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Despite the increasing utilization of renewable energy resources, such as solar and wind energy, most residential buildings still rely on conventional energy supply by public utility services. Such utility services often use time-of-use energy pricing, which compels residential consumers to reduce their energy usage. This paper presents a wireless home energy management (HEM) system that enables the automatic control of home appliances to reduce energy consumption to assist such energy users. The system consists of multiple smart sockets that measure the energy that is consumed by the connected appliances and are capable of implementing on/off commands. The system includes other support components for supplying data to a central controller, which utilizes a rule-based HEM algorithm. The control rules were designed, such that the lifestyle of the user would be preserved while the energy consumption and daily energy cost were reduced. The experimental results showed that the central controller could effectively receive data and control multiple devices. The system was also found to afford significant reductions of 23.5 kWh and $2.898 in the total daily energy consumption and bill of the considered household setup, respectively. The proposed HEM system promises to be particularly useful for households with a high daily energy consumption.

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Solar energy is a source of free, clean energy which avoids the destructive effects on the environment that have long been caused by power generation. Solar energy technology rivals fossil fuels, and its development has increased recently. Photovoltaic (PV) solar farms can only produce active power during the day, while at night, they are completely idle. At the same time, though, active power should be supported by reactive power. Reactive power compensation in power systems improves power quality and stability. The use during the night of a PV solar farm inverter as a static synchronous compensator (or PV-STATCOM device) has recently been proposed which can improve system performance and increase the utility of a PV solar farm. In this paper, a method for optimal PV-STATCOM placement and sizing is proposed using empirical data. Considering the objectives of power loss and cost minimization as well as voltage improvement, two sub-problems of placement and sizing, respectively, are solved by a power loss index and adaptive particle swarm optimization (APSO). Test results show that APSO not only performs better in finding optimal solutions but also converges faster compared with bee colony optimization (BCO) and lightening search algorithm (LSA). Installation of a PV solar farm, STATCOM, and PV-STATCOM in a system are each evaluated in terms of efficiency and cost.