Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System
Abstract
:1. Introduction
2. Materials and Methods
2.1. System Description
2.2. Modeling
2.2.1. Modeling of the Photovoltaic Generator
2.2.2. Inverter Modeling
2.2.3. Battery Energy Modeling
2.3. Power Flow Control and Management
2.3.1. Power Control
2.3.2. Load Management Strategy
- Loads type 01: These are the loads that have priority over the other load types. They include the different lamps and the refrigerator.
- Loads type 02: These loads are less important than the load type 01 and include fans and TV.
- Loads type 03: This load is mainly a water pump that will be used to redirect any energy excess, which increases the energy efficiency of the system.
- The ambient temperature (Ta);
- The natural irradiance (Ga);
- The occupancy— if the house is occupied and if not;
- The predicted battery state of charge (PSOC) at time t + Δta;
- The predicted PV generated energy PEpv at time t + Δta.
- ≥
- = 1) & ( ≤ )
- ≥ & Oc = 1
- ≥
2.3.3. LSTM and Forecasting Algorithm
3. Simulation Results
3.1. Application Site and Load Profile
3.2. Forecasting Results
3.3. Management Algorithm Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
Description | Symbol | Unit |
Photovoltaic | PV | - |
State of charge | SOC | - |
Loss of power supply probability | LPSP | - |
Battery management system | BMS | - |
Alternative current | AC | - |
Maximum power point tracking | MPPT | - |
PV power | W | |
PV efficiency | % | |
Irradiance | ||
Surface | ||
Efficiency of the module at the reference temperature | % | |
Power tracking efficiency | % | |
Thermal efficiency coefficient | %/°C | |
The temperature of the cell | °C | |
The ambient air temperature | °C | |
Nominal operating cell temperature | °C | |
Inverter output power | W | |
Inverter efficiency | % | |
Inverter input power | W | |
Inverter maximum input power | W | |
No load losses coefficient | - | |
The instant power of the battery | ,Pnet | W |
The instant load power | W | |
The nominal capacity of the battery | Wh | |
The battery state of charge | % | |
The predicted SOC | PSOC | % |
The predicted energy of the PV | Pepv | W |
The nominal irradiance | ||
The nominal temperature | °C |
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Component | Characteristics |
---|---|
Battery | 1300 Whr |
PV | 400 Wc |
Inverter | 1 kW |
Symbol | Value |
---|---|
2.3 m2 | |
16% | |
0.98 | |
0.5%/°C | |
25 °C |
Symbol | Value |
---|---|
95% | |
1 kW | |
0.005 | |
0.005 | |
0.06 |
Constant | Value |
---|---|
10% | |
30% | |
30% | |
95% | |
20 °C | |
100 W/m2 |
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Alnejaili, T.; Labdai, S.; Chrifi-Alaoui, L. Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System. Sensors 2021, 21, 6427. https://doi.org/10.3390/s21196427
Alnejaili T, Labdai S, Chrifi-Alaoui L. Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System. Sensors. 2021; 21(19):6427. https://doi.org/10.3390/s21196427
Chicago/Turabian StyleAlnejaili, Tareq, Sami Labdai, and Larbi Chrifi-Alaoui. 2021. "Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System" Sensors 21, no. 19: 6427. https://doi.org/10.3390/s21196427
APA StyleAlnejaili, T., Labdai, S., & Chrifi-Alaoui, L. (2021). Predictive Management Algorithm for Controlling PV-Battery Off-Grid Energy System. Sensors, 21(19), 6427. https://doi.org/10.3390/s21196427