1. AI Applications to Power Systems.
- Author
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Tjing Lie, Tek and Tjing Lie, Tek
- Subjects
History of engineering & technology ,Technology: general issues ,Markov decision process ,artificial neural networks ,automatic P2P energy trading ,cellular computational networks ,change detection ,contingency ,data analytics ,data mining ,deep Q-network ,deep reinforcement learning ,dynamic mode decomposition ,event detection ,feature extraction ,filtering ,fuzzy system ,inter-area oscillations ,load flow prediction ,long short-term delayed reward ,machine learning ,machine-learning ,manta ray foraging optimization algorithm ,modal analysis ,multi-objective function ,n/a ,optimal power flow ,optimization ,optimization techniques ,power quality ,radial networks ,reduced order modeling ,self-healing grid ,signal processing ,situation awareness ,steady-state security assessment ,total variation smoothing - Abstract
Summary: Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.