A review: Agents in smart grids

Malik, Farhan H.; Lehtonen, Matti

doi:10.1016/j.epsr.2015.10.004

Cited by 60 publications

(27 citation statements)

References 23 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

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“…During the recent years, increased interest is recorded in the field of smart grids (Malik andLehtonen 2016, Tuballa andAbundo 2016) and in the development of novel demand side management (DSM) techniques (Haider et al 2016, Stathopoulos et al 2014) that can facilitate the latter operation. One of the essential elements for the effective implementation of such techniques is the ability to reliably forecast load demand several hours ahead (Pérez-García and Moral-Carcedo 2016).…”

Section: Introductionmentioning

confidence: 99%

One Day-Ahead Prognosis of Energy Demand Using Artificial Intelligence and Biometeorological Indices

Zafirakis¹,

Moustris²,

Alamo³

et al. 2016

Perspectives on Atmospheric Sciences

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Nowadays demand side management has become an important issue. Managing the energy resources in an optimal manner has become imperative among energy planners and policy makers. An integrated energy management approach is essential for the sustainable development of any electricity grid. The main objective of this work is the development of a forecasting model in order to predict one day ahead the energy demand of Tilos Island, Greece. For this purpose, an artificial neural network (ANN) forecasting model was developed to predict the energy demand of the entire island region. Prediction concerns 24-hours ahead on an hourly basis, with the developed ANN model being fed with historical data of energy demand, historical data of solar irradiation and historical data of a biometeorological index known as Cooling Power Index. Results show that the proposed methodology gives a sufficient forecast of energy demand in order to design an automated energy demand information tool for end-users such as distribution and transmission system operators.

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“…The bidirectional flow of energy enables the integration of distributed generation into the grid, bringing the opportunity to introduce local energy production based on renewable resources in the system, and reducing the carbon emissions [11]. Storage systems can also be introduced into the grid thanks to the bidirectional flow of energy.…”

Section: Smart Gridsmentioning

confidence: 99%

Lessons Learned in Building a Middleware for Smart Grids

Macarulla

Albano

Ferreira

et al. 2016

JGE

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Smart grids play an important role in the modernization and optimization of the existing electrical grid, to accomplish the current European Union Energy and Climate targets. Smart grids require distributed applications to manage the grid more efficiently. The performance of the distributed applications impacts on the communications delay time and on the timely interaction with the devices located in the users' Home Area Networks. This paper presents the results of the ENCOURAGE project related to the development of a software platform to support smart grids. The work presented in this paper assesses four different middleware configurations and analyses the results on the delay performance tests. The results show that the mean end-to-end delay is between 310 ms and 453 ms in proper conditions. In terms of operational costs, the optimal configuration enables managing houses with less than 0.25 Euros per month per house. This paper justifies the maturity of the technology to support smart grids, and the possibility to transfer the ENCOURAGE project results to the industry.

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A review: Agents in smart grids

Cited by 60 publications

References 23 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

One Day-Ahead Prognosis of Energy Demand Using Artificial Intelligence and Biometeorological Indices

Lessons Learned in Building a Middleware for Smart Grids

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