In this work, we propose a hierarchical distributed model predictive control strategy to operate interconnected microgrids (MGs) with the goal of increasing the overall infeed of renewable energy sources. In particular, we investigate how renewable infeed of MGs can be increased by using a transmission network allowing the exchange of energy. To obtain an model predictive control scheme which is scalable with respect to the number of MGs and preserves their independent structure, we make use of the alternating direction method of multipliers leading to local controllers communicating through a central entity. This entity is in charge of the power lines and ensures that the constraints on the transmission capacities are met. The results are illustrated in a numerical case study.

Abstract-We propose a model predictive control (MPC) approach for the operation of islanded microgrids that takes into account the stochasticity of wind and load forecasts. In comparison to worst case approaches, the probability distribution of the prediction is used to optimize the operation of the microgrid, leading to less conservative solutions. Suitable models for time series forecast are derived and employed to create scenarios. These scenarios and the system measurements are used as inputs for a stochastic MPC, wherein a mixedinteger problem is solved to derive the optimal controls. In the provided case study, the stochastic MPC yields an increase of wind power generation and decrease of conventional generation.

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 scenariobased 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 quadraticallyconstrained 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.

Due to the steady growth of decentralised distributed generation, the operational management of small, local electricity networks (microgrids) is becoming an increasing challenge to meet: How to provide an operational control for microgrids with a high share of renewable energy sources (RES) that is robust to perturbations? In this paper we address an optimal control problem (OCP) that maintains all of the stated properties in the presence of an uncertain load and RES infeed in islanded operation. Assuming that the uncertainty is within a bounded region along a given load and RES trajectory prediction, the problem is posed as a worst-case hybrid OCP, where the RES output can be curtailed. We propose a minimax (MM) model predictive control (MPC) scheme that adjusts according to the present uncertainty and can be formulated as a mixed-integer linear program (MILP) and solved numerically online.

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