Abstract
This paper proposes a stochastic framework for residential energy management (REM) in a multi-carrier building. The proposed REM program minimizes the purchase cost of electricity and natural gas of a building, considering the operational constraints of energy dispatch and various building components such as micro-combined heat and power (CHP), heat storage system, heater, gas boiler, plug-in electric vehicles (PEVs), and renewable energy resources (RERs). A part of the electrical and thermal load of the building is assumed to be flexible in time and the amount of energy consumption. Uncertainties of renewable generation, the traveling time of PEVs, energy prices, and electricity/thermal loads are addressed in the program. Since the conventional stochastic methods are time-consuming or rely on asymptotic approximation, the proposed stochastic method in this paper has a low computational burden and a high precision level, which make it a suitable solution for large-scale stochastic programming problems. This method is examined on a test building for the stochastic day-ahead REM problem, and the results are compared with the other conventional methods to demonstrate the advantages of the proposed one.
Published Version
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