A Lagrangian Decomposition Algorithm for Optimal Emergency Voltage Control

Beccuti, Marco; Demiray, Turhan; Andersson, Göran; Morari, Manfred

doi:10.1109/tpwrs.2010.2043749

Cited by 38 publications

(30 citation statements)

References 36 publications

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“…Yet, these approaches are designed for preventive control and are not applicable to corrective actions. In contrast to these, the authors in [5], [6] and [7] focus on corrective control of voltage instability that is performed centrally based on information obtained Phasor Measurement Units (PMUs) or based on model predictive control. General shortcomings of centralized approaches are their dependency on comprehensive and accurate system information and the thread of non-convergence in case of complex optimization problems.…”

Section: Context and State Of The Artmentioning

confidence: 99%

Agent‐based identification and control of voltage emergency situations

Robitzky

Weckesser²,

Häger

et al. 2018

IET Generation, Transmission & Distribution

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Changing dynamics of power systems caused by the migration from conventional to distributed energy sources increase the risk of blackouts due to voltage instability, especially in case of unforeseen network conditions (e.g., (N-k)-cases). To enable a both secure and efficient power supply, novel monitoring and emergency control systems for the identification of voltage emergency situations as well as the execution of control actions are required that react reliably in due time and adaptively in case of changing network situations. This paper presents a distributed agent-based approach to counteract voltage instability that is based solely on local measurements and limited inter-agent communication. Distributed agents located at substations in the (sub-)transmission network monitor distribution and transmission voltages as well as load tap changer positions and are able to autonomously curtail load in case system stability is endangered. The applicability of the approach is demonstrated in a co-simulation environment interfacing the multi-agent system with a dynamic power system simulation. The presented approach allows for an early detection of voltage instability as well as a coordinated execution of available control actions. IntroductionThe large-scale integration of renewable energy sources and the related decommissioning of conventional power plants change the complexity and dynamics of future power grids. Especially in case of (N-k) situations, the risk of regional or large-scale blackouts due to long-term voltage instability has to be carefully assessed. To counteract critical network conditions, novel emergency control concepts acting timely and making use of available measures are essential for maintaining system stability in future power systems [1]. The major challenges for such systems are (i) a reliable reaction in due time, (ii) the adaptability in case of changing network conditions, (iii) the robustness to communication failures, and (iv) the coordinated activation of countermeasures.As an alternative to centralized system integrity protection schemes requiring significant computational effort in case of largescale grids or the comprehensive installation of wide-area monitoring and control systems, as proposed in [2]. is proposed. Future substations will be connected through high speed communication links which allows for distributed controllers communicating between each other. In particular, a Multi-Agent System (MAS) is developed with intelligent agents situated at substations in the transmission and subtransmission network. Agents monitor the trend of local bus voltages, communicate with other agents and decide about corrective control actions autonomously. The developed approach allows for fast detection of an evolving voltage instability after the system is subjected to severe disturbances as well as the autonomous activation of countermeasures in order to restore a stable state of equilibrium with acceptable bus voltages.The paper is structured as follows. First, in Section 2 the...

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Section: Context and State Of The Artmentioning

confidence: 99%

Agent‐based identification and control of voltage emergency situations

Robitzky

Weckesser²,

Häger

et al. 2018

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

show abstract

“…With this trend, the risk of failures has increased as demonstrated by the large blackouts observed (Fairley, 2004;Hines et al, 2009). As emphasized by Beccuti et al (2010), the situation is complicated by the fact that electrical power grids are large interconnected systems covering large areas where the failures of components or control actions of a regional operator may adversely affect other components located hundreds of kilometers away. This observation suggests that the problem can be modeled by a PFF-game.…”

Section: The Modelmentioning

confidence: 99%

Cooperation with Externalities and Uncertainty

Habis

Csercsik

2014

Netw Spat Econ

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We introduce a new solution concept to problems with externalities, which is the first in the literature to take into account economic, regulatory and physical stability aspects of network problems in the very same model. A new class of cooperative games is defined where the worth of a coalition depends on the behavior of other players and on the state of nature as well. We allow for coalitions to form both before and after the resolution of uncertainty, hence agreements must be stable against both types of deviations. The appropriate extension of the classical core concept, the Sustainable Core, is defined for this new setup to test the stability of allocations in such a complex environment.A prominent application, a game of consumers and generators on an electrical energy transmission network is examined in details, where the power in-and outlets of the nodes have to be determined in a way, that if any line (0)46 222

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“…A cooperative distributed MPC, using a linear time-invariant model of the system, is applied in [7] to automatic generation control aiming at frequency and tie-line interchange regulation, where each subsystem requires the full knowledge of all other subsystems. A centralized MPC optimization in [8] is solved in a distributed fashion using a classic Lagrangian decomposition algorithm to select optimal combinations of generator voltage setpoints and load shedding. Reference [9] employs a centralized MPC formulation, using an explicit model for time evolution of the load power, to select a combination of generator voltage setpoints, shunt capacitors and load shedding to correct non-viable transmission voltages.…”

Section: Mpc-based Voltage Controlmentioning

confidence: 99%

Distributed communication-based Model Predictive Control for long-term voltage instability

Moradzadeh

Boel

Vandevelde

2013

2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)

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Abstract-This paper deals with a form of long-term voltage instability which can arise from uncoordinated control actions taken by interacting voltage controllers. An effective coordination paradigm, based on Model Predictive Control (MPC), is proposed in order to properly coordinate local control actions taken by Load Tap Changing transformers (LTCs). This coordination is achieved by exchange of information on local planned LTC moves among immediate neighboring control agents (CAs) only, within a prediction horizon. Each MPC-based voltage controller, knows only a reduced-order local hybrid system model of its own area, and uses approximate models for its immediate neighboring areas, as well as even more approximate models for remote areas. Simulation results on well-known Nordic32 test system illustrate the good performance of the proposed real-time coordinating voltage controller.

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A Lagrangian Decomposition Algorithm for Optimal Emergency Voltage Control

Cited by 38 publications

References 36 publications

Agent‐based identification and control of voltage emergency situations

Agent‐based identification and control of voltage emergency situations

Cooperation with Externalities and Uncertainty

Distributed communication-based Model Predictive Control for long-term voltage instability

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