Smart microgrids belong to a set of networks that operate independently. These networks have technologies such as electric vehicle battery swapping stations that aim to economic welfare with own resources. Improper handling of electric vehicles services represents overload, congestion or surplus energy. This study addresses both management and support of electric vehiculos battery swapping stations. The formulation of a decision matrix examines economically viable alternatives that contributes to scheduling battery swapping stations. The decision matrix is implemented to manage the swapping, charging and discharging of electric vehicles. In addition, the smart microgrid model evaluates operation issues. The smart microgrid model used extends with considerations of demand response, generators with renewable energies, the wholesale market, local market and electricity spot price for electric vehicles. Additionally, uncertainty issues related to the planning for the infrastructure of the electric vehicle battery swapping station, variability of electricity prices, weather conditions and load forecasting are used. Mentioned stakeholders must maximize their economic benefits optimizing their uncertain day-ahead resources. The proposed hybrid optimization algorithm supports aggregator decision making. This algorithm achieves a 72% reduction in total cost. This percentage of feasible reduction is obtained by calculating the random solution with respect to the suboptimal solution.
Optimal charging strategies of electric vehiclesNishant Gupta
1) The document presents a framework for modeling optimal charging of electric vehicles in the UK power system to minimize costs and emissions.
2) It describes a time-coordinated optimal power flow tool that balances the needs of distribution network operators, power markets, and vehicle owners.
3) Results show that under current carbon prices, emissions costs have a negligible impact on optimization compared to electricity costs, but emissions could influence charging if carbon prices increase.
In this paper, a new method of integration between PV inverter system with utility grid for electric vehicle charging station based on the extended boost quasi-Z-source (q-ZSI) topology is proposed. The proposed system realizes a bidirectional power flow management between PV sources, energy storage unit and the utility grid. The extended boost q-ZSI is a most efficient topology that provides a single stage conversion for PV systems by providing low ratings for components, reduced number of components used, high input voltage gain, increased voltage boost property , reduced voltage stress across switches and simple control strategies .Its unique capability in single stage conversion with improved voltage gain is used for voltage buck and boost function. A simulation model of the grid connected q-ZSI for electric vehicle charging station has been built in MATLAB/ SIMULINK. The hardware setup was developed and the results are validated.
This document proposes using a mobile app to coordinate electric vehicle (EV) charging with local renewable energy production. The app would allow photovoltaic panel owners to sell excess electricity directly to EVs. It would also help utilities manage electricity load by influencing EV charging behavior in real time. The app is described as a two-sided market connecting EV drivers and electricity producers. It would provide services like booking charging stations and optimizing charging suggestions based on travel needs and energy costs. This could balance local renewable energy use while making EV charging more convenient.
This document presents a strategic design optimization model for microgrids with multiple energy storage technologies and demand response aggregation. The model (1) uses game theory to model the strategic behavior of utilities, aggregators, and consumers, (2) determines optimal tradeoffs between power imported from the grid and demand response resources, and (3) allocates various storage technologies cost-optimally. The model was tested on a 100% renewable energy microgrid in New Zealand, reducing lifetime costs by an estimated 21% compared to a business-as-usual approach without demand response.
The Benefits of Distributed Generation in Smart-Grid Environment- A Case StudyJitendra Bhadoriya
This document summarizes a paper that examines the benefits of distributed generation in smart grid environments through a case study. It discusses different types of distributed generation technologies like micro-turbines, fuel cells, storage devices, and renewable energy sources. It also discusses the importance of using accurate load models, as the optimal placement and sizing of distributed generation resources can be significantly impacted by the load model assumptions. The paper aims to present a critical survey of incorporating new distributed generation technologies into conventional grids to create smart grids.
Harvesting in electric vehicles: Combining multiple power tracking and fuel-c...IJECEIAES
This document summarizes a research article that proposes a power electronic platform and energy management strategy (EMS) to harvest energy from multiple sources in electric vehicles. The platform allows simultaneous operation of sources like solar panels, fuel cells, energy-generating dampers, and others. The EMS aims to minimize degradation of the battery bank and fuel cell by filtering current transients and ensuring sources operate at their maximum power points. A mathematical model of the platform is presented and stability analysis was performed. Numerical, hardware-in-the-loop, and experimental validations supported the effectiveness of the approach.
Two tier energy compansation framework based on smart electric charging stationSandeshPatil99
Abstract: Traditional cars produce a lot of carbon dioxide (CO2) emissions that are ejected into the atmosphere, causes pollution and greenhouse gases today, electric vehicles (EVs) have received much attention as an alternative to traditional vehicles. The traditional vehicles are powered by internal combustion engines. The electric vehicle is developed because of the advancement in battery technology and motor efficiency. The secondary batteries are the main energy sources of the EV. Thus, energy management is the key factor in EV or Hybrid Electric Vehicles (HEV) design. Moreover, the charge capacity of the battery will influence the endurance of electric vehicles. The main challenge in the HEV is the charging time required for the batteries and insufficiency of charging stations (CS) and therefore charging within existing distribution system infrastructure is problematic. Up to now the HEV‟s CS is in the range of 100 km per charge due to the on-board energy which is needed to be optimized. The second challenge for the EV‟s is their battery capacity which ranges from 8.6KWh to 15.2KWh. The consequent disadvantage is that the charging time for above mentioned size, in a level 1 household charger (120V, 50Hz, 15-20A) is more than 15 hours. Due to these constraints, the vendor (EV charging station supplier) needs a convenient distribution system to cater to customer demand as well as maximize benefits. A special attention must be given to the charging station. In future, the number of electric vehicles will be increasing to a greater extent; these electric vehicles have to re-charge their battery in a place (i.e.) charging station, so there will be a growing need of public accessing charging stations. This will have a significant impact on the power systems like transformers, protection devices etc. With respect to the varying load, it will have an impact on the consumers and vendors due to the traffic at this station, waiting time for charging the vehicles will increase etc. Therefore both the consumer and vendor will get assistance from a communication system which shares useful data regarding the charging station, whether the charging slots are free, rate at which charging is done, cost per unit etc.
Optimal charging strategies of electric vehiclesNishant Gupta
1) The document presents a framework for modeling optimal charging of electric vehicles in the UK power system to minimize costs and emissions.
2) It describes a time-coordinated optimal power flow tool that balances the needs of distribution network operators, power markets, and vehicle owners.
3) Results show that under current carbon prices, emissions costs have a negligible impact on optimization compared to electricity costs, but emissions could influence charging if carbon prices increase.
In this paper, a new method of integration between PV inverter system with utility grid for electric vehicle charging station based on the extended boost quasi-Z-source (q-ZSI) topology is proposed. The proposed system realizes a bidirectional power flow management between PV sources, energy storage unit and the utility grid. The extended boost q-ZSI is a most efficient topology that provides a single stage conversion for PV systems by providing low ratings for components, reduced number of components used, high input voltage gain, increased voltage boost property , reduced voltage stress across switches and simple control strategies .Its unique capability in single stage conversion with improved voltage gain is used for voltage buck and boost function. A simulation model of the grid connected q-ZSI for electric vehicle charging station has been built in MATLAB/ SIMULINK. The hardware setup was developed and the results are validated.
This document proposes using a mobile app to coordinate electric vehicle (EV) charging with local renewable energy production. The app would allow photovoltaic panel owners to sell excess electricity directly to EVs. It would also help utilities manage electricity load by influencing EV charging behavior in real time. The app is described as a two-sided market connecting EV drivers and electricity producers. It would provide services like booking charging stations and optimizing charging suggestions based on travel needs and energy costs. This could balance local renewable energy use while making EV charging more convenient.
This document presents a strategic design optimization model for microgrids with multiple energy storage technologies and demand response aggregation. The model (1) uses game theory to model the strategic behavior of utilities, aggregators, and consumers, (2) determines optimal tradeoffs between power imported from the grid and demand response resources, and (3) allocates various storage technologies cost-optimally. The model was tested on a 100% renewable energy microgrid in New Zealand, reducing lifetime costs by an estimated 21% compared to a business-as-usual approach without demand response.
The Benefits of Distributed Generation in Smart-Grid Environment- A Case StudyJitendra Bhadoriya
This document summarizes a paper that examines the benefits of distributed generation in smart grid environments through a case study. It discusses different types of distributed generation technologies like micro-turbines, fuel cells, storage devices, and renewable energy sources. It also discusses the importance of using accurate load models, as the optimal placement and sizing of distributed generation resources can be significantly impacted by the load model assumptions. The paper aims to present a critical survey of incorporating new distributed generation technologies into conventional grids to create smart grids.
Harvesting in electric vehicles: Combining multiple power tracking and fuel-c...IJECEIAES
This document summarizes a research article that proposes a power electronic platform and energy management strategy (EMS) to harvest energy from multiple sources in electric vehicles. The platform allows simultaneous operation of sources like solar panels, fuel cells, energy-generating dampers, and others. The EMS aims to minimize degradation of the battery bank and fuel cell by filtering current transients and ensuring sources operate at their maximum power points. A mathematical model of the platform is presented and stability analysis was performed. Numerical, hardware-in-the-loop, and experimental validations supported the effectiveness of the approach.
Two tier energy compansation framework based on smart electric charging stationSandeshPatil99
Abstract: Traditional cars produce a lot of carbon dioxide (CO2) emissions that are ejected into the atmosphere, causes pollution and greenhouse gases today, electric vehicles (EVs) have received much attention as an alternative to traditional vehicles. The traditional vehicles are powered by internal combustion engines. The electric vehicle is developed because of the advancement in battery technology and motor efficiency. The secondary batteries are the main energy sources of the EV. Thus, energy management is the key factor in EV or Hybrid Electric Vehicles (HEV) design. Moreover, the charge capacity of the battery will influence the endurance of electric vehicles. The main challenge in the HEV is the charging time required for the batteries and insufficiency of charging stations (CS) and therefore charging within existing distribution system infrastructure is problematic. Up to now the HEV‟s CS is in the range of 100 km per charge due to the on-board energy which is needed to be optimized. The second challenge for the EV‟s is their battery capacity which ranges from 8.6KWh to 15.2KWh. The consequent disadvantage is that the charging time for above mentioned size, in a level 1 household charger (120V, 50Hz, 15-20A) is more than 15 hours. Due to these constraints, the vendor (EV charging station supplier) needs a convenient distribution system to cater to customer demand as well as maximize benefits. A special attention must be given to the charging station. In future, the number of electric vehicles will be increasing to a greater extent; these electric vehicles have to re-charge their battery in a place (i.e.) charging station, so there will be a growing need of public accessing charging stations. This will have a significant impact on the power systems like transformers, protection devices etc. With respect to the varying load, it will have an impact on the consumers and vendors due to the traffic at this station, waiting time for charging the vehicles will increase etc. Therefore both the consumer and vendor will get assistance from a communication system which shares useful data regarding the charging station, whether the charging slots are free, rate at which charging is done, cost per unit etc.
V2G allows electric vehicles to provide power to the electrical grid during periods of peak demand by allowing two-way power flow. There are three main versions of V2G involving battery-powered vehicles that can provide power to the grid from excess battery capacity during peak times and recharge during off-peak times. V2G systems provide benefits like peak load leveling and spinning reserves but challenges include potential grid overloading and high vehicle costs compared to ICE vehicles.
V2G Opportunities And Impacts E3 2008 S Mullenmull0197
The document discusses vehicle-to-grid opportunities and impacts, including how plug-in hybrid electric vehicles (PHEVs) can provide power to the electric grid through two-way communication. PHEVs could supply power for ancillary grid services, reduce peak loads, and help integrate renewable energy. The author models how aggregated PHEVs could support the electric system and designs vehicle controls to optimize energy use while meeting driver needs. Batteries in PHEVs are seen as ideal for short-term grid services and providing customer choice if communication and control infrastructure is improved.
Opportunities for v2 g integrating plug-in vehicles and the electric grid (to...CALSTART
CALSTART New Fuel Program Manager, Dr Jasna Tomic, presented on vehicle-to-grid technology at UCLA in April 2011. In this presentation, Dr Tomic addresses some of the barriers and opportunities to use battery power generated by a new generation of electric vehicles by putting it back onto the grid.
Dr. Praveen Kumar presented on the concept of Grid to Vehicle (G2V) power. He explained that as electric vehicles become more common, their batteries could provide power storage and generation back to the electric grid. This would allow electric vehicles to provide ancillary power services to help maintain grid stability. G2V power could benefit both vehicle owners through additional revenue and utilities by reducing costs and emissions compared to traditional peak power generation. However, integrating large numbers of electric vehicles into the grid also presents technical and regulatory challenges that would need to be addressed.
This document discusses opportunities for electric vehicle (EV) charging infrastructure and vehicle-to-grid (V2G) technology. It notes that greater adoption of renewable energy and efforts to improve grid efficiency have increased demand for customer demand management solutions, making V2G a promising option. An integrated home solution could manage energy use and enable V2G benefits. V2G infrastructure could allow EVs to support the grid during peak demand periods using stored battery power, helping supplement generation and reduce the need for load shedding. Aggregating many EVs could provide substantial benefits to utilities and grid stability with renewable energy integration.
South caspian summit cook 14th april 2015ChrisJCook
The document proposes funding the development of a Caspian Energy Grid through innovative financing mechanisms to improve energy infrastructure connectivity and efficiency in the Caspian region. It suggests establishing a "Clearing Union" platform where investors can purchase energy credits at a discount, which producers and consumers can then use to fund infrastructure upgrades. As a proof of concept, it outlines plans for a Kazakhstan-Azerbaijan-Turkmenistan energy triangle connecting Turkmen gas and power generation assets to the other countries via undersea high voltage direct current cables, which could help Turkmenistan export surplus energy while conserving domestic gas resources. The proposal aims to minimize development costs through "energy loans" that are repaid with energy rather than interest, increase energy market integration
This document proposes funding the development of a Caspian Energy Grid through a prepay energy credit system and energy loans. It would connect energy generation and transmission infrastructure across the Caspian region to improve efficiency. Currently infrastructure is fragmented, inefficient, and wastes carbon fuels. The proposal suggests upgrading legacy systems and building new high-voltage direct current connections. This would allow countries like Turkmenistan to export excess power. Investors could provide funding by buying discounted prepay energy credits or lending through energy loans repaid in energy units. A pilot project called KAT Core involving Turkmenistan, Kazakhstan, and Azerbaijan is presented as a proof of concept.
This document discusses vehicle-to-grid (V2G) technology which allows electric vehicles to provide power to the electric grid during periods when they are parked and connected to the grid. V2G technology integrates electric vehicles into the smart grid and allows them to provide services like frequency regulation. It describes how V2G works by establishing communication between vehicles and charging stations that can then form a "virtual storage network" to provide balancing services to the electric grid. V2G provides benefits like improving power quality and reducing electric bills but requires standards and legislation to fully integrate electric vehicles into grid operations.
APPLICATION OF PHEVs FOR SMART GRID IN INDIAN POWER SECTOR1Eshwar Pisalkar
This document discusses the application of plug-in hybrid electric vehicles (PHEVs) for smart grids in the Indian power sector. It describes how PHEVs can help with demand side management by charging from renewable energy sources and providing power back to the grid. The document also examines issues like harmonic distortion caused by PHEV charging and its impact on transformers. It proposes a distributed strategy for PHEV charging and discharging that involves regional dispatch centers and charging stations. Finally, it suggests that introducing electric 2-wheelers widely in India first could significantly reduce household power demand.
This document discusses a proposed prototype for an ultracapacitor-operated city bus. Ultracapacitors provide advantages over lithium-ion batteries such as longer lifespan, ability to charge and discharge quickly, and increased safety. The prototype would use multiple 3V motors powered by ultracapacitors instead of batteries. Calculations are shown for selecting the capacitor, booster, and motor specifications needed. The objectives are to design and demonstrate a hybrid electric transit bus with reduced emissions and operating costs compared to diesel buses.
Hosted by KTN, this event brought together the projects that were funded as part of the £30 million UK government funding to support and develop vehicle-to-grid (V2G) technologies - aiming to enable electric cars and other vehicles to deliver electricity back to the smart grid, to light homes and power businesses.
A great opportunity for local councils, fleet owners (looking to go electric) or those in the Energy and Infrastructure sectors.
The event looked at:
- Benefits and learnings from the V2G cohort projects;
- V2G Grid connections (as it was noted that the G99 process could be elongated at times)
- Update from Ofgem (including a Q&A session)
Find out more: https://ktn-uk.co.uk/news/v2g-vehicle-to-grid-cohort-the-future
Demand response (DR) resources can provide various services to wholesale electricity markets including energy, capacity, and ancillary services depending on the market rules. DR is seen as an important tool for reliable grid operations and preventing market power issues. Federal regulations and funding aim to improve the role of DR by requiring comparable treatment of DR and generation in markets and supporting smart grid demonstration projects. Individual ISO/RTO markets define different products DR can provide and have requirements for metering and communications to enable participation.
This presentation discusses power transfer issues in vehicle-to-grid (V2G) and grid-to-vehicle (G2V) systems. It outlines some of the major challenges including high installation costs, battery life degradation from frequent charging/discharging, needs for frequency regulation when vehicles connect and disconnect from the grid, effects of harmonics on power transfer, and losses that increase costs. Solutions proposed include using more advanced battery materials, installing filters to reduce harmonics, developing controls for frequency regulation, and improving converter efficiencies to reduce losses. The presentation concludes that while V2G/G2V is possible, further work is needed to address technical issues and make the concept practical for real-world implementation.
The document discusses smart vehicle-to-grid (V2G) applications from the EnerWare team's final project report. It notes that V2G technologies allow electric vehicles to provide energy storage and ancillary grid services when not in use. By 2017, it is projected there will be over 5 million plug-in electric vehicles worldwide. The technologies have the potential to provide revenue to vehicle owners and help buildings and power grids balance energy supply and demand. The team's smart V2G applications aim to create this win-win situation through investments in infrastructure, vehicle technology, V2G-ready parking lots, and wireless charging spots.
IRJET- A Review on Designing of 100KV Grid Power using Hybrid ParametersIRJET Journal
This document reviews the design of a 100kV grid power system using hybrid wind and solar energy sources. It discusses using a converter and inverter to integrate the wind and solar energy inputs and provide high quality AC power to the grid. The aim is to maximize energy extraction from both sources and maintain a stable output voltage. Several previous studies on hybrid renewable energy systems, multilevel inverters, and maximum power point tracking algorithms are also summarized to provide relevant background.
The Role of Energy Storage in the Future Electricity SystemLorenzo Kristov
Energy storage at various scales can be the key to integrating large amounts of renewable generating resources into the electric power system. Growth of storage is advanced by a combination of policies and economics. Presentation for the Portuguese National Committee of CIGRE, 2017.
This document discusses replacing retiring coal plants with either energy storage or combustion turbines (CTs) for peaking capacity. CTs have historically been the preferred option but are inefficient and emit pollutants. The document argues that advanced energy storage is becoming cost competitive with CTs and is more flexible, providing capacity and grid balancing services at both regional and local levels. By 2017-2018, the capital costs of 4-hour energy storage are projected to be on par with or cheaper than CTs, making storage the better option for meeting peaking capacity needs.
V2X is an umbrella term to explain the use of Electric Vehicle (EV) batteries to provide energy services and derive additional value from the battery asset during times of non-use. V2X services aim to generate revenue from the battery asset through dynamic or bi-directional charge control to provide benefits to the electric grid or to reduce/flatten/shift peak energy consumption of buildings and can be classified in the following operating modes:
· Vehicle-to-Grid (V2G): Using an EV battery to interact with/provide value to the electric grid
· Vehicle-to Building (V2B): Operating EV batteries to optimize building energy consumption
· Vehicle-to-Home (V2H): Optimizing home energy consumption or as emergency back-up power
· Vehicle-to-Load (V2L): Any other instance of an EV battery providing energy to a load
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Electric vehicle (EV) charging station powered by the scattered energy sources with DC Nanogrid (NG) provides an option for uninterrupted charging. The NG powered by the renewable energy sources (RES) of photovoltaic (PV) and wind energy. When the excess power produced by the renewable energy stored in the local energy storage unit (ESU) utilized during shortage power from the renewable sources. During the overloading of NG and demand of energy in ESU; the mobile charging station (MCS) provides an uninterrupted charging. The MCS provides an option for battery swapping and vehicle to grid feasibility. The MCS required to monitor the state of charge (SOC) and state of health (SOH) of the battery. Monitoring of SOC and SOH related to the various battery parameters like voltage, current and temperature. A laboratory prototype is developed and tested the practical possibility of EV to NG and Internet of things (IoT) based monitoring of battery parameters.
V2G allows electric vehicles to provide power to the electrical grid during periods of peak demand by allowing two-way power flow. There are three main versions of V2G involving battery-powered vehicles that can provide power to the grid from excess battery capacity during peak times and recharge during off-peak times. V2G systems provide benefits like peak load leveling and spinning reserves but challenges include potential grid overloading and high vehicle costs compared to ICE vehicles.
V2G Opportunities And Impacts E3 2008 S Mullenmull0197
The document discusses vehicle-to-grid opportunities and impacts, including how plug-in hybrid electric vehicles (PHEVs) can provide power to the electric grid through two-way communication. PHEVs could supply power for ancillary grid services, reduce peak loads, and help integrate renewable energy. The author models how aggregated PHEVs could support the electric system and designs vehicle controls to optimize energy use while meeting driver needs. Batteries in PHEVs are seen as ideal for short-term grid services and providing customer choice if communication and control infrastructure is improved.
Opportunities for v2 g integrating plug-in vehicles and the electric grid (to...CALSTART
CALSTART New Fuel Program Manager, Dr Jasna Tomic, presented on vehicle-to-grid technology at UCLA in April 2011. In this presentation, Dr Tomic addresses some of the barriers and opportunities to use battery power generated by a new generation of electric vehicles by putting it back onto the grid.
Dr. Praveen Kumar presented on the concept of Grid to Vehicle (G2V) power. He explained that as electric vehicles become more common, their batteries could provide power storage and generation back to the electric grid. This would allow electric vehicles to provide ancillary power services to help maintain grid stability. G2V power could benefit both vehicle owners through additional revenue and utilities by reducing costs and emissions compared to traditional peak power generation. However, integrating large numbers of electric vehicles into the grid also presents technical and regulatory challenges that would need to be addressed.
This document discusses opportunities for electric vehicle (EV) charging infrastructure and vehicle-to-grid (V2G) technology. It notes that greater adoption of renewable energy and efforts to improve grid efficiency have increased demand for customer demand management solutions, making V2G a promising option. An integrated home solution could manage energy use and enable V2G benefits. V2G infrastructure could allow EVs to support the grid during peak demand periods using stored battery power, helping supplement generation and reduce the need for load shedding. Aggregating many EVs could provide substantial benefits to utilities and grid stability with renewable energy integration.
South caspian summit cook 14th april 2015ChrisJCook
The document proposes funding the development of a Caspian Energy Grid through innovative financing mechanisms to improve energy infrastructure connectivity and efficiency in the Caspian region. It suggests establishing a "Clearing Union" platform where investors can purchase energy credits at a discount, which producers and consumers can then use to fund infrastructure upgrades. As a proof of concept, it outlines plans for a Kazakhstan-Azerbaijan-Turkmenistan energy triangle connecting Turkmen gas and power generation assets to the other countries via undersea high voltage direct current cables, which could help Turkmenistan export surplus energy while conserving domestic gas resources. The proposal aims to minimize development costs through "energy loans" that are repaid with energy rather than interest, increase energy market integration
This document proposes funding the development of a Caspian Energy Grid through a prepay energy credit system and energy loans. It would connect energy generation and transmission infrastructure across the Caspian region to improve efficiency. Currently infrastructure is fragmented, inefficient, and wastes carbon fuels. The proposal suggests upgrading legacy systems and building new high-voltage direct current connections. This would allow countries like Turkmenistan to export excess power. Investors could provide funding by buying discounted prepay energy credits or lending through energy loans repaid in energy units. A pilot project called KAT Core involving Turkmenistan, Kazakhstan, and Azerbaijan is presented as a proof of concept.
This document discusses vehicle-to-grid (V2G) technology which allows electric vehicles to provide power to the electric grid during periods when they are parked and connected to the grid. V2G technology integrates electric vehicles into the smart grid and allows them to provide services like frequency regulation. It describes how V2G works by establishing communication between vehicles and charging stations that can then form a "virtual storage network" to provide balancing services to the electric grid. V2G provides benefits like improving power quality and reducing electric bills but requires standards and legislation to fully integrate electric vehicles into grid operations.
APPLICATION OF PHEVs FOR SMART GRID IN INDIAN POWER SECTOR1Eshwar Pisalkar
This document discusses the application of plug-in hybrid electric vehicles (PHEVs) for smart grids in the Indian power sector. It describes how PHEVs can help with demand side management by charging from renewable energy sources and providing power back to the grid. The document also examines issues like harmonic distortion caused by PHEV charging and its impact on transformers. It proposes a distributed strategy for PHEV charging and discharging that involves regional dispatch centers and charging stations. Finally, it suggests that introducing electric 2-wheelers widely in India first could significantly reduce household power demand.
This document discusses a proposed prototype for an ultracapacitor-operated city bus. Ultracapacitors provide advantages over lithium-ion batteries such as longer lifespan, ability to charge and discharge quickly, and increased safety. The prototype would use multiple 3V motors powered by ultracapacitors instead of batteries. Calculations are shown for selecting the capacitor, booster, and motor specifications needed. The objectives are to design and demonstrate a hybrid electric transit bus with reduced emissions and operating costs compared to diesel buses.
Hosted by KTN, this event brought together the projects that were funded as part of the £30 million UK government funding to support and develop vehicle-to-grid (V2G) technologies - aiming to enable electric cars and other vehicles to deliver electricity back to the smart grid, to light homes and power businesses.
A great opportunity for local councils, fleet owners (looking to go electric) or those in the Energy and Infrastructure sectors.
The event looked at:
- Benefits and learnings from the V2G cohort projects;
- V2G Grid connections (as it was noted that the G99 process could be elongated at times)
- Update from Ofgem (including a Q&A session)
Find out more: https://ktn-uk.co.uk/news/v2g-vehicle-to-grid-cohort-the-future
Demand response (DR) resources can provide various services to wholesale electricity markets including energy, capacity, and ancillary services depending on the market rules. DR is seen as an important tool for reliable grid operations and preventing market power issues. Federal regulations and funding aim to improve the role of DR by requiring comparable treatment of DR and generation in markets and supporting smart grid demonstration projects. Individual ISO/RTO markets define different products DR can provide and have requirements for metering and communications to enable participation.
This presentation discusses power transfer issues in vehicle-to-grid (V2G) and grid-to-vehicle (G2V) systems. It outlines some of the major challenges including high installation costs, battery life degradation from frequent charging/discharging, needs for frequency regulation when vehicles connect and disconnect from the grid, effects of harmonics on power transfer, and losses that increase costs. Solutions proposed include using more advanced battery materials, installing filters to reduce harmonics, developing controls for frequency regulation, and improving converter efficiencies to reduce losses. The presentation concludes that while V2G/G2V is possible, further work is needed to address technical issues and make the concept practical for real-world implementation.
The document discusses smart vehicle-to-grid (V2G) applications from the EnerWare team's final project report. It notes that V2G technologies allow electric vehicles to provide energy storage and ancillary grid services when not in use. By 2017, it is projected there will be over 5 million plug-in electric vehicles worldwide. The technologies have the potential to provide revenue to vehicle owners and help buildings and power grids balance energy supply and demand. The team's smart V2G applications aim to create this win-win situation through investments in infrastructure, vehicle technology, V2G-ready parking lots, and wireless charging spots.
IRJET- A Review on Designing of 100KV Grid Power using Hybrid ParametersIRJET Journal
This document reviews the design of a 100kV grid power system using hybrid wind and solar energy sources. It discusses using a converter and inverter to integrate the wind and solar energy inputs and provide high quality AC power to the grid. The aim is to maximize energy extraction from both sources and maintain a stable output voltage. Several previous studies on hybrid renewable energy systems, multilevel inverters, and maximum power point tracking algorithms are also summarized to provide relevant background.
The Role of Energy Storage in the Future Electricity SystemLorenzo Kristov
Energy storage at various scales can be the key to integrating large amounts of renewable generating resources into the electric power system. Growth of storage is advanced by a combination of policies and economics. Presentation for the Portuguese National Committee of CIGRE, 2017.
This document discusses replacing retiring coal plants with either energy storage or combustion turbines (CTs) for peaking capacity. CTs have historically been the preferred option but are inefficient and emit pollutants. The document argues that advanced energy storage is becoming cost competitive with CTs and is more flexible, providing capacity and grid balancing services at both regional and local levels. By 2017-2018, the capital costs of 4-hour energy storage are projected to be on par with or cheaper than CTs, making storage the better option for meeting peaking capacity needs.
V2X is an umbrella term to explain the use of Electric Vehicle (EV) batteries to provide energy services and derive additional value from the battery asset during times of non-use. V2X services aim to generate revenue from the battery asset through dynamic or bi-directional charge control to provide benefits to the electric grid or to reduce/flatten/shift peak energy consumption of buildings and can be classified in the following operating modes:
· Vehicle-to-Grid (V2G): Using an EV battery to interact with/provide value to the electric grid
· Vehicle-to Building (V2B): Operating EV batteries to optimize building energy consumption
· Vehicle-to-Home (V2H): Optimizing home energy consumption or as emergency back-up power
· Vehicle-to-Load (V2L): Any other instance of an EV battery providing energy to a load
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Electric vehicle (EV) charging station powered by the scattered energy sources with DC Nanogrid (NG) provides an option for uninterrupted charging. The NG powered by the renewable energy sources (RES) of photovoltaic (PV) and wind energy. When the excess power produced by the renewable energy stored in the local energy storage unit (ESU) utilized during shortage power from the renewable sources. During the overloading of NG and demand of energy in ESU; the mobile charging station (MCS) provides an uninterrupted charging. The MCS provides an option for battery swapping and vehicle to grid feasibility. The MCS required to monitor the state of charge (SOC) and state of health (SOH) of the battery. Monitoring of SOC and SOH related to the various battery parameters like voltage, current and temperature. A laboratory prototype is developed and tested the practical possibility of EV to NG and Internet of things (IoT) based monitoring of battery parameters.
DESIGN AND SIMULATION OF SOLAR BASED FAST CHARGING STATION FOR ELECTRIC VEHIC...IRJET Journal
This document discusses the design and simulation of a solar-based fast charging station for electric vehicles using MATLAB. It aims to provide a reliable and sustainable charging solution by integrating solar PV panels, energy storage batteries, power electronics components like DC-DC converters and inverters, and advanced control strategies. The design process involves sizing the solar panels to generate sufficient power for fast charging and ensuring continuous power availability through an energy storage system. Power flow is efficiently managed between the solar panels, batteries, and EV charging units through electronic components and control strategies that regulate the charging process based on battery state of charge and other factors.
Adverse effects of fossil fuel burning and internal combustion engine vehicles have alarmed nations worldwide. Governments are taking steps to promote the use of Electric Vehicles due to less carbon emissions and to pacify the environmental issues. The added load of Electric Vehicles poses a threat to the existing grid which leads to instability of the grid. The problem of demand supply mismatching can be solved by integrating the renewable energy sources with Electric vehicle charging station resulting in bi-directional flow of power. Vehicle to Grid technology helps the utility with active and reactive power support by feeding power from battery pack to grid and vice versa. Vehicle to Grid describes a system in which electric vehicles, plug-in hybrid, fuel cells electric vehicles are connected to the power grid to provide high power, spinning reserves, regulation services etc. The perspective of this study is to evolve a smart charging schedule based on the load on grid, time of use of the EV and other factors in order to minimize cost of charging for electric utilities and EVs as well as promote profits to EV owners.
This document is a thesis proposal by S. Kaveripriya on optimally allocating distributed energy resources (DERs) in a distribution system considering electric vehicles (EVs). The objectives are to minimize power losses. The methodology uses loss sensitivity factor and grasshopper optimization algorithm to determine optimal DER locations and sizes throughout the day for different EV charging patterns. Various charging patterns are analyzed to find the best approach for minimizing power losses. The results show smart charging EVs along with optimally placed DERs further reduces power losses compared to other cases.
Energy management strategy for photovoltaic powered hybrid energy storage sys...IJECEIAES
Nowadays, electric vehicles (EVs) using additional energy sources frequently deliver a safe ride without concern about the distance. The energy sources including a battery, an ultra-capacitor (UC), and a photovoltaic (PV) are considered in this research for driving the EV. Vehicles that only use battery-oriented technologies experience problems with charging and quick battery discharge. EVs are used with an ultracapacitor to decrease the quick discharge effects and increase the lifetime of the battery. Furthermore, bidirectional DC-DC converters are a type of power electronics device used to verify the smooth transfer of generated power from energy sources to the motor throughout various stages of the driving cycle. Therefore, this study proposes a perturb and observe (P&O) energy management control technique based on tuna swarm optimization (TSO). The suggested TSOP&O completely uses UC while regulating the battery because it lowers dynamic battery charging and discharging currents. Due to the aforementioned aspect, the suggested TSO-P&O increases battery life and demonstrates a very dependable, long range power source for an electric car. The TSO-P&O technique achieves the EVs by obtaining the maximum speed of 91.93 km/hr. with a quicker settling time of 4,930 ms when compared with the existing zero-fuel zero-emission (ZFZE) method.
Grid reactive voltage regulation and cost optimization for electric vehicle p...nooriasukmaningtyas
Expecting large electric vehicle (EV) usage in the future due to environmental issues, state subsidies, and incentives, the impact of EV charging on the power grid is required to be closely analyzed and studied for power quality, stability, and planning of infrastructure. When a large number of energy storage batteries are connected to the grid as a capacitive load the power factor of the power grid is inevitably reduced, causing power losses and voltage instability. In this work large-scale 18K EV charging model is implemented on IEEE 33 network. Optimization methods are described to search for the location of nodes that are affected most due to EV charging in terms of power losses and voltage instability of the network. Followed by optimized reactive power injection magnitude and time duration of reactive power at the identified nodes. It is shown that power losses are reduced and voltage stability is improved in the grid, which also complements the reduction in EV charging cost. The result will be useful for EV charging stations infrastructure planning, grid stabilization, and reducing EV charging costs.
A Generalized Multistage Economic Planning Model for Distribution System Cont...IJERD Editor
This document presents a generalized multistage economic planning model for distribution systems containing distributed generation (DG) units. The model minimizes total investment and operation costs over a planning horizon divided into multiple periods, taking into account load growth, equipment capacities and voltages limits. Constraints include power flow equations and logical constraints relating planning periods. The model is applied to a sample 11kV distribution network with one substation, 23 load buses and 32 feeders over 4 annual periods. The mixed integer nonlinear optimization problem is solved using LINGO software to obtain the least-cost expansion plan.
This document summarizes a research paper that presents a model for optimizing merchant investments in energy storage units that can compete in both energy and reserve markets. The model uses a bilevel programming framework to maximize the expected lifetime profit for the energy storage investor while considering market clearing decisions over characteristic operating days. The bilevel model is converted to a single-level mixed-integer linear program using Karush-Kuhn-Tucker conditions and solved using Benders' decomposition. A case study on the ISO New England test system provides insights into how optimal energy storage siting and sizing is affected by changing capital costs and reserve requirements.
Design and development of smart interoperable electric vehicle supply equipme...IJECEIAES
The transportation industry at present is moving towards electrification and the number of electric vehicles in the market increased with the different policies of the directorate. Consumers, who wish to contribute to green mobility are concerned about the limited availability of charging points due to high manufacturing costs and the interoperability issues related to smart charging. This work proposes an internet of things-based low-cost, interoperable smart electric vehicle supply equipment for deploying in all charging stations. The device hardware is designed to monitor, analyze, and collect consumed energy by the vehicle and transfer this data to a connected network. The pre-defined messages associated with the firmware will help to record this data with a remote management server for further processing. The messages are defined in JavaScript Object Notation (JSON), which helps to overcome the interoperability issue. The device is smart because it can gather energy usage, detect device faults, and be intimate with the controller for a better operational environment. The associated management servers and mobile applications help to operate the smart device remotely and keep track of the usage statics. The developed low-cost, interoperable smart model is most suitable for two and three-wheeler vehicles.
This document presents a two-stage model for daily volt/var control (VVC) of distribution systems that includes distributed energy resources (DERs) like wind turbines and synchronous machine-based distributed generations. The first stage is a day-ahead market that minimizes electrical energy costs and gas emissions from generation units to determine an initial schedule. The second stage examines this schedule from an operational perspective to determine optimal daily dispatches of VVC devices while minimizing losses, adjustment of scheduled active powers, and depreciation costs. It uses Benders decomposition to solve the mixed integer nonlinear programming problem, and tests the approach on two distribution test networks.
Simulation studies on developed Solar PV Array based Multipurpose EV Charger ...IRJET Journal
This document describes a solar photovoltaic array-powered electric vehicle charger that can operate in both grid-connected and standalone modes. The charger is designed to charge EVs using power from the PV array or grid. It can also power local household loads using PV, EV, or grid power. The charger controls include maximum power point tracking for the PV array, regulation of the DC bus voltage, synchronization between the grid and charger voltages, and seamless switching between operating modes. Simulation studies were conducted to validate the charger's multipurpose and integrated functionality. The charger aims to make better use of distributed energy resources while providing uninterruptible power for EV charging and household loads.
A Review on Electric Vehicle Charging SystemsIRJET Journal
This document reviews electric vehicle charging systems. It discusses different types of chargers and charging methods, including onboard and off-board chargers. It also covers battery management systems and their functions in monitoring battery state, balancing cells, and ensuring safety. The document reviews literature on electric vehicle charger topologies and control algorithms. It aims to understand lithium-ion battery operation and charging, accurately determine state of charge, and design an intelligent onboard level 1 charging system and automatic payment system. Level 1 charging uses a standard outlet but is slow, making it suitable for multi-unit dwellings and backup charging. Pricing strategies can influence when drivers remove fully charged vehicles from stations.
The document provides an overview of electric vehicles and their integration into smart grids. Some key points:
- Electric vehicles (EVs) are becoming more prevalent and can provide flexibility to smart grids through technologies like vehicle-to-grid that allow EVs to send power back to the grid.
- Unmanaged EV charging could strain the grid during peak times. Smart charging, where charging is shifted to off-peak periods, helps address this issue.
- The smart grid allows for remote monitoring and management of EV charging stations to optimize charging and provide services to the grid like demand response.
- Widespread EV adoption could both challenge and benefit utilities by requiring grid upgrades but also opening opportunities through customer relationships and
High Performance Smart on Board Battery ChargerIRJET Journal
This document discusses high performance smart onboard battery chargers for electric vehicles. It contains the following key points:
1. It describes the various components of a battery charger, including the grid connection, filters, front-end converter, DC-DC converter with galvanic isolation, and lithium-ion battery.
2. It explains the functions of each component and technologies involved like current sensors, capacitors, voltage sensors, and converters.
3. It concludes that increasing public charging infrastructure is needed to support the growing adoption of electric vehicles and addresses remaining challenges in areas like thermal management, fast charging impacts, and battery degradation.
Three Receiver Coil System for Dynamic Wireless EV Charging Dynamic Charging ...IRJET Journal
Three Receiver Coil System for Dynamic Wireless EV Charging
The document proposes a three-receiver coil system for wireless charging of electric vehicles. A single-coil system requires high current, resulting in increased losses and bulkier circuits. A three-coil system spaced evenly on the vehicle can draw high power more efficiently. Various transmitter coil cluster configurations are analyzed, including four coil, six coil, and ten coil designs. A ten coil design best maintains flux intensity across the lane width. The three-coil system reduces no-load coils compared to single-coil, lowering losses. It allows universal retrofitting to different vehicle sizes while providing continuous high power transfer.
Design hybrid micropower system in mistah village using homer modelIAEME Publication
The document presents a case study analyzing different hybrid micropower system designs for a remote village in Iraq using the HOMER modeling software. Three cases are analyzed: 1) diesel generator only, 2) wind, hydro, diesel generator, converter, and batteries, and 3) PV, wind, hydro, diesel generator, converter, and batteries. The optimal configuration was found to be case 2, with a total net present cost of $705,643 and cost of energy of $0.422/kWh. This hybrid system was shown to have lower costs than grid extension for distances over 3.55km from the main power station. Emissions were also calculated and found to be lower for the hybrid systems compared to diesel generator
Allocation of Transmission Cost Using Power Flow Tracing MethodsIJERA Editor
In the open access restructured power system market, it is necessary to develop an appropriate pricing scheme that can provide the useful economic information to market participants, such as generation, transmission companies and customers. Though many methods have already been proposed, but accurately estimating and allocating the transmission cost in the transmission pricing scheme is still a challenging task. This work addresses the problem of allocating the cost of the transmission network to generators and demands. In this work four methods using DC Power flow and AC power flow have been attempted. They are MW-Mile Method, MVA-Mile Method, GGDF method and Bialek Tracing method.MVA-Mile method and Bialek Tracing method applies AC power flow and considers apparent power flows. The purpose of the present work is to allocate the cost pertaining to the transmission lines of the network to all the generators and demands. A load flow solution is run and, the proposed method determines how line flows depend on nodal currents. This result is then used to allocate network costs to generators and demands. The technique presented in this work is related to the allocation of the cost to GENCO‘s TRANSCO‘s and DISCO‘s. A technique for tracing the flow of electricity of lines among generators with GGDF and Bialek upstream looking algorithm is proposed. With these methods correct economic signals are generated for all players. All these methods are tested on IEEE 14 bus system.
Design and Implementation of Real Time Charging Optimization for Hybrid Elect...IAES-IJPEDS
This document describes a proposed real-time charging optimization system for hybrid electric vehicles using an Android application. The system would provide information like the vehicle's battery state of charge and location to help users find and reserve charging slots. It would calculate estimated time and distance to charging stations to allocate parking. Emergency mobile charging stations would also be available. The hardware implementation monitors a battery's voltage using an Arduino board and communicates the state of charge to an Android device via Bluetooth. Algorithms are developed to predict if a destination can be reached based on state of charge and provide alternate routes to nearby charging stations if needed. The system aims to make electric vehicle charging more convenient and help users efficiently plan routes and charging.
IRJET- Real-Time Forecasting of EV Charging Station Scheduling for Smart Ener...IRJET Journal
This document proposes a real-time forecasting system for scheduling electric vehicle (EV) charging stations. It develops models to predict spatial and temporal distribution of EV charging demand based on traffic flow, optimal path planning and queuing theory. The system would allocate charging spaces using estimated battery parameters from an interactive client app. It communicates with charging stations to obtain availability data and schedules EV charging to avoid wait times and ensure vehicles do not run out of battery. The proposed server-based forecasting infrastructure aims to improve the current EV charging framework.
Similar to Optimal scheduling of smart microgrids considering electric vehicle battery swapping stations (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
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ESS
P
Charge power of ESS (kW)
fcN Number of fully-charged batteries
EV
P
Charge power of EV (kW)
cN Number of batteries to charge
curtP Power reduction of load (kW)
dN Number of batteries to discharge
imbP
Unsatisfied power for load (kW)
swN Number of batteries to swap
imb
P
Exceeded power of DG unit (kW)
iN Number of initial batteries in the station
buyP Power bought to the market (kW)
DN Initial EV station visits (EV demand)
sellP Power sold to the market (kW)
evN Number of EVs
PVP Photovoltaic generation (kW) J Demand welfare
Parameters
Full charging battery interval
buyC Cost for buying energy (m.u./kWh)
Full discharging battery interval
sellC Cost for selling energy (m.u./kWh)
DGN Number of DG
s Probability of scenario 𝑠
kN Number of external suppliers
PVP Photovoltaic generation (kW)
eN Number of ESSs
loadP Forecasted load (kW)
LN Number of loads MP Market price in wholesale and local markets
(m.u./kWh)
mN Number of markets
swP Price for swapping a batterie (m.u.)
sN Number of scenarios
mP Price for missing a battery (m.u.)
T Number of periods
eMP EVs market price (m.u.)
DGC Generation cost of DG (m.u./kWh) d
Efficiency for discharging
extC Cost of external supplier (m.u./kWh) c
PVC Cost of PV generation (m.u./kWh)
ESSC Cost of ESS (m.u./kWh)
Charger rate of the on-board EV (kW)
loadC Cost of load (m.u./kWh)
Charger rate from the charger level specification
(kW)
imbC Grid imbalance cost (m.u./kWh) ev Type of EV
1. INTRODUCTION
Electric vehicles (EVs) have shown additional benefits compared with their fossil fuel vehicle
counterparts [1]. They produce fewer emissions even when considering their whole process of energy
production, independently of their energy source [1]. In addition to lower emissions, EVs may also use
renewables energies as a source of power [1]. However, one of the greatest deficiencies of EVs is that
conventional vehicles can easily be refueled, while EVs require long charging times that need planning.
This is combined with the difficulties in setting up an infrastructure with specialized equipment to facilitate
the use of EVs [1]. Given these scenarios, the sustainable EV operation must rely on the efficient EV
scheduling, among others.
There are still challenges in the deployment of EV charging infrastructure. First, EVs increase
the power demand on the grid [2]. Second, when distributed generation is available, uncertainty over
the photovoltaic and wind generation, electricity prices, load forecasting, and swapping demand can
influence the operation of the electrical network [3]. Furthermore, demand management considers strategies
for flatting the demand of EV´s and load with demand response (DR), such as moving the peak load to valley
hours [3]. Third, time role is crucial to schedule events, such as swapping, charging, and discharging of EVs
batteries [4]. Fourth, the longevity of battery life is a concern for end users given the level of charging and
discharging that users must undergo [4]. Fifth, consumers may have range anxiety on whether EVs batteries
can last all their trips. These barriers can also represent restrictions to the proper use of the microgrid with
EVs. As an illustration, if the customers distrust both EV technology and the associated infrastructure.
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This risky behaviour may result in the overuse of charging stations [4]. Finally, the overall inherent
uncertainties that the aggregator must manage when implementing the smart microgrid (SMG) is significant.
In fact, SMG is defined as a group of loads with DR and energy storage systems (ESSs), as much as they act
as a unified entity. SMG operates in both connected and isolated modes [5]. However, demand of users,
market prices, and uncertain renewable energy production may lead to barriers in creating efficient charging
station infrastructures [6]. To countermeasure these challenges, an aggregator can be formulated [6, 7].
Energy aggregators can be made by integrating EVs and ESSs. Furthermore, battery swapping
stations (BSSs) have more competitive visit times of EVs in comparison with traditional stations, which only
have the option of charging batteries [6]. Regarding the schedule, public transport would prefer swapping
the battery instead of charging batteries due to the critical operating time [6, 7]. In SMGs, a forecasting issue
is the prediction of EVs visits [6]. An aggregator may deal with this problem, as it manages components such
as photovoltaic panels, loads with DR, ESSs, and EVs [8]. The last two can both discharge (or charge) power
and buy (or sell) energy in SMGs [9]. The aggregator coordinates the service reliability among
the stakeholders, EVs scheduling, hours of load shifting, or valley filling of EVs. Aggregators should also
consider uncertainties such as EVs trips planning, forecasting load, electricity prices, and generation with
renewable energies [10]. Finally, the increase in SMG profits has been addressed with metaheuristic
optimization tools, producing outstanding solutions in acceptable times [9].
Under this context, the proposed SMG model introduces an aggregator, who optimizes EV BSS.
Equally notable, this approach is formulated using a decision matrix, which aims to simulate operating
conditions such as charging (or discharging) times, stochastic visits, and batteries swapping of EVs. Without
overlooking other elements, this study formulates the programming of traditional and renewable energies,
ESSs, electricity markets, and loads with DR. The main contributions of this paper are described below:
The SMG model introduces stochastic scheduling due to the EV station visits based on the K-means
clustering approach, and price variability in electricity markets. Both aspects are formulated according to
the operation approach of EV BSS. To be more comprehensive, the SMG model includes uncertainty
conditions, such as weather forecast, load prediction, and wholesale and local markets.
A decision matrix to EV BSS is scheduled introducing significant elements in EVs, such as state of
charge (SOC) of EVs, type of vehicle, probability of EVs visiting the BSS, state of the battery (swap or do
nothing) and idle spaces (per hour). The idle spaces are generated when the charge of a battery must be
mandatory, to exemplify a reserve of batteries that guarantees a good service in the case of unexpected visits
of EVs in the BSS. The proposed SMG model has continuous and discrete variables, as well as a decision
matrix that introduces a new problem related to integer numbers, sequence of numbers and idle spaces.
Therefore, the optimization algorithm “variable neighbourhood search-differential evolutionary particle
swarm” (VNS-DEEPSO) is implemented in order to reduce the cost in SMGs with uncertainty environments.
This document is organized as follows: section 2 presents the state of the art of SMG models that
include EV BSS. Section 3 formulates the SMG model and emphasizes the constraint of the SMG with EV
BSSs. In section 4, the uncertainty sources are highlighted and the EV BSS matrix with uncertainty is
presented, showing the operational strategy. In section 5, the case study and results are discussed. Finally,
section 6 outlines the conclusions.
2. STATE OF THE ART
SMGs have challenges related to the planning of the day-ahead operation of EVs. At the same time,
other elements (load with demand response - DR, generation - Gen and ESSs) must be coordinated [7].
Six elements are tackled as main contribution of this study: (1) load with DR, (2) Gen, (3) ESSs,
(4) EV-prosumer (EVP), (5) BSS, and (6) uncertainty of EV BSS, Gen, loads with DR and energy markets.
Hereinafter, the six terms are called uncertainty sources. According to the information collected, Table 1
organizes SMG models from 1 to 10, based on their main characteristics.
SMG model 1 schedules the day ahead, where the optimization problem is solved by assembling
the genetic algorithm with the feasible solution region [3]. Therefore, an economical operation of BSS is
proposed when it chooses the level of tolerable risk. This BSS model operates as an isolated network, that is,
it does not integrate other elements of the grid and eliminates the discharged function of EVs batteries [3].
SMG model 2 coordinates the charging, discharging, and swapping of batteries. When EVs can
buy/sell energy to the grid, this role is called EVP. The swapping prices and charging intervals are compared
with traditional strategies. Then, the results demonstrate that the profit of BSS coordination is acceptable in
comparison with other strategies. Nevertheless, the associated cost does not take into account other elements
of the grid [6].
SMG model 3 includes other elements such as EV operation, photovoltaic (PV) generation,
electricity market pricing, and DR. In this case, the EV model has no comprehensive description of scenarios
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and constraints [9]. SMG model 4 proposes the dispatch of energy for EVs, wind and photovoltaic power
generation. In fact, this EV model associates EVs with each node and only the load power varies. Therefore,
the model lacks an approach of discharging, swapping batteries, and constraints [11].
BSS model 5 gathers the EVs in programs of DR. The case study demonstrates that ignoring
the uncertainty of EVs traffic and PV generation leads to an inappropriate planning. Furthermore, BSS model
5 assumes simplifications without considering other elements of the grid and the uncertainty of the market
prices [12]. The model 6 for battery charging stations considers the uncertainty over charging EVs.
EV uncertainties are modeled through a Gaussian distribution model. In addition, the EVs are connected to
the grid, PV generation, and battery-ESSs. In spite of this, the last variables have uncontrolled stochastic
uncertainties, since there are no details about these uncertainty models [13].
BSS model 7 forecasts wind-power generation and uses real time prices to plan in short time.
However, the EV model takes into account the uncertainty over charging EVs [14]. BSS model 8 considers
the stochasticity in energy markets and demand management of batteries. Nevertheless, model 8 simplifies
the interaction between stakeholders and EV BSS [15].
BSS model 9 purchases power in an upstream network and maximizes the day-ahead incomes.
This bi-level model is divided into lower and upper levels. This model has acceses to the mechanism of
real-time prices. The BSS model includes a micro-turbine, PV and wind generation. The uncertainty of
the last two is considered together with the uncertainty of the load demand and the arrival time of EV.
However, this model includes neither the ESS nor the load shedding restrictions [16]. A framework is
proposed to restrict the load shedding in [17]. Other research also considers the uncertainty of PV and wind
generation and introduces an index of probability of islanding operation [18].
BSS model 10 is formulated in this paper and integrates constraints to EVs through BSS, where BSS
uncertainty is simulated by means of a K-means clustering approach. Moreover, it integrates SMG with
uncertainty elements such as PV generation, electricity markets, and loads with DR. SMG planning is
performed by the aggregator, which tries to increase the profits of the SMG. Then, the aggregator minimizes
the operational cost while increasing the incomes. Indeed, the transactions in electricity markets generate
profit. Besides, the aggregator has assets, such as EVs BSSs and ESSs, which become prosumers as they can
either buy or sell energy.
Table 1. Review of microgrids with EVs BSS
No DR Gen ESS EVP BSS Uncertainty sources
1 No Yes No No Yes EVs and PV generation [3]
2 No No No Yes Yes EVs and electricity markets [6]
3 Yes Yes Yes Yes No EVs, PV generation, electricity markets, and loads with DR [9]
4 Yes No No No No EVs, wind-power, and PV generation [11]
5 Yes Yes No No Yes EVs and PV generation [12]
6 No Yes Yes No No EVs [13]
7 No Yes No No Yes Electricity markets and wind-power generation [14]
8 No Yes No Yes Yes Battery demand and electricity markets [15]
9 No Yes No Yes Yes PV and wind-power generation, load demand and EVs [16]
10 Yes Yes Yes Yes Yes EVs, PV generation, electricity markets, and loads with DR (This SMG
model is formulated in this paper)
The BSS model formulation can be applied for DR programs [19]. The load is usually involved in
DR programs [9]. In the BSS model, the aggregator negotiates energy in the electricity spot price. In the BSS,
one available asset is the battery bank in the BSS that can be encoded with a EVs BSS matrix.
The aggregator deals with the uncertainty in the arrival time of EVs and the volatility of market prices.
For both, the uncertain scenarios are estimated with the K-means grouping method. In the case study, vehicle
driving patterns are forecast based on NWS traffic [6]. Load management for EVs should tackle congestion
times and relieve the scarcity of energy [20]. The SOC eases the scheduling the batteries charge or discharge
as indicator [20].
In this way, SMG model 10 is the most comprehensive, taking into account the previous revision in
Table 1. Satisfactory solutions of vital importance are inspected, through the formulation of heuristic
methods [9, 21]. These methods have been widely studied, however, there are still development gaps related
to yields, and exploration and exploitation methods [21]. A vast majority of metaheuristics proposes
a sequential combination of heuristics to be more successful [21]. For example, a hierarchy analysis is set out
among several algorithms such as enhanced velocity differential evolutionary particle swarm optimization
algorithm. In Figure 1, the main algorithms reported in the literature are organized according to the hierarchy
of a better suboptimal solution [9, 22]. In summary, VNS-DEEPSO algorithm turns out to have a greater
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hierarchy regarding the SMG models proposed in [9, 22] and it is described in two (VNS and DEEPSO) main
components below. VNS-DEEPSO is widely tested in a robust optimization approach [9]. This research finds
out a robust solution with low sensibility to some uncertainty parameters, such as PV power forecast, load
prediction, EVs planning, and wholesale and local markets [9].
Figure 1. Hierarchy of the best suboptimal solutions based on [9, 22]
2.1. Variable neighborhood search-VNS
VNS is used in nonlinear optimization, as it can find a local optimum with quality solutions [23].
The algorithm makes automatics neighborhood changes and aims to extend the local search. The VNS
algorithm is defined in two main stages: initialization and repetition. The initialization stage consists in
defining the neighborhood structures. In the repetition is carried out in three steps. First step, the initial
solution is found in the neighborhood. Second step, the search method looks for an initial solution called
local optimum. In the third step, the local optimum is stored. Afterward, when the new solution improves
the local optimum solution, then the local optimum solution will be updated. Two cases can happen:
first, the new solution found in the process is worse than the local optimum or second, the new solution found
is the same as the local optimum. In both cases, the search continues for the next neighborhood structure.
These steps are repeated until the last neighborhood structure [23, 24].
2.2. Differential evolutionary particle swarm optimization-DEEPSO
DEEPSO algorithm was developed based on three metaheuristics: differential evolution (DE),
evolutionary algorithm (EA) and particle swarm optimization (PSO). These methods try to find the global
correct direction and jointly search for a robust solution [25]. EA algorithm uses biological operators such as
reproduction, mutation, recombination, and selection. Therefore, the population evolves through biological
techniques. Furthermore, the fitness determines the solution quality [26].
PSO algorithm is a set of candidate solutions as swarm particles. They flow in tracks of the search
space. The search motivation is the best performance of them and their neighbors [27]. DE algorithm has
three or four operational parameters and in roughly 20 lines, it includes a simple and adaptable mutation
operator [26]. Finally, DEEPSO algorithm was introduced as a metaheuristic technic and is a hybrid among
EA, PSO, and DE. It has best outcomes than other techniques, however, it does not provide a deductive
demonstration [25].
3. SMART MICROGRID MODEL
This section presents the main assumptions in the BSS model 10 of SMG that represents the profits
(including operation costs and incomes). Figure 2 represents the main elements of the SMG (ESS, EV BSS,
load with DR, day-ahead markets, PV generation and DG). The arrows mean buy or sell energy: selling is
symbolized by the line entering to the aggregator and buying by the line coming out from the aggregator.
The red line highlights the elements with uncertainty. The SMG model is obtained by codifying a black box
model of SMG from [28] and a decision matrix is developed by collecting available data from [29, 30],
together with the EV uncertainty model based on the methodology from [6]. The main SMG assumptions are
listed below.
Variable neighborhood search differential evolutionary particle swarm (VNS-DEEPSO)
First
hierarchy
Evolutionary particle swarm optimization (EVDEPSO)
Second
hierarchy
Ant colony optimization (ACO), chaotic evolutionary swarm optimization (CESO), cuckoo
search (CS), evolutionary particle swarm optimization (EPSO), flower pollination (FP),
differential evolution (DE), genetic algorithm (GA), hybrid simulated annealing (HSA),
hybrid differential search algorithm (HDSA), particle swarm optimization (PSO), quantum
particle swarm optimization (QPSO), simulated annealing (SA) and Tabu search (TS).
Third
hierarchy
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- SMGs is a component of the smart grid, but also SMGs operate independently and self-sufficiently.
By the same token, SMGs consist of a set of loads and generators operating as a unique system [31].
In this context, SMGs have aggregators that try to increase the SMG profits.
- SMGs are flexible in connecting distributed generation (DG) and PV generation [32].
- Two electricity markets are considered wholesale and local, and the EV prices are estimated with
the Australian Market Operator from January 2016 to June 2019. Prices from electricity markets lead to
revenues for selling or expenses for buying energy [28].
- A realistic approach of SMG includes energy resources with uncertainty. The uncertainty sources come
from (a) PV renewable generation, (b) load profiles, (c) visit of EVs to BSSs, and (d) electricity market
prices for wholesale, local, and electricity spot for EVs [6, 28].
- The aggregator uses ESS and BSS as their own assets and supports the programming of energy
resources of SMG. Indeed, ESS has constraints related to the state of the battery, while EVs have
constraints related to stochastic visits to BSS, brand, and plug type of EV.
- The improvement of SMG profits is formulated with a heuristic optimization algorithm. Specifically,
the VNS-DEEPSO algorithm is a recently studied technique that has demonstrated good performance
for these types of problems [21].
Figure 2. Depiction of energy transactions in SMG
According to the main aspects in the SMG operation, the objective function aims to minimize Z,
that is, the negative of the profits (1), which means that profits are maximized. It is defined as the addition
and subtraction of six terms. The first term introduces the value of buying energy to load with DR.
The second term, Gen can be negative when the energy of DGs and PV generation are sold. The third, fourth
and fifth terms are the prosumers, represented by the ESS, BSS of EVs, and market transactions (MTs).
These costs or revenues are quantified based on the type of transfer. In the first place, the incomes are
represented with a negative value and, in the second place, costs are estimated with a positive value.
The sixth term of (1) is penalties (Pen), which are defined for an imbalance, when the demand is not satisfied
or the generation is exceeded [28]. Fixed costs are related to the purchase of batteries, degradation of
components, and maintenance. As in math, the derivative of a constant is zero. Fixed costs are also constant
(they do not vary regardless of the decision variables), therefore, they are not included in the objective
function, because their change rate is zero (1) [6]. For explanations purposes, the six terms on the right side
of (1) are formulated from (2) to (7).
Equation (2) represents the prices (𝑃) per capacity (𝐶) of loads with DR and the scenarios (s) have
a probability (𝜋(𝑠)). In (3), the total generation is denoted as the sum of DGs, external supplier, and PV
generation. Equation (4) details the operation of the ESS. In (5), BSS revenue is calculated from allocating
a swapped battery 𝑁𝑆𝑊 per price 𝑃𝑆. If EVs visit the station, but there is no battery to swap, then BSS owners
will receive penalties. The penalty is calculated based on the subtraction of an assigned battery 𝑁𝑠𝑤 from EVs
battery demand 𝑁 𝐷 [15]. In (5), the efficiencies (𝜂 𝑑
and 𝜂 𝑐
) refer to charging and discharging of batteries,
Aggregator optimizes energy
transactions of SMG
ESS BSS
Stochastic visit of EVs
Electricity spot price for EVs
Load with
DR
Day-ahead markets
DG
PV
generation
Uncertainty Bidirectional transaction One-directional transaction
Decision matrix for EV BSS, data from [29, 30]
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while 𝑁𝑐 and 𝑁𝑑 represent the number of batteries that are charged and discharged in the electricity
spot price for EVs (𝑀𝑃𝑒). In (6), MTs represent the sale (𝐶 𝑏𝑢𝑦) and purchase (𝐶𝑠𝑒𝑙𝑙) of energy. Finally,
the penalizations (Pen) are made such that the demand is not satisfied, or the generation is exceeded as shown
in (7). This model is structured with mixed-integer linear programming. However, in (9) the constraint for
demand with DR is nonlinear, so it can be classified as mixed-integer nonlinear programming.
minimize Z DR Gen ESS BSS MT Pen (1)
24
, , ,
1 1 1
s LNT
load l t s load l t
s t l
N
DR P C s
(2)
24 24
, , , ,
1 1 1 1
24
, , ,
1 1 1
DG k
s PV
N NT T
DG i t DG i t ext k t ext k t
t i t k
N NT
PV j t s PV j t
s t l
P P C
G
P C s
C
en
(3)
24
, , ,
1 1 1
s eN NT
ESS j t s ESS j t
s t e
ESS P C s
(4)
,
24
, ,
1 1
,
, ,
s
sw t s sw t
N T
m t D t s sw t s
s t
c t sd
e t s d t s c
N P
BSS P N N s
N
MP N
(5)
24
, , , ,
1 1 1
s mN NT
buy m t sell m t m t s
s t m
MT C C MP s
(6)
, ,24
1
1 1
, ,
1
L
s
DG
N
N imb j t s imb tT
l
N
s t
imb j t s imb t
i
P C
Pen s
P C
(7)
The energy balance has constraints shown in (8). The conservative balance should be zero.
The energy balance is composed of the total generation (DG, an external supplier, and PV), ESS, load with
DR, transfer in the market, and imbalance in the generation of energy [28]. EVs BSS has another analysis
equal to the energy balance shown from (10) to (17). This model also considers that EVs and loads are
involved in DR programs. Equation (9) formulates a constraint for the load DR that they should be different
from zero and the demand welfare 𝐽 represents at least 10 % of the total energy being supplied for all
periods [32]. Equation (1) is subject to (8) and (9). EVs encompass the DR program and schedule the BSS
arrival time with the following assumptions. The amount of visits of EVs is expected to be fixed [6].
However, visiting hours are estimated with the K-means clustering method [6]. Therefore, the visiting hours
of EVs is uncertain [16]. The priority in the BSS is that the swapping batteries must be charged. Once
the batteries are charged, the aggregator decides whether to discharge or swap them. This option is only
possible when one or more EVS eventually demand to swap a battery.
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, , , , , , , ,
1 1 1 1
, , , , , , , ,
1 1
, , , ,
1 1
0
DG k PV DG e
mL
DG L
N N N N
DG i t ext k t PV j t s ESS e t s ESS e t s
i k j e
NN
curt l t s load l t s buy m t s sell m t s
l m
N N
imb i t s imb l t s
i l
P P P P P
P P P P
P P
,t T s S (8)
, , ,1 1
, , ,1 1
10
1
T NL
load l t s load l tt l
T NL
load l t s load l tt l
P C
max P C
J e
,t T s S (9)
Fully charged batteries are available per hour. At least one fully charged battery is guaranteed every
period in the BSS. In the event that electric vehicles visit the substation, two cases may occur. In the first
case, the battery is not swapped because the aggregator does not consider it compelling or there are no
batteries available. These assumptions contemplate that the aggregator is penalized, so these situations are
undesirable [15]. In the second case, the battery is swapped. The day begins with a fully charged battery
bank. EVs arriving at the BSS have completely depleted batteries [6]. The charging / discharging capacity of
EVs batteries is constant. This is estimated with the average charging time according to the type of charger
and the EV brand [6]. The battery swapping time is considered negligible [16].
The substation has a constant number of batteries [33]. However, the SOC depends on factors, such
as the visits of EVs and the decisions of the aggregator [16]. Battery degradation is estimated as a fixed cost.
In other words, the variability of costs for battery degradation is assumed negligible for day-ahead
planning [33]. The aggregator can swap several batteries at the same time [33]. In accordance with
the aforementioned considerations, the restrictions for the programming of EVs are formulated.
In (10), fully-charged batteries (𝑁𝑓𝑐) include the previous fully-charged batteries in each period, new
batteries that are fully-charged (𝑁𝑐) and depleted batteries that are swapped for fully-charged batteries (𝑁𝑠𝑤).
An additional function of EV batteries consists in supplying the SMG demand. In (11), the waiting time for
charging or discharging is represented by the interval charging time (𝛿) or interval discharging time (𝜁).
𝛿 is calculated as the ratio between the vehicle battery capacity (𝜐 𝛽
) and the minimum between the power of
the on-board EV (𝜐 𝛼
) and the allowed power from the charger level specification (𝜐 𝛾
).
The acceptance rate is limited by the EV technology for charging or discharging in BSS.
EV charging levels are frequently grouped into three categories as shown in Table 2 [34]. Level 1 chargers
are required either at home or in overnight process. Level 2 chargers have two uses in public or private
facilities. Level 3 chargers are developed with technology that allows a quick charge in a short time;
however, these advances are at a premature phase yet [34].
The next EV restriction (12) establishes the initial batteries in the station (𝑁𝑖). Fully-charged
batteries (𝑁𝑓𝑐) should maintain this limit. Furthermore, discharging (𝑁𝑑) and swapping (𝑁𝑠𝑤) batteries are
allowed without surpassing the number of initial batteries (13). In addition, EV customers demand (𝑁 𝐷)
should be higher than the batteries being swapped (14).
The batteries to be charged (𝑁𝑐) depend (𝑁𝑖 − 𝑁𝑓𝑐(𝑡,𝑠)) on the battery availability and the allocated
batteries to swap 𝑁𝑠𝑤 in the actual period (15). In (16), the SOC is represented by the succession of
the charge (∑ 𝑁𝑐(𝑡−𝑓,𝑠)
𝛿−1
𝑓=0 ) and discharge (∑ 𝑁𝑑(𝑡−𝑓,𝑠)
𝜁−1
𝑓=0 ) periods. The sum of these two terms cannot exceed
the initial number of batteries. In (17), decision variables are greater than or equal to zero.
Equation (1) is then subject to
1, , , , ,fc t s c t s d t s sw t s fc t s
N N N N N
,t T s S (10)
min ,
or
(11)
,
0 ifc t s
N N ,t T s S (12)
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, , id t s sw t s
N N N ,t T s S (13)
,t T s S (14)
, , ,ic t s fc t s sw t s
N N N N ,t T s S (15)
11
, ,
0 0
ic t f s d t f s
f f
N N N
,t T s S (16)
, , ,
, , , 0i sw t s c t s d t s
N N N N ,t T s S (17)
,iN ,sw t s
N , ,c t s
N , ,d t s
N ℤ
Table 2. Specifications of the charger power level [34]
Power Level Typical use Charger rate (𝜐 𝛾
) Variant
Level 1 Charging at home or office 1.4 kW (12 A)
1.9 kW (20 A)
A
B
Level 2 Charging at private or public outlets 7.7 kW (32 A)
19.2 kW (80 A)
A
B
Level 3 Commercial Up to 50 kW
Up to 100 kW
A
B
4. UNCERTAINTY SOURCES
The uncertainty is represented in the three groups presented below.
4.1. Uncertainty of PV generation, electricity markets, and loads with DR
The case study is created by using 5000 scenarios based on [28]. This research looks for robust
solutions to the uncertainty parameters, such as PV power forecast, load prediction, and wholesale and local
markets [28]. The PV power geration, the loads with DR and the variations of energy markets (wholesale and
local) have forecast errors of 15%, 10% and 20% respectively. In (18), the Monte Carlo method generates
the scenarios. A normal distribution function is used to forecast the error (
,error s
x ) through historical data.
The forecast errors (
forecast
x ) with uncertainty are represented by a normal distribution function. In the second
step, 5000 scenarios are reduced to 100 using a reduction technique based on low probabilities [35].
,forecast error s
SX t x t x t (18)
4.2. Uncertainty of driven pattern and electricity spot price for EVs
Data on EV trips and electricity spot prices for EVs use the transportation and electricity sources
from New South Wales (NSW) [30]. The data on electricity spot prices is obtained from January 2016 to
June 2019. They are estimated with the Australian Energy Market Operator. The time resolution is per hour
and the K-means clustering is employed to forecast electricity spot prices from hour 1 to hour 24 [6].
Electricity spot prices for EVs are grouped into 10 clusters together with their probability percentages,
as shown in Figure 3. The percentages represent the probability of occurrence. A similar feature is called
a scenario and the prices are normalized so that the sum is unity for each day [6].
The cluster dataset identifies the EV expected visits in a specified region [6]. It is illustrated in
Figure 4, EVs are group by arrival time. Then, the traffic data includes the amount of cars for a 24-hour time
interval. The numer of vehicles and market prices have been normalized with a value of 0.1. Cars passing in
southbound and northbound areas are separated. The scenarios are produced through probability of
occurrence, where each cluster has some associated feature sets. In addition, the probability of occurrence
depends on the number of characteristic patterns [6].
, ,sw t s D t s
N N
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Figure 3. Clustering of scenarios to EV market
Figure 4. Clustering of scenarios for the driven pattern of EV
4.3. EVs BSS matrix with uncertainty
Scenarios of EVs arrival time from January 2016 to June 2019 in NSW are modeled and reduced to
100 stochastic scenarios. For example, the EV BSS matrix illustrates a stochastic scenario and a feasible
solution. Table 3 shows the encoding for a random scenario. The charging (𝛿) and discharging (𝜁) time are
computed with Table 2. The time for charging and discharging are 8, 12, and 9 hours for Toyota, Nissan, and
Mitsubishi at levels 1 A, 1 B, and 2 A, respectively. To emphasize, the decision matrix is formulated to
quantify the number of fully charged batteries (Nfc), the number of batteries to charge (Nc), the number of
batteries to discharge (Nd) and the number of batteries to swap (Ns). Fully charged batteries are estimated for
the current (t) and previous (t-1) instant.
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Table 3. Sample of EVs BSS matrix
State of change of 15 EVs Batteries EVs Batteries counter
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Nd Nc Ns
Nfc
(t-1)
Nfc
(t1)
Ni-Nfc
(t-1)-Ns
Planningday-ahead
1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 14 0 0 0 1 15
2 1 2 1 1 3 3 3 3 3 3 3 3 3 3 3 11 2 0 2 3 13
3 8 1 1 1 2 4 4 4 4 4 4 4 4 4 4 10 2 1 2 3 12
4 7 8 9 1 1 3 3 3 3 3 5 5 5 5 5 5 7 2 1 2 12
5 6 7 8 1 1 2 2 2 2 2 4 4 4 6 6 2 11 0 2 2 13
6 5 6 7 8 1 1 1 1 1 1 3 3 3 5 5 0 13 1 1 6 13
7 4 5 6 7 8 9 8 8 9 1 2 2 2 4 4 0 9 5 1 1 9
8 3 4 5 6 7 8 7 7 8 8 1 1 1 3 3 0 14 1 0 3 14
9 2 3 4 5 6 7 6 6 7 7 8 9 1 2 2 0 12 2 1 1 12
10 1 2 3 4 5 6 5 5 6 6 7 8 1 1 1 0 14 0 1 4 14
11 2 1 2 3 4 5 4 4 5 5 6 7 8 12 1 1 11 2 1 2 12
12 1 1 1 2 3 4 3 3 4 4 5 6 7 11 9 0 13 1 1 3 13
13 8 8 1 1 2 3 2 2 3 3 4 5 6 10 8 0 12 2 1 2 12
14 7 7 9 1 1 2 1 1 2 2 3 4 5 9 7 0 13 1 1 4 13
15 6 6 8 8 2 1 1 1 1 1 2 3 4 8 6 1 11 1 2 5 12
16 5 5 7 7 1 2 2 2 2 2 1 2 3 7 5 5 10 0 0 2 15
17 4 4 6 6 1 1 1 1 3 3 2 1 2 6 4 3 11 0 1 5 14
18 3 3 5 5 9 1 1 1 2 2 3 2 1 5 3 2 9 1 3 4 11
19 2 2 4 4 8 12 8 8 1 1 2 1 2 4 2 1 11 3 0 3 12
20 1 1 3 3 7 11 7 7 9 8 1 1 1 3 1 0 12 2 1 6 12
21 2 2 2 2 6 10 6 6 8 7 8 9 8 2 1 2 9 3 1 1 11
22 3 3 1 1 5 9 5 5 7 6 7 8 7 1 12 2 12 1 0 3 14
23 2 2 2 2 4 8 4 4 6 5 6 7 6 1 11 2 12 0 1 1 14
24 1 1 1 1 3 7 3 3 5 4 5 6 5 9 10 0 14 1 0 4 14
Free spots: Swapping batteries: Penalties: 4 Total swapping: 30
Nomenclature
Nd: Number of batteries to dischange Nc Number of batteries to change
Nfc Number of fully changed batteries Ns Number of batteries to swap
The SOC is calculated each hour for 24 hours with 15 batteries to swap. Vehicles such as Toyota,
Nissan, and Mitsubishi use the services of BSS. The countdown decreases from 8, 12, and 9 hours (battery
completely discharged) to 1 hour (battery fully charged). Regarding the charging of EV batteries,
it is assumed that when the battery is swapped in BSS, it will be connected to the BSS charging point.
Swapping batteries are represented by the red spots, while the free spots (blue spots) can make any decision:
charging, discharging, or nothing. Regarding swapping, the aggregator does not know when the EV visits
the BSS, because it depends on a random scenario. However, it can decide if it accepts or not the swapping
battery. This BSS model assumes that vehicles arrive with depleted batteries. Nevertheless, the EV BSS
matrix can also quantify different SOCs. For example, for the Toyota brand that requires 8 hours of charge,
the SOC can be replaced by a different value between one and seven for the remaining hours. The arrival of
vehicles with different SOCs into the BSS- can be further explored in future research.
5. RESULTS AND DISCUSSION
This section develops the case study and the results are discussed after its formulation.
5.1. Development of the case study
The structure of the problem is based on the modified case study proposed in the IEEE-
CEC/GECCO 2019 competition [28]. This case study is carried out in Matlab 2016. Figure 5 shows the SMG
components. Therefore, a 26-bus SMG has 5 DGs, 1 external supplier, 1 PV generation, 15 swapping
batteries for EVs, 34 EVs, 90 loads with DR, and 2 ESSs. Moreover, the wholesale and local markes, and
electricity spot price for EVs are considered. The capacity of energy resources is represented in Table 4.
Prices of the wholesale and local markets are taken from [28], while the prices for EVs are taken from
the New South Wales (NSW) electricity market given in monetary units (m.u.) per kWh [30].
The capacity of energy resources is representedn Table 4. Prices of the wholesale and local markets
are taken from [28], while the prices of the EVs market are taken from the New South Wales (NSW)
electricity market given in monetary units (m.u.) per kWh [30]. EVs are selected based on the market reach
of top EV manufacturers. Under this context, three types of vehicles are chosen: Toyota, Nissan, and
Mitsubishi. In Table 5, surveys of EVs sales allow to compute the probability of EV transit [29].
Additionally, the battery characteristics are provided in [36].
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Figure 5. Representation of a 26-bus SMG
Table 4. Energy resources [28]
Energy resources Prices (m.u.*/kWh) Capacity (kW) Units
Dispatchable DGs 0.07-0.11 10-100 5
External suppliers 0.074-0.16 0-150 1
ESS:
Charge - 0-16.6
Discharge 0.03 0-16.6 2
Loads with DR 0.0375 4.06-8.95 90
Forecast (kW)
Photovoltaic - 0-106.81 1
Load - 35.82-83.39 90
Limits (kW)
Market 1 (WS) 0.021-0.039 0-100 1
Market 2 (LM) 0.021-0.039 0-10 1
Electricity spot price (EVs) 0.014-0.119 0.0476-4.076 1
* Monetary units (m.u)
Table 5. Characteristic and transiting probability
Price sales
[29]
Percentage
(%)
Cars Battery capacity (𝜐 𝛽
)
(kWh) [36]
Charger rate (𝜐 𝛼
)
(kW)
Toyota 27,595 59 20 10 1.25
Nissan 14,715 32 11 23 [34] 3
Mitsubishi 4,166 9 3 16 1.78
Total 46,476 100 34
5.2. Discussion of case study results
In this study case, the SMGs are optimized through the VNS-DEEPSO algorithm with 100
probabilistic scenarios. The cost is reduced by 72 % in the overall cost, which is obtained by calculating
the reduction percentage of the random solution with respect to the suboptimal solution. Additionally,
according to [9], the VNS algorithm is selected (simple version) and the DEEPSO algorithm is formed by
the following parameters: the mutation rate, the communication probability, the population and the local
DEEPSO search probability as 0.8, 0.8, 2 and 0.1, respectively. The number of evaluations is restricted to
a maximum of 50,000 [28].
Results are summarized in Figure 6, where the generation is presented for a period of 24 hours
(in day-ahead markets). For this period, the maximum generation is supplied by the external supplier. DG 5
has relevant participation followed by PV generation. The other generators (DG 1, DG 2, DG 3, and DG 4)
have a lower participation rate. To summarize, the generation presents a stochastic generation. Load with DR
is calculated by using a coefficient 10 from (9), as suggested by [32]. However, it is adjusted to 20 to avoid
consumption equal to zero. The peak consumption in Figure 7 is 0.0722 kW at midday. Other peaks are
found at 10 a.m., 7 p.m., and 9 p.m. with consumption of 0.0147 kW, 0.0198 kW, and 0.0117 kW,
respectively. In [9], they report that the load is reduced because it is more convenient for the aggregator to
carry out load shedding.
PV 5 DGs G
External
supplier
22
23 25
24 19
18
20
21 14 15 16 17
1
11
12 13 10 5
4
3 6-9
BSS
26
90 Controllable loads ; 34 Electric vehicles; 2 Energy storage systems;
26-Bus Microgrid; 15 Swapping batteries to EVs
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EV batteries have events such as swapping, charging, and discharging as shown in Figure 8. A BSS
of EVs has 15 EV batteries and it has scheduled visits of 34 EVs at any given time. The EV batteries can be
partially charged. Figure 8 shows a smooth increase in the number of battery discharge at 2 a.m. and
the number of discharging batteries decreases close to zero at 9 a.m. Afterward, from 2 p.m. the number of
discharging batteries increases smoothly until 11 p.m. and ends steadily.
The number of swapping batteries have stochastic behavior. It depends on the EVs visit and the EVs
batteries that are accepted. The number of charging EVs batteries increases from 2 a.m. to 5 a.m. as shown in
Figure 8. Then, it remains steady until 7 a.m. and decreases close to zero at 1 p.m. ESS 1 as shown in
Figure 9 has a discharging peak with 0.1986 kW at 4 a.m. and a charging peak with 0.3144 kW at 9 p.m.
ESS 2 has lower participation, presumably due to the use of EVs. In addition, [22] reports ESS 2 with
a suboptimal solution of 0.87 kW. Transfer power in electricity spot price for EVs is computed as result of
EVs matrix per average charger rate (19). This equation takes into account the average type of electric
vehicle and type of charger rate.
Figure 6. Power generated for 24 hours Figure 7. Consumption time of load with DR
Figure 8. EV battery event programming for
an average of 100 scenarios
Figure 9. Planning of the energy storage system
for one day
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑐ℎ𝑎𝑟𝑔𝑒𝑟 𝑟𝑎𝑡𝑒 =
∑ min(𝜐 𝛼,𝜐 𝛾)∗𝑒𝑣
𝑁 𝑠
𝑒𝑣=1
𝑁 𝑒𝑣
(19)
Electricity markets in Figure 10 show a tendency to sell and not to buy energy for higher profits.
The wholesale market has the higher participation with transactions close to 85 kW and some fluctuations.
The lowest energy sale takes place at 12 p.m. Sales in the local market have fluctuations around 36 kW.
The electricity spot price for EVs carries out a small percentage of transactions per day. In the ESS, this is
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highlighted when there are batteries charging and discharging. The transfer power is not carried out in
the EVs market, but it is performed between the same batteries of the BSS. Therefore, the energy transaction
of electricity spot price for EVs shown in Figure 10 is lower.
Figure 10. Electricity markets
6. CONCLUSIONS
The optimization of EVs in SMG with uncertain scenarios has not been fully explored, thus this
study proposes a new methodology that involves uncertainty decisions in battery swapping stations.
In the proposed method, a decision matrix is formulated to schedule EVs BSS. Furthermore, the stocastic
visit is based on the K-means clustering approach. Once the visit schedule to EV BSS has been generated,
idle spaces appear in the probabilistic scenarios within a decision matrix. This means that the idle spaces are
generated when the charge of a battery must be mandatory.
As a result, optimization is only allowed for free spaces, that is, charging, discharging, or no action.
In addition, other elements of the SMG are included in the model, such as DG, PV generation, ESS, loads
with DR, and day-ahead markets. Finally, the best schedule of charging, discharging, and swapping for EVs
batteries in BSS, and the transfer of power in the market are identified satisfactorily. The generation, loads
with DR, and ESS are stochastic, however, the patterns are recognized of the main generators, peak hours in
loads with DR (at midday), and ESS participation.
APPENDIX
Supplementary Materials
- The SMG is available online at: http://www.gecad.isep.ipp.pt/WCCI2018-SG-
COMPETITION/ and http://www.gecad.isep.ipp.pt/ERM2019-Competition/
- EV brands are available online at: https://evadoption.com/ev-sales/evs-percent-of-vehicle-sales-by-
brand/. Electricity spot price for EVs is available online at:
https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM/Data-dashboard
REFERENCES
[1] H.-Y. Mak, Y. Rong, and Z.-J. M. Shen, “Infrastructure Planning for Electric Vehicles with Battery Swapping,”
Manage. Sci., vol. 59, no. 7, pp. 1557-1575, Jul. 2013.
[2] L. Zhang, S. Zhou, J. An, Q. K.-I., “Demand-Side Management Optimization in Electric Vehicles Battery
Swapping Service,” IEEE Access, vol. 7, pp. 95224-95232, 2019.
[3] H. Liu, Y. Zhang, S. Ge, C. Gu, and F. Li, “Day-Ahead Scheduling for an Electric Vehicle PV-Based Battery
Swapping Station Considering the Dual Uncertainties,” IEEE Access, vol. 7, pp. 115625-115636, 2019.
[4] J. Yang, F. Guo, and M. Zhang, “Optimal planning of swapping/charging station network with customer
satisfaction,” Transp. Res. Part E Logist. Transp. Rev., vol. 103, pp. 174-197, 2017.
[5] A. Valibeygi, et al., “Robust Power Scheduling for Microgrids with Uncertainty in Renewable Energy Generation,”
2019 IEEE Power Energy Soc. Innov. Smart Grid Technol. Conf., pp. 1-5, Feb. 2019.
[6] W. Infante, J. Ma, X. Han, and A. Liebman, “Optimal Recourse Strategy for Battery Swapping Stations
Considering Electric Vehicle Uncertainty,” IEEE Trans. Intell. Transp. Syst., pp. 1-11, 2019.
15. Int J Elec & Comp Eng ISSN: 2088-8708
Optimal scheduling of smart microgrids considering electric vehicle… (J. Garcia-Guarin)
5107
[7] B. Sun, X. Sun, D. Tsang, and W. Whitt, “Optimal Battery Purchasing and Charging Strategy at Electric Vehicle
Battery Swap Stations,” Eur. J. Oper. Res., vol. 279, no. 2, pp. 524-539, 2019.
[8] J. Garcia-Guarin, S. Rivera, and H. Rodriguez, “Smart grid review: Reality in Colombia and expectations,” IOP
Conf. Series: J. Phys. Conf. Ser., vol. 1257, 012011, pp. 1-7, Jun. 2019.
[9] J. Garcia-Guarin et al., “Smart Microgrids Operation Considering a Variable Neighborhood Search:
The Differential Evolutionary Particle Swarm Optimization Algorithm,” Energies, vol. 12, no. 16, pp. 1-13,
Aug. 2019.
[10] J. Garcia Guarin, F. Lezama, J. Soares, and S. Rivera, “Operation scheduling of smart grids considering stochastic
uncertainty modelling,” Far East J. Math. Sci., vol. 115, no. 1, pp. 77-98, 2019.
[11] J. Arévalo, F. Santos, and S. Rivera, “Uncertainty cost functions for solar photovoltaic generation, wind energy
generation, and plug-in electric vehicles: mathematical expected value and verification by Monte Carlo simulation,”
Int. J. Power and Energy Convers., vol. 10, no. 2, pp. 171-207, 2019.
[12] Y. Cheng and C. Zhang, “Configuration and operation combined optimization for EV battery swapping station
considering PV consumption bundling,” Prot. Control Mod. Power Syst., vol. 2, pp. 1-18, Dec. 2017.
[13] T. Li et al., “An optimal design and analysis of a hybrid power charging station for electric vehicles considering
uncertainties,” in Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society,
pp. 5147-5152, 2018.
[14] J. Li, X. Tan, and B. Sun, “Optimal Power Dispatch of a Centralized Electric Vehicle Battery Charging Station with
Renewables,” IET Communications, vol. 12, no. 5, pp. 579-585, 2018.
[15] M. R. Sarker, H. Pandžić, and M. A. Ortega-Vazquez, “Optimal operation and services scheduling for an electric
vehicle battery swapping station,” IEEE Trans. Power Syst., vol. 30, no. 2, pp. 901-910, Mar. 2015.
[16] M. Hemmati, M. Abapour, B. Mohammadi-ivatloo, “Optimal scheduling of smart Microgrid in presence of battery
swapping station of electrical vehicles,” Electric Vehicles in Energy Systems, Springer, pp. 249-267, 2020.
[17] M. Hemmati, S. Ghasemzadeh, and B. Mohammadi-Ivatloo, “Optimal scheduling of smart reconfigurable
neighbour micro-grids,” IET Gener. Transm. Distrib., vol. 13, no. 3, pp. 380–389, Feb. 2019.
[18] M. Hemmati, B. M.-Ivatloo, M. Abapour, and A. Anvari-Moghaddam, “Optimal chance-constrained scheduling of
reconfigurable microgrids considering islanding operation constraints,” IEEE Systems Journal, pp. 1-10, 2020.
[19] F. Moaidi and M. A. Golkar, “Demand response application of battery swap station using a stochastic model,”
in 2019 IEEE Milan PowerTech, PowerTech 2019, pp. 1-6, 2019.
[20] W. Sutopo et al., “A technical review of BMS performance standard for electric vehicle applications in Indonesia,”
TELKOMNIKA (Telecommunication, Computing, Electronics and Control), vol. 16, no. 2, pp. 544-549, 2018.
[21] P. J. García-Guarín, et al., “Implementación del algoritmo VNS-DEEPSO para el despacho de energía en redes
distribuidas inteligentes - Implementation of the VNS-DEEPSO algorithm for energy dispatch in intelligent
distributed networks (in English),” INGE CUC, vol. 15, no. 1, pp. 142-154, 2019.
[22] D. Dabhi and K. Pandya, “Enhanced velocity differential evolutionary particle swarm optimization for optimal
scheduling of a distributed energy resources with uncertain scenarios,” IEEE access, vol. 8, pp. 27001-27017, 2020.
[23] E. de V. Fortes, et al., “A VNS algorithm for the design of supplementary damping controllers for small-signal
stability analysis,” Int. J. Electr. Power Energy Syst., vol. 94, pp. 41-56, 2018.
[24] P. Hansen and N. Mladenović, “Variable neighborhood search: Principles and applications,” Eur. J. Oper. Res.,
vol. 130, no. 3, pp. 449–467, May 2001.
[25] V. Miranda and R. Alves, “Differential evolutionary particle swarm optimization (deepso): A successful hybrid,”
IEEE BRICS Congr. Comput. Intell. 11th Brazilian Congr. Comput. Intell., pp. 368-374, 2013.
[26] D. G. Mayer, B. P. Kinghorn, and A. A. Archer, “Differential evolution – an easy and efficient evolutionary
algorithm for model optimisation,” Agric. Syst., vol. 83, no. 3, pp. 315–328, Mar. 2005.
[27] F. Marini and B. Walczak, “Particle swarm optimization (PSO): A tutorial,” Chemom. Intell. Lab. Syst., vol. 149,
pp. 153–165, Dec. 2015.
[28] F. Lezama, J. Soares, Z. Vale, and J. Rueda, “Evolutionary computation in uncertain environments: a smart grid
application,” IEEE World Congress on Computational Intelligence 2018 – WCCI 2018, 2018.
[29] Automaker, “EVAdoption,” Analyzing key factors that will drive mass adoption of electric vehicles, 2019.
Accessed: 29 Dec 2019. [Online]. Available: https://evadoption.com/ev-sales/evs-percent-of-vehicle-sales-by-
brand/.
[30] AEMO, “Electricity price and demand,” Australian Energy Market Operator-, 2019. Accessed: 29 Dec 2019.
[Online]. Available: https://www.aemo.com.au/Electricity/National-Electricity-Market-NEM/Data-dashboard.
[31] J. P. Fossati, “Revisión bibliográfica sobre micro redes inteligentes,” Lit. Rev. microgrids, vol. 9, pp. 13-20, 2011.
[32] J. Garcia-Guarin, S. Rivera, and L. Trigos, “Multiobjective optimization of smart grids considering market power,”
J. Phys. Conf. Ser., vol. 1409, 012006, Nov. 2019.
[33] M. Mahoor, Z. Hosseini, A. K., “Least-cost operation of a battery swapping station with random customer
requests,” Energi, Elsevier, vol. 172, pp. 913-921, Apr. 2019.
[34] M. Yilmaz and P.T. Krein, “Review of battery charger topologies, charging power levels, and infrastructure for
plug-in electric and hybrid vehicles,” IEEE Transactions on Power Electr., vol. 28, no. 5. pp. 2151–2169, 2013.
[35] J. Soares, et al., “Two-Stage Stochastic Model Using Benders’ Decomposition for Large-Scale Energy Resource
Management in Smart Grids,” IEEE Trans. Ind. Appl., vol. 53, no. 6, pp. 5905–5914, Nov. 2017.
[36] J. Ferreira, “Mobi-System: towards an information system to support sustainable mobility with electric vehicle
integration,” Doctoral Thesis, University of Minho, Portugal, 2013.