Risk-Averse Model Predictive Operation Control of Islanded Microgrids

Hans, Christian A.; Sopasakis, Pantelis; Raisch, Jörg; Reincke-Collon, Carsten; Patrinos, Panagiotis

doi:10.1109/tcst.2019.2929492

Cited by 35 publications

(44 citation statements)

References 67 publications

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“…Recently, MPC has been extended to solve the optimal load frequency control (LFC) problems of power systems [12][13][14][15][16] and operation optimization issues of microgrids [17][18][19][20]. Especially, it should be noted that there are some existing works regarding MPC-based frequency control of microgrids [12,[19][20][21][22][23]. Reference [12] developed a MPC-based coordinated load frequency control method for a microgrid under the condition regarding varied number of electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid

et al. 2018

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Optimal frequency control of an islanded microgrid has been a challenging issue in the research field of microgrids. Recently, fractional-order calculus theory and some related control methods have attempted to handle this issue. In this paper, a novel fractional-order model predictive control (FOMPC) method is proposed to achieve the optimal frequency control of an islanded microgrid by introducing a fractional-order integral cost function into model predictive control (MPC) algorithm. Firstly, a discrete state-space model is derived for the optimal frequency control problem of an islanded microgrid. Afterward, a fractional-order integral cost function is designed to guide the FOMPC algorithm to obtain optimal control law by borrowing the Grünwald-Letnikov (GL) definition of fractional order calculus. Six simulation studies have been carried out to illustrate the superiority of FOMPC to conventional MPC under dynamical load disturbances, perturbed system parameters and random dynamical power fluctuation of wind turbines.

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Section: Introductionmentioning

confidence: 99%

Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid

et al. 2018

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“…Risk-averse optimal control formulations are nowadays making their way in applications such as power systems [10], and economics [2] as their favorable properties are becoming evident. Yet, their applicability is hindered by their complexity and computational cost of associated multistage formulations.…”

Section: A Background Motivation and Contributionsmentioning

confidence: 99%

“…When the system dynamics is linear (or affine) and functions t and φ t are convex in x and u, then (19) is a convex conic problem which can be solved very efficiently with solvers such as MOSEK [28], SuperSCS [29] and more. Problems (10) and (19) are equivalent in the sense that the optimal values of the objective function at the solution are the same. If all involved risk measures are strictly monotone, then the respective sets of minimizers are equal.…”

Section: B Decomposition Of Nested Formulationmentioning

confidence: 99%

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Risk-averse risk-constrained optimal control

Sopasakis

Schuurmans

Patrinos

2019

2019 18th European Control Conference (ECC)

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Multistage risk-averse optimal control problems with nested conditional risk mappings are gaining popularity in various application domains. Risk-averse formulations interpolate between the classical expectation-based stochastic and minimax optimal control. This way, risk-averse problems aim at hedging against extreme low-probability events without being overly conservative. At the same time, risk-based constraints may be employed either as surrogates for chance (probabilistic) constraints or as a robustification of expectationbased constraints. Such multistage problems, however, have been identified as particularly hard to solve. We propose a decomposition method for such nested problems that allows us to solve them via efficient numerical optimization methods. Alongside, we propose a new form of risk constraints which accounts for the propagation of uncertainty in time. † P. Sopasakis is with Queen's University Belfast,

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“…The sampling time of the MPC is several minutes and therefore this model is the steady-state model. It is based on our previous work in [5].…”

Section: System Modelmentioning

confidence: 99%

Fallback Strategies in Operation Control of Microgrids with Communication Failures

Löser

Sampathirao

Hofmann

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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This paper proposes a model predictive control (MPC)-based energy management system to address communication failures in an islanded microgrid (MG). The energy management system relies on a communication network to monitor and control power generation in the units. A communication failure to a unit inhibits the ability of the MPC to change the power of the unit. However, this unit is electrically connected to the network and ignoring this aspect could have adverse effect in the operation of the microgrid. Therefore, this paper considers an MPC design that includes the electrical connectivity of the unit during communication failures. This paper also highlights the benefit of including the lower layer control behaviour of the MG to withstand communication failures. The proposed approaches are validated with a case study.arXiv:1908.03736v1 [eess.SY]

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Risk-Averse Model Predictive Operation Control of Islanded Microgrids

Cited by 35 publications

References 67 publications

Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid

Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid

Risk-averse risk-constrained optimal control

Fallback Strategies in Operation Control of Microgrids with Communication Failures

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