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Power Plant Operation Optimization: Unit Commitment of Combined Cycle Power Plants Using Machine Learning and MILP
mohamed-ahmed@siemens.com.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In modern days electric power systems, the penetration of renewable resources and the introduction of free market principles have led to new challenges facing the power producers and regulators. Renewable production is intermittent which leads to fluctuations in the grid and requires more control for regulators, and the free market principle raises the challenge for power plant producers to operate their plants in the most profitable way given the fluctuating prices. Those problems are addressed in the literature as the Economic Dispatch, and they have been discussed from both regulator and producer view points. Combined Cycle Power plants have the privileges of being dispatchable very fast and with low cost which put them as a primary solution to power disturbance in grid, this fast dispatch-ability also allows them to exploit price changes very efficiently to maximize their profit, and this sheds the light on the importance of prices forecasting as an input for the profit optimization of power plants. In this project, an integrated solution is introduced to optimize the dispatch of combined cycle power plants that are bidding for electricity markets, the solution is composed of two models, the forecasting model and the optimization model. The forecasting model is flexible enough to forecast electricity and fuel prices for different markets and with different forecasting horizons. Machine learning algorithms were used to build and validate the model, and data from different countries were used to test the model. The optimization model incorporates the forecasting model outputs as inputs parameters, and uses other parameters and constraints from the operating conditions of the power plant as well as the market in which the plant is selling. The power plant in this mode is assumed to satisfy different demands, each of these demands have corresponding electricity price and cost of energy not served. The model decides which units to be dispatched at each time stamp to give out the maximum profit given all these constraints, it also decides whether to satisfy all the demands or not producing part of each of them.

Place, publisher, year, edition, pages
2019. , p. 72
Series
TVE-MFE ; 19009
Keywords [en]
Unit Commitment, Economic Dispatch, Forecasting, Machine Learning, Mixed Integer Linear Programming, Optimization, Electricity prices, Electricity markets, Time series
National Category
Energy Engineering Engineering and Technology Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:uu:diva-395304OAI: oai:DiVA.org:uu-395304DiVA, id: diva2:1361827
External cooperation
Siemens Industrial Turbomachinery
Educational program
Master Programme in Renewable Electricity Production
Presentation
(English)
Supervisors
Examiners
Available from: 2019-10-30 Created: 2019-10-17 Last updated: 2019-11-25Bibliographically approved

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Energy EngineeringEngineering and TechnologyOther Electrical Engineering, Electronic Engineering, Information Engineering

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CiteExportLink to record
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Citation style
  • apa
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Language
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Output format
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