Economics of Power Stations using Data Science

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Price: 59.99$

What is the course about: This course teaches economics about the most important part of Electricity systems: Power Stations, also known as electricity generation units, or simply units. We begin with an in-depth presentation of the Theory of Power Station technologies going through Hydro Electric power stations, which we also model on Python, and also wind farms – and we compare offshore versus onshore farms in terms of investment – and also tidal/geothermal / biomass units as well as we model fundamental techno economics of wind farms such as the development of wind patterns using Python. We also discuss, in-depth, the technical characteristics of power stations, such as capacity factor, ramp rate, efficiency, minimum stable generation, installed capacity accounting for transmission and distribution losses, dispatchability and flexibility among others. We move on to develop a Python executable file, from scratch, which models the operation of electricity generators and show how they dynamically affect the wholesale electricity price. We can use this application for studying the interaction between wholesale electricity price, merit order and marginal generation costs, which we define and view in practice, using Python. We then proceed with the Economics of Power Stations., starting with fundamental costs, such as Capital Costs, and Levelized Cost of Electricity for different electricity generation types; we develop the LCOE, and we plot it and explain it. We proceed to the Revenue, and specifically – subsidies for electricity generation units. We analyse contracts for difference, and the Renewables Obligation scheme – we build the model from scratch in Excel and Python. We also use Pyomo and perform optimization to determine the optimal strategy of power stations in spot electricity markets and wholesale electricity markets with the objective being to maximize the revenue. Finally, we learn about how to perform Data Analysis on all possible structures of datasets used for Power Stations and generally electricity generation. Who: Senior Research Scientists, part of high-tech projects at the intersection of Academia & Industry.  Doctor of Philosophy (Ph. D.) in Analytics & Mathematical Optimization applied to Energy Investments, from Imperial College London, and Masters of Engineering (M. Eng.) in Power Systems and Economics. Acknowledgements: To Himalaya Bir Shrestha who has been contributing to the development of Python scripts for this course and to Medium with insightful posts. Important: We start from scratch so that you do not need to have done any preparatory work in advance at all.  Just follow what is shown on screen.