Experimental validation of feedback optimization in power distribution grids

Ortmann, Lukas; Hauswirth, Adrian; Caduff, Ivo; Dörfler, Florian; Bolognani, Saverio

doi:10.1016/j.epsr.2020.106782

Cited by 36 publications

(48 citation statements)

References 11 publications

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“…In this way it differs from classical offline optimization algorithms that only forward output values upon convergence. Due to advantages like a low complexity and a high robustness against modelling errors, online optimization has gained a lot of interest recently, especially in the context of OPF [4][5][6]. The algorithm utilized here is based on the framework proposed in [4].…”

Section: Operation Management Controllermentioning

confidence: 99%

“…Hence, such approaches can be characterized as feedforward: they predict the voltages and currents resulting from applying the new set values solely based on the power flow model and previous input values. As shown in [6], this might result in persisting limit violations in case of an inaccurate model of the power grid. Another difficulty stems from the fact that the DER's allowed active and reactive power ranges (3)-(4) have to be known, which might not be the case especially for weather-dependent DERs like PV plants.…”

Section: St (1)-(5)mentioning

confidence: 99%

“…The closed-loop approach increases the robustness to model errors. Also, the iterative procedure significantly reduces the computational burden within each control cycle compared to feedforward optimization [4][5][6].…”

Section: Online Feedback-based Optimization Algorithmmentioning

confidence: 99%

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Utility-based operation management for low voltage distribution grids using online optimization

Ipach

Fisser

Becker

et al. 2021

Elektrotech. Inftech.

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We present an operation management controller for low voltage (LV) grids that coordinates a multitude of distributed energy resources (DER) in real time to maximize the utilization of renewable energy production. It utilizes an LTE radio network that connects the DERs to the distribution system operator. In our approach, utility functions are assigned to the DERs, and the utility maximization is formulated as an optimization problem. The optimization problem is solved by an iterative algorithm that performs incremental updates of the DER power set values to achieve the optimum. In order to take the state of the grid in the optimization process into account, the state of the grid is estimated. During the simulation of a use case, we demonstrate the applicability and identify the benefits of our approach compared to an established optimal power flow (OPF) method. Particular emphasis is put on evaluating the communication delay and feasibility of the required communication network, as the iterative approach leads to a high communication load.

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Section: Operation Management Controllermentioning

confidence: 99%

Section: St (1)-(5)mentioning

confidence: 99%

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Utility-based operation management for low voltage distribution grids using online optimization

Ipach

Fisser

Becker

et al. 2021

Elektrotech. Inftech.

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“…A projected dynamical systems approach is explored in [13]. The robustness of FO is investigated theoretically [14] and experimentally [15]. All these works assume that the plant is a static map and thus neglect dynamical interactions.…”

Section: Introductionmentioning

confidence: 99%

“…While we do not explicitly address the issue here, feedback optimization is known to be robust to modelling errors, see e.g.,[14],[15].…”

mentioning

confidence: 99%

Sampled-Data Online Feedback Equilibrium Seeking: Stability and Tracking

Belgioioso

Liao‐McPherson

Badyn

et al. 2021

2021 60th IEEE Conference on Decision and Control (CDC)

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This paper proposes a general framework for constructing feedback controllers that drive complex dynamical systems to "efficient" steady-state (or slowly varying) operating points. Efficiency is encoded using generalized equations which can model a broad spectrum of useful objectives, such as optimality or equilibria (e.g. Nash, Wardrop, etc.) in noncooperative games. The core idea of the proposed approach is to directly implement iterative solution (or equilibrium seeking) algorithms in closed loop with physical systems. Sufficient conditions for closed-loop stability and robustness are derived; these also serve as the first closed-loop stability results for sampleddata feedback-based optimization. Numerical simulations of smart building automation and game-theoretic robotic swarm coordination support the theoretical results.

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