (remove the white spaces and place dots).
Prerequisites:
No prerequisites because we learn everything needed and gradually build the entire case.
The Instructor is an Independent Researcher with:
— a PhD in Optimization & Data Science applied to Energy, from Imperial College London.
— an MEng in Energy Economics.
— More than 10 years of Academic and Industry Research experience in Energy
— Extensive experience supervising students in real-life (academia) and online.
— CEO of Energy-Complete which shows how to apply Machine Learning, Optimization, Finance, and Economics in Energy. Visit www energy-complete com (remove the white spaces and place dots).
