Abstract
Energy management systems in residential areas have attracted the attention of many researchers along the deployment of smart grids, smart cities, and smart homes. This paper presents the implementation of a Home Energy Management System (HEMS) based on the fuzzy logic controller. The objective of the proposed HEMS is to minimize electricity cost by managing the energy from the photovoltaic (PV) to supply home appliances in the grid-connected PV-battery system. A fuzzy logic controller is implemented on a low-cost embedded system to achieve the objective. The fuzzy logic controller is developed by the distributed approach where each home appliance has its own fuzzy logic controller. An automatic tuning of the fuzzy membership functions using the Genetic Algorithm is developed to improve performance. To exchange data between the controllers, wireless communication based on WiFi technology is adopted. The proposed configuration provides a simple effective technology that can be implemented in residential homes. The experimental results show that the proposed system achieves a fast processing time on a ten-second basis, which is fast enough for HEMS implementation. When tested under four different scenarios, the proposed fuzzy logic controller yields an average cost reduction of 10.933% compared to the system without a fuzzy logic controller. Furthermore, by tuning the fuzzy membership functions using the genetic algorithm, the average cost reduction increases to 12.493%.
Highlights
Home Energy Management Systems (HEMS) are used to manage energy consumption in the home, for example, by scheduling the load operation time, managing renewable energy resources, and providing battery storage [1]
To provide optimal energy consumption, a residential demand response (DR) program, consisting of the following attributes can be employed [1]: (a) real-time pricing, as electricity price varies over the day and month; (b) time of use pricing as the electricity price varies between peak and off-peak hours; (c) critical peak pricing as the electricity price changes within a short period; (d) direct load program as the electricity price can change at any time from the utility side; (e) curtailable program as the electricity price changes during emergencies; and (f) demand bidding where the electricity price depends on the customer’s bid
We only focus on the implementation of fuzzy logic controllers (FLCs) on the embedded system and examine the wireless communication issues of the WiFi module
Summary
Home Energy Management Systems (HEMS) are used to manage energy consumption in the home, for example, by scheduling the load operation time, managing renewable energy resources, and providing battery storage [1]. The DR method controls the load and energy resources using optimization techniques, such as the Genetic Algorithm (GA) and Mixed Integer Linear Programming (MILP). Typical HEMS consist of sensing devices (current, voltage, temperature, light sensors); measuring devices (electricity, gas, water meters); smart appliances (home appliances that can be monitored and controlled remotely); wireless communication (WiFi, Zigbee, Z-wave) and an energy management system (informative, automated, advanced function, integrated system) [3]. Mixed Integer Linear Programming (MILP) was employed to solve the optimization problem in the HEMS proposed in Ref. The authors in [8] proposed a HEMS algorithm to limit the power consumption of home appliances while keeping a comfortable level based on priority. The hardware demonstration of the HEMS was implemented on the embedded personal computer (PC), the load controller, and the ZigBee communication module [9]. The FLC is designed according to the particular condition, where the variables, membership functions, and the inference rules should be defined
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