Approximate closed-loop minimax model predictive operation control of microgrids

Hans, Christian A.; Nenchev, Vladislav; Raisch, Jörg; Reincke-Collon, Carsten

doi:10.1109/ecc.2015.7330551

Cited by 10 publications

(7 citation statements)

References 19 publications

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“…The MPC approaches for islanded MGs in [10], [12] include power flow over the lines, power sharing of grid-forming units and curtailable renewable infeed. However, these approaches have been found to be overly conservative as they try to minimise the worst-case objective [10], [12].…”

Section: A Operation Control Of Mgsmentioning

confidence: 99%

“…To compensate for this lack of robustness, some authors have proposed worst-case MPC approaches for the operation of MGs. Uncertain parameters, such as model parameters [11] and forecasts [10], [12] are treated as bounded quantities and an MPC formulation is used to minimise the worst-case cost. The MPC approaches for islanded MGs in [10], [12] include power flow over the lines, power sharing of grid-forming units and curtailable renewable infeed.…”

Section: A Operation Control Of Mgsmentioning

confidence: 99%

“…Uncertain parameters, such as model parameters [11] and forecasts [10], [12] are treated as bounded quantities and an MPC formulation is used to minimise the worst-case cost. The MPC approaches for islanded MGs in [10], [12] include power flow over the lines, power sharing of grid-forming units and curtailable renewable infeed. However, these approaches have been found to be overly conservative as they try to minimise the worst-case objective [10], [12].…”

Section: A Operation Control Of Mgsmentioning

confidence: 99%

“…There are small MGs in which all units are directly connected to a single bus, e.g., in [7], [22]. Such MGs can be modelled in two ways with (12). (i) Discard constraints (12a) and (12b) and only keep (12c).…”

Section: E Transmission Networkmentioning

confidence: 99%

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Risk-Averse Model Predictive Operation Control of Islanded Microgrids

Hans

Sopasakis

Raisch

et al. 2020

IEEE Trans. Contr. Syst. Technol.

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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.

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Section: A Operation Control Of Mgsmentioning

confidence: 99%

Section: A Operation Control Of Mgsmentioning

confidence: 99%

Section: A Operation Control Of Mgsmentioning

confidence: 99%

Section: E Transmission Networkmentioning

confidence: 99%

See 2 more Smart Citations

Risk-Averse Model Predictive Operation Control of Islanded Microgrids

Hans

Sopasakis

Raisch

et al. 2020

IEEE Trans. Contr. Syst. Technol.

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“…Early works on deterministic MPC [1], [5], [6] are giving way to methods that take the associated uncertainty into consideration. Worst-case approaches [7], [8] can prove overly conservative, especially in presence of higly uncertain wind, irradiance and load. Expectation-based (risk-neutral) stochastic MPC formulations have been proposed involving either (typically independent) processes with continuous distributions [9] or scenario-based formulations [2].…”

Section: Introductionmentioning

confidence: 99%

Optimal operation of microgrids with risk-constrained state of charge

Lei

Hans

Sopasakis³

2021

Preprint

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In this paper we present a stochastic scenariobased model predictive control (MPC) approach for the operation of islanded microgrids with high share of renewable energy sources. We require that the stored energy remains within given bounds with a certain probability using risk-based constraints as convex approximations of chance constraints. We show that risk constraints can generally be cast as conic constraints and, unlike chance constraints, can control both the number and average magnitude of constraint violations. Lastly, we demonstrate the risk-constrained stochastic MPC in a numerical case study.

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