Applications of Artificial Intelligence in Wind and Solar Power for Electricity Production: Methodologies and Case Study
With the Key Targets for 2030 of at least 32% share of renewable energy, together with a cut of 40% in greenhouse gas emissions, EU is paving the way towards a massive increment of renewable energy generation systems. However, renewable energy sources (RES) solutions have to face some major challenges. For example, solar and wind, leading sources of renewable energy, are strongly dependent on the unpredictability of the weather; several storage facilities have been introduced, however, improved control and dispatch logics still have to be developed. In this context, the electric grid is evolving rapidly with the addition of variable renewable energy sources and many challenges have to be solve prior to a fruitful accommodation of different RES based technologies.
Artificial Intelligence (AI) and Machine Learning (ML) technologies can review and analyze the past events learning from them, optimize the present enabling quick event-response. Thus, a proper AI introduction in the renewable energy sector can help in solving most of the challenges, improving the overall reliability of renewable energy.
The aims of this thesis are: firstly, to perform a literature review on the applicability of artificial technologies in renewable energy (particularly focused on wind and solar systems), secondly to analyze and model a wind or solar based case study where AI can provide positive gains in terms of energy production, reliability and overall system optimization.
The main deliverables of the project include:
• Final project report and presentation comprising description of project, literature review on AI methodologies, introduction of possible different AI exploitation in RES and energy sector, case study identification, implementation of models, results analysis and final suggestions.
• Flexible models: model scripts and user guidelines / instructions.
The project should start in January 2020 the latest, and should not extend for more than 6 months.
Specific earlier starting date to be discussed.
Location: KTH - Energy Department.
Researcher, Division of Heat and Power Technology
Contact person, MSc, PhD Student, Heat and Power Division
MSc, Researcher, Heat and Power Division
Examiner, Professor and Head of Energy Department