Skip to main content
Top
Published in: Mobile Networks and Applications 5/2019

31-05-2017

Agent-Based Microgrid Scheduling: An ICT Perspective

Authors: Fernando Lezama, Jorge Palominos, Ansel Y. Rodríguez-González, Alessandro Farinelli, Enrique Munoz de Cote

Published in: Mobile Networks and Applications | Issue 5/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

New Information and Communications Technologies (ICT), such as the Internet of Things (IoT), are enabling the evolution of energy grids towards a sophisticated power network called Smart Grid (SG). In the context of SGs, a microgrid is a self-sustained network that can operate in both grid-connected or stand-along modes. The long-term scheduling of the operation of distributed generators (DG) and renewable energy resources (RES) in microgrids is a problem that requires tough planning and the use of advanced tools to be efficiently addressed. This paper discusses different ICT technologies that can enable microgrid communication for control and management of distributed energy resources (DER). Based on such ICT, we propose a novel agent-based model to address the long-term scheduling of DER in microgrids as a distributed constraint optimization problem (DCOP). However, finding the optimal solution for a DCOP is known to be an NP-Hard problem, making difficult to guarantee optimal solutions even for short optimization periods. Hence, for the long-term scheduling of DER, we propose to split the problem into small time windows that can be effectively solved sequentially by off-the-shelf DCOP algorithms. A particular, but general enough case study is used to compare different DCOP algorithms under the proposed model. Results show that DCOP algorithms can find optimal and near-optimal solutions depending on the window size and the scenario considered.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Show more products
Footnotes
1
Nonetheless, we believe that using decentralized learning approaches for scheduling on/off cycles of devices in the microgrid deserves further investigation, and is definitely an interesting direction for future work.
 
2
We refer the interested reader to [21] for further details on pseudotrees, separators and on the DPOP approach.
 
3
We implemented the DCOP algorithms in FRODO2 and JaCoP. Both available in http://​frodo2.​sourceforge.​net and http://​www.​jacop.​eurespectively.
 
4
Notice that the “total” size of messages used by MB-DPOP is higher compared with the “total” size of messages used by DPOP, which seems counterintuitive since MB stands for memory bounded. This however is explained by the fact that, even when DPOP uses messages of larger size, it uses less total number of messages compared with MB-DPOP, thus resulting in an overall total size of messages smaller compared with MB-DPOP.
 
Literature
1.
go back to reference Gungor V, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, Hancke G (2013) A survey on smart grid potential applications and communication requirements. IEEE Trans Ind Inf 9(1):28– 42CrossRef Gungor V, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, Hancke G (2013) A survey on smart grid potential applications and communication requirements. IEEE Trans Ind Inf 9(1):28– 42CrossRef
2.
go back to reference Yan Y, Qian Y, Sharif H, Tipper D (2013) A survey on smart grid communication infrastructures: motivations, requirements and challenges. IEEE Commun Surv Tutorials 15(1):5–20CrossRef Yan Y, Qian Y, Sharif H, Tipper D (2013) A survey on smart grid communication infrastructures: motivations, requirements and challenges. IEEE Commun Surv Tutorials 15(1):5–20CrossRef
3.
go back to reference Karnouskos S (2010) The cooperative internet of things enabled smart grid. IEEE International Symposium on Consumer Electronics Karnouskos S (2010) The cooperative internet of things enabled smart grid. IEEE International Symposium on Consumer Electronics
4.
go back to reference Vega A, Santamaria F, Rivas E (2015) Modeling for home electric energy management: a review. Renew Sust Energ Rev 52:948–959CrossRef Vega A, Santamaria F, Rivas E (2015) Modeling for home electric energy management: a review. Renew Sust Energ Rev 52:948–959CrossRef
5.
go back to reference Olivares DE, Mehrizi-Sani A, Etemadi AH, Cañizares CA, Iravani R, Kazerani M, Hajimiragha AH, Gomis-Bellmunt O, Saeedifard M, Palma-Behnke R, Jiménez-Estévez GA, Hatziargyriou ND (2014) Trends in microgrid control. IEEE Trans Smart Grid 5(4):1905–1919CrossRef Olivares DE, Mehrizi-Sani A, Etemadi AH, Cañizares CA, Iravani R, Kazerani M, Hajimiragha AH, Gomis-Bellmunt O, Saeedifard M, Palma-Behnke R, Jiménez-Estévez GA, Hatziargyriou ND (2014) Trends in microgrid control. IEEE Trans Smart Grid 5(4):1905–1919CrossRef
6.
go back to reference Sousa T, Vale Z, Carvalho JP, Pinto T, Morais H (2014) A hybrid simulated annealing approach to handle energy resource management considering an intensive use of electric vehicles. Energy 67:81–96CrossRef Sousa T, Vale Z, Carvalho JP, Pinto T, Morais H (2014) A hybrid simulated annealing approach to handle energy resource management considering an intensive use of electric vehicles. Energy 67:81–96CrossRef
7.
go back to reference Morais H, Pinto T, Vale Z, Praça I (2012) Multilevel negotiation in smart grids for vpp management of distributed resources. IEEE Intell Syst 27(6):8–16CrossRef Morais H, Pinto T, Vale Z, Praça I (2012) Multilevel negotiation in smart grids for vpp management of distributed resources. IEEE Intell Syst 27(6):8–16CrossRef
8.
go back to reference Pinto T, Morais H, Sousa TM, Sousa T, Vale Z, Praça I, Faia R, Pires EJS (2016) Adaptive portfolio optimization for multiple electricity markets participation. IEEE Transactions on Neural Networks and Learning Systems 27(8):1720–1733MathSciNetCrossRef Pinto T, Morais H, Sousa TM, Sousa T, Vale Z, Praça I, Faia R, Pires EJS (2016) Adaptive portfolio optimization for multiple electricity markets participation. IEEE Transactions on Neural Networks and Learning Systems 27(8):1720–1733MathSciNetCrossRef
9.
go back to reference Lopes JP, Hatziargyriou N, Mutale J, Djapic P, Jenkins N (2007) Integrating distributed generation into electric power systems: a review of drivers, challenges and opportunities. Electr Power Syst Res 77(9):1189–1203. Distributed GenerationCrossRef Lopes JP, Hatziargyriou N, Mutale J, Djapic P, Jenkins N (2007) Integrating distributed generation into electric power systems: a review of drivers, challenges and opportunities. Electr Power Syst Res 77(9):1189–1203. Distributed GenerationCrossRef
10.
go back to reference Karnouskos S (2013) Smart houses in the smart grid and the search for value-added services in the cloud of things era IEEE international conference on industrial technology, pp 2016– 2021 Karnouskos S (2013) Smart houses in the smart grid and the search for value-added services in the cloud of things era IEEE international conference on industrial technology, pp 2016– 2021
11.
go back to reference Morais H, Kádár P, Faria P, Vale ZA, Khodr HM (2010) Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming. Renew Energy 35:151–156CrossRef Morais H, Kádár P, Faria P, Vale ZA, Khodr HM (2010) Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming. Renew Energy 35:151–156CrossRef
12.
go back to reference Su W, Wang J, Roh J (2014) Stochastic energy scheduling in microgrids with intermittent renewable energy resources. IEEE Trans Smart Grid 5(4):1876–1883CrossRef Su W, Wang J, Roh J (2014) Stochastic energy scheduling in microgrids with intermittent renewable energy resources. IEEE Trans Smart Grid 5(4):1876–1883CrossRef
13.
go back to reference Chaouachi A, Kamel RM, Andoulsi R, Nagasaka K (2013) Multiobjective intelligent energy management for a microgrid. IEEE Trans Ind Electron 60(4):1688–1699CrossRef Chaouachi A, Kamel RM, Andoulsi R, Nagasaka K (2013) Multiobjective intelligent energy management for a microgrid. IEEE Trans Ind Electron 60(4):1688–1699CrossRef
14.
go back to reference Logenthiran T, Srinivasan D, Khambadkone AM (2011) Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system. Electr Power Syst Res 81(1):138–148CrossRef Logenthiran T, Srinivasan D, Khambadkone AM (2011) Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system. Electr Power Syst Res 81(1):138–148CrossRef
15.
go back to reference Miller S, Ramchurn SD, Rogers A (2012) Optimal decentralised dispatch of embedded generation in the smart grid Proceedings of the international conference on autonomous agents and multiagent systems, pp 281–288 Miller S, Ramchurn SD, Rogers A (2012) Optimal decentralised dispatch of embedded generation in the smart grid Proceedings of the international conference on autonomous agents and multiagent systems, pp 281–288
16.
go back to reference Ghazvini MAF, Abedini R, Pinto T, Vale Z (2014) Multiagent system architecture for short-term operation of integrated microgrids. {IFAC} Proceedings Volumes 47(3):6355–6360. 19th {IFAC} World CongressCrossRef Ghazvini MAF, Abedini R, Pinto T, Vale Z (2014) Multiagent system architecture for short-term operation of integrated microgrids. {IFAC} Proceedings Volumes 47(3):6355–6360. 19th {IFAC} World CongressCrossRef
17.
go back to reference Davin J, Modi PJ (2005) Impact of problem centralization in distributed constraint optimization algorithms International joint conference on autonomous agents and multiagent systems, ACM, pp 1057–1063 Davin J, Modi PJ (2005) Impact of problem centralization in distributed constraint optimization algorithms International joint conference on autonomous agents and multiagent systems, ACM, pp 1057–1063
18.
go back to reference Petcu A, Faltings B (2005) Superstabilizing, fault-containing distributed combinatorial optimization Proceedings of the national conference on artificial intelligence, pp 449–454 Petcu A, Faltings B (2005) Superstabilizing, fault-containing distributed combinatorial optimization Proceedings of the national conference on artificial intelligence, pp 449–454
19.
go back to reference Nguyen DT, Yeoh W, Lau HC, Zilberstein S, Zhang C (2014) Decentralized multi-agent reinforcement learning in average-reward dynamic DCOPs Proceedings of the international conference on autonomous agents and multi-agent systems, pp 1341–1342 Nguyen DT, Yeoh W, Lau HC, Zilberstein S, Zhang C (2014) Decentralized multi-agent reinforcement learning in average-reward dynamic DCOPs Proceedings of the international conference on autonomous agents and multi-agent systems, pp 1341–1342
20.
go back to reference Dowling J, Haridi S (2008) Decentralized reinforcement learning for the online optimization of distributed systems. INTECH Open Access Publisher Dowling J, Haridi S (2008) Decentralized reinforcement learning for the online optimization of distributed systems. INTECH Open Access Publisher
21.
go back to reference Petcu A (2009) A class of algorithms for distributed constraint optimization. Volume 194 Ios Press Petcu A (2009) A class of algorithms for distributed constraint optimization. Volume 194 Ios Press
22.
go back to reference Lezama F, Palominos J, Rodríguez-González AY, Farinelli A, de Cote EM (2017) Optimal scheduling of on/off cycles: a decentralized iot-microgrid approach, applications for future internet: international summit, revised selected papers, Cham. Springer, pp 79–90 Lezama F, Palominos J, Rodríguez-González AY, Farinelli A, de Cote EM (2017) Optimal scheduling of on/off cycles: a decentralized iot-microgrid approach, applications for future internet: international summit, revised selected papers, Cham. Springer, pp 79–90
23.
go back to reference Safdar S, Hamdaoui B, Cotilla-Sanchez E, Guizani M (2013) A survey on communication infrastructure for micro-grids International wireless communications and mobile computing conference, pp 545–550 Safdar S, Hamdaoui B, Cotilla-Sanchez E, Guizani M (2013) A survey on communication infrastructure for micro-grids International wireless communications and mobile computing conference, pp 545–550
24.
go back to reference Al-Omar B, Al-Ali A, Ahmed R, Landolsi T (2012) Role of information and communication technologies in the smart grid. Journal of Emerging Trends in Computing and Information Sciences 3(5):707–716 Al-Omar B, Al-Ali A, Ahmed R, Landolsi T (2012) Role of information and communication technologies in the smart grid. Journal of Emerging Trends in Computing and Information Sciences 3(5):707–716
25.
go back to reference Liang H, Choi BJ, Zhuang W, Shen X, Awad ASA, Abdr A (2012) Multiagent coordination in microgrids via wireless networks. IEEE Wirel Commun 19(3):14–22CrossRef Liang H, Choi BJ, Zhuang W, Shen X, Awad ASA, Abdr A (2012) Multiagent coordination in microgrids via wireless networks. IEEE Wirel Commun 19(3):14–22CrossRef
26.
go back to reference Gungor VC, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, Hancke GP (2011) Smart grid technologies: communication technologies and standards. IEEE Trans Ind Inf 7(4):529–539CrossRef Gungor VC, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, Hancke GP (2011) Smart grid technologies: communication technologies and standards. IEEE Trans Ind Inf 7(4):529–539CrossRef
27.
go back to reference Karfopoulos EL, Hatziargyriou ND (2013) A multi-agent system for controlled charging of a large population of electric vehicles. IEEE Trans Power Syst 28(2):1196–1204CrossRef Karfopoulos EL, Hatziargyriou ND (2013) A multi-agent system for controlled charging of a large population of electric vehicles. IEEE Trans Power Syst 28(2):1196–1204CrossRef
28.
go back to reference Mourshed M, Robert S, Ranalli A, Messervey T, Reforgiato D, Contreau R, Becue A, Quinn K, Rezgui Y, Lennard Z (2015) Smart grid futures: perspectives on the integration of energy and ict services. Energy Procedia 75:1132–1137CrossRef Mourshed M, Robert S, Ranalli A, Messervey T, Reforgiato D, Contreau R, Becue A, Quinn K, Rezgui Y, Lennard Z (2015) Smart grid futures: perspectives on the integration of energy and ict services. Energy Procedia 75:1132–1137CrossRef
29.
go back to reference Pipattanasomporn M, Feroze H, Rahman S (2009) Multi-agent systems in a distributed smart grid: design and implementation IEEE/PES Power systems conference and exposition, pp 1–8 Pipattanasomporn M, Feroze H, Rahman S (2009) Multi-agent systems in a distributed smart grid: design and implementation IEEE/PES Power systems conference and exposition, pp 1–8
30.
go back to reference Farinelli A, Rogers A, Jennings NR (2014) Agent-based decentralised coordination for sensor networks using the max-sum algorithm. Auton Agent Multi-Agent Syst 28(3):337–380CrossRef Farinelli A, Rogers A, Jennings NR (2014) Agent-based decentralised coordination for sensor networks using the max-sum algorithm. Auton Agent Multi-Agent Syst 28(3):337–380CrossRef
31.
go back to reference Hirayama K, Yokoo M (1997) Distributed partial constraint satisfaction problem Principles and practice of constraint programming-CP97: third international conference, CP97 Linz, Austria, October 29 – November 1, 1997 Proceedings, pp 222–236CrossRef Hirayama K, Yokoo M (1997) Distributed partial constraint satisfaction problem Principles and practice of constraint programming-CP97: third international conference, CP97 Linz, Austria, October 29 – November 1, 1997 Proceedings, pp 222–236CrossRef
32.
go back to reference Petcu A, Faltings B (2005) DPOP: a scalable method for multiagent constraint optimization Proceedings of the international joint conference on artificial intelligence, pp 266–271 Petcu A, Faltings B (2005) DPOP: a scalable method for multiagent constraint optimization Proceedings of the international joint conference on artificial intelligence, pp 266–271
33.
go back to reference Petcu A, Faltings B (2007) MB-DPOP: a new memory-bounded algorithm for distributed optimization Proceedings of the international joint conference on artificial intelligence, pp 1452–1457 Petcu A, Faltings B (2007) MB-DPOP: a new memory-bounded algorithm for distributed optimization Proceedings of the international joint conference on artificial intelligence, pp 1452–1457
34.
go back to reference Gershman A, Meisels A, Zivan R (2009) Asynchronous forward bounding for distributed COPs. J Artif Intell Res (JAIR) 34:61–88MathSciNetCrossRef Gershman A, Meisels A, Zivan R (2009) Asynchronous forward bounding for distributed COPs. J Artif Intell Res (JAIR) 34:61–88MathSciNetCrossRef
35.
go back to reference Zhang W, Wang G, Xing Z, Wittenburg L (2005) Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks. Artif Intell 161 (12):55–87MathSciNetCrossRef Zhang W, Wang G, Xing Z, Wittenburg L (2005) Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks. Artif Intell 161 (12):55–87MathSciNetCrossRef
36.
go back to reference Maheswaran RT, Pearce JP, Tambe M (2004) Distributed algorithms for DCOP: a graphical-game-based approach Proceedings of the 17th international conference on parallel and distributed computing systems (PDCS), pp 432–439 Maheswaran RT, Pearce JP, Tambe M (2004) Distributed algorithms for DCOP: a graphical-game-based approach Proceedings of the 17th international conference on parallel and distributed computing systems (PDCS), pp 432–439
37.
go back to reference Motevasel M, Seifi AR (2014) Expert energy management of a micro-grid considering wind energy uncertainty. Energy Convers Manag 83:58–72CrossRef Motevasel M, Seifi AR (2014) Expert energy management of a micro-grid considering wind energy uncertainty. Energy Convers Manag 83:58–72CrossRef
38.
go back to reference Su W, Wang J, Roh J (2014) Stochastic energy scheduling in microgrids with intermittent renewable energy resources. IEEE Trans Smart Grid 5(4):1876–1883CrossRef Su W, Wang J, Roh J (2014) Stochastic energy scheduling in microgrids with intermittent renewable energy resources. IEEE Trans Smart Grid 5(4):1876–1883CrossRef
39.
go back to reference Chvatal V (1983) Linear programming. Series of books in the mathematical sciences. W. H Freeman Chvatal V (1983) Linear programming. Series of books in the mathematical sciences. W. H Freeman
Metadata
Title
Agent-Based Microgrid Scheduling: An ICT Perspective
Authors
Fernando Lezama
Jorge Palominos
Ansel Y. Rodríguez-González
Alessandro Farinelli
Enrique Munoz de Cote
Publication date
31-05-2017
Publisher
Springer US
Published in
Mobile Networks and Applications / Issue 5/2019
Print ISSN: 1383-469X
Electronic ISSN: 1572-8153
DOI
https://doi.org/10.1007/s11036-017-0894-x

Other articles of this Issue 5/2019

Mobile Networks and Applications 5/2019 Go to the issue