Low-cost modular PV-battery microgrid emulator for testing of energy management algorithms

Within the context of microgrids, the need for a smart energy management system (EMS) has become increasingly important for users to maximise use of local energy generation and/or trade power effectively with the energy market if there is a grid connection. Many researchers have been developing algorithms to forecast the availability of renewable energy and load demands whilst optimising the energy flows within required constraints. Recently, control systems for peer-to-peer (P2P) microgrid architectures, which involve complex information and communication technologies, have also been given much attention. However, not all of these algorithms have been implemented and tested with real hardware, which may be attributed to the high cost involved and the safety concerns of a larger system. This paper describes the design, build and demonstration of a scaled down (100 W) P2P microgrid system to provide a low cost, modular, safe, portable testing environment for new EMS algorithms. The system nonetheless has realistic behaviour in terms of control interfaces, measurements and dynamics, and therefore provides a valuable insight into EMS implementation that cannot be obtained through simulations alone. In this work, three microgrid emulators were built and they can communicate with each other via TCP/IP, enabling development and demonstrations of distributed forecasting, control and optimisation algorithms.

[1]  John K. Kaldellis,et al.  Stand-alone and hybrid wind energy systems , 2010 .

[2]  Freyr Sverrisson,et al.  Renewables Global Status Report 2016 (GSR2016) , 2016 .

[3]  Fernando Gustavo Tinetti,et al.  Distributed systems: principles and paradigms (2nd edition): Andrew S. Tanenbaum, Maarten Van Steen Pearson Education, Inc., 2007 ISBN: 0-13-239227-5 , 2011 .

[4]  Kenneth M. Hopkinson,et al.  Using a Distributed Agent-Based Communication Enabled Special Protection System to Enhance Smart Grid Security , 2013, IEEE Transactions on Smart Grid.

[5]  P. T. Krein,et al.  Review of Battery Charger Topologies, Charging Power Levels, and Infrastructure for Plug-In Electric and Hybrid Vehicles , 2013, IEEE Transactions on Power Electronics.

[6]  Pascal Benoit,et al.  What the term agent stands for in the Smart Grid definition of agents and multi-agent systems from an engineer's perspective , 2012, 2012 Federated Conference on Computer Science and Information Systems (FedCSIS).

[7]  Kostas Kalaitzakis,et al.  Development of a microcontroller-based, photovoltaic maximum power point tracking control system , 2001 .

[8]  H. Kakigano,et al.  Loss evaluation of DC distribution for residential houses compared with AC system , 2010, The 2010 International Power Electronics Conference - ECCE ASIA -.

[9]  R. P. Saini,et al.  A review on Integrated Renewable Energy System based power generation for stand-alone applications: Configurations, storage options, sizing methodologies and control , 2014 .

[10]  Hirohisa Aki,et al.  Optimal management of fuel cells in a residential area by Integrated-Distributed Energy Management System (IDEMS) , 2016, 2016 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).

[11]  Pierluigi Siano,et al.  A Review of Architectures and Concepts for Intelligence in Future Electric Energy Systems , 2015, IEEE Transactions on Industrial Electronics.

[12]  Rachid Beguenane,et al.  Energy Management and Control System for Laboratory Scale Microgrid Based Wind-PV-Battery , 2017, IEEE Transactions on Sustainable Energy.

[13]  Boon-Hee Soong,et al.  An adaptive model for vanadium redox flow battery and its application for online peak power estimation , 2017 .

[14]  Thillainathan Logenthiran,et al.  Multiagent System for Real-Time Operation of a Microgrid in Real-Time Digital Simulator , 2012, IEEE Transactions on Smart Grid.

[15]  Mario Tokoro,et al.  Peer-to-Peer Control System for DC Microgrids , 2018, IEEE Transactions on Smart Grid.

[16]  Zhongwei Deng,et al.  Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery , 2016 .

[17]  Zhengyu Liu,et al.  Base on the ultra-short term power prediction and feed-forward control of energy management for microgrid system applied in industrial park , 2016 .

[18]  Peng Kou,et al.  Distributed Coordination of Multiple PMSGs in an Islanded DC Microgrid for Load Sharing , 2017, IEEE Transactions on Energy Conversion.

[19]  Lei Zhang,et al.  Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model , 2016 .

[20]  Kazuto Yukita,et al.  AC/DC microgrids , 2016 .

[21]  Daniel Weisser,et al.  Instantaneous wind energy penetration in isolated electricity grids: concepts and review , 2005 .

[22]  Maria Skyllas-Kazacos,et al.  Online state of charge and model parameter co-estimation based on a novel multi-timescale estimator for vanadium redox flow battery , 2016 .

[23]  Giuseppe A. Laudani Demand-driven energy conversion Comparison of cost and efficiency of DC versus AC in office buildings , 2015 .

[24]  Xiaofeng Yin,et al.  Stochastic Optimal Energy Management of Smart Home With PEV Energy Storage , 2018, IEEE Transactions on Smart Grid.

[25]  Pierluigi Siano,et al.  A Review of Agent and Service-Oriented Concepts Applied to Intelligent Energy Systems , 2014, IEEE Transactions on Industrial Informatics.

[26]  Udaya K. Madawala,et al.  An intelligent hybrid communication system for a distributed renewable energy management , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[27]  Aie World Energy Outlook 2015 , 2015 .

[28]  Mario Vasak,et al.  Optimal charging of valve-regulated lead-acid batteries based on model predictive control , 2017 .

[29]  Ye Li,et al.  A Module-Integrated Distributed Battery Energy Storage and Management System , 2016, IEEE Transactions on Power Electronics.

[30]  Richard D. Braatz,et al.  State-of-charge estimation in lithium-ion batteries: A particle filter approach , 2016 .

[31]  David A. J. Rand,et al.  Lead–acid battery fundamentals , 2017 .

[32]  P. Eng CO2 emissions from fuel combustion: highlights , 2009 .