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.
In the REM framework, high-power-rated electric cars transfer energy to smaller vehicles like electric scooters and bicycles to enable quick charging without drawing additional power from the grid during peak times.
Integrating electric vehicles into residential demand-side management (RDSM) improves grid stability, minimizes the need for additional generation capacity, and maximizes the utilization of existing resources through load shifting and optimization.
The Binary Whale Optimization Algorithm (BWOA) is proposed as an efficient algorithm for scheduling electric vehicle energy utilization within residential demand side management.
Residential load modelling categorizes appliances into two groups: Flexible/Interruptible Loads (I), such as washing machines, cloth dryers, electric vehicles, and water heaters; and Base/Essential Loads (B), such as refrigerators and lighting.
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).
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.
The integration of electric vehicles requires modern infrastructure planning, including communication technology, sensors, IoT, measurement instruments, and bidirectional power electronics devices to accommodate increased charging demand and support demand-side management (DSM) strategies.
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.
Strategic planning of Electric Vehicle (EV) charging via Demand Side Management (DSM) techniques enhances grid stability by preventing sudden demand spikes, thereby reducing the risk of brownouts or blackouts in areas with high EV adoption rates.
The bidirectional power flow mode of electric vehicle operation, involving both charge and discharge scheduling, provides greater flexibility for regulating energy consumption in the residential sector and beyond.
Singh et al. conducted a review of electric vehicle charging technologies, infrastructure expansion, and grid integration strategies, providing an outlook on the role of electric vehicles in sustainable e-mobility.
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 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 exponential deployment of electric vehicles in residential sectors enables improved energy utilization at both decentralized and centralized distribution levels due to their bidirectional operation and energy storage capabilities.
N. Dharavat et al. utilized the political optimization algorithm to determine the optimal allocation of renewable distributed generators and electric vehicles in a distribution system in 2022.
Electric vehicles function as energy consumption devices during peak periods and energy storage devices during off-peak periods in residential load scenarios.
Electric Vehicles (EVs) participating in Residential Demand Side Management (RDSM) require supportive regulatory frameworks, clear standards, market mechanisms, and financial incentives to facilitate effective energy management and grid modernization efforts.
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 participation of electric vehicles in residential demand-side management (RDSM) as energy storage systems requires supportive regulatory frameworks, clear standards, market mechanisms, and financial incentives to facilitate effective energy management and grid modernization.
Electric vehicles (EVs) function as mobile storage units within the residential energy management framework, utilizing bidirectional energy flow to absorb and supply energy, thereby enhancing system flexibility and resilience.
Liao, W. et al. conducted a comparative study of demand-side energy management strategies for building-integrated photovoltaics-battery systems and electric vehicles in diversified building communities, published in Applied Energy, volume 361, in 2024.
The integration of electric vehicles (EVs) into Residential Demand Side Management (RDSM) reduces the stress on energy storage devices.
In the REM framework's Case III (Vehicle to Vehicle), high-power electric cars transfer energy to smaller electric vehicles like scooters and bicycles to facilitate quick charging without drawing additional power from the grid during peak times.
Electric vehicles (EVs) function as mobile storage units in the residential energy management framework, utilizing bidirectional energy flow to absorb and supply energy, thereby enhancing system flexibility and resilience.
Figures 10, 11, 12, 13, and 14 illustrate electric vehicle integration for Case I, while Figures 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, and 29 depict results for Cases II, III, and IV, respectively.
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.
Cost, measured in Indian rupees (₹), represents the monetary expenditure associated with energy consumption by household appliances and electric vehicles during each time slot h.
Electric vehicles can act as load-shifting devices during off-peak periods, which flattens the demand curve, reduces energy demand during peak hours, and establishes optimal energy utilization.
Wang, Xie, and Ding (2024) analyzed the stability of load frequency control systems in the presence of electric vehicles and time-varying delays.
Leveraging the energy storage capacity of electric vehicle (EV) batteries in residential settings enhances grid flexibility, improves resilience, and promotes sustainable energy practices while providing benefits to consumers and utilities.
Residential Demand Side Management (RDSM) must address environmental factors and source balancing by coordinating the charging and discharging of storage devices and Electric Vehicles (EVs) through planned scheduling strategies.
Dharavat, N. et al. utilized the political optimization algorithm for the optimal allocation of renewable distributed generators and electric vehicles in a distribution system, published in Energies, volume 15, issue 18, in 2022.
Effective implementation of Demand Side Management (DSM) strategies for electric vehicles requires infrastructure planning, including the installation of smart meters and charging infrastructure to accommodate larger charging demands.
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 abbreviation 'EVs' stands for Electric vehicles.
Integrating electric vehicles (EVs) into Residential Demand Side Management (RDSM) improves grid stability, minimizes the need for additional generation capacity, and maximizes the utilization of existing resources through load shifting and optimization.
Implementing Demand Side Management (DSM) with electric vehicles requires data management and privacy protections regarding charging patterns, electricity consumption, and grid conditions to ensure consumer security while optimizing DSM strategies.
Integrating electric vehicles (EVs) with renewable energy sources (RES), specifically solar and wind power, reduces the carbon footprint associated with EV charging.
Data collection and analysis regarding electric vehicle (EV) charging patterns, electricity consumption, and grid conditions enable utilities to optimize charging schedules and improve overall grid management.
A., Bayati, N., and Charoenlarpnopparut, C. proposed an energy management scheme for optimizing multiple smart homes equipped with electric vehicles in 2024.
Electric vehicles can act as load-shifting devices during off-peak periods, which flattens the demand curve, reduces energy demand during peak hours, and establishes optimal energy utilization.
M. A. Ravindran et al. published a technological review on fast charging infrastructure for electric vehicles, covering challenges, solutions, and future research directions in 2023.
Energy consumption is defined in the study as the total electrical energy (measured in kWh) used by household appliances and Electric Vehicles (EVs) during each time slot h over the simulation period.
Liao et al. performed a comparative study on demand-side energy management strategies for buildings equipped with photovoltaics, batteries, and electric vehicles, revealing synergies between distributed generation and adaptive load control.
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.
Wang, Xie, and Ding (2024) analyzed the stability of load frequency control systems in the presence of electric vehicles and time-varying delays.
Electric vehicle owners participating in residential demand-side management (RDSM) initiatives contribute to grid support services, including load shifting, peak shaving, and emergency power supply.
In the Residential Energy Management (REM) framework, the 'Vehicle and Battery Interaction' strategy involves electric vehicles interacting with home battery storage systems to store excess renewable energy and discharge it during peak demand periods to optimize energy usage.
The paper outlines a mathematical model to formulate a scheduling strategy for integrating Electric Vehicles (EVs) into Residential Demand Side Management (RDSM), focusing on a simple representation that accounts for real-time energy consumption.
VikramGoud et al. (2022) published a survey on strategies, challenges, modeling, and optimization techniques for demand-side management of electric vehicles in smart grids in Energy Reports.
Residential Demand Side Management (RDSM) models electric vehicles (EVs) in three primary interaction scenarios: Home to Vehicle (H2V) for charging, Vehicle to Home (V2H) for storage/discharge, and Vehicle to Vehicle (V2V) for energy transfer.
Electric vehicles (EVs) possess bidirectional capability, allowing them to function as energy loads during charging and as energy sources during discharging, particularly in emergency or high-demand scenarios.
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.
Intelligent users are residential energy consumers who leverage smart devices, on-site renewable energy sources, electric vehicles, and energy storage facilities to optimize energy consumption and implement scheduling strategies.
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.
Maximizing efficiency and grid support through dynamic electric vehicle charging schedules requires intelligent charging infrastructure and grid communication in the residential sector.
The integration of Electric Vehicles (EVs) into residential energy management allows households to act as both energy consumers and suppliers through Home-to-Vehicle (H2V) charging and Vehicle-to-Home (V2H) discharging, which enhances grid stability and creates cost savings.
The major influential factors for integrating electric vehicles into residential demand-side management (RDSM) include cost savings for consumers, grid optimization, environmental sustainability, and enhanced grid resilience.
Prosumers strategically manage electric vehicle (EV) charging (Home-to-Vehicle, H2V) and discharging (Vehicle-to-Home, V2H) to optimize energy use and costs, allowing households to act as both energy consumers and suppliers.
Ravindran et al. provided a technological review on fast-charging infrastructure for electric vehicles, identifying challenges, solutions, and future research directions to address grid impact concerns.
Mohanty et al. published a survey in Energy Reports in 2022 titled 'Demand side management of electric vehicles in smart grids: A survey on strategies, challenges, modeling, and optimization' which reviews demand-side management strategies for electric vehicles.
Intelligent users are defined as residential energy consumers who leverage smart devices, on-site renewable energy sources, electric vehicles, and energy storage facilities to optimize energy consumption and respond dynamically to real-time pricing signals and time-of-use rates.
Researchers surveyed algorithms for distributed charging control of electric vehicles in smart grids in a 2019 study published in IEEE Transactions on Intelligent Transportation Systems.
Electric vehicles function as energy consumption devices during off-peak periods and as energy storage devices during peak periods within the residential load scenario.
Dharavat et al. investigated optimal renewable distributed generator and electric vehicle allocations in a distribution network using the Political Optimization Algorithm, highlighting the role of heuristic optimization in energy planning.
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.
The study investigates the impact of electric vehicle (EV) integration in residential demand-side management (RDSM) to develop an optimal energy utilization strategy focused on economic efficiency and improved energy management.
The integration of electric vehicles into demand-side management (DSM) requires developing market incentives and fair pricing mechanisms to encourage consumer participation while maintaining fairness.
Ravindran, M. A. et al. reviewed fast charging infrastructure for electrical vehicles, including challenges, solutions, and future research directions, published in the Alexandria Engineering Journal, volume 82, in 2023.
Electric vehicles support environmental sustainability goals, including carbon emission reduction, fossil fuel independence, and the enhancement of efficient transportation facilities.
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.
Residential Demand Side Management (RDSM) is critical when electric vehicle charging penetration is excessive in distribution systems.
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.
Electric vehicles function as loads during off-peak periods and as energy storage devices during peak periods within the residential load scenario.
Maximizing the local, decentralized utilization of electric vehicle resources contributes to the reduction of greenhouse gas emissions and the mitigation of climate change.
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.
Mohanty et al. (2019) surveyed algorithms for distributed charging control of electric vehicles within smart grid systems in the IEEE Transactions on Intelligent Transportation Systems.
Electric vehicles (EVs) possess bidirectional capability, allowing them to function as energy loads during charging periods and as energy sources during discharging, particularly in high-demand or emergency scenarios.
Residential Demand Side Management (RDSM) strategies must handle environmental factors and balance energy sources by properly charging and discharging storage devices and Electric Vehicles (EVs) using coordinated and planned scheduling.
Korkas et al. (2022) developed an approximate dynamic programming approach for nearly optimal demand-side management of energy, thermal, electric vehicle, and storage loads in buildings.
A., Bayati, N. & Charoenlarpnopparut, C. proposed an energy management scheme for optimizing multiple smart homes equipped with electric vehicles in the journal Energies, volume 17, issue 1, published in 2024.
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.
In the REM framework's Case II (Vehicle to Home), electric vehicles act as storage devices, discharging energy to the home during peak load times to reduce grid dependence and lower energy costs.
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.
A home battery system discharges energy to the home grid between 6 pm and 9 pm at a rate of 2.0 kW to meet household demand alongside electric vehicles in vehicle-to-home (V2H) mode.
Grid reliability and load management strategies must be implemented when integrating electric vehicles into demand-side management (DSM) to prevent grid instability while maximizing economic and environmental benefits.
Electric vehicles (EVs) possess bidirectional capability, allowing them to function as energy loads during charging periods and as energy sources during discharging periods, particularly in emergency or high-demand scenarios.
The integration of electric vehicles (EVs) into residential demand side management (RDSM) reduces stress on energy storage devices.
In the Residential Energy Management (REM) framework, the 'Vehicle to Home' (V2H) strategy utilizes electric vehicles as storage devices to supply energy back to the home during peak load times, which reduces grid dependence, lowers energy costs, and flattens the household load curve.
Implementing Demand Side Management (DSM) with electric vehicles requires data management and privacy protections for charging patterns, electricity consumption, and grid conditions to ensure security while optimizing DSM strategies.
Electric vehicles play a vital role in the hierarchical control strategies adopted in modern smart grid distribution systems because the centralized approach of power sharing through electric vehicles indirectly impacts centralized control levels.
Vehicle-to-grid (V2G) and grid-to-vehicle (G2V) integration in Residential Demand Side Management (RDSM) enhances grid resilience and reliability by utilizing electric vehicles as storage devices and leveraging distributed renewable energy resources to address peak demand and grid fluctuations.
Electric vehicles can support grid stability, flexibility, and energy regulation, particularly when residential consumers act as prosumers during periods of excess energy availability.
Simulation performance parameters are critical indicators for assessing the behavior and efficiency of appliance scheduling schemes, particularly when Electric Vehicles (EVs) are integrated into the system.
High electric vehicle charging demand necessitates infrastructure planning, including the installation of smart meters and charging infrastructure to support Demand Side Management (DSM) strategies.
Regulating energy use with electric vehicles in residential sectors changes consumer behavior, requiring efficient Demand Side Management (DSM) techniques to incentivize electric vehicle owners to maximize benefits.
During active PV solar generation periods, energy is utilized to meet load demand, charge electric vehicles (EVs), and store surplus energy in energy storage devices (ESDs) for later use.
In the REM framework, electric vehicles (including cars, scooters, and bicycles) charge from the home grid during off-peak hours to minimize costs and prevent grid overloading.
Prum et al. introduced an energy management scheme for optimizing multiple smart homes equipped with electric vehicles, focusing on cooperative control strategies to enhance local grid stability.
Optimizing residential load scheduling requires considering grid source availability, energy delivery costs, renewable energy source utilization potential based on environmental conditions, the state of charge of storage systems, and the bidirectional energy flow of electric vehicles.
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.
Intelligent users are defined as consumers who leverage smart devices, on-site renewable energy sources, electric vehicles, and energy storage facilities to optimize energy consumption and implement residential energy scheduling strategies.
The effective implementation of electric vehicles in residential demand-side management (RDSM) reduces the stress placed on energy storage devices.
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.
Prum et al. introduced an energy management scheme for optimizing multiple smart homes equipped with electric vehicles, focusing on cooperative control strategies to enhance local grid stability.
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).
Dharavat et al. investigated optimal renewable distributed generator and electric vehicle allocations in a distribution network using the Political Optimization Algorithm, highlighting the role of heuristic optimization in energy planning.
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.
In the REM framework's Case I (Home to Vehicle), electric vehicles including cars, scooters, and bicycles charge from the home grid during off-peak hours to minimize costs and prevent grid overloading.
Liao, W. et al. conducted a comparative study of demand-side energy management strategies for building-integrated photovoltaics-battery systems and electric vehicles in diversified building communities in 2024.
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.
Maximizing the potential of electric vehicle (EV) integration into Residential Demand Side Management (RDSM) requires effective energy scheduling and mutual collaboration between utilities, regulators, EV owners, and technology providers.
In the REM framework, electric vehicles interact with home battery storage systems to store excess renewable energy and discharge it during peak demand periods, ensuring efficient renewable energy utilization.
Figures 10-14 illustrate Electric Vehicle (EV) integration for Case I, while Figures 15-19, 20-24, and 25-29 depict results for Cases II, III, and IV, respectively, showcasing load patterns, electricity costs, and the performance of smart users employing Salp Swarm Algorithm (SSA) and Black Widow Optimization Algorithm (BWOA).
Kumar et al. proposed an electric vehicle charging strategy focused on user-centric scheduling and grid integration benefits to ensure efficient and reliable charging operations for sustainable transportation systems.
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.
Ravindran et al. provided a technological review on fast-charging infrastructure for electric vehicles, identifying challenges, solutions, and future research directions to address grid impact concerns.
The residential energy management framework optimizes energy usage by considering grid availability, energy costs, renewable energy utilization, storage systems' state of charge (SOC), and bidirectional energy flow from electric vehicles.
Time-of-use (ToU) pricing models combined with electric vehicle (EV) support can lead to efficient energy consumption patterns and cost savings in energy utilization.
Dharavat et al. investigated optimal renewable distributed generator and electric vehicle allocations in a distribution network using the Political Optimization Algorithm, highlighting the role of heuristic optimization in energy planning.
The integration of electric vehicles into residential energy systems requires monitoring of Home-to-Vehicle (H2V) charging and Vehicle-to-Home (V2H) discharging activities to optimize household energy profiles and reduce costs.
Implementing Demand Side Management (DSM) with electric vehicles requires robust data management and privacy protections regarding charging patterns, electricity consumption, and grid conditions to ensure consumer security while optimizing DSM strategies.
Bidirectional energy flow enables the implementation of vehicle-to-grid (V2G), vehicle-to-home (V2H), grid-to-vehicle (G2V), home-to-vehicle (H2V), and vehicle-to-vehicle (V2V) energy transfer at the electric vehicle integration level within the residential load sector.
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.
Datta et al. (2019) developed a price-regulated charge-discharge strategy for electric vehicles supporting grid-to-vehicle (G2V), vehicle-to-home (V2H), and vehicle-to-grid (V2G) operations.
Optimal economic energy utilization in the residential sector is facilitated by charging scheduling for electric vehicles through time-of-use (ToU) pricing plans prescribed by utilities, provided the sector has intelligent charging infrastructure and grid communication for dynamic schedule adjustment.
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.
Korkas et al. (2022) developed an approximate dynamic programming approach to achieve nearly optimal demand-side management for energy, thermal, electric vehicle, and storage loads in buildings.
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.
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.
Vehicle-to-grid (V2G) integration involves using electric vehicles (EVs) as an energy source during peak hours, rather than just as a load during off-peak periods.
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 integration of electric vehicles into demand-side management (DSM) requires addressing equitable access to charging infrastructure to prevent regional disparities in adoption and energy access.
Liao et al. performed a comparative study on demand-side energy management strategies for buildings equipped with photovoltaics, batteries, and electric vehicles, revealing the synergies between distributed generation and adaptive load control.
Realizing the full potential of electric vehicles in residential Demand Side Management (DSM) requires careful planning, investment, and load scheduling to address associated challenges.
Prosumers manage Electric Vehicle (EV) charging (Home-to-Vehicle, H2V) and discharging (Vehicle-to-Home, V2H) to optimize energy use and costs, allowing households to act as both energy consumers and suppliers.
Ravindran, M. A. et al. reviewed fast charging infrastructure for electrical vehicles, including challenges, solutions, and future research directions in 2023.
In the Home to Vehicle (H2V) charging scenario, electric cars charge at 7.2 kW from 11 pm to 5 am, electric scooters charge at 2.0 kW from 10 pm to 1 am, and electric bicycles charge at 0.5 kW from 12 am to 2 am to minimize costs and avoid grid overloading.
Electric vehicle (EV) integration into Residential Demand Side Management (RDSM) replaces traditional energy storage systems, such as batteries, within residential settings.
The deployment of electric vehicles in residential sectors enables improved energy utilization in decentralized and centralized distribution systems due to their bidirectional operation and energy storage capabilities.
Electric vehicles support environmental sustainability goals by facilitating carbon emission reduction, reducing dependence on fossil fuels, and enhancing efficient transportation facilities.
The residential energy management framework optimizes energy usage by considering grid availability, energy costs, renewable energy utilization, storage systems' state of charge (SOC), and bidirectional energy flow from electric vehicles.
In the Vehicle to Home (V2H) discharging scenario, electric cars discharge at 3.6 kW from 6 pm to 9 pm, electric scooters discharge at 1.0 kW from 7 pm to 9 pm, and electric bicycles discharge at 0.2 kW from 8 pm to 9 pm to reduce grid dependence during peak hours.
Cost is defined in the study as the monetary expenditure (measured in Indian rupees—₹) associated with energy consumption by household appliances and Electric Vehicles (EVs) during each time slot h.
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.
Integrating electric vehicles (EVs) with renewable energy sources (RES), specifically solar and wind power, reduces the carbon footprint associated with EV charging.
Electric vehicles support environmental sustainability goals, including carbon emission reduction, fossil fuel independence, and the enhancement of efficient transportation facilities.
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 integration of electric vehicles into Residential Demand Side Management involves utilizing the energy storage capacity of electric vehicle batteries to store surplus energy during off-peak hours and discharge that energy during peak hours or in case of emergencies.
Electric vehicles (EVs) function as mobile storage units in the residential energy management framework, utilizing bidirectional energy flow to absorb or supply energy, thereby enhancing system flexibility and resilience.
In the REM framework, electric vehicles act as storage devices, supplying energy back to the home during peak load times to reduce grid dependence, lower energy costs, and flatten the household load curve.
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.
The residential energy management framework optimizes energy usage by considering grid availability, energy costs, renewable energy utilization, storage systems' state of charge (SOC), and bidirectional energy flow from electric vehicles.
Singh et al. conducted a review of electric vehicle charging technologies, infrastructure expansion, and grid integration strategies, providing an outlook on the role of electric vehicles in sustainable e-mobility.
In the Residential Energy Management (REM) framework, the 'Vehicle to Vehicle' (V2V) strategy involves high-power-rated electric cars transferring energy to smaller vehicles like electric scooters and bicycles to facilitate quick charging without drawing additional power from the grid during peak times.
Prum et al. introduced an energy management scheme for multiple smart homes equipped with electric vehicles, utilizing cooperative control strategies to enhance local grid stability.
The integration of electric vehicles (EVs) into Residential Demand Side Management (RDSM) reduces dependence on centralized infrastructure, minimizes the impact of power outages, and improves overall grid stability.
In the Home to Vehicle (H2V) charging scenario, electric cars charge at 7.2 kW from 11 pm to 5 am, electric scooters charge at 2.0 kW from 10 pm to 1 am, and electric bicycles charge at 0.5 kW from 12 am to 2 am to minimize costs and avoid grid overloading.
Time-of-use (ToU) pricing models with electric vehicle support can lead to efficient energy consumption patterns and cost savings in energy utilization.
Monitoring electric vehicle energy consumption during Home-to-Vehicle (H2V) and Vehicle-to-Home (V2H) scenarios provides data necessary for understanding how electric vehicles interact with other household appliances and for optimizing energy distribution.
The study incorporates the State of Charge (SOC) of electric vehicles and energy storage devices into its computational model by assigning specific charging and discharging time durations to ensure real-time feasibility.
In the Residential Energy Management (REM) framework, the 'Home to Vehicle' (H2V) charging strategy involves electric cars, scooters, and bicycles charging from the home grid primarily during off-peak hours to minimize costs and prevent grid overloading.
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.
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.
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.
Ravindran et al. provided a technological review on fast-charging infrastructure for electric vehicles, identifying challenges, solutions, and future research directions to address grid impact concerns.
Liao et al. performed a comparative study on demand-side energy management strategies for buildings equipped with photovoltaics, batteries, and electric vehicles, revealing the synergies between distributed generation and adaptive load control.
The study investigates the impact of electric vehicle (EV) integration in residential demand-side management (RDSM) across various roles, including EVs as loads, energy storage, and sources of bidirectional energy flow to the grid, to develop optimal energy utilization strategies.
W. Liao et al. conducted a comparative study of demand-side energy management strategies for building-integrated photovoltaics, battery systems, and electric vehicles in diversified building communities in 2024.
The abbreviation 'EVs' stands for Electric vehicles.
Mohanty et al. (2022) surveyed strategies, challenges, modeling, and optimization techniques for demand-side management of electric vehicles in smart grids.
Users can capitalize on favorable tariff rates by charging electric vehicles during off-peak hours or by using electric vehicles as energy sources during peak hours through Vehicle-to-Home (V2H) technology.
The study investigates the impact of electric vehicle (EV) integration in residential demand-side management (RDSM) to develop an optimal energy utilization strategy focused on economic efficiency and improved energy management.
The integration of electric vehicles into demand-side management frameworks has driven extensive research into optimization algorithms, grid resilience, renewable energy microgrid feasibility, and smart energy management.
Users can capitalize on favorable tariff rates by charging electric vehicles during off-peak hours or using them as energy sources during peak hours, a practice known as Vehicle-to-Home (V2H).
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.
Integrating electric vehicles into Residential Demand Side Management (RDSM) as a load during peak and off-peak periods is essential for regulating electricity patterns and constraints in residential settings.
The integration of electric vehicles into demand-side management (DSM) requires implementing regulatory mechanisms that promote Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) participation without overburdening consumers.
Electric vehicles introduce new dimensions to household energy management by requiring monitoring of Home-to-Vehicle (H2V) charging and Vehicle-to-Home (V2H) discharging activities.
Data collection and analysis in residential demand-side management (RDSM) provides insights into electric vehicle (EV) charging patterns, electricity consumption, and grid conditions, which allows utilities to optimize charging schedules and improve grid management.
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.
Realizing the potential of electric vehicles in residential Demand Side Management (DSM) requires careful planning, investment, and load scheduling to address associated challenges.
Time-of-use (ToU) pricing models that support electric vehicles (EVs) can create efficient energy consumption patterns, leading to cost savings in energy utilization.
The integration of electric vehicles into demand-side management (DSM) requires managing grid reliability and load to prevent instability while maximizing economic and environmental benefits.
Wang, Xie, and Ding (2024) conducted a stability analysis of load frequency control systems in the presence of electric vehicles, accounting for time-varying delays.
The major influential factors for integrating electric vehicles (EVs) into Residential Demand Side Management (RDSM) include cost savings for consumers, grid optimization, environmental sustainability, and enhanced grid resilience.
Users can capitalize on favorable tariff rates by charging electric vehicles during off-peak hours or using them as energy sources during peak hours, a practice known as Vehicle-to-Home (V2H).
The paper 'Critical Review on Electric Vehicles: Chargers, Charging Techniques, and Standards' by Karuppiah, N., Mounica, P., Balachandran, P. K. & Muniraj, R. was published in 'Renewable Energy for Plug-In Electric Vehicles' in 2024, pages 81–94.
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.
The Binary Whale Optimization Algorithm (BWOA) is proposed as an efficient algorithm for scheduling energy utilization in residential demand side management, specifically considering the impact of electric vehicles.
Singh et al. conducted a review of electric vehicle charging technologies, infrastructure expansion, and grid integration strategies, providing an outlook on the role of electric vehicles in sustainable e-mobility.
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.
Vehicle-to-grid (V2G) integration involves using electric vehicles as an energy source during peak hours, in addition to their utilization as a load during off-peak periods.
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 integration of electric vehicles into demand-side management (DSM) requires addressing consumer data privacy concerns, specifically emphasizing compliance with regulations like GDPR regarding data collection, storage, and usage.
Cost, measured in Indian rupees (₹), represents the monetary expenditure associated with energy consumption by household appliances and Electric Vehicles (EVs) during each time slot h.
Electric vehicles play a vital role in the hierarchical control strategies adopted in modern smart grid distribution systems by facilitating centralized power sharing.
Energy consumption, measured in kilowatt-hours (kWh), is defined as the total electrical energy used by household appliances and electric vehicles during each time slot h over the simulation period.
Vehicle-to-grid (V2G) and grid-to-vehicle (G2V) integration in residential demand-side management (RDSM) enhances grid resilience and reliability by utilizing electric vehicles as storage devices and leveraging distributed renewable energy resources to address peak demand and grid fluctuations.
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.
Regulatory frameworks for integrating electric vehicles into Residential Demand Side Management (RDSM) must include standards for interoperability, fair pricing mechanisms, and equitable access to benefits for all consumers.
The exponential deployment of electric vehicles in residential sectors allows for improved energy utilization at both decentralized and centralized levels of distribution systems due to the vehicles' bidirectional operation and energy storage capabilities.
During active solar generation periods, residential energy is utilized to meet load demand, charge electric vehicles, and store surplus energy in storage devices for later use.
Integrating electric vehicles into Residential Demand Side Management (RDSM) as a load during peak and off-peak periods is essential for regulating electricity patterns and constraints in residential settings.
Integrating electric vehicles (EVs) into residential demand side management (RDSM) improves grid stability, minimizes the need for additional generation capacity, and maximizes the utilization of existing resources through load shifting and optimization.
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).
Energy consumption, measured in kilowatt-hours (kWh), is defined as the total electrical energy used by household appliances and Electric Vehicles (EVs) during each time slot h over the simulation period.
Electric vehicles (EVs) can replace traditional residential energy storage systems by utilizing their battery capacity to store surplus energy during off-peak hours and discharge it during peak hours or emergencies.
Dharavat, N. et al. utilized the political optimization algorithm for the optimal allocation of renewable distributed generators and electric vehicles in a distribution system in 2022.
Residential Demand Side Management (RDSM) strategies should incorporate environmental factors and balance energy sources by coordinating the charging and discharging of storage devices and Electric Vehicles (EVs).
The significant integration of Electric Vehicles (EVs) into the power grid necessitates modern infrastructure planning, including communication technology, sensors, IoT, measurement instruments, and bidirectional power electronics devices to support Demand Side Management (DSM) strategies.
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).
The authors of the study developed a mathematical model to formulate a scheduling strategy for Electric Vehicle (EV) integration in Residential Demand Side Management (RDSM) that considers real-time energy consumption without sacrificing energy utilization and demand flow.
The integration of electric vehicles into demand-side management frameworks has driven extensive research into optimization algorithms, grid resilience, renewable energy microgrid feasibility, and smart energy management.
Intelligent loads and appliances, such as electric vehicles, can function as smart energy hubs for energy dispatch and storage within demand-side management (DSM) strategies and may be integrated as Virtual Power Plants (VPPs) to enhance residential energy management.
Regulatory frameworks for electric vehicle integration in Residential Demand Side Management (RDSM) must include interoperability standards, fair pricing mechanisms, and equitable access to DSM benefits.
Integrating electric vehicles into demand-side management (DSM) requires addressing consumer data privacy concerns, specifically emphasizing compliance with regulations like GDPR regarding data collection, storage, and usage.
The integration of electric vehicles into demand-side management (DSM) requires addressing equitable access to charging infrastructure to prevent regional disparities in adoption and energy access.
Intelligent loads and appliances, such as electric vehicles, can function as smart energy hubs for energy dispatch and storage in demand-side management, potentially acting as Virtual Power Plant (VPP) integration for improved energy management.
Vehicle-to-grid (V2G) integration involves using electric vehicles as an energy source during peak hours, in addition to their utilization as a load during off-peak periods.
Regulatory frameworks are necessary to support the effective integration of electric vehicles into Residential Demand Side Management (RDSM), specifically by developing standards for interoperability, establishing fair pricing mechanisms, and ensuring equitable access to DSM benefits for all consumers.
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.
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.
Regulating energy utilization with electric vehicles in residential sectors changes consumer behavior, necessitating efficient Demand Side Management (DSM) techniques to encourage electric vehicle owners to maximize benefits through information and incentives.
Data collection and analysis in residential demand side management (RDSM) provides insights into electric vehicle (EV) charging patterns, electricity consumption, and grid conditions, which allows utilities to optimize charging schedules and improve grid management.
In the Vehicle to Home (V2H) discharging scenario, electric cars discharge at 3.6 kW from 6 pm to 9 pm, electric scooters discharge at 1.0 kW from 7 pm to 9 pm, and electric bicycles discharge at 0.2 kW from 8 pm to 9 pm to reduce grid dependence during peak hours.
Rajagopalan et al. proposed an iterative map-based self-adaptive crystal structure algorithm for multi-objective energy management in renewable and electric vehicle-integrated microgrids, addressing cost efficiency and operational stability.
Residential Demand Side Management (RDSM) utilizes the energy storage capacity of electric vehicle (EV) batteries to store surplus energy during off-peak hours and discharge it during peak hours or in case of emergencies.
The smart scheduler application in the Residential Energy Management (REM) framework flattens demand peaks and distributes load by strategically scheduling appliance operation and integrating electric vehicles, which reduces grid stress and allows residential users to utilize lower off-peak energy rates.
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.
The integration of electric vehicles into demand-side management (DSM) requires addressing ethical and policy considerations, including equitable access to charging infrastructure, consumer data privacy (specifically compliance with GDPR), market incentives for participation, regulatory mechanisms for V2G and V2H, and grid reliability management.
Rajagopalan et al. proposed an iterative map-based self-adaptive crystal structure algorithm for multi-objective energy management in renewable and electric vehicle-integrated microgrids, addressing both cost efficiency and operational stability.
Vehicle-to-grid (V2G) and grid-to-vehicle (G2V) integration in residential demand side management (RDSM) enhances grid resilience and reliability by utilizing electric vehicles (EVs) as storage devices and leveraging distributed renewable energy resources to address peak demand and grid fluctuations.
A. Bayati and C. Charoenlarpnopparut proposed an energy management scheme designed to optimize multiple smart homes equipped with electric vehicles in 2024.
The study incorporates the state of charge (SOC) of electric vehicles and energy storage devices (ESD) into its computational model by assigning specific charging and discharging time durations to ensure the research is applicable to real-time conditions.
The major influential factors for integrating electric vehicles (EVs) into residential demand side management (RDSM) include cost savings for consumers, grid optimization, environmental sustainability, and enhanced grid resilience.
Electric vehicles acting as loads can perform load shifting during off-peak periods, which flattens the demand curve and reduces energy demand during peak hours.
Residential load modelling categorizes appliances into two groups: Flexible/Interruptible Loads (I), such as washing machines, cloth dryers, electric vehicles, and water heaters; and Base/Essential Loads (B), such as refrigerators and lighting.