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This article presents a predictive home energy management system (HEMS) for a residential building with integration of a plug-in electric vehicle (PEV), a photovoltaic array, and a heat pump. A stochastic model predictive control (MPC) strategy is applied in the HEMS in order to minimize the home's electricity cost and reduce the PEV battery degradation cost. Moreover, the MPC ensures that home load demand, PEV battery charging requirements, and household thermal comfort conditions are met. The MPC operates in real-time and thus minimizes the effects of gap between the forecasted and real data on the HEMS performance by updating its control decisions and the forecast data as the stochastic parameters are realized in each time step. The obtained simulation results demonstrate that the proposed control strategy reaches 96% to 97% of ideal performance achieved by off-line optimization counterpart with perfect data.
- Home energy management system (HEMS)
- optimal plug-in electric vehicle (PEV) charging/discharging
- photovoltaic array
- stochastic model predictive control (MPC)
- user thermal comfort
Hajizadeh, A., N. Soltani, M., Hredzak, B. & Kianpoor, N., Nov 2020, In : Applied Energy. 277, 13 p., 115618.
Research output: Contribution to journal › Journal article › Research › peer-review