Autonomous agents and cooperation for the control of cascading failures in electric grids

Hines, Paul; Liao, Huaiwei; Jia, Dong; Talukdar, Sarosh N.

doi:10.1109/icnsc.2005.1461200

Cited by 28 publications

(12 citation statements)

References 5 publications

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“…Moreover, one could conceivably extended this approach to other parameters or to other optimization strategies that can be used to assess vulnerabilities and to allocate protection devices and preventive maintenance responses, as described in [24]. The model can also be generalized to replace the unique power grid operator with a network of distributed, autonomous agents who share local information in order to coordinate their local responses to finding good global optimization solution as described in [15]. A decentralized approach may also benefit from the information provided by the structure of the underlying network of flows, as proposed in [20].…”

Section: Optimal Responsementioning

confidence: 99%

Stochastic Model for Power Grid Dynamics

Anghel

Werley

Motter³

2007

2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07)

126

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Abstract-We introduce a stochastic model that describes the quasi-static dynamics of an electric transmission network under perturbations introduced by random load fluctuations, random removing of system components from service, random repair times for the failed components, and random response times to implement optimal system corrections for removing line overloads in a damaged or stressed transmission network. We use a linear approximation to the network flow equations and apply linear programming techniques that optimize the dispatching of generators and loads in order to eliminate the network overloads associated with a damaged system. We also provide a simple model for the operator's response to various contingency events that is not always optimal due to either failure of the state estimation system or due to the incorrect subjective assessment of the severity associated with these events. This further allows us to use a game theoretic framework for casting the optimization of the operator's response into the choice of the optimal strategy which minimizes the operating cost. We use a simple strategy space which is the degree of tolerance to line overloads and which is an automatic control (optimization) parameter that can be adjusted to trade off automatic load shed without propagating cascades versus reduced load shed and an increased risk of propagating cascades. The tolerance parameter is chosen to describes a smooth transition from a risk averse to a risk taken strategy. We present numerical results comparing the responses of two power grid systems to optimization approaches with different factors of risk and select the best blackout controlling parameter.

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Section: Optimal Responsementioning

confidence: 99%

Stochastic Model for Power Grid Dynamics

Anghel

Werley

Motter³

2007

2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07)

126

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“…Subnetworks can however also be defined differently, e.g., based on a fixed "radius" around input nodes. Nodes that are reachable within a certain number of arcs from a particular node with an actuator are then included in a particular subnetwork [9]. Or, subnetworks can be defined using an influence-based approach [8].…”

Section: Control Of Subnetworkmentioning

confidence: 99%

A Novel Coordination Strategy for Multi-Agent Control Using Overlapping Subnetworks with Application to Power Systems

Negenborn

Hug-Glanzmann

Schutter

et al. 2010

Efficient Modeling and Control of Large-Scale Systems

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Power networks are huge interconnected systems controlled by a large number of different control authorities. As the nature of power networks is evolving from a hierarchically structured system toward a much more decentralized system, the need to adequately control the power flows over the network using distributed control strategies increases. Currently available distributed control methods assume that the various subnetworks that individual control agents, i.e., the control authorities, control are usually touching, in the sense that the border of one subnetwork is at the same time also the border of a neighboring subnetwork. Such touching networks, however, do not necessarily capture the subnetwork that an agent can influence in the best way. To capture in the best way the subnetwork that an agent can influence overlapping subnetworks will usually have to be defined. In this chapter, we propose a strategy for coordinating multiple control agents that control overlapping subnetworks in a network. Simulations are carried out on an adjusted IEEE 57-bus power network in which the controlled entities are Flexible Alternating Current Transmission Systems (FACTS) and the objective is to improve system security.

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“…The theoretical research in multi-agent MPC started in the 90s (Aicardi et al, 1992;Acar, 1992;Katebi and Johnson, 1997;Krogh, 2001, 2002;Camponogara et al, 2002), with applications to water distribution systems (Georges, 1999), delivery canals (Sawadogo et al, 1998), irrigation systems (El Fawal et al, 1998), multi-reach canals (Gomez et al, 1998), dynamic routing (Baglietto et al, 1999), cascading failures in power networks (Hines et al, 2005), distributed vehicle coordination (Dunbar and Murray, 2006), and distributed emergency voltage control (Beccuti and Morari, 2006).…”

Section: Multi-agent Mpcmentioning

confidence: 99%

“…1. In the literature on multi-agent MPC mainly parallel schemes have been proposed, e.g., (Hines et al, 2005;Camponogara et al, 2002;El Fawal et al, 1998;Georges, 1999), in which all agents simultaneously perform a local step, then exchange information, then solve their next local step, and so on. In this paper we propose a novel serial scheme, in which only one agent at a time performs a local step, sends information to a next agent, after which this next agent performs a local computation step, sends information to a next agent, etc.…”

Section: Parallel Versus Serial Schemesmentioning

confidence: 99%

Multi-Agent Model Predictive Control for Transportation Networks: Serial Versus Parallel Schemes

Negenborn

Schutter

Hellendoorn

2006

IFAC Proceedings Volumes

124

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We consider the control of large-scale transportation networks, like road traffic networks, power distribution networks, water distribution networks, etc. Control of these networks is often not possible from a single point by a single intelligent control agent; instead control has to be performed using multiple intelligent agents. We consider multi-agent control schemes in which each agent employs a model-based predictive control approach. Coordination between the agents is used to improve decision making. This coordination can be in the form of parallel or serial schemes. We propose a novel serial coordination scheme based on Lagrange theory and compare this with an existing parallel scheme. Experiments by means of simulations on a particular type of transportation network, viz., an electric power network, illustrate the performance of both schemes. It is shown that the serial scheme has preferable properties compared to the parallel scheme in terms of the convergence speed and the quality of the solution.

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Autonomous agents and cooperation for the control of cascading failures in electric grids

Cited by 28 publications

References 5 publications

Stochastic Model for Power Grid Dynamics

Stochastic Model for Power Grid Dynamics

A Novel Coordination Strategy for Multi-Agent Control Using Overlapping Subnetworks with Application to Power Systems

Multi-Agent Model Predictive Control for Transportation Networks: Serial Versus Parallel Schemes

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