Cloud-based optimal energy scheduling of photovoltaics and electric vehicle-integrated community microgrids

Abstract

Community microgrid is one of the promising pathways to achieve higher levels of penetration of distributed generation from intermittent renewables and energy storage, further electrify heat and transport and enable active energy customers. Optimal energy scheduling of wide range and large number of flexible asset, using operational information from stakeholders (such dynamic pricing rates) and relying on customer preferences has been a processing power intensive major challenge. The inconsistencies between the common assumptions, simplifications in modeling, scenario determination and observations gained from field pilots require design and investigation of improved models and scenarios that can better represent reality. This study presents a cloud-based optimal energy scheduling approach for community microgrids with large penetration of photovoltaics and electric vehicle chargers. A case study for an urban energy community is explored highlighting the promising flexibility potential of EV charging coordination of wide range of car models and charger options.