Distributed multi-energy systems, in addition to their advantages, pose significant challenges to future energy networks. One of these challenges is how these systems participate in energy markets. To overcome this issue, this paper introduces a virtual energy hub plant (VEHP) comprised of multiple energy hubs (EHs) to participate in the energy market in a cost-effective manner. Each EH is equipped with multiple distributed energy resources (DERs) in order to supply electrical, heating and cooling loads. Moreover, an integrated demand response (IDR) program and vehicle-to-grid (V2G) capable electric vehicles (EVs) are taken into consideration to enhance the flexibility to EHs. The manager of the VEHP participates in the existing dayahead markets on behalf of EHs after collecting their bids. Since EHs are independent entities, a hybrid model of mobile edge computing system and analytical target cascading theory (MEC-ATC) is proposed to preserve data privacy of EHs. Further, to tackle the uncertainty of renewables, a robust optimization method is applied. Obtained results corroborated the proposed scheduling is efficient and could increase the VEHP's profit about 21.4% in light of using flexible technologies.Index Terms-Virtual energy hub plant, mobile edge computing (MEC), analytical target cascading theory (ATC), combined heat and power (CHP) unit, uncertainty
This paper proposes a novel stochastic agent-based framework to predict the day-ahead charging demand of electric vehicles (EVs) considering key factors including the initial and final state of charge (SOC), the type of the day, traffic conditions, and weather conditions. The accurate forecast of EVs charging demand enables the proposed model to optimally determine the location of common prime urban parking lots (PLs) including residential, offices, food centers, shopping malls, and public parks. By incorporating both macro-level and micro-level parameters, the agents used in this framework provide significant benefits to all stakeholders, including EV owners, PL operators, PL aggregators, and distribution network operators. Further, the path tracing algorithm is employed to find the nearest PL for the EVs and the probabilistic method is applied to evaluate the uncertainties of driving patterns of EV drivers and the weather conditions.The simulation has been carried out in an agent-based modeling software called NETLOGO with the traffic and weather data of the city of Newcastle Upon Tyne, while the IEEE 33 bus system is mapped on the traffic map of the city. The findings reveal that the total charging demand of EVs is significantly higher on a sunny weekday than on a rainy weekday during peak hours, with an increase of over 150kW. Furthermore, on weekdays higher load demand could be seen during the night time as opposed to weekends where the load demand usually increases during the day time.
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