Reinforcement Learning-Enabled Electric Vehicle Load Forecasting for Grid Energy Management
Abstract
:1. Introduction
1.1. Literature Survey
1.2. Motivation and Problem Statement
1.3. Real Contribution
- A comprehensive investigation of load forecasting challenges for PHEV charging remains imperative through implementing RL, a potent tool for combining multiple ML models [6]. To address this matter, a new approach based on QL for forecasting load at EV charging stations has been introduced in this publication. QL has been frequently employed in problems with distinct states and actions. Our proposed QL model is suitable for the control task that demands ongoing response to the prevailing circumstances. The system comprises neural networks and deduces the differential for the state evolution of unknown epistemic uncertainty. This solution presents an opportunity to enhance the operational efficiency of PHEV charging, while also serving as a mechanism of reinforcement for energy dispatching within power grids.
- The recommended RL methodology for determining the optimal framework for PHEV load forecasting encompasses smart, cooperative, and non-cooperative scenarios. The developed QL approach exhibits superior efficiency, precision, and flexibility in PHEV load estimation when compared to traditional ANN and RNN models. Furthermore, incorporating modifications such as adjusting the epoch, hidden layer, and node quantities can significantly augment the accuracy of PHEV charging load predictions, as evidenced by empirical analyses.
1.4. Paper Organization
2. Technical Background
2.1. ANN
2.2. RNN
2.3. QL
3. Charging Behavior of PHEVs
3.1. Non-Cooperative PHEV Charging
3.2. Cooperative PHEV Charging
3.3. Smart PHEV Charging
4. Proposed QL-Based PHEV Charging Load Forecasting Framework
4.1. Data Source
4.2. Preprocessing Module
4.3. QL-Based Forecasting Module
- When using the ANN technique to forecast the load on PHEVs, the input and output ANN units should be chosen appropriately. Due to the time series nature of PHEVS load data, the ANN unit utilized prior PHEV load data. The baseline 24-h PHEVs load data were helpful for more accurate one-hour-ahead load forecasting. The deployment of ANN and RNN for predicting one hour ahead is shown in Figure 4.
- The proposed QL method for the PHEVs load forecasting used the previous days’ ANN and RNN forecasting results. In hopes of identifying the best day-ahead PHEV load forecasting, the proposed QL approach chose the best course of action based on the output of ANN and RNN. The proposed QL model’s reward function is shown in Equation (14):
5. Evaluation Criteria
6. QL-Model Forecasting Performance with Different Network Depths
Convergence of the QL
7. Test Cases Simulations and Results
7.1. Load Forecasting of Non-Cooperative PHEVs Charging
7.2. Load Forecasting of Cooperative PHEVs Charging
7.3. Load Forecasting of Smart PHEVs Charging
8. Sensitivity Analysis of Three Charging Techniques
9. Validation of Proposed QL
10. QL in Terms of Speed, Flexibility and Accuracy
10.1. Faster Speed
10.2. Improved Accuracy
10.3. Flexibility
11. Discussion
12. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Charging Type | I/P Voltage | (KW) |
---|---|---|
Level-I (AC) | 120 | 1.42 |
Level-II (AC) | 208–240 | 11.5 |
Level-III (AC) | 208–240 | 97 |
Level-III (DC) | 208–600 | 239 |
Class | Market Share | (Min–Max) |
---|---|---|
Mini Vehicle | 0.2 | 8–12 |
Mid Size Vehicle | 0.3 | 14–18 |
Economy Vehicle | 0.3 | 10–14 |
Light Truck | 0.3 | 19–23 |
Field | Description |
---|---|
Time of connection | The plugs in time of users. |
Accomplished charging time | The time of last non-zero charging rate. |
Time of disconnection | The unplugs time of users. |
kWh supply | Supplied energy measurement. |
Session ID | Unique identity for the session. |
Station ID | Unique identity of the EV Supply Equipment. |
Layer of QL | CPU Time (s) |
---|---|
2 | 499.48 |
3 | 798.21 |
4 | 1401.98 |
5 | 2001.73 |
6 | 2312.19 |
Techniques | Charging Strategy | Penetration (%) | MSE (KW) | Epoch | MAPE (%) |
---|---|---|---|---|---|
ANN-1 (non-cop) | Non-cooperative | 30 | 4.2 | 3000 | 4.3371 |
RNN-1 (non-cop) | 9.3 | 1000 | 2.9189 | ||
QL-1 (non-cop) | 0.79 | 10,000 | 2.4741 | ||
ANN-1 (Cop) | Cooperative | 30 | 7.06 | 3000 | 4.4901 |
RNN-1 (Cop) | 9.12 | 500 | 3.0129 | ||
QL-1 (Cop) | 6.21 | 10,000 | 2.7210 | ||
ANN-1 (Smart) | Smart | 30 | 6.23 | 3000 | 4.3210 |
RNN-1 (Smart) | 6.38 | 500 | 2.7112 | ||
QL-1 (Smart) | 5.30 | 10,000 | 2.4214 | ||
ANN-2 (non-cop) | Non-cooperative | 60 | 1.67 | 3000 | 5.2121 |
RNN-2 (non-cop) | 25.63 | 500 | 3.1489 | ||
QL-2 (non-cop) | 1.30 | 10,000 | 2.9741 | ||
ANN-2 (Cop) | Cooperative | 60 | 9.54 | 3000 | 5.7371 |
RNN-2 (Cop) | 9.67 | 500 | 4.2189 | ||
QL-2 (Cop) | 9.37 | 10,000 | 3.1451 | ||
ANN-2 (Smart) | Smart | 60 | 7.12 | 3000 | 5.9871 |
RNN-2 (Smart) | 7.34 | 500 | 4.0189 | ||
QL-2 (Smart) | 5.23 | 10,000 | 2.9741 | ||
ANN-3 (non-cop) | Non-cooperative | 90 | 0.0031 | 1000 | 3.2171 |
RNN-3 (non-cop) | 0.0019 | 1000 | 2.7189 | ||
QL-3 (non-cop) | 0.0019 | 1000 | 2.2741 | ||
ANN-3 (Cop) | Cooperative | 90 | 1.23 | 3000 | 3.3371 |
RNN-3 (Cop) | 10.3 | 5000 | 2.8189 | ||
QL-3 (Cop) | 0.889 | 10,000 | 2.1741 | ||
ANN-3 (Smart) | Smart | 90 | 7.11 | 3000 | 3.6371 |
RNN-3 (Smart) | 7.45 | 5000 | 2.7189 | ||
QL-3 (Smart) | 4.45 | 10,000 | 2.0741 |
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Zulfiqar, M.; Alshammari, N.F.; Rasheed, M.B. Reinforcement Learning-Enabled Electric Vehicle Load Forecasting for Grid Energy Management. Mathematics 2023, 11, 1680. https://doi.org/10.3390/math11071680
Zulfiqar M, Alshammari NF, Rasheed MB. Reinforcement Learning-Enabled Electric Vehicle Load Forecasting for Grid Energy Management. Mathematics. 2023; 11(7):1680. https://doi.org/10.3390/math11071680
Chicago/Turabian StyleZulfiqar, M., Nahar F. Alshammari, and M. B. Rasheed. 2023. "Reinforcement Learning-Enabled Electric Vehicle Load Forecasting for Grid Energy Management" Mathematics 11, no. 7: 1680. https://doi.org/10.3390/math11071680