Closing the loop: Dynamic state estimation and feedback optimization of power grids

Picallo, Miguel; Bolognani, Saverio; Dörfler, Florian

doi:10.1016/j.epsr.2020.106753

Cited by 25 publications

(32 citation statements)

References 12 publications

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“…As a result, our method allows to consider constraints on grid state variables that are not directly measured. The combination of online optimization and SE for OPF has been investigated in [7], where a Kalman-filter based SE is utilized. In contrast to [7], we employ the modified branch-current based SE proposed in [8] that avoids repeated matrix inversions and thus keeps the computational complexity low.…”

Section: Operation Management Controllermentioning

confidence: 99%

“…The combination of online optimization and SE for OPF has been investigated in [7], where a Kalman-filter based SE is utilized. In contrast to [7], we employ the modified branch-current based SE proposed in [8] that avoids repeated matrix inversions and thus keeps the computational complexity low. Furthermore, an emphasis is placed on the accurate communication system modelling.…”

Section: Operation Management Controllermentioning

confidence: 99%

“…For these unobserved prosumers, we assume that at least their annual energy consumption is known. Then, scaled standard load profiles (SLP) are used as pseudo-measurements with low weighting factors, which is a common approach to gain observability in distribution grid SE with sparse measurements [7]. The power factor is assumed to equal 0.95.…”

Section: State Estimationmentioning

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: Operation Management Controllermentioning

confidence: 99%

Section: State Estimationmentioning

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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show abstract

“…To cope with the incomplete measurable system states, reference [20] used a weighted least squares state estimator as feedback and formulated a closed-loop distributed primal-dual gradient algorithm in a singleperiod optimization problem. For a multi-period problem with random process noise, reference [21] utilized a dynamic SE based on the Kalman filter in a centralized gradient projection method, where the seeking performance of the dynamic SE was theoretically proved to converge to the offline optimal solution in expectation.…”

Section: Online Algorithm Via Measurement Feedbackmentioning

confidence: 99%

Online distributed tracking of generalized Nash equilibrium on physical networks

Liu

Wang

et al. 2021

Auton. Intell. Syst.

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In generalized Nash equilibrium (GNE) seeking problems over physical networks such as power grids, the enforcement of network constraints and time-varying environment may bring high computational costs. Developing online algorithms is recognized as a promising method to cope with this challenge, where the task of computing system states is replaced by directly using measured values from the physical network. In this paper, we propose an online distributed algorithm via measurement feedback to track the GNE in a time-varying networked resource sharing market. Regarding that some system states are not measurable and measurement noise always exists, a dynamic state estimator is incorporated based on a Kalman filter, rendering a closed-loop dynamics of measurement-feedback driven online algorithm. We prove that, with a fixed step size, this online algorithm converges to a neighborhood of the GNE in expectation. Numerical simulations validate the theoretical results.

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“…OFO is based on a controller that uses grid measurements as feedback to iteratively steer the controllable input set-points towards the AC-OPF solutions, and has already been successfully tested in both simulations and experimental settings [7]. Furthermore, OFO neither requires full grid observability [8], nor an accurate nonlinear grid model. It only needs measurements of the outputs that need to be controlled, and the input-output sensitivity that matches a change in the input to a change in the output.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Real-Time Grid Operation via Online Feedback Optimization with Sensitivity Estimation

Picallo¹,

Ortmann²,

Bolognani³

et al. 2021

Preprint

Self Cite

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In this paper we propose an approach based on an Online Feedback Optimization (OFO) controller with grid input-output sensitivity estimation for real-time grid operation, e.g., at subsecond time scales. The OFO controller uses grid measurements as feedback to update the value of the controllable elements in the grid, and track the solution of a time-varying AC Optimal Power Flow (AC-OPF). Instead of relying on a full grid model, e.g., grid admittance matrix, OFO only requires the steady-state sensitivity relating a change in the controllable inputs, e.g., power injections set-points, to a change in the measured outputs, e.g., voltage magnitudes. Since an inaccurate sensitivity may lead to a model-mismatch and jeopardize the performance, we propose a recursive least-squares estimation that enables OFO to learn the sensitivity from measurements during real-time operation, turning OFO into a model-free approach. We analytically certify the convergence of the proposed OFO with sensitivity estimation, and validate its performance on a simulation using the IEEE 123-bus test feeder, and comparing it against a state-of-the-art OFO with constant sensitivity.

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Closing the loop: Dynamic state estimation and feedback optimization of power grids

Cited by 25 publications

References 12 publications

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

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

Online distributed tracking of generalized Nash equilibrium on physical networks

Adaptive Real-Time Grid Operation via Online Feedback Optimization with Sensitivity Estimation

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