A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network
Abstract
:1. Introduction
- In the electricity market, dynamic electric pricing policies have chief importance to influence the customer’s electricity consumption. We have explored various pricing policies from the perspective of EV charging to highlight their effects on EV charging behavior.
- In each pricing domain, we have explored various optimization techniques employed to schedule the charging demand of EVs.
- The optimization objectives realized during this charge scheduling process are also featured in this study.
2. Smart Grid and EV Charging
2.1. EV Charging International Standards
2.2. Coordinated EV Charging Framework
3. Dynamic Electricity Pricing Policies
3.1. Real Time Pricing (RTP)
3.2. Time of Use (ToU)
3.3. Critical Peak Pricing (CPP)
3.4. Peak Time Rebates (PTR)
4. Electric Vehicle Charging in Dynamic Electricity Pricing Environment
4.1. EV Charging under RTP
4.2. EV Charging under ToU
5. Optimization Techniques for EV Charging Scheduling
5.1. Mathematical Optimization Techniques
5.2. Computational Intelligence Techniques
5.2.1. Heuristic Method
5.2.2. Particle Swarm Optimization (PSO)
5.2.3. Genetic Algorithm (GA)
5.2.4. Fuzzy Logic (FL)
6. Computational Performance of Optimization Techniques
7. Optimization Objectives for EV Charging Scheduling
7.1. Power Loss Minimization
7.2. Electricity Cost Minimization
7.3. Peak Load Minimization
7.4. Voltage Regulation
7.5. Distribution Transformer and Distribution Lines Overloading Minimization
8. Discussion
9. Conclusions and Future Research Directions
- It is important to recognize the EV customers’ readiness to accept dynamic pricing for further advancement.
- A detailed research contribution is required to estimate the charging demand and electricity price relationship at domestic level. Besides the electricity price, there exist a number of factors that impact the electricity demand of one EV customer to another. In a dynamic pricing environment, the recognition of these aspects is an impending research area.
- In execution of dynamic electricity pricing from EVs charging perspective, a research on electricity market is a potential area to consider.
- Considering flexible charging demand of EVs optimization of electricity prices, incorporation of renewable energy system and storage units is a potential research direction.
- Locational incentive plans can be introduced in future research work to facilitate the charging activities.
- Although the RTP offers a flexibility to EV customers to manage their demand, a sophisticated communication infrastructure is required for real time information between EV customer and aggregator so that charging activities can be monitored and controlled. Although there exist several research studies on the communication system, a privacy improvement needs further attentions.
- The EVs arrival and departure pattern is a significant aspect to consider while executing charging activities. Therefore, forecasting the arrival and departure behavior of EVs is important for selection of optimal charging time and duration and it is a potential research topic.
Author Contributions
Funding
Conflicts of Interest
References
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Charging Mode | Charging Characteristics | Advantages | Disadvantages | |||||
---|---|---|---|---|---|---|---|---|
Charging Outlets | Voltage Rating (V) | Current Rating (A) | Power Rating (kW) | Supply Connection | Charging Period (Hour) | |||
Mode 1 | Domestic | 120 VAC | 12–16 | 1.4–1.9 | Single phase | 6–10 | Low installation cost Less impact on utility | Slow charging rate Long charging period |
Mode 2 | Domestic, Public | 240 VAC | 80 | 19.2 | Single/Three phase | 1–3 | Fast charging time Energy efficient | High installation cost Impact on the utility |
Mode 3 | Public | 480 VDC | 80–200 | 20–120 | Three phase | 0.5 | Very fast charging time High energy efficient | High installation cost High impact on the utility |
Characteristics | Charging Control Logic | |
---|---|---|
Centralized | Distributed | |
Charging Decision | The aggregator | The EV customer |
Control Action | Direct control | Price-Based Control |
Ancillary Services | Fully supported | Partially supported |
Computational Complexity | More | Less |
Flexibility | Less | More |
Scalability | Less | More |
Considerations | RTP | ToU | CPP | PTR | Flat Rate |
---|---|---|---|---|---|
Economic efficiency | **** | *** | *** | *** | ** |
Bill steadiness | ** | *** | *** | **** | **** |
System complexity | **** | *** | *** | *** | ** |
Price uncertainty | **** | ** | *** | *** | ** |
Fairness | **** | *** | *** | ** | * |
Risk incentive | **** | *** | **** | ** | * |
Reference | Year | Major Objective Achieved | Pricing Scheme |
---|---|---|---|
Mohsenian et al. [46] | 2010 | Electricity cost minimization | RTP |
Deilami et al. [8] | 2011 | Reducing potential stresses, performance degradations, and overloads in distribution system. | RTP |
Masoum et al. [63] | 2011 | Power loss minimization, peak shaving, and voltage regulation | ToU |
Cao et al. [60] | 2012 | Minimize charging cost and reduce peak and fill valley | ToU |
Taheri et al. [19] | 2013 | EV load scheduling | CAP |
Lian et al. [66] | 2013 | Optimized time based pricing schemes | UDP |
Martinenes et al. [67] | 2014 | charging cost minimization | RTP |
Andreson et al. [68] | 2014 | charging cost minimization | Two-tier policy |
Yin et al. [50] | 2015 | resolving peak on peak | CPP |
Misra et al. [69] | 2015 | Cost optimization and reduction of extra load during peak hours | RTP |
Binitti et al. [70] | 2015 | Minimization of power losses, voltage deviation, load variance, operational cost, and emission control | Discrete charging rates |
Soltani et al. [44] | 2015 | Reducing load peaks | RTP |
Dubey et al. [71] | 2015 | Mitigating the impacts if EV load is on residential distribution circuit. | ToU |
Yang et al. [59] | 2015 | EV route optimization | ToU |
Soares et al. [34] | 2016 | Reducing distribution transformer overloading, voltage irregularities | UDP |
Hajforoosh et al. [72] | 2016 | Reducing unwanted peaks, transformer over-loading | Variable charging rate |
Crow et al. [61] | 2017 | Load factor improvement, electricity cost reduction, mitigating line overloading | ToU |
Chen et al. [62] | 2017 | Solution of power congestion, under voltage, and grid instability | ToU |
Xu et al. [55] | 2017 | Reducing imbalance usage and long charging delays at charging stations | RTP |
Chen et al. [56] | 2017 | Electricity cost minimization and flattening peak power demand curve | RTP |
Bitencourt et al. [48] | 2017 | Reducing peak load demand and transformer overloading | RTP |
Korolko et al. [43] | 2017 | Reducing distribution transformer overloading, voltage irregularities, and uncontrolled charging effect | RTP |
Yang et al. [57] | 2017 | Resolving large and unpredictable peaks | RTP |
Latinopoulos et al. [41] | 2017 | EV load scheduling | Dynamic pricing (DP) |
Zhang et al. [73] | 2017 | Minimize the peak–valley and economical improvements | ToU |
Moon et al. [74] | 2017 | Balanced charging | ToU |
Zhang et al. [58] | 2017 | Provides benefits to electricity supplier, charging station, EV user | RTP |
Optimization Method | References | Time (Seconds) |
---|---|---|
Mathematical optimization techniques | [19] | 40 |
[43] | 60 | |
[44] | Not given | |
[48] | Not given | |
[55] | Not given | |
[61] | Not given | |
[67] | Not given | |
[69] | 03 | |
[70] | 10 | |
Heuristic method | [60] | Not given |
[31] | Not given | |
Particle swarm optimization (PSO) | [50] | Not given |
[72] | 0.035 | |
[74] | Not given | |
[75] | 02 | |
[76] | Not given | |
[77] | 0.054 | |
[78] | Not given | |
Fuzzy logic (FL) | [56] | Not given |
[62] | Not given | |
[28] | Not given | |
[83] | Not given | |
[84] | 0.5 | |
Genetic algorithm | [59] | 375 |
[79] | Not given | |
[80] | Not given | |
[81] | Not given | |
[82] | Not given |
Ref. | Year | Research Focus | Optimization Technique | Objective | Pricing Schemes |
---|---|---|---|---|---|
Deilami et al. [8] | 2011 | Real-Time Coordination of Electric Vehicle Charging in Smart Grids | Maximum sensitivities selection (MSS) | Cost Minimization and Load Management | RTP |
Taheri et al. [19] | 2013 | A dynamic algorithm for EV charging of | Dual clustered linear programming (DCLP) | EV load scheduling | Constraint-adjusted prices (CAP) |
Soares et al. [34] | 2016 | Dynamic electricity pricing for electric vehicles | Mixed integer nonlinear optimization formulation (MINLP) | Reducing distribution transformer overloading, voltage irregularities | Usage Based Dynamic Pricing (UDP) |
Korolko et al. [43] | 2017 | Robust optimization of EV charging schedules | Mixed integer nonlinear optimization formulation (MINLP) | Reducing distribution transformer overloading, voltage irregularities, and uncontrolled charging effect | RTP |
Sultani et al. [44] | 2015 | Real-time load elasticity tracking and pricing for EV | Online convex optimization | Reducing load peaks | Conditional random field CRF based RTP |
Mohsenain et al. [46] | 2010 | Optimal Residential Load Control with Price Prediction | Mixed integer linear programming (MILP) | Electricity cost minimization | RTP |
Bitencourt et al. [48] | 2017 | Optimal EV charging and discharging under dynamic pricing | Linear Programming | Reducing peak load demand and transformer overloading | RTP & ToU |
Yin et al. [50] | 2015 | Dynamic decision model of CPP considering electric vehicles′ charging load | Particle swarm optimization algorithm (PSO) | Resolving peak on peak | Critical Peak Pricing (CPP) |
Xu et al. [55] | 2017 | Dynamic Pricing at Electric Vehicle Charging Stations for Queuing Delay Reduction | Poisson process | To reduce the long delay at the crowded charging station, load balancing | Dynamic pricing policy |
Chen et al. [56] | 2017 | Dynamic Price Vector Formation Model-Based Automatic DR Strategy for PV-integrated EV Charging Stations | Fuzzy C-means (FCM) Fuzzy K-means (FKM) algorithm. | Electricity cost minimization and flatten peak power demand curve | RTP |
Yang et al. [57] | 2017 | Regulating Load of Electric Taxi Fleet via Real-Time Pricing | Probabilistic decision model | Resolving large and unpredictable peaks | RTP |
Zahang et al. [58] | 2017 | Pricing model for the charging of electric vehicles | SD modelling technique | Balancing the benefits of electricity supplier, charging station, EV user | RTP |
Yang et al. [59] | 2015 | Electric Vehicle Route Optimization | learnable partheno genetic algorithm (LPGA) | EV route optimization | ToU |
Cao et al. [60] | 2012 | An Optimized EV Charging Model | Heuristic algorithm | Minimize charging cost and reduce peak and fill valley | ToU |
Crow et al. [61] | 2017 | Cost-constrained dynamic optimal electric vehicle charging | Linear, quadratic, and quadratic constrained formulations moving horizon optimization | Load factor improvement, Electricity cost reduction, mitigating line overloading | RTP & ToU |
Chen et al. [62] | 2017 | Optimal regional time-of-use charging price model for electric vehicles | Membership function, Fuzzy C mean. FCM | Minimizing the peak valley difference and charging cost | Regional ToU |
Martinenas et al. [67] | 2014 | Electric vehicle smart charging using dynamic price signal | Linear programming | Charging cost minimization | RTP |
Misra et al. [69] | 2015 | Distributed dynamic pricing policy | Linear optimization | Cost optimization and reduction of extra load during peak hours | RTP |
Binetti et al. [70] | 2015 | Charging with discrete charging rates | Real-time greedy (RTG) and the enhanced scale able S-RTG algorithms | Minimization of power losses, voltage deviation, load variance, operational cost, and emission control | Discrete Pricing |
Dubey et al. [71] | 2015 | EV Charging on Residential Distribution Systems | Dynamic Programming | Mitigating the impacts if EV load on residential distribution circuit. | ToU |
Hajforoosh et al. [72] | 2016 | Online optimal variable charge-rate coordination of EV | Coordinated aggregated particle swarm optimization (CAPSO). | Reducing undesirable peaks in power consumption, transformer over-loading | variable charge-rate |
Moon et al. [74] | 2017 | Balanced charging strategies for EV | Coordinated aggregated particle swarm optimization (CAPSO). | Balanced charging | ToU |
Xu et al. [76] | 2016 | Dynamic Optimization of Charging Strategies for EV | Improved particle swarm optimization (PSO) | Great profit improvement for the vehicle parking lot | RTP |
Arif et al. [29] | 2016 | Online scheduling of EV in dynamic pricing schemes | Learning Automata, Reinforcement Learning, Online Algorithm, | Cost minimization, Customer satisfaction | RTP |
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Amin, A.; Tareen, W.U.K.; Usman, M.; Ali, H.; Bari, I.; Horan, B.; Mekhilef, S.; Asif, M.; Ahmed, S.; Mahmood, A. A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network. Sustainability 2020, 12, 10160. https://doi.org/10.3390/su122310160
Amin A, Tareen WUK, Usman M, Ali H, Bari I, Horan B, Mekhilef S, Asif M, Ahmed S, Mahmood A. A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network. Sustainability. 2020; 12(23):10160. https://doi.org/10.3390/su122310160
Chicago/Turabian StyleAmin, Adil, Wajahat Ullah Khan Tareen, Muhammad Usman, Haider Ali, Inam Bari, Ben Horan, Saad Mekhilef, Muhammad Asif, Saeed Ahmed, and Anzar Mahmood. 2020. "A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network" Sustainability 12, no. 23: 10160. https://doi.org/10.3390/su122310160
APA StyleAmin, A., Tareen, W. U. K., Usman, M., Ali, H., Bari, I., Horan, B., Mekhilef, S., Asif, M., Ahmed, S., & Mahmood, A. (2020). A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network. Sustainability, 12(23), 10160. https://doi.org/10.3390/su122310160