Voltage Regulation For Residential Prosumers Using a Set of Scalable Power Storage
Abstract
:1. Introduction
1.1. Research Motivation
1.2. Contribution of This Work
2. Characteristic Profiles of Solar Radiation
3. Solar Irradiation Forecast
Recurrent Neural Networks
4. Electrical Problems Associated to the High Variability of the Solar Radiation
5. Restrictions of Voltage Limits
6. Case Study
7. Mitigation Method for Overvoltage
7.1. Database
7.2. Forecasting Model of RNN LSTM
7.3. Storage System Planning and Definition
8. Results
8.1. Result of the Forecast of Solar Irradiation and Cell Temperature
8.2. Determination of Power Flow Produced by the Photovoltaic Generator
8.3. Short-Term Voltage Regulation
9. Conclusions
- The mitigation technique proposed in this work was able to reduce voltage levels above the critical limit. For the days of high variability, there was a greater need for energy storage, of 84% more than the day of clear sky, in order to guarantee the mitigation of all points of overvoltage.
- For a SoC of 70% of the minimum level of total storage capacity, the day of high variability reached full loading of the initial 3 levels of storage. In percentage terms, for W, W, and W, it needed supplements of: 252%, 144%, and 134%, respectively, at each level of power. The quantitative percentage is related to the capacity of accumulation of electric energy for the day of low variability.
- The number of excessive commutations was classified for the power class W, where the number of commutations for the day of high variability was double compared to the day of low variability. The relationship between the number of commutations and accumulated energy was the opposite for the clear day, when looking at the class of power W, and this fact is justified by the time that the battery system remained in a state of charge.
- The RNN forecasting model for solar irradiance performed well in the absence of variability, the clear sky, and cloudy sky days presented with a Pearson’s correlation coefficient of 98%. The parameters of RMSE were better for the day of clear sky and cloudy sky, at 47.84 W/m2 and 5.69 W/m2, respectively. As well as the MAE, for the days of clear sky and cloudy sky, they reached, respectively, 33.44 W/m2 and 3.56 W/m2. The days characterized as high and low variability showed higher values of RMSE and MAE, as demonstrated throughout the work, concluding with the fact that the neural network had low relative performance for the days with the presence of solar irradiation variability.
- The model used to calculate the power converted by a photovoltaic generator in the OpenDSS software also presented an excellent profile when compared to the data obtained experimentally. The power flow calculation model based on the irradiation prediction achieved good results for all sky conditions. The days with low and high intermittence were validated with a Pearson’s coefficient of 96% for both days, and the RMSE and the MAE were established in a range of 23.03% and 28.80%, respectively, tolerating the maximum load of the predefined inverter (5000 W).
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Voltage Classification | Single Phase 220 V Range (Volts) | Three Phases 380 V Range (Volts) |
---|---|---|
Normal Operating | ||
Critical Limit (Upper) | ||
Critical Limit (Low) | ||
Precarious Limit (Upper) | 3 | |
Precarious Limit (Low) |
Line | Start | End | Length (m) | Line | Start | End | Length (m) |
---|---|---|---|---|---|---|---|
1 | B2 | B3 | 31 | 11 | B12 | B13 | 25 |
2 | B3 | B4 | 34 | 12 | B10 | B14 | 18 |
3 | B4 | B5 | 20 | 13 | B14 | B15 | 17 |
4 | B5 | B6 | 31 | 14 | B2 | B20 | 10 |
5 | B6 | B7 | 32 | 15 | B20 | B19 | 40 |
6 | B7 | B8 | 27 | 16 | B19 | B18 | 32 |
7 | B8 | B9 | 42 | 17 | B18 | B17 | 30 |
8 | B4 | B10 | 40 | 18 | B17 | B16 | 30 |
9 | B10 | B11 | 22 | 19 | B16 | B15 | 20 |
10 | B11 | B12 | 24 |
Line | Conductor | Line Position | Line | Conductor | Line Position |
---|---|---|---|---|---|
1 | Al70 mm2 | Trunk | 11 | Al25 mm2 | Middle |
2 | Al70 mm2 | Trunk | 12 | Al25 mm2 | Middle |
3 | Al70 mm2 | Trunk | 13 | Al25 mm2 | Middle |
4 | Al50 mm2 | Middle | 14 | Al50 mm2 | Neutral |
5 | Al25 mm2 | Trunk | 15 | Al50 mm2 | End |
6 | Al25 mm2 | Trunk | 16 | Al50 mm2 | Middle |
7 | Al25 mm2 | End | 17 | Al50 mm2 | Middle |
8 | Al25 mm2 | End | 18 | Al70 mm2 | Trunk |
9 | Al50 mm2 | Middle | 19 | Al70 mm2 | Trunk |
10 | Al25 mm2 | Trunk |
Operation Mode | Battery Power | PV Power | Demand Power |
---|---|---|---|
Charge (Passive) | > 0 | > 0 | > 0 |
Idle (Neutral) | = 0 | > 0 | > 0 |
Discharger (Active) | < 0 | = 0 | ∀ |
Day | Day Classification | RMSE (W/m2) | MAE (W/m2) | R2 |
---|---|---|---|---|
Day 1 | Low variability | 96.39 | 58.03 | 0.93 |
Day 2 | Clear sky | 47.84 | 33.44 | 0.99 |
Day 3 | Cloudy sky | 5.69 | 3.56 | 0.97 |
Day 4 | High variability | 121.36 | 73.47 | 0.89 |
Classification | > > | < > | > < | < < |
---|---|---|---|---|
Low Variability | 15% | 18% | 0% | 67% |
High Variability | 31% | 11% | 0% | 58% |
Cloudy Sky | 0% | 0% | 0% | 100% |
Clear Sky | 11% | 46% | 0% | 43% |
Classification | (Wh) 500 W | (Wh) 1000 W | (Wh) 1500 W | (Wh) 2000 W |
---|---|---|---|---|
Low Variability | 111 | 1611 | 3917 | 3556 |
High Variability | 361 | 3944 | 9167 | 3333 |
Clear Sky | 0 | 0 | 667 | 8444 |
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Torres, I.C.; Farias, D.M.; Aquino, A.L.L.; Tiba, C. Voltage Regulation For Residential Prosumers Using a Set of Scalable Power Storage. Energies 2021, 14, 3288. https://doi.org/10.3390/en14113288
Torres IC, Farias DM, Aquino ALL, Tiba C. Voltage Regulation For Residential Prosumers Using a Set of Scalable Power Storage. Energies. 2021; 14(11):3288. https://doi.org/10.3390/en14113288
Chicago/Turabian StyleTorres, Igor Cavalcante, Daniel M. Farias, Andre L. L. Aquino, and Chigueru Tiba. 2021. "Voltage Regulation For Residential Prosumers Using a Set of Scalable Power Storage" Energies 14, no. 11: 3288. https://doi.org/10.3390/en14113288
APA StyleTorres, I. C., Farias, D. M., Aquino, A. L. L., & Tiba, C. (2021). Voltage Regulation For Residential Prosumers Using a Set of Scalable Power Storage. Energies, 14(11), 3288. https://doi.org/10.3390/en14113288