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Simulation-based methodology for optimizing Energy Community Controllers
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DOI：10.1109/ISIE45552.2021.9576277.png)
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Abstract
En 中文
In recent years, the trend to provide more and more energy from renewable sources and less from conventional forms of electricity production reduces worldwide carbon emissions. In general, this is welcome but it also introduces new challenges. The diversity of electrical energy production grows and puts stress on the grid, its operators, and energy distribution planning. New ways for compensating the introduced instabilities are needed. Energy communities can address distributed production issues by adjusting the local demand as good as locally possible. This paper presents a methodology for simulating an energy community controller to test mechanism for smoothing grid load by aggregating available flexibilities of energy community members on a communal level. The controller tries to minimize load from or to the community by using different flexibilities and prediction algorithms within the community.
Keywords:
Energy management
Smart Grids
Batteries
Photovoltaic systems
Machine learning
Prediction methods
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