Stochastic distributed model predictive control of microgrid with uncertain PV power prediction
This paper is concerned with a stochastic distributed Model Predictive Control (MPC) technique for power management of a photovoltaic (PV) generators-installed microgrid. The photovoltaic power supply has large uncertainty because it depends on weather conditions. To keep stable power supply to the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-01-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/18824889.2020.1863614 |
| Summary: | This paper is concerned with a stochastic distributed Model Predictive Control (MPC) technique for power management of a photovoltaic (PV) generators-installed microgrid. The photovoltaic power supply has large uncertainty because it depends on weather conditions. To keep stable power supply to the microgrid, both accurate predictions of PV power supplies and efficient energy management based on the prediction are essential. We propose a distributed MPC method for microgrid management by combining the alternating direction method of multipliers-based distributed optimization and the randomized algorithm approach under the situation that a stochastic prediction model for the PV power prediction is available. The proposed method enables us to efficient energy management in a distributed way as well as the probabilistic guarantee of the line and battery capacity constraints. We demonstrate the effectiveness of the proposed method by a numerical simulation. |
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| ISSN: | 1884-9970 |