Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles
Abstract
:1. Introduction
2. Technological Infrastructure
2.1. Energy Storage and Fast Charging Systems
2.2. Storage Battery and Controller
2.3. Converters
3. Appropriate Renewable Energy Sources
4. Siting
4.1. Home Charging
4.2. Workplace Charging
4.3. Public Charging
4.3.1. Opportunity Charging Stations
4.3.2. Fast Charging Stations
4.3.3. Battery Exchange Station
5. Optimal Planning
6. Optimal Sizing
7. Control and Energy Management
8. Pricing Programs
9. Challenges of Renewable Energy-Based Charging Infrastructure
10. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Study | Modeling Technique | Source | Station Type |
---|---|---|---|
[73] | Stochastic programming | Grid, Solar | Charging Station |
[67] | Mixed-integer linear programming (MILP) | Grid, Wind, vehicle to grid (V2G) | Charging Station |
[70] | Two-stage stochastic MILP | Grid, Solar | Battery Exchange Station & Charging Station |
[65] | Two-stage stochastic MILP | Grid, wind, V2G | Charging Station |
[68] | Stochastic Optimization | Grid, Wind | Charging Station |
[69] | Probabilistic Model | Grid, Wind, Solar | Charging Station |
[71] | Two-stage stochastic MILP | Grid, Solar | Battery Exchange Station |
[66] | MILP | Grid, Wind | Charging Station |
Study | Modeling Technique | Problem to Solve | Findings |
---|---|---|---|
[74] | Power requirement model | The behavior of the power grid | Lacks the electrical behavior information of the network while charging, so these models have their importance if connected to an electrical network |
[75] | Queueing model | The probability distribution of getting charged EVs | The EVs can determine the siting of charging stations by providing waiting spots; in addition to charging spots, the utilization of chargers increases, and the number of required chargers at each site decreases |
[76] | The distributional robust travel time information gain sensor location (DRTTIGSL) model | Uncertainty in the prior travel time distribution | The model can reduce the worst-case situation with a small price of the average objective value, especially when the total budget is not high |
[77] | The data-driven constraints are reformulated into tractable counterparts by the sample average approximation (SAA) approach. | Siting and sizing standalone electric-vehicle charging stations | The SAA approach merely investigates the empirical probability distribution and ignores the true one |
Study | Configuration | Aim | Method | Remarks |
---|---|---|---|---|
[102] | Standalone hybrid renewable systems | Minimizing the use of battery storage and maximizing the use of renewable sources with zero charging rejection | The simulation was developed to find the minimum of a constrained non-linear multi-variable function | Different scenarios are introduced and analyzed |
[103] | Standalone hybrid renewable systems | Optimal scheduling for power supply | The energy resources and realistic EV charging data were simulated | The power scheduling was optimized |
[104] | PV–WT-Grid | Maximizing use of renewable sources | Experimenting with the maximum power point tracking technique | The infrastructure is capable of providing sufficient energy in response to the load demand |
[105] | PV–BESS–Grid | Support of high charging rates and penetration of the energy system into the grid | Simulation and prototype experimental | They demonstrated the effectiveness and benefits of a hybrid grid-connected energy system |
[106] | PV–Grid | Discussing critical aspects of renewable resources-based fast charging | Review | Recommendations and useful information related to renewable energy-based DC fast charging |
[107] | WT–Diesel Generator–BESS | Minimizing use of the dump load normally associated with diesel operation | Simulation | Optimizes charging/discharging cycles of the storage system and system operation cost |
[108] | PV–Grid | Improving self-consumption of PV energy and lower its impacts on the grid | Simulation-based on real-time data acquisition of the demand and generation without forecasting | Proves the proposed strategy’s efficiency that can be used in embedded systems for real-time allocation of the EV charging rate |
[19] | PV–Grid | Comparing an optimal charge-scheduling strategy with an uncontrolled charging case | An hourly simulation was used by considering statistical data for driving distances, different types of vehicles, parking time, installation cost, tax rebates and incentives | Confirms feasibility of PV-based infrastructure, benefits to EVs’ drivers and the garage owner and the need for an optimal charging controller |
[96] | PV–Grid | Determining optimal schedules of EV according to the predicted PV power and demand | Simulation and prototype | Demonstrates the effectiveness of the proposed smart EV charging method |
[109] | PV–Grid | Minimizing operation costs | Simulation and economic analysis | Confirms applicability of the strategy to DC distribution buildings, for energy cost reduction |
[110] | PV–Grid | Providing a day-ahead upper limit profile of the charging infrastructure’s power consumption | Simulations and sensitivity analysis | Demonstrates feasibility and relevance of the proposed strategy |
[111] | PV–WT–Fuel Cells–Grid | Minimizing the total cost | Simulations based on the genetic algorithm method | Presents the optimal number of parking lots under optimal scheduling of PHEVs |
[112] | PV–WT– Thermal Storage –BESS–Grid | Minimizing operating costs and CO2 emissions | Case study | Demonstrates reduction in costs and CO2 emissions |
[113] | PV–WT– Fuel Cells –Grid | Integrating scheduling and management of intermittent renewable generation and EVs in a microgrid | Case study | Satisfies technical and financial objectives of infrastructure and economic and security issues of the microgrid |
[114] | PV–BESS–Grid | Reducing operation cost | Simulation based on two algorithms and a case study | The case study confirms effectiveness of the proposed algorithms in reducing the cost |
Approach | Advantages | Programs/Plans | Example of Costing | Utility |
---|---|---|---|---|
Renewables’ network charging | –Enables customers to charge with renewable sources. –Encourages drivers to charge at beneficial times | –Pay per use. –Monthly flat fee | 2 USD per h 4.17 USD per month | Austin Energy |
Offers for time-shifting and renewables’ access. | –Encourages charging at more suitable times for the grid by considering the availability of renewables and avoiding peak hours | –Charging with renewable energy. –Merge TOU rates with renewables’ pricing program | No extra cost for wind energy or 0.02 USD per kWh | Great River Energy & Potomac Electric Power Co. |
Pair on-site renewables’ charging with EV charging | –Free management and control charging | –Beneficial charging rate –Free employee charging | Cost varied | San Diego’s Solar and Google LLC |
Smart charging | –Allows utilities to control charging remotely to meet grid needs. | –Managed charging program | Cost varied | Pacific Gas & Electric/BMW |
Matching rate with surplus renewable energy | –Shifts charging loads to times when there is excess renewable energy generation on the grid | –Time of use (TOU) rates | Varies from 0.9 to 1.5 USD per kWh | Xcel Energy |
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Share and Cite
Alkawsi, G.; Baashar, Y.; Abbas U, D.; Alkahtani, A.A.; Tiong, S.K. Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles. Appl. Sci. 2021, 11, 3847. https://doi.org/10.3390/app11093847
Alkawsi G, Baashar Y, Abbas U D, Alkahtani AA, Tiong SK. Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles. Applied Sciences. 2021; 11(9):3847. https://doi.org/10.3390/app11093847
Chicago/Turabian StyleAlkawsi, Gamal, Yahia Baashar, Dallatu Abbas U, Ammar Ahmed Alkahtani, and Sieh Kiong Tiong. 2021. "Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles" Applied Sciences 11, no. 9: 3847. https://doi.org/10.3390/app11093847