Energy Management Strategy for an Autonomous Hybrid Power Plant Destined to Supply Controllable Loads
Abstract
:1. Introduction
- ➢
- The authors apply the fuzzy logic to decide on the switching time of the switches that links the hybrid plant’ components (Figure 1). For this, an EMS is designed and tested using the measured climatic data of Mostoles, (Madrid, Spain), during three typical days of July, March, and December.
- ➢
- The load operation is also deduced using the EMS.
- ➢
- The EMS is applied in a hybrid plant with the objective of maximizing the use of the power generated from the renewable energy components, on the one hand, and minimizing the use of the battery bank and especially the diesel generator, on the other one.
2. Materials and Methods
2.1. System Components Modeling
2.1.1. Photovoltaic Power Generation Model
- : the estimated photovoltaic current (A),
- : the generated photo-current at a given irradiance G (A),
- : the short circuit current for a given temperature (A),
- : the reverse saturation current for a given temperature (A),
- : the reverse saturation current for the reference temperature (A),
2.1.2. Wind Turbine Generation Model
2.1.3. Diesel Generator Model
2.1.4. Battery Bank Model
- : the available energy in the battery bank (Wh),
- : the batteries number,
- : battery’s nominal capacity (Ah),
- : Peukert constant,
- : battery bank nominal voltage (V).
2.1.5. Loads Model
2.2. Fuzzy Management Algorithm (FMA)
2.2.1. Management Strategy
- The load must be supplied with stability. For this:
- The renewable energy sources supply the loads (without the battery bank participation) only when the power demanded by the loads is lower than the power generated by the RE sources.
- The time between switches of the switches is controlled. Consequently, the switching of the switches is minimized.
- The battery bank is used to save the unused RE, which is later used by the loads during the night or when the renewable power generated is less than the power required by the controllable loads.
- To protect the batteries against deep discharges and excessive charges. For this, during the charging or discharging process, the SOC should be maintained between the following limits ( = 10% and = 90%).
- The battery bank can only be charged by the power generated by the renewable sources .
- Deducing the time and identifying which load(s) should be operating, depending on the power demanded.
- Minimizing the switching of the loads.
2.2.2. Switching Modes
- Mode 1: During this mode, only the switch is switched ON. The battery is moderately or almost discharged. Thus, all the renewable power is used to feed the battery.
- Mode 2: The switches and are switched ON. In this case, the RE sources provide the power required by the loads. If the batteries are not fully charged, the excessed RE power is used to charge the battery bank. Otherwise, this energy is not used.
- Mode 3: In this mode, the switches and are switched ON, while is switched OFF. In this mode, the battery bank is moderately or fully charged. Thus, it supplies the loads together with the photovoltaic panels and the wind turbine with the required electrical power. Indeed, it is common to have this mode during the earliest hours of the morning or the latest hours of the afternoon during which the RE power is not sufficient for supply the loads with the required power. Hence, the battery bank provides the loads with the remaining power needed.In this mode,
- Mode 4: This mode is possible during the night. In this mode, only is switched ON. In this case, the battery moderately or fully charged supplies the loads.
- Mode 5: This mode is obtained in the case that the power generated by the RE sources is not sufficient and the battery bank is totally discharged or in the case that there is no power required by the load. Hence, the renewable energy is used to charge the batteries and the loads are supplied by the diesel generator. Therefore, the switches and are switched ON, while the switches and are switched OFFOFF.
- Mode 6: This mode is activated when only the switch is ON, while the rest of the switches are OFF.
2.3. Fuzzy Management Algorithm
2.3.1. The Knowledge Base
- First partition (Renewable power )
- Second partition (State of Charge SOC)
- Third partition (total required loads’ power )
- Fourth partition (Loads’ operation)
- Fifth partition (switches
- ❖
- First case: SOC X = [0, 0.1]: in this interval, charging the battery is preferred to supplying the loads.
- ❖
- Second case: SOC Y = [0.1, 0.8]: in this interval, supplying the loads is preferred to charging the battery bank.
- ❖
- Third case: SOC Z = [0.8, 1]: in this case, the loads are supplied by the RE sources and/or the battery.
2.3.2. Fuzzification
2.3.3. Inference Diagram
2.3.4. Defuzzification
2.4. Algorithm Execution
- Estimation of the RE power generation based on the measured solar radiation, the ambient temperature, and the wind speed.
- Estimation of the demanded power
- Estimation of the SOC based on the measured battery current.
- Fuzzification of the fuzzy inputs.
- Inference diagram performance.
- Defuzzification: Deduction of the fuzzy outputs using the trapezoidal method.
3. Results
4. Discussions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
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Yahyaoui, I.; de la Peña, N.V. Energy Management Strategy for an Autonomous Hybrid Power Plant Destined to Supply Controllable Loads. Sensors 2022, 22, 357. https://doi.org/10.3390/s22010357
Yahyaoui I, de la Peña NV. Energy Management Strategy for an Autonomous Hybrid Power Plant Destined to Supply Controllable Loads. Sensors. 2022; 22(1):357. https://doi.org/10.3390/s22010357
Chicago/Turabian StyleYahyaoui, Imene, and Natalia Vidal de la Peña. 2022. "Energy Management Strategy for an Autonomous Hybrid Power Plant Destined to Supply Controllable Loads" Sensors 22, no. 1: 357. https://doi.org/10.3390/s22010357
APA StyleYahyaoui, I., & de la Peña, N. V. (2022). Energy Management Strategy for an Autonomous Hybrid Power Plant Destined to Supply Controllable Loads. Sensors, 22(1), 357. https://doi.org/10.3390/s22010357