Optimizing energy cost in the residential sector through home energy management systems in a smart grid environment
Worldwide energy demand is increasing exponentially, presenting significant challenges for existing power generation systems to meet this demand. Enhancing energy efficiency has become critical for reducing consumption and addressing the ongoing environmental crisis. Consequently, there is a need fo...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2025-07-01
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| Series: | Energy Exploration & Exploitation |
| Online Access: | https://doi.org/10.1177/01445987251325344 |
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| Summary: | Worldwide energy demand is increasing exponentially, presenting significant challenges for existing power generation systems to meet this demand. Enhancing energy efficiency has become critical for reducing consumption and addressing the ongoing environmental crisis. Consequently, there is a need for smart control systems that optimize system costs and improve efficiency. Because of the introduction of smart grids, customers can now participate in demand-side management and integrate renewable energy sources (RESs). Electricity consumption during peak hours often leads to increased grid demand and higher costs. However, the integration of RESs enables consumers to operate appliances during peak hours, thereby reducing reliance on grid power. Therefore, residential load management seeks to reduce power peaks and electrical energy costs. In home energy management systems (HEMS), appliance scheduling is crucial because it continually monitors appliance usage, ensuring that energy supply and demand are balanced. This research aims to optimize power usage by reducing peak loads and electricity costs through the integration of RESs, such as solar or photovoltaic (PV) systems, while considering grid limitations, PV capacity, appliance ON/OFF schedules, and time-of-use tariffs. A genetic algorithm (GA) based optimization technique was employed to evaluate the performance of a HEMS and validated with particle swarm optimization (PSO) technique under identical initial conditions for each appliance and their corresponding energy pricing over different periods. The results show that GA achieved a 48% cost reduction compared to PSO, with significant peak load reduction and improved energy optimization when integrated with PV systems. GA also demonstrated better appliance scheduling, with appliances in the “ON” state for 82% of the time, compared to 52% with PSO. |
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| ISSN: | 0144-5987 2048-4054 |