Risk-Averse Model Predictive Operation Control of Islanded Microgrids

TL;DR

This paper introduces a risk-averse model predictive control approach for islanded microgrids with high renewable energy, enhancing robustness against forecast errors and allowing a trade-off between performance and safety.

Contribution

It proposes a novel risk-averse MPC scheme that handles uncertainty in renewable infeed and load, using low-dimensional scenario-based optimization suitable for real-time control.

Findings

Robustness demonstrated through sensitivity analysis.

Effective trade-off between performance and safety.

Suitable for high renewable share microgrids.

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 optimisation 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 favourable characteristics are demonstrated in a case study. This includes a sensitivity analysis that illustrates the robustness to load and renewable power prediction errors.