Enabling the Analysis of Emergent Behavior in Future Electrical Distribution Systems Using Agent-Based Modeling and Simulation

In future electrical distribution systems, component heterogeneity and their cyber-physical interactions through electrical lines and communication lead to emergent system behavior. As the distribution systems represent the largest part of an energy system with respect to the number of nodes and components, large-scale studies of their emergent behavior are vital for the development of decentralized control strategies. This paper presents and evaluates DistAIX, a novel agent-based modeling and simulation tool to conduct such studies. The major novelty is a parallelization of the entire model—including the power system, communication system, control, and all interactions—using processes instead of threads. Thereby, a distribution of the simulation to multiple computing nodes with a distributed memory architecture becomes possible. This makes DistAIX scalable and allows the inclusion of as many processing units in the simulation as desired. The scalability of DistAIX is demonstrated by simulations of large-scale scenarios. Additionally, the capability of observing emergent behavior is demonstrated for an exemplary distribution grid with a large number of interacting components.

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

[2]  Marija D. Ilic,et al.  Interaction variables for distributed numerical integration of nonlinear power system dynamics , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[3]  S. Soltani,et al.  Reliability models for wind farms in generation system planning , 2010, 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems.

[4]  Antonello Monti,et al.  Automated deserializer generation from CIM ontologies: CIM$${+}{+}$$++—an easy-to-use and automated adaptable open-source library for object deserialization in C$${+}{+}$$++ from documents based on user-specified UML models following the Common Information Model (CIM) standards for the energy sector , 2018, Computer Science - Research and Development.

[5]  Cindy E. Hmelo-Silver,et al.  Understanding Complex Systems: Some Core Challenges , 2006 .

[6]  Antonello Monti,et al.  Swarm behavior for distribution grid control , 2017, 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe).

[7]  Chris Develder,et al.  Combining Power and Communication Network Simulation for Cost-Effective Smart Grid Analysis , 2014, IEEE Communications Surveys & Tutorials.

[8]  Ryozo Ooka,et al.  Metaheuristic optimization methods for a comprehensive operating schedule of battery, thermal energy storage, and heat source in a building energy system , 2015 .

[9]  Jianhua Feng,et al.  Google protocol buffers research and application in online game , 2013, IEEE Conference Anthology.

[10]  Cuong P. Nguyen,et al.  A Novel Agent-Based Distributed Power Flow Solver for Smart Grids , 2015, IEEE Transactions on Smart Grid.

[11]  Michael J. North,et al.  Parallel agent-based simulation with Repast for High Performance Computing , 2013, Simul..

[12]  Laurent Philippe,et al.  A survey on parallel and distributed multi-agent systems for high performance computing simulations , 2016, Comput. Sci. Rev..

[13]  H. Morais,et al.  MASGriP — A Multi-Agent Smart Grid Simulation Platform , 2012, 2012 IEEE Power and Energy Society General Meeting.

[14]  J. C. Fuller,et al.  Communication simulations for power system applications , 2013, 2013 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES).

[15]  Bojan Mrazovac,et al.  Performance evaluation of using Protocol Buffers in the Internet of Things communication , 2016, 2016 International Conference on Smart Systems and Technologies (SST).

[16]  Wolf Fichtner,et al.  Agent-based modelling and simulation of smart electricity grids and markets – A literature review , 2016 .

[17]  Tilmann Rabl,et al.  Solving Big Data Challenges for Enterprise Application Performance Management , 2012, Proc. VLDB Endow..

[18]  E. Yücesan,et al.  AGENT-BASED SIMULATION TUTORIAL-SIMULATION OF EMERGENT BEHAVIOR AND DIFFERENCES BETWEEN AGENT-BASED SIMULATION AND DISCRETE-EVENT SIMULATION , 2010 .

[19]  Ray D. Zimmerman,et al.  Comprehensive distribution power flow: modeling, formulation, solution algorithms and analysis , 1996 .

[20]  T. C. Hu Parallel Sequencing and Assembly Line Problems , 1961 .

[21]  Hongbin Sun,et al.  Distributed power flow calculation for whole networks including transmission and distribution , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[22]  Alberto Borghetti,et al.  Simulation of the Volt/Var Control in Distribution Feeders by Means of a Networked Multiagent System , 2014, IEEE Transactions on Industrial Informatics.

[23]  Ned Djilali,et al.  GridLAB-D: An Agent-Based Simulation Framework for Smart Grids , 2014, J. Appl. Math..

[24]  Robert J. Meijer,et al.  Sensor Data Storage Performance: SQL or NoSQL, Physical or Virtual , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[25]  Wei Zhou,et al.  A novel model for photovoltaic array performance prediction , 2007 .

[26]  Shuangshuang Jin,et al.  Thread Group Multithreading: Accelerating the Computation of an Agent-Based Power System Modeling and Simulation Tool -- C GridLAB-D , 2014, 2014 47th Hawaii International Conference on System Sciences.

[27]  Amit Narayan,et al.  GridSpice: A Distributed Simulation Platform for the Smart Grid , 2013, IEEE Transactions on Industrial Informatics.

[28]  Yong Meng Teo,et al.  An integrated approach for the validation of emergence in component-based simulation models , 2012, Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC).

[29]  Marian Gheorghe,et al.  Exploitation of High Performance Computing in the FLAME Agent-Based Simulation Framework , 2012, 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems.

[30]  D. Shirmohammadi,et al.  A compensation-based power flow method for weakly meshed distribution and transmission networks , 1988 .

[31]  Peter Palensky,et al.  Co-simulation of components, controls and power systems based on open source software , 2013, 2013 IEEE Power & Energy Society General Meeting.

[32]  K. Schneider,et al.  GridLAB-D: An open-source power systems modeling and simulation environment , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[33]  Zhang Boming,et al.  A new distributed power flow algorithm between multi-control-centers based on asynchronous iteration , 2006, 2006 International Conference on Power System Technology.

[34]  Enrique Kremers,et al.  Agent based modeling of energy networks , 2014 .

[35]  David A. Cartes,et al.  Quantification of complexity of power electronics based systems , 2012 .

[36]  L. Hofmann,et al.  Multi-agent based distributed power flow calculation , 2010, IEEE PES General Meeting.

[37]  Siobhán Clarke,et al.  Towards Decentralised Detection of Emergence in Complex Adaptive Systems , 2014, SASO.

[38]  Winfried Lamersdorf,et al.  Jadex: A BDI Reasoning Engine , 2005, Multi-Agent Programming.

[39]  Arun Ravindran,et al.  Accelerating the Gauss-Seidel Power Flow Solver on a High Performance Reconfigurable Computer , 2009, 2009 17th IEEE Symposium on Field Programmable Custom Computing Machines.

[40]  Laurent Ciarletta,et al.  Multi-agent Multi-Model Simulation of Smart Grids in the MS4SG Project , 2015, PAAMS.

[41]  Ulrich Rüde,et al.  High-performance simulation of nation-sized smart grids , 2017, Int. J. Parallel Emergent Distributed Syst..