Towards Agent-Based Model Specification in Smart Grid: A Cognitive Agent-based Computing Approach

A smart grid can be considered as a complex network where each node represents a generation unit or a consumer. Whereas links can be used to represent transmission lines. One way to study complex systems is by using the agent-based modeling (ABM) paradigm. An ABM is a way of representing a complex system of autonomous agents interacting with each other. Previously, a number of studies have been presented in the smart grid domain making use of the ABM paradigm. However, to the best of our knowledge, none of these studies have focused on the specification aspect of ABM. An ABM specification is important not only for understanding but also for replication of the model. In this study, we focus on development as well as specification of ABM for smart grid. We propose an ABM by using a combination of agent-based and complex network-based approaches. For ABM specification, we use ODD and DREAM specification approaches. We analyze these two specification approaches qualitatively as well as quantitatively. Extensive experiments demonstrate that DREAM is a most useful approach as compared with ODD for modeling as well as for replication of models for smart grid.

[1]  Haitham Abu-Rub,et al.  Smart grid customers' acceptance and engagement: An overview , 2016 .

[2]  Jinde Cao,et al.  A consensus control strategy for dynamic power system look-ahead scheduling , 2015, Neurocomputing.

[3]  Lingfeng Wang,et al.  Adaptive Negotiation Agent for Facilitating Bi-Directional Energy Trading Between Smart Building and Utility Grid , 2013, IEEE Transactions on Smart Grid.

[4]  Haibo He,et al.  Fault-tolerant location of transient voltage disturbance source for DG integrated smart grid , 2017 .

[5]  Muaz A. Niazi,et al.  Cognitive Agent-based Computing-I: A Unified Framework for Modeling Complex Adaptive Systems using Agent-based & Complex Network-based Methods , 2012 .

[6]  Gerhard P. Hancke,et al.  Opportunities and Challenges of Wireless Sensor Networks in Smart Grid , 2010, IEEE Transactions on Industrial Electronics.

[7]  Muaz A. Niazi,et al.  Towards modeling complex wireless sensor networks using agents and networks: A systematic approach , 2014, TENCON 2014 - 2014 IEEE Region 10 Conference.

[8]  Long Chen,et al.  Routing in scale-free networks based on expanding betweenness centrality , 2011 .

[9]  Ali Feliachi,et al.  A Multiagent Design for Power Distribution Systems Automation , 2016, IEEE Transactions on Smart Grid.

[10]  José Vicente Canto dos Santos,et al.  New genetic algorithms for contingencies selection in the static security analysis of electric power systems , 2015, Expert Syst. Appl..

[11]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[12]  Ratnesh K. Sharma,et al.  Dynamic Energy Management System for a Smart Microgrid , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[13]  Jianfeng Ma,et al.  Check-in based routing strategy in scale-free networks , 2017 .

[14]  Neeraj Suri,et al.  Robust QoS-aware communication in the smart distribution grid , 2017, Peer-to-Peer Netw. Appl..

[15]  Taskin Koçak,et al.  Smart Grid Technologies: Communication Technologies and Standards , 2011, IEEE Transactions on Industrial Informatics.

[16]  Christian Posse,et al.  Evaluating North American Electric Grid Reliability Using the Barabasi-Albert Network Model , 2004, nlin/0408052.

[17]  Muaz A. Niazi,et al.  Complex Adaptive Systems Modeling: A multidisciplinary Roadmap , 2013, Complex Adapt. Syst. Model..

[18]  Antonio De Nicola,et al.  A lightweight methodology for rapid ontology engineering , 2016, Commun. ACM.

[19]  Lapas Pradittasnee,et al.  Efficient Route Update and Maintenance for Reliable Routing in Large-Scale Sensor Networks , 2017, IEEE Transactions on Industrial Informatics.

[20]  Mikel Armendariz,et al.  Multiagent-Based Distribution Automation Solution for Self-Healing Grids , 2015, IEEE Transactions on Industrial Electronics.

[21]  Tsung-Hui Chang,et al.  Communication-Efficient Distributed Demand Response: A Randomized ADMM Approach , 2017, IEEE Transactions on Smart Grid.

[22]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[23]  Dirk Helbing,et al.  Collective navigation of complex networks: Participatory greedy routing , 2017, Scientific Reports.

[24]  Yongduan Song,et al.  Distributed Economic Dispatch for Smart Grids With Random Wind Power , 2016, IEEE Transactions on Smart Grid.

[25]  Xu Zhang,et al.  Agent-Based Distributed Volt/Var Control With Distributed Power Flow Solver in Smart Grid , 2016, IEEE Transactions on Smart Grid.

[26]  Birgit Müller,et al.  A standard protocol for describing individual-based and agent-based models , 2006 .

[27]  Montserrat Ros,et al.  Balancing Energy in the Smart Grid Using Distributed Value Function (DVF) , 2015, IEEE Transactions on Smart Grid.

[28]  Ilhami Colak,et al.  Smart grid technologies and applications , 2016 .

[29]  Jonas Hinker,et al.  A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain , 2017 .

[30]  Ian F. Akyildiz,et al.  Channel-aware routing and priority-aware multi-channel scheduling for WSN-based smart grid applications , 2016, J. Netw. Comput. Appl..

[31]  Muaz A. Niazi,et al.  Towards a novel unified framework for developing formal, network and validated agent-based simulation models of complex adaptive systems , 2011, ArXiv.

[32]  Chengwei Wang Synchronisation in complex networks with applications to power grids , 2017 .

[33]  Tingwen Huang,et al.  Reinforcement Learning in Energy Trading Game Among Smart Microgrids , 2016, IEEE Transactions on Industrial Electronics.

[34]  Jun Li,et al.  An efficient probability routing algorithm for scale-free networks , 2017 .

[35]  Enrique Kremers,et al.  Multi-agent modeling for the simulation of a simple smart microgrid , 2013 .

[36]  Xi Fang,et al.  3. Full Four-channel 6.3-gb/s 60-ghz Cmos Transceiver with Low-power Analog and Digital Baseband Circuitry 7. Smart Grid — the New and Improved Power Grid: a Survey , 2022 .

[37]  Muaz A. Niazi,et al.  Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models , 2017, Complex Adapt. Syst. Model..

[38]  O. Wolkenhauer Why model? , 2013, Front. Physiol..

[39]  Les M. Sztandera,et al.  A neural network approach to adaptive protective systems problem in the complex power generating units , 2000, Int. J. Intell. Syst..

[40]  Zhao Xu,et al.  Security analysis of smart grids-A complex network perspective , 2012 .

[41]  Chi K. Tse,et al.  Advanced Algorithms for Local Routing Strategy on Complex Networks , 2016, PloS one.

[42]  Jun Li,et al.  A reliable opportunistic routing for smart grid with in-home power line communication networks , 2016, Science China Information Sciences.

[43]  Hortensia Amaris,et al.  Integration of renewable energy sources in smart grids by means of evolutionary optimization algorithms , 2012, Expert Syst. Appl..

[44]  Yves Demazeau,et al.  Agent-Based Integration of Complex and Heterogeneous Distributed Energy Resources in Virtual Power Plants , 2017, PAAMS.

[45]  Enrico Zio,et al.  Reinforcement learning for microgrid energy management , 2013 .

[46]  M. Vetterli,et al.  Lattice sensor networks: capacity limits, optimal routing and robustness to failures , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[47]  Antonio Alfredo Ferreira Loureiro,et al.  Centrality-based routing for Wireless Sensor Networks , 2010, 2010 IFIP Wireless Days.

[48]  Pao-Ann Hsiung,et al.  A fair energy resource allocation strategy for micro grid , 2016, Microprocess. Microsystems.

[49]  Giancarlo Fortino,et al.  Modeling AIDS Spread in Social Networks - An In-Silico Study Using Exploratory Agent-Based Modeling , 2013, MATES.

[50]  J. Gareth Polhill,et al.  The ODD protocol: A review and first update , 2010, Ecological Modelling.

[51]  Reka Albert,et al.  Mean-field theory for scale-free random networks , 1999 .

[52]  Jianhui Wang,et al.  Resilient Distribution System by Microgrids Formation After Natural Disasters , 2016, IEEE Transactions on Smart Grid.

[53]  Muaz A. Niazi,et al.  Agent-based tools for modeling and simulation of self-organization in peer-to-peer, ad hoc, and other complex networks , 2009, IEEE Communications Magazine.

[54]  Ali Mohammad Ranjbar,et al.  Dynamic load management for a residential customer; Reinforcement Learning approach , 2016 .

[55]  Muaz A. Niazi Emergence of a Snake-Like Structure in Mobile Distributed Agents: An Exploratory Agent-Based Modeling Approach , 2014, TheScientificWorldJournal.

[56]  Ahad Kazemi,et al.  Multi-agent systems for reactive power control in smart grids , 2016 .

[57]  Carlos R. Minussi,et al.  An artificial immune system with continuous-learning for voltage disturbance diagnosis in electrical distribution systems , 2016, Expert Syst. Appl..

[58]  Nathalie Mitton,et al.  Geographic routing protocol for the deployment of virtual power plant within the smart grid , 2016 .

[59]  Vincent W. S. Wong,et al.  Load Scheduling and Power Trading in Systems With High Penetration of Renewable Energy Resources , 2016, IEEE Transactions on Smart Grid.

[60]  Muhammad Faheem,et al.  Spectrum-aware bio-inspired routing in cognitive radio sensor networks for smart grid applications , 2017, Comput. Commun..