Kanakadhurga and Prabaharan (2024) researched smart home energy management using demand response, incorporating uncertainty analysis of electric vehicles in the presence of renewable energy sources.
Storage systems are essential for residential energy management because they store surplus energy generated by renewable sources during periods of availability and supply it during peak demand or when renewable sources are inactive.
Flattening demand peaks improves system reliability and supports the integration of Renewable Energy Sources (RES) by better aligning energy demand with their availability.
The study evaluates the impact of electric vehicle (EV) integration in residential demand-side management (RDSM) on the establishment of renewable energy sources (RES) and energy storage devices (ESD).
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.
The study results demonstrated that the prosumer model incorporating Renewable Energy Sources (RES) achieved the highest savings in both energy consumption and costs compared to conventional users and smart homes without RES.
In the REM framework's Case IV (Vehicle and Battery Interaction), electric vehicles interact with home battery storage systems to store excess renewable energy and discharge it during peak demand periods to optimize energy usage.
Implementing electric vehicles in residential Demand Side Management (DSM) improves grid efficiency, promotes Renewable Energy Source (RES) integration, generates cost savings, and supports sustainable transportation initiatives.
The integration of renewable energy sources (RES) with Advanced Metering Infrastructure (AMI) ensures efficient resource utilization, reduces reliance on grid energy, and promotes sustainability.
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.
Energy storage devices (ESD) store surplus energy from renewable sources or off-peak grid purchases, which is then dispatched during peak demand periods to reduce grid dependency.
Systematic scheduling of electric vehicle charging during off-peak hours and discharging during peak hours provides a solution for peak load management, reduces grid stress, and decreases the need for additional renewable energy source (RES) and energy storage device (ESD) integration.
The integration of renewable energy sources (RES) with Advanced Metering Infrastructure (AMI) promotes sustainability by ensuring efficient utilization of renewable resources and reducing reliance on grid energy.
The proposed residential demand-side management (RDSM) strategy incorporates electric vehicles (EVs), local renewable energy sources (RES), and energy storage devices (ESD) to improve energy utilization from economic, environmental, and operational perspectives.
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.
Optimizing residential load scheduling requires consideration of grid source availability, the cost of energy delivery, the utilization potential of renewable energy sources (RES) based on environmental conditions, the state of charge and energy delivery capability of storage systems, and the bidirectional energy flow from electric vehicles (EVs).
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.
Integrating electric vehicles (EVs), renewable energy sources (RES), and energy storage devices (ESDs) into residential load management provides operational benefits such as peak load reduction, the ability to meet unpredictable demand, and the facilitation of customers based on energy consumption priority.
The integration of renewable energy sources (RES) with Advanced Metering Infrastructure (AMI) promotes sustainability by ensuring efficient utilization of renewable resources and reducing reliance on grid energy.
The vision for implementing Demand Side Management (DSM) in the residential sector is to provide optimal energy scheduling to reduce electricity bills, minimize load, maximize the integration of renewable energy sources, and optimize energy consumption efficiency.
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).
Integrating electric vehicles (EVs) with renewable energy sources (RES), specifically solar and wind power, reduces the carbon footprint associated with EV charging.
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).
In Scenario 1 (No Renewable Energy Management or Renewable Energy Sources), the Beluga Whale Optimization Algorithm (BWOA) achieved 7.99% electricity cost savings compared to 4.70% savings achieved by the Salp Swarm Algorithm (SSA).
On-site photovoltaic (PV) systems are widely preferred for residential use due to their ease of access and implementation compared to other 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.
Utilizing electric vehicle batteries as alternative energy storage devices promotes environmental sustainability by enabling the integration of renewable energy sources and reducing dependency on fossil fuels.
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.
Babu, Balachandran, and Nwulu published 'Renewable Energy for Plug-In Electric Vehicles: Challenges, Approaches, and Solutions for Grid Integration' in 2024, focusing on the integration of renewable energy with plug-in electric vehicles.
A coordinated approach using electric vehicles, renewable energy sources (RES), and energy storage devices (ESD) improves reliability, security, uncertainty handling, and peak load management for both consumers and the grid.
Integrating electric vehicles, renewable energy sources, and energy storage devices into residential load management provides operational benefits such as peak load reduction, the ability to meet unpredictable demand, and the facilitation of customers based on energy consumption priority.
Implementing electric vehicles in residential Demand Side Management (DSM) improves grid efficiency, promotes Renewable Energy Source (RES) integration, enables cost savings, and supports sustainable transportation.
Kanakadhurga and Prabaharan examined smart home energy management using demand response while incorporating uncertainty analysis of electric vehicles in the presence of renewable energy sources to enhance decision-making frameworks for dynamic load control.
Electric vehicles can support grid stability, flexibility, and energy regulation alongside energy storage devices and renewable energy sources, particularly when consumers act as prosumers during periods of excess energy availability.
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.
The study on residential demand-side management considers the grid and renewable energy sources (RES) as primary energy sources, while energy storage devices (ESDs) and electric vehicles (EVs) are considered secondary sources, factoring in operational and economic constraints.
Nagarajan et al. developed an enhanced cheetah-inspired algorithm for optimizing dynamic economic dispatch in integrated renewable energy and demand-side management systems, contributing to advancements in computational efficiency for large-scale energy distribution networks.
Nagarajan et al. proposed an Enhanced Wombat Optimization Algorithm (EWOA) to solve the multi-objective optimal power flow problem in systems integrated with renewable energy and electric vehicles, aiming to optimize operational cost and grid stability.
In Scenario 2 (With Renewable Energy Management, no Renewable Energy Sources), the Beluga Whale Optimization Algorithm (BWOA) achieved 16.26% electricity cost savings compared to 13.56% savings achieved by the Salp Swarm Algorithm (SSA).
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 research paper 'An optimized home energy management system with integrated renewable energy and storage resources' was published in Energies 10(4), 549 in 2017.
The study results demonstrated that the Smart Scheduler (SS) achieved significant reductions in energy consumption and costs across all scenarios, with the highest savings observed in the prosumer model incorporating Renewable Energy Sources (RES).
Integrating Renewable Energy Sources (RES) like solar or wind, Energy Storage Devices (ESD), and Electric Vehicles (EVs) into residential load scenarios requires adequate infrastructure and synchronized, balanced, and stable grid operation.
Approaches to handling energy management issues at the architecture and planning stage include analyzing the load profiles of each appliance, the integration capacity and output of renewable energy sources, load arrangement based on basic characteristics, day-ahead dynamic electricity tariffs, and consumer categorization based on usage patterns.
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.
The study evaluates the impact of electric vehicle (EV) integration in residential demand-side management (RDSM) on the establishment of renewable energy sources (RES) and energy storage devices (ESD).
Nagarajan et al. (2025) introduced an enhanced Wombat optimization algorithm for multi-objective optimal power flow in systems integrated with renewable energy and electric vehicles in Results in Engineering.
Nagarajan et al. proposed the Enhanced Wombat Optimization Algorithm (EWOA) to address the multi-objective optimal power flow problem in systems integrating renewable energy and electric vehicles, aiming to optimize operational costs and grid stability.
Integrating storage systems alongside renewable energy sources (RES) enhances usability by storing surplus energy generated during periods of availability and supplying it during peak demand or when RES are inactive.
The study on smart residential demand side management considers two primary energy sources (the electrical grid and renewable energy sources) and two secondary sources (energy storage devices and electric vehicles) while factoring in operational and economic constraints.
The paper 'Comprehensive framework for smart residential demand side management' is organized into sections covering a literature review of research gaps, mathematical modeling of RDSM (including EVs, RES, and ESD), the proposed BWOA approach for optimal scheduling, an optimization model, result analysis, and future research directions.
In a vehicle-to-home (V2H) and battery interaction scenario, an electric car can charge from a home battery system between 1 am and 3 am at a rate of 3.0 kW when excess renewable energy is available.
Electric vehicle participation in residential demand-side management (RDSM) combined with renewable energy sources and other energy storage device integration can help regulate consumer behavior regarding load consumption and address uncertainties in load consumption for emergency load sharing.
The integration of electric vehicles with renewable energy sources, such as solar and wind power, reduces the carbon footprint associated with electric vehicle charging.
Nagarajan et al. introduced an enhanced Wombat optimization algorithm for multi-objective optimal power flow in systems integrated with renewable energy and electric vehicles, published in Results in Engineering in 2025.
Integrating electric vehicles, renewable energy sources, and energy storage devices into residential load management provides operational benefits such as peak load reduction, the ability to meet unpredictable demand, and the prioritization of energy consumption for customers.
Residential Demand Side Management (RDSM) aims to provide optimal energy scheduling to reduce electricity bills, minimize load, maximize the integration of renewable energy sources, and optimize energy consumption efficiency.
Integrating Renewable Energy Sources (RES) like solar or wind, Energy Storage Devices (ESD), and Electric Vehicles (EVs) into residential load scenarios requires adequate infrastructure and synchronized, balanced, and stable grid operation.
Nagarajan et al. proposed an Enhanced Wombat Optimization Algorithm (EWOA) to solve the multi-objective optimal power flow problem in systems integrated with renewable energy and electric vehicles, aiming to optimize operational costs and grid stability.
The optimization model aims to minimize reliance on grid utility energy during peak pricing periods and maximize the use of renewable energy whenever it is available.
Babu et al. (2024) analyzed challenges, approaches, and solutions for integrating renewable energy with plug-in electric vehicles.
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.
Kanakadhurga and Prabaharan (2024) developed a smart home energy management system using demand response and uncertainty analysis of electric vehicles in the presence of renewable energy sources, published in Applied Energy.
Smart scheduling of electric vehicle charging and discharging activities in residential settings can reduce energy costs, optimize grid load, and improve the utilization of renewable energy sources.
Integrating electric vehicles (EVs) with renewable energy sources (RES), specifically solar and wind power, reduces the carbon footprint associated with EV charging.
On-site photovoltaic (PV) systems are widely preferred for residential use due to their ease of access and implementation compared to other renewable energy sources.
Kanakadhurga and Prabaharan examined smart home energy management using demand response and uncertainty analysis of electric vehicles in the presence of renewable energy sources to improve decision-making frameworks for dynamic load control.
Optimizing residential load scheduling requires consideration of grid source availability, the cost of energy delivery, the utilization potential of renewable energy sources based on environmental conditions, the state of charge of storage systems, and the bidirectional energy flow from electric vehicles.
Flattening demand peaks improves system reliability and supports the integration of Renewable Energy Sources (RES) by aligning energy demand with their availability.
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.
Electric Vehicle (EV) participation in Residential Demand Side Management (RDSM) combined with Renewable Energy Sources (RES) and Energy Storage Devices (ESD) can regulate random consumer behavior and mitigate uncertainties in load consumption through emergency load sharing assessments.
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.
Electric vehicles in residential demand side management can function as loads, storage devices, or mutually supportive devices alongside renewable energy sources and energy storage devices.
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.
The proposed optimization approach in the study 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.
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.
Prosumers reduce their reliance on grid energy by following this procedure: (1) utilize stored Renewable Energy Sources during peak periods, and (2) schedule energy-intensive tasks during off-peak hours.
Nagarajan et al. developed an enhanced cheetah-inspired algorithm for optimizing dynamic economic dispatch in integrated renewable energy and demand-side management systems, contributing to advancements in computational efficiency for large-scale energy distribution networks.
Residential Demand Side Management (RDSM) has become an active area of interest to address the exponential rise of energy demand in the context of microgrids that integrate Renewable Energy Sources (RES), Energy Storage Devices (ESD), and electric vehicles.
Prosumers reduce reliance on grid energy by utilizing stored Renewable Energy Sources (RES) during peak periods and scheduling energy-intensive tasks during off-peak hours.
Scenario-III in the study integrates Residential Energy Management (REM) systems with Renewable Energy Sources (RES) to achieve further energy optimization.
Electric vehicles can support grid stability, flexibility, and overall energy regulation when integrated with other energy storage devices and renewable energy sources, particularly when consumers act as prosumers during periods of excess energy availability.
Khan et al. (2024) developed an enhanced Cheetah-inspired algorithm to optimize dynamic economic dispatch for integrated renewable energy and demand-side management systems.
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.
Nagarajan et al. (2025) developed an enhanced Wombat optimization algorithm for multi-objective optimal power flow in systems integrating renewable energy and electric vehicles.
The participation of electric vehicles in residential demand-side management (RDSM) helps balance supply and demand, improves grid resilience, and facilitates the integration of renewable energy sources.
Utilizing Electric Vehicle (EV) batteries as energy storage devices promotes environmental sustainability by enabling the integration of renewable energy sources (RES), reducing dependency on fossil fuels, and mitigating greenhouse gas emissions.
Integrating Renewable Energy Sources (RES) like solar or wind, Energy Storage Devices (ESD), and Electric Vehicles (EVs) into residential load scenarios requires adequate infrastructure and synchronized, balanced, and stable grid operation.
The research paper 'A comprehensive review on demand side management and market design for renewable energy support and integration' was published in Energy Rep. 10, 2228–2250 in 2023.
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.
The smart scheduler application described in the REM framework flattens demand peaks and distributes load by integrating Renewable Energy Sources (RES), electric vehicles, and energy management strategies, leading to a more sustainable and cost-effective energy ecosystem for residential prosumers.
Renewable energy sources are prioritized over other energy sources in residential load management whenever they are available due to their renewable nature.
Researchers suggest addressing architecture and planning issues in Residential Demand Side Management (DSM) by considering factors such as appliance load profiles, renewable energy source (RES) integration capacity and output, load arrangement characteristics, day-ahead dynamic electricity tariffs, and consumer categorization based on usage patterns.
Intelligent prosumers are residential energy consumers who possess all the capabilities of intelligent users but also have the ability to supply excess energy back to the utility grid using on-site renewable energy sources, energy storage systems, and bidirectional energy flow technologies.
The study proposes an optimal and smart scheduling strategy for the residential load sector by incorporating electric vehicles into the residential demand-side management (RDSM) concept alongside local renewable energy sources (RES) and energy storage devices (ESD).
Kanakadhurga and Prabaharan published research in Applied Energy in 2024 on smart home energy management using demand response, incorporating uncertainty analysis of electric vehicles in the presence of renewable energy sources.
Kanakadhurga and Prabaharan examined smart home energy management using demand response and uncertainty analysis of electric vehicles in the presence of renewable energy sources to improve decision-making frameworks for dynamic load control.
The study examines the impact of electric vehicle (EV) integration in residential demand-side management (RDSM) on the establishment of renewable energy sources (RES) and energy storage devices (ESD).
In Scenario 3 (With Renewable Energy Management and Renewable Energy Sources), the Beluga Whale Optimization Algorithm (BWOA) achieved 25.29% electricity cost savings compared to 16.82% savings achieved by the Salp Swarm Algorithm (SSA).
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.
The study results demonstrated significant reductions in energy consumption and costs across all three scenarios, with the highest savings observed in the prosumer model that incorporates Renewable Energy Sources (RES).
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.
Nagarajan et al. developed an enhanced cheetah-inspired algorithm for optimizing dynamic economic dispatch in integrated renewable energy and demand-side management systems, contributing to advancements in computational efficiency for large-scale energy distribution networks.
In the study 'Comprehensive framework for smart residential demand side', the grid and renewable energy sources (RES) are classified as primary energy sources, while energy storage devices (ESDs) and electric vehicles (EVs) are classified as secondary energy sources for residential load management.
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).
Javaid, N. et al. published 'An intelligent load management system with renewable energy integration for smart homes' in IEEE Access, volume 5, pages 13587–13600, in 2017.
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.
Flattening demand peaks improves system reliability and supports the integration of Renewable Energy Sources (RES) by aligning energy demand with their availability.
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.
Electric Vehicle (EV) participation in Residential Demand Side Management (RDSM) provides grid support services, including load shifting, peak shaving, and emergency power supply, which helps balance supply and demand, improves grid resilience, and integrates renewable energy sources.
Integrating storage systems alongside renewable energy sources is essential to ensure a reliable and efficient energy management system by storing surplus energy for use during peak demand or when renewable sources are inactive.
Babu et al. (2024) examined challenges, approaches, and solutions for integrating renewable energy with plug-in electric vehicles for grid integration.
Smart scheduling of electric vehicle charging and discharging activities allows households to reduce energy costs, optimize grid load, and effectively utilize 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.
Systematic scheduling of electric vehicle charging during off-peak hours and discharging during peak hours provides a solution for peak load management, reduces grid stress, and decreases the need for additional renewable energy sources (RES) and energy storage devices (ESD) integration during peak demand.
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.