A Review on Stochastic Approach for PHEV Integration Control in a Distribution System with an Optimized Battery Power Demand Model
Abstract
:1. Introduction
- (1)
- A precise estimation of PHEV power demand during the charging process was overlooked in most previous works and if mentioned it was vaguely explained, in this paper the power demand is expressed in the function of battery SoC and the rated power of the charger.
- (2)
- The study in this paper focuses on the technical impacts (voltage deviation and power losses in the distribution grid).
- (3)
- The developed framework investigates the seasonal effect by taking two sets of daily residential power consumption for summer and winter as well, while ignoring the seasonal effect that can cause an underestimation or an overestimation of the effects.
2. Framework Assumptions and Parameters
3. Stochastic Modeling for the PHEV’s Mobility and Charging Behaviors
3.1. PHEV Departure Time
3.2. PHEV Arrival Home Time
3.3. PHEV Daily Trip Length
3.4. PHEV Battery Charge Model
3.5. PHEV Battery Discharge Model
3.6. PHEV Battery Power Demand
4. Daily Residential Load Profiles
4.1. Measured Data for Fixed Daily Residential Load
4.2. Scenario Reduction Algorithm
- 1)
- Determine the scenario ‘k’ to be eliminated from the original set, mathematically this scenario is determined as follows:
- 2)
- Update the remaining load profile set and calculate the distance between each pair of scenarios.
- 3)
- Update the probability of the nearest load profile Sn to the removed one Sk
4.3. Genetic Algorithm for Scenario Generation
5. Methodology and Numerical Results
5.1. Case 1: “Uncontrolled Charging”
- 1-
- The Uncontrolled Deterministic Summer (UDS).
- 2-
- The Uncontrolled Deterministic Winter (UDW).
- 3-
- The Uncontrolled Stochastic Summer (USS).
- 4-
- The Uncontrolled Stochastic Winter (USW).
5.2. Case 2: ‘Controlled Charging’
- It estimates the total residential power demand (household demand and the power demand to charge the EVs) and runs the power flow analysis.
- Check for the state of charge of the electric vehicles and proceed to charge them following the rule “first came-first served”.
- Before the charging demand is approved, another power flow analysis is performed to ensure the voltage profile is still within the margin of voltage prior to the charging power request. Otherwise, the charging is delayed to the next time-frame (i.e. temporal load shifting).
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
BEV | battery electrical vehicles |
PHEV | Plug-in hybrid electric vehicles |
FCEV | Fuel Cell Electric Vehicles |
NHTS | National Household Travel Survey |
AER | All-electric range |
AVD | Average Voltage Drop |
MVD | Maximum Voltage Drop |
MDRPL | Maximum Daily Real Power Losses |
UDS | The Uncontrolled Deterministic Summer case |
UDW | The Uncontrolled Deterministic Winter case |
USS | The Uncontrolled Stochastic Summer case |
USW | The Uncontrolled Stochastic Winter case |
Nomenclature | |
tdep | Departure time |
µdep | The mean value of the departure time PDF |
σdep | The standard deviation of the departure time PDF |
tarr | Arrival time |
µarr | The mean value of the arrival time PDF |
σarr | The standard deviation of the arrival time PDF |
d | The daily travelled distance |
µd | The mean value of daily travelled distance PDF |
σd | The standard deviation of the daily travelled distance PDF |
SoC | State of Charge |
N | The number of hours required to fully charge the battery from an empty state |
D | Traveled distance |
SoCreg | SoC recharged from the regenerative braking |
Dk | The Kantorovich distance between two scenarios |
Si | The ith Scenario |
K | the number of scenarios in the original set |
Fk | fitness level to each member in the set |
P(i) | the probability of ith representative load profile |
Vref | The rated bus voltage |
Vt,i | The node voltage at time t |
Nb | The numbers of nodes in the power system |
Nl | The numbers of branches in the power system |
T | The number of time intervals |
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Parameters | Value | Unit |
---|---|---|
Average battery capacity | 8.8 | kWh |
All-electric range | 25 | Mile |
Full Charging Time (Level 1) | 5.5 | Hour |
Full Charging Time (Level 2) | 2.5 | Hour |
Depletion Mode Energy Use (η) | 0.37 | kWh/mile |
1 | 2 | 3 | … | N | |
---|---|---|---|---|---|
1 | 0 | 0.48877 | 0.61957 | … | 0.2168 |
2 | 0.48877 | 0 | 0.35714 | … | 0.6437 |
3 | 0.61957 | 0.34375 | 0 | … | 0.77604 |
. | . | . | . | . | |
N | 0.2168 | 0.6437 | 0.77604 | … | 0 |
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Boudina, R.; Wang, J.; Benbouzid, M.; Yao, G.; Zhou, L. A Review on Stochastic Approach for PHEV Integration Control in a Distribution System with an Optimized Battery Power Demand Model. Electronics 2020, 9, 139. https://doi.org/10.3390/electronics9010139
Boudina R, Wang J, Benbouzid M, Yao G, Zhou L. A Review on Stochastic Approach for PHEV Integration Control in a Distribution System with an Optimized Battery Power Demand Model. Electronics. 2020; 9(1):139. https://doi.org/10.3390/electronics9010139
Chicago/Turabian StyleBoudina, Rabah, Jie Wang, Mohamed Benbouzid, Gang Yao, and Lidan Zhou. 2020. "A Review on Stochastic Approach for PHEV Integration Control in a Distribution System with an Optimized Battery Power Demand Model" Electronics 9, no. 1: 139. https://doi.org/10.3390/electronics9010139
APA StyleBoudina, R., Wang, J., Benbouzid, M., Yao, G., & Zhou, L. (2020). A Review on Stochastic Approach for PHEV Integration Control in a Distribution System with an Optimized Battery Power Demand Model. Electronics, 9(1), 139. https://doi.org/10.3390/electronics9010139