Real-Time Building Smart Charging System Based on PV Forecast and Li-Ion Battery Degradation
Abstract
:1. Introduction
1.1. Literature Study
1.2. Contribution
- a Lithium-ion degradation model used to accurately assess and optimize the operational costs of EV and BES. It will be shown that the degradation costs are equal to 68% of the total grid electricity costs and are therefore nonnegligible;
- a two-stage model predictive controller consisting of an optimal charging algorithm and real-time controller implemented in a moving horizon control scheme to compensate forecasting and estimation errors, such as PV power or SoC estimation, at a one-minute resolution. It was found that using a moving horizon window and real-time control scheme furthers reduces the costs by 9.7% compared to the reduction in cost of only optimal scheduling;
- a forecast of PV power and load demand in 15-min resolution up to 48 h ahead. Even using advanced irradiance forecasting, root mean square errors can be up to 45% [30], showing the necessity of a model predictive controller; and
- Smart grid implementation in order for the system to be integrated into a future smart grid, allowing for power curtailment and optimization of available reserved capacity for primary frequency regulation, further reducing the cost by 7.8%.
1.3. Paper Organization
2. System Description and Smart Grid Implementation
3. Second-Life Batteries
4. Materials and Methods
4.1. Forecasting
4.2. Optimal Charging Algorithm
4.2.1. Objective Function
4.2.2. Constraints
4.2.2.1. Lithium-Ion Degradation Model
4.2.2.2. Battery Energy Storage Constraints
4.2.2.3. Electric Vehicle Constraints
4.2.2.4. Power Balance Constraints
4.2.2.5. Grid Constraints
4.2.2.6. Regulation Market Constraints
4.2.2.7. Inverter Constraints
4.2.2.8. Photovoltaic Constraints
4.3. Moving Horizon Window and Real-Time Control
5. Use Case and Price Mechanism
6. Results and Discussion
6.1. Comparison
- case 1: Uncontrolled case (only EV, PV, and load)
- case 2: Proposed optimal and real-time control scheme
- case 3: Proposed optimal control and error compensation using grid power
- case 4: Proposed optimal and real-time control scheme without V2G.
- case 5: Proposed optimal and real-time control scheme without up/downregulation.
Demand-Side Management: Power Curtailment
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
t | optimization time index (-) |
optimization time step (-) | |
k | real-time control time index (-) |
Total cost of energy (Euro) | |
BES costs (Euro) | |
Electric vehicle costs (Euro) | |
PV costs (Euro) | |
Grid energy costs (Euro) | |
up/downregulation revenue (Euro) | |
New BES price per kWh (500 Euro) | |
New EV price per kWh (500 Euro) | |
2nd life BES price per kWH (250 Euro) | |
2nd life EV price per kWH (250 Euro) | |
PV energy price (0.03 Euro) | |
Grid energy buying price (Euro) | |
Grid energy buying price (Euro) | |
up regulation price (Euro) | |
down regulation price (Euro) | |
BES capacity at time t (kWh) | |
Initial maximum BES capacity (10 kWh) | |
Max BES capacity at time t (kWh) | |
Initial BES capacity (5 kWh) | |
Total degraded BES capacity (kWh) | |
Total degraded EV capacity (kWh) | |
EV capacity at time t (kWh) | |
Initial maximum EV capacity (80 kWh) | |
Max EV capacity at time t (kWh) | |
Initial EV capacity (35 kWh) | |
Final EV capacity (35 kWh) | |
EV departure charge (50 kWh) | |
inverter power at time t (kW) | |
Max inverter power (10 kW) | |
negative (draw) inverter power (kW) | |
positive (feed-in) inverter power (kW) | |
BES power at time t (kW) | |
Max BES power (10 kW) | |
discharging BES power (kW) | |
charging BES power (kW) | |
EV power at time t (kW) | |
Max EV power (kW) | |
discharging EV power (kW) | |
charging EV power (kW) | |
produced PV power at time t (kW) | |
forecasted PV power at time t (kW) | |
forecasted PV power at time t (kW) | |
total load power at time t (kW) | |
grid power at time t (kW) | |
Maximum grid power (kW) | |
feed-in grid power (kW) | |
buying grid power (kW) | |
available up-regulation capacity (kW) | |
available down-regulation capacity (kW) | |
Average BES cell voltage (3.7 V) | |
open circuit voltage BES (V) | |
open circuit voltage BES (V) | |
BES cell current (A) | |
BES cell current (A) | |
BES Energy Storage State of Charge (-) | |
Electric vehicle State of Charge (-) | |
MPPT efficiency (98%) | |
Inverter efficiency (96%) | |
EV/BES charging efficiency (97.5%) | |
EV/BES discharging efficiency (97.5%) | |
cable efficiency (99%) | |
Electric vehicle departure time (8:00 h) | |
Bank account interest rate (1%/year) | |
a1 (3.679) | |
a2 (−0.2528) | |
a3 (0.9386) | |
b1 (−0.1101) | |
b2 (−6.829) | |
c (0.00054) | |
c (0.35) | |
c (14,876) | |
c (2.64 ) | |
Amount of EV battery cell groups in parallel (145) | |
Amount of EV battery cell in series (100) | |
Amount of cells in series in BES (100) | |
Amount of cells in parallel in BES [18] |
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Symbol | Quantity | Value |
---|---|---|
installed PV capacity power | 10 kWp | |
Maximum EV (dis)charging power | 10 kW | |
Maximum battery (dis)charging power | 10 kW | |
Initial full EV capacity | 80 kWh | |
Initial full battery capacity | 10 kWh | |
EV voltage | 325–430 V | |
BES voltage | 325–430 V |
Symbol | Quantity | Value |
---|---|---|
Number of battery cells in parallel | 14 | |
Number of battery cells in series | 100 | |
curve fit parameter | 3.679 | |
curve fit parameter | −0.1101 | |
curve fit parameter | −0.2528 | |
curve fit parameter | −6.829 | |
curve fit parameter | 0.9386 | |
ageing curve fit parameter | 0.00054 | |
ageing curve fit parameter | 0.35 | |
ageing curve fit parameter | 14,876 | |
averaged calendar ageing per | 2.64 × 10 |
Use Case | [€] | [€] | [€] | [€] | [€] | [€] |
---|---|---|---|---|---|---|
1 | 679.5 | 169.14 | 0 | 10.38 | 0 | 859.01 |
2 | −247.27 | 169.14 | 64.61 | 91.88 | −66.74 | 11.76 |
3 | −163.09 | 169.14 | 64.34 | 91.7 | −66.74 | 95.24 |
4 | 93.4 | 169.14 | 65.68 | 16.94 | −66.83 | 278.3 |
5 | −230.74 | 169.14 | 65.54 | 98.367 | 0 | 102.3 |
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Vermeer, W.; Chandra Mouli, G.R.; Bauer, P. Real-Time Building Smart Charging System Based on PV Forecast and Li-Ion Battery Degradation. Energies 2020, 13, 3415. https://doi.org/10.3390/en13133415
Vermeer W, Chandra Mouli GR, Bauer P. Real-Time Building Smart Charging System Based on PV Forecast and Li-Ion Battery Degradation. Energies. 2020; 13(13):3415. https://doi.org/10.3390/en13133415
Chicago/Turabian StyleVermeer, Wiljan, Gautham Ram Chandra Mouli, and Pavol Bauer. 2020. "Real-Time Building Smart Charging System Based on PV Forecast and Li-Ion Battery Degradation" Energies 13, no. 13: 3415. https://doi.org/10.3390/en13133415
APA StyleVermeer, W., Chandra Mouli, G. R., & Bauer, P. (2020). Real-Time Building Smart Charging System Based on PV Forecast and Li-Ion Battery Degradation. Energies, 13(13), 3415. https://doi.org/10.3390/en13133415