Optimal resource planning of smart home energy system under dynamic pricing based on invasive weed optimization algorithm

This study deals with optimal resources planning of energy resources in smart home energy system (SHES), where PV (Photovoltaic) panel, battery and electrical heater (EH) are employed to meet all electrical and thermal demands. The advantage of this configuration is avoiding the use of two different energy carriers including electricity and natural gas in downstream users, in addition, reduction of SHES operation cost. An energy management controller (EMC) based on invasive weed optimization (IWO) algorithm is designed in order to optimize the energy routing and scheduling of different resources to obtain an efficient look-up table, by which the power generation of each energy resource is determined for all hourly time intervals. Different electricity tariffs and all operational constraints of energy resources are considered. Several scenarios are defined and the impacts of system components on SHES operation cost are investigated. Moreover, feasibility study of the proposed SHES based on different electricity and gas prices is conducted. It is observed that the proposed optimal scheduling based on IWO algorithm outperforms other approaches. Furthermore, it is shown that self-discharge rate of the battery, affects the system operational performance, especially time of charging and discharging of the battery.