Specialized K-Means Framework for Identifying Characteristic Energy Community Operation Scenarios Considering Distributed Generation from PV, Residential Consumption, EV Charging, Real-time Prices, and Grid Carbon Intensity

Realistic time-series profiles are critically needed in investigation of novel technologies for energy communities. Residential electricity consumption profiles, solar irradiation related to photovoltaic (PV) panels, hourly dynamic retail electricity prices, EV charging sections information and unit grid carbon intensity information are provided by different organizations with data and file format, different resolution and in different databases. It is a challenging for the researcher to determine representative scenarios that cover different cases in long term operation .This study proposes a generic specialized K-means clustering method to determine characteristically different energy community daily operation scenarios combining long-term data of local generation from PV, residential electricity demand of multiple houses, charging demand of EV with different brand and models, carbon emission rates of grid electricity mix and hourly retail electricity prices. Exploring different clustering options up to 10 cluster and using elbow method to select the most suitable cluster number. Each considered daily time-series profile ranging from 1 month to 1 year are clustered under 3 to 5 clusters. Each cluster represents 2 to 68 precent of the whole dataset. Each identified cluster can be represented by a single profile that is closest to the average values of the cluster. The developed method reduces time and effort for the long-term analysis by reducing the number of scenarios to be explored, still ensuring consideration of wide range of operational cases in the field.

Kaynakça

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