Abstract
As an integral part of multi-energy systems, the energy hub acts a major task in developing the flexibility, efficiency, and reliability. Due to the increasing progress of science and human communities and the rise in air pollutants and Earth temperature, the need for renewable energies and electric vehicles has increased. The only challenge to the use of new energies is the uncertainty in their production due to the lack of continuous solar irradiation and wind in different hours of the day. Accordingly, this paper addresses an optimal load dispatch form for an energy hub to decrease the total costs of the energy hub, such as exploitation costs and CO2 emission costs. This energy hub includes a heat storage unit, combined heat and power (CHP) unit, photovoltaic (PV) arrays, gas boiler, wind turbine (WT), and electric vehicles (EV). EV uncertainty is modeled via Monte Carlo simulation and a developed algorithm based on grasshopper search is adopted for dealing with future uncertainties in electricity price. Moreover, the proposed model considers the electric and thermal demand response (DR) methods comprehensively. Herein, three scheduling scenarios are discussed with different charge/discharge and DR settings. The numerical and graphical results demonstrate that, by choosing a coordinate charge/discharge mode for the EVs, the final costs are successfully reduced. Compared to Scenario 1, the total costs of Scenario 2 are reduced by 12%. Consequently, it can be obvious that the EVs’ matched charge/discharge is successfully decreased the energy costs for the consumers. Compared to Scenario 2, the total costs of Scenario 3 are decreased by 5.76%. The results also indicate that by implementing the DR programs, total consumer costs can be further decreased.
Published Version
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