Agent-based modeling and simulation of a smart grid: A case study of communication effects on frequency control

Kilkki, Olli; Kangasrääsiö, Antti; Nikkilä, Raimo; Alahäivälä, Antti; Seilonen, Ilkka

doi:10.1016/j.engappai.2014.04.007

Cited by 22 publications

(9 citation statements)

References 26 publications

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“…They are also straightforwardly related to the distributed information and intelligence behind to manage the Internet of Things of assets placed in a network [147,148]. Agent-based solutions have shown to be suitable for smart-grids [149,150], transportation [151,152], water distribution systems [153], and telecommunication infrastructure [154]. The works of Cardellini et al [155], Setola et al [156], and Iturriza et al [157] showed how MAS are suitable to model network interdependence.…”

Section: Agent-based Complex Networkmentioning

confidence: 99%

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

et al. 2020

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Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.Processes 2020, 8, 312 2 of 29 in a telecommunication system network, nodes (e.g., routers and switches) may suffer over time a degradation in their properties that may lead them to diminish their performance. However, just the daily variation on traffic demand also has an impact on such nodes performance. Unexpected, external intervention such as cyber-attacks or extreme weather conditions may affect the properties, even the topology of a telecommunication system.Intelligent distributed systems are capable of modelling how different parts of the network might work individually and collectively [10]. Ultimately, this is used as support for any decision-making system aiming to achieve better overall system functioning and its consequent performance. Intelligent distributed systems encompass autonomous learning units that can be associated with the nodes of a complex network. These intelligent network nodes are able to act independently and also interact with other nodes to pursue both individual (local) and general system-level targets. The necessary communication between individuals can be represented by links of the complex network connecting their nodes. It is natural to understand such nodes as intelligent agents within a MAS framework. Each agent separately obeying simple rules but working together with other agents makes it possible to approach complex engineering challenges. Intelligent distributed systems have been proposed in a broad range of engineered complex systems [11,12]. Precisely, the review proposed herein focuses only on engineered systems and manufacturing processes, given the wide spectrum of the approached topic. Out of this review fall, then, topics related to social systems analysis such as migration models [13,14] or online social media [15,16]. Similarly, natural systems are out of this review scope. This is the case of computational chemistry [17,18], biology [19,20], and metabolic networks [21,22].Automatic optimisation and control in an engineering system is associated with a near real-time data acquisition and an optimal decision-making. Th...

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Section: Agent-based Complex Networkmentioning

confidence: 99%

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

et al. 2020

View full text Add to dashboard Cite

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“…They are also straightforwardly related to the distributed information and intelligence behind to manage the Internet of Things of assets placed in a network [138,139]. Agent-based solutions have shown to be suitable for smart-grids [140,141], transportation [142,143], water distribution systems [144], and telecommunication infrastructure [145]. The works of Cardellini et al [146], Setola et al [147] and Iturriza et al [148] showed how MAS are suitable to model network interdependence.…”

Section: Agent-based Complex Networkmentioning

confidence: 99%

A Review on Control and Optimisation of Multi-Agent Systems and Complex Networks for Systems Engineering

Herrera¹,

Hernández²,

Parlikad³

et al. 2020

Preprint

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Systems Engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address Systems Engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in Industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks; along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.

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“…The communication infrastructure of the smart grid is envisioned to consist of a combination of existing networks and technologies (Güngör et al, 2011), where the latencies and other characteristics are important considerations (Lu et al, 2013). These communication channels can be included in the models by integrating external communication network simulators (Mets et al, 2011), or more directly by utilizing various probability distributions for the relevant parameters (Kilkki et al, 2014). In addition, constraints on locational grid limits might have to be taken into account (Wang et al, 2015).…”

Section: Other Elementsmentioning

confidence: 99%

On Demand Response Modeling and Optimization of Power in a Smart Grid

Kilkki¹,

Zenger²

2018

Linköping Electronic Conference Proceedings

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The electrical grid is under reform; the increasing volatile renewable energy production and distributed local generation compel the development of a future where the consumption of electricity can also participate in maintaining the production-consumption balance of the grid. There is a vast amount of recent research activity related to exploiting residential and industrial consumption elasticity. This paper presents a selected overview of various facets of modeling, optimizing and simulating the demand response potential and effects, especially with emergent soft computing methods. In addition, some illustrating examples are presented, where various relevant approaches from recent state-of-the-art research, including soft computing methods, are reviewed.

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Agent-based modeling and simulation of a smart grid: A case study of communication effects on frequency control

Cited by 22 publications

References 26 publications

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

A Review on Control and Optimisation of Multi-Agent Systems and Complex Networks for Systems Engineering

On Demand Response Modeling and Optimization of Power in a Smart Grid

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