Analysis of a Multi-Timescale Framework for the Voltage Control of Active Distribution Grids

Din, Edoardo De; Bigalke, Fabian; Pau, Massimiliano; Ponci, Ferdinanda; Monti, Antonello

doi:10.3390/en14071965

Cited by 6 publications

(4 citation statements)

References 37 publications

(68 reference statements)

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“…Moreover the results obtained by solving the centralized MPC will be use to validate the results of the distributed MPC. The MPC formulation used in our work is based on [17], where the MPC problem has been formulated for a radial distribution grid described using the linearized branch-flow equations defined in [15], [26], [28], with voltage phase angles considered equal to zero. When applying MPC, at every time step k we solve a finite-time optimal control problem over a prediction horizon of…”

Section: B Centralized Mpc Formulationmentioning

confidence: 99%

“…A possible voltage control strategy that can be applied in both centralized and distributed architecture is the Model Predictive Control (MPC), where the optimization problem is solved by minimizing the objective function over a finite prediction horizon. As described in [17], since the MPC performs an optimization using measurements and predictions, it is able to track reference values provided with larger timescale while correcting voltage deviations with online measurements. Purpose of the MPC described in this paper is to track the reference value for the State of Charge (SoC) over a finite time horizon while maintaining the voltage in the operational limits.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed Decomposition

et al. 2022

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Advanced control techniques for modern distribution grids are becoming fundamental for the reduction of grid reinforcements while maintaining network performances. In particular, as shown in literature, distributed algorithms for voltage control have gained much interests in comparison with the typical centralized formulation for its feasibility. Distributed model predictive control (MPC) is shown to optimally manage the Distributed Generators (DGs) over time and it can be implemented locally at the controllable sources. For this purpose, this paper adopts a distributed algorithm recently presented in the literature for solving a constraint-coupled optimization problem for model predictive voltage control. A detailed reformulation of the original MPC problem for the specific application is presented. Besides, this paper provides a calculation of the convergence limit for the value of the iteration step size of the algorithm, supported by numerical results. The proposed distributed solution of model predictive voltage control is compared with a centralized formulation via numerical simulation in terms of the percentage of error with the centralized solution and number of iteration for the convergence.INDEX TERMS Voltage control, distributed optimization, smart grids, networked control systems.

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Section: B Centralized Mpc Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed Decomposition

et al. 2022

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“…To maintain the voltage deviations within the allowable ranges, Alam et al [9] have designed a centralized ESS charging-discharging strategy with the functions of long-timescale peak-load shifting and short-timescale power supply and demand balance. More similar works can be found in [10][11][12][13][14]. However, with the increase of PV penetration proportion and the expansion of distribution network scale, the requirement of adequate communication, computation, and data storage resources in the centralized methods will lead to low solution efficiency and complex control operation problems [15].…”

Section: Introductionmentioning

confidence: 99%

Decomposition-Coordination-Based Voltage Control for High Photovoltaic-Penetrated Distribution Networks under Cloud-Edge Collaborative Architecture

Han

2022

International Transactions on Electrical Energy Systems

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With the integration of high proportional photovoltaics (PVs) into distribution networks, the superposition of uncertain output power of PVs and stochastic load demand fluctuations have posed a serious challenge to voltage stability. In this paper, a multitimescale decomposition-coordination-based voltage control method is proposed under the cloud-edge cooperative architecture. The distribution network is firstly decomposed into several subnetworks. In each subnetwork, the edge computing device is equipped to realize the distributed control and provide external computing resources for the cloud center. Then, a multitimescale control scheme containing the interval dispatch stage and the real-time time-window stage is proposed. In the interval dispatch stage, a cloud-edge collaborative calculation strategy considering balanced resource allocation is well designed to obtain the global optimal power flows and the corresponding reference operating points of the voltage control equipment. Meanwhile, in the real-time time-window stage, a consensus-based voltage correction mechanism under the optimal power flow boundary constraints is designed to avoid the voltage violations caused by unexcepted power fluctuations deviating from the representative scenarios. Simulation results with the improved 33-bus and IEEE 123-bus systems have demonstrated the effectiveness of our proposed method.

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“…Alternative DNO-based control solutions are needed to accelerate the uptake of residential PV systems [10][11][12]. One of the potential solutions is to reduce voltages at the LV busbar of distribution transformers to allow additional headroom of voltage rise at customers' connection points [13].…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Using OLTC-Fitted Distribution Transformer to Increase Residential PV Hosting Capacity: Decentralized Voltage Management Approach

2022

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The increasing Photovoltaic (PV) penetration in residential Low Voltage (LV) networks is likely to result in a voltage rise problem. One of the potential solutions to deal with this problem is to adopt a distribution transformer fitted with an On-Load Tap Changer (OLTC). The control of the OLTC in response to local measurements reduces the need for expensive communication channels and remote measuring devices. However, this requires developing an advanced decision-making algorithm to estimate the existence of voltage issues and define the best set point of the OLTC. This paper presents a decentralized data-driven control approach to operate the OLTC using local measurements at a distribution transformer (i.e., active power and voltage at the secondary side of the transformer). To do so, Monte Carlo simulations are utilized offline to produce a comprehensive dataset of power flows throughout the distribution transformer and customers’ voltages for different PV penetrations. By the application of the curve-fitting technique to the resulting dataset, models to estimate the maximum and the minimum customers’ voltages are defined and embedded into the control logic to manage the OLTC in real time. The application of the approach to a real UK LV feeder shows its effectiveness in improving PV hosting capacity without the need for remote monitoring elements.

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Analysis of a Multi-Timescale Framework for the Voltage Control of Active Distribution Grids

Cited by 6 publications

References 37 publications

Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed Decomposition

Distributed Model Predictive Voltage Control for Distribution Grid Based on Relaxation and Successive Distributed Decomposition

Decomposition-Coordination-Based Voltage Control for High Photovoltaic-Penetrated Distribution Networks under Cloud-Edge Collaborative Architecture

Using OLTC-Fitted Distribution Transformer to Increase Residential PV Hosting Capacity: Decentralized Voltage Management Approach

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