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• To simplify the modeling process, the residential energy management framework represents the power grid and renewable energy sources as a unified node to facilitate efficient formulation and maintain accuracy in energy distribution analysis.
• In Scenario 2 (With REM, no RES), the Beluga Whale Optimization Algorithm (BWOA) achieves 16.26% electricity cost savings compared to the Salp Swarm Algorithm's (SSA) 13.56% savings.
• The proposed residential energy management framework integrates electric vehicles (EVs), renewable energy sources (RES), and energy storage devices (ESD) to serve as a testbed for analyzing various energy scenarios.
• The study published in Nature (https://www.nature.com/articles/s41598-025-93817-5) models energy consumption patterns under three conditions: conventional residential users without Residential Energy Management (REM), smart homes using REM systems, and prosumers integrating REM with Renewable Energy Sources (RES).
• The residential energy management framework utilizes grid energy as a supply source when renewable energy sources and other alternatives are insufficient, while minimizing reliance on the grid during peak pricing periods.
• The residential energy management strategy reduces overall energy costs, enhances grid stability by flattening peak loads, and increases system reliability by integrating renewable energy sources, energy storage devices, and electric vehicles.
• To simplify modeling, the residential energy management framework represents the power grid and renewable energy sources as a unified node to facilitate efficient formulation and energy distribution analysis.
• In Scenario 3 (With REM and RES), the Beluga Whale Optimization Algorithm (BWOA) achieves 25.29% electricity cost savings compared to the Salp Swarm Algorithm's (SSA) 16.82% savings.
• Renewable energy sources (RES), such as solar or wind, are prioritized in the residential energy management framework for their sustainability and cost-effectiveness.
• The study models energy consumption patterns under three conditions: conventional residential users, smart homes utilizing Residential Energy Management (REM) systems, and prosumers who integrate REM with Renewable Energy Sources (RES).
• The study analyzed three scenarios for residential energy management: (1) conventional users without Residential Energy Management (REM), (2) smart homes implementing REM, and (3) prosumers integrating REM with Renewable Energy Sources (RES).
• The proposed residential energy management framework integrates electric vehicles (EVs), renewable energy sources (RES), and energy storage devices (ESD) to serve as a testbed for analyzing various energy scenarios.
• The study evaluated energy and cost savings for residential users through optimized load scheduling by analyzing three scenarios: (1) conventional users without Residential Energy Management (REM), (2) smart homes implementing REM, and (3) prosumers integrating REM with Renewable Energy Sources (RES).
• In the residential energy management framework, grid energy supplies electricity when renewable energy sources and other alternatives are insufficient, with usage minimized during peak pricing periods.
• During periods of high grid energy prices, residential energy management systems prioritize the use of renewable energy sources (RES), energy storage devices (ESDs), and electric vehicles (EVs) to achieve cost-efficient utilization.
• In Scenario 1 (No REM or RES), the Beluga Whale Optimization Algorithm (BWOA) achieves 7.99% electricity cost savings compared to the Salp Swarm Algorithm's (SSA) 4.70% savings.
• Renewable energy sources, such as solar or wind, are prioritized in the residential energy management framework for their sustainability and cost-effectiveness.
• Energy storage devices (ESD) in the residential energy management framework store surplus energy from renewable sources or energy purchased during off-peak hours, which is then dispatched during peak demand periods to reduce grid dependency.
• The study models and simulates energy consumption patterns under three conditions: conventional residential users without Residential Energy Management (REM), smart homes utilizing REM systems, and prosumers integrating REM with Renewable Energy Sources (RES), all using Time-of-Use (ToU) based tariffs.
• The smart scheduler application flattens demand peaks and distributes load by strategically scheduling appliance operation and integrating electric vehicles, renewable energy sources, and residential energy management.
• Comparative analyses with existing literature validate that the proposed Residential Energy Management (REM) and Renewable Energy Sources (RES) approaches deliver improvements in cost efficiency, grid stability, and energy management.
• Renewable energy sources, such as solar or wind, are prioritized in the residential energy management framework for their sustainability and cost-effectiveness.
• The study evaluated three residential energy scenarios: (1) conventional users without Residential Energy Management (REM), (2) smart homes implementing REM, and (3) prosumers integrating REM with Renewable Energy Sources (RES).
• The integration of Renewable Energy Sources (RES) into Residential Energy Management (REM) systems enhances the ability to flatten demand peaks, improving overall grid efficiency and resilience.
• The proposed optimization approach for residential energy management aims to balance direct consumption, storage charging, and grid energy usage by ensuring efficient utilization of renewable energy, adequate charging of storage devices, and redistribution of energy demand during high-demand hours.
• To simplify modeling, the residential energy management framework represents the power grid and renewable energy sources as a unified node to facilitate efficient formulation and analysis.
• The proposed residential energy management framework integrates electric vehicles (EVs), renewable energy sources (RES), and energy storage devices (ESD) to analyze various operational scenarios and validate optimization approaches for energy efficiency and cost reduction.
• During periods of high grid energy prices, residential energy management systems prioritize energy from renewable energy sources, energy storage devices, and electric vehicles to ensure cost-efficient utilization.
• Scenario-III in the study integrates Residential Energy Management (REM) systems with Renewable Energy Sources (RES) to achieve further energy optimization.
• Integrating Residential Energy Management (REM) systems with on-site Renewable Energy Sources (RES), such as photovoltaic systems, and leveraging differential pricing mechanisms allows prosumers to reduce electricity costs while contributing to grid stability.
• In the residential energy management framework, grid energy serves as a supply source when renewable energy sources and other alternatives are insufficient, with usage minimized during peak pricing periods.
• By combining Residential Energy Management (REM) architectures with on-site Renewable Energy Sources (RES), such as photovoltaic (PV) systems, and leveraging differential pricing mechanisms, prosumers can reduce electricity costs while contributing to grid stability and sustainability.
• In Scenario 2 (With Residential Energy Management, no Renewable Energy Sources), the Beluga Whale Optimization Algorithm (BWOA) achieved 16.26% savings compared to the Salp Swarm Algorithm's (SSA) 13.56% savings.
• A Residential Energy Management System (REMS) is a framework developed to optimize the scheduling of electrical appliances in a household, utilizing three energy sources: the grid, renewable energy sources, and storage devices.
• During periods of high grid energy prices, residential energy management systems prioritize the use of renewable energy sources (RES), energy storage devices (ESDs), and electric vehicles (EVs) to reduce reliance on the grid and minimize costs.
• A Residential Energy Management System (REMS) is designed to optimize the scheduling of electrical appliances in a household by utilizing three energy sources: the electrical grid, renewable energy sources (RES), and energy storage devices (ESDs).
• The integration of Residential Energy Management (REM) systems with Renewable Energy Sources (RES), such as photovoltaic (PV) systems, allows prosumers to reduce electricity costs and contribute to grid stability by leveraging differential pricing mechanisms.
• Smart scheduling of electric vehicle charging and discharging activities in residential energy management frameworks can reduce energy costs, optimize grid load, and improve the utilization of renewable energy sources.
• In Scenario 1 (No Residential Energy Management or Renewable Energy Sources), the Beluga Whale Optimization Algorithm (BWOA) achieved 7.99% savings compared to the Salp Swarm Algorithm's (SSA) 4.70% savings.
• In Scenario 3 (With Residential Energy Management and Renewable Energy Sources), the Beluga Whale Optimization Algorithm (BWOA) achieved 25.29% savings compared to the Salp Swarm Algorithm's (SSA) 16.82% savings.