Abstract
In this paper, we present a risk-averse model predictive control (MPC) scheme for the operation of islanded microgrids with very high share of renewable energy sources. The proposed scheme mitigates the effect of errors in the determination of the probability distribution of renewable infeed and load. This allows to use less complex and less accurate forecasting methods and to formulate low-dimensional scenario-based optimization problems, which are suitable for control applications. Additionally, the designer may trade performance for safety by interpolating between the conventional stochastic and worst case MPC formulations. The presented risk-averse MPC problem is formulated as a mixed-integer quadratically constrained quadratic problem and its favorable characteristics are demonstrated in a case study. This includes a sensitivity analysis that illustrates the robustness to load and renewable power prediction errors.
| Original language | English |
|---|---|
| Number of pages | 16 |
| Journal | IEEE Transactions on Control Systems Technology |
| DOIs | |
| Publication status | Published - 08 Aug 2019 |
Keywords
- Probability distribution
- Stochastic processes
- Load modeling
- Microgrids
- Computational modeling
- Predictive models
- Optimization
- Average value-at-risk (AVaR)
- energy management
- islanded microgrids
- model predictive control (MPC)
- operation control
- risk-averse control
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- Risk-Averse Model Predictive Operation Control of Islanded Microgrids
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