Abstract
In power systems, the traditional, non-interactive, and manually controlled power grid has been transformed to a cyber-dominated smart grid. This cyber-physical integration has provided the smart grid with communication, monitoring, computation, and controlling capabilities to improve its reliability, energy efficiency, and flexibility. A microgrid is a localized and semi-autonomous group of smart energy systems that utilizes the above-mentioned capabilities to drive modern technologies such as electric vehicle charging, home energy management, and smart appliances. Design, upgrading, test, and verification of these microgrids can get too complicated to handle manually. The complexity is due to the wide range of solutions and components that are intended to address the microgrid problems. This article presents a novel Model-Based Design (MBD) methodology to model, co-simulate, design, and optimize microgrid and its multi-level controllers. This methodology helps in the design, optimization, and validation of a microgrid for a specific application. The application rules, requirements, and design-time constraints are met in the designed/optimized microgrid while the implementation cost is minimized. Based on our novel methodology, a design automation, co-simulation, and analysis tool, called GridMAT, is implemented. Our experiments have illustrated that implementing a hierarchical controller reduces the average power consumption by 8% and shifts the peak load for cost saving. Moreover, optimizing the microgrid design using our MBD methodology considering smart controllers has decreased the total implementation cost. Compared to the conventional methodology, the cost decreases by 14% and compared to the MBD methodology where smart controllers are not considered, it decreases by 5%.
- Jamshid Aghaei and Mohammad-Iman Alizadeh. 2013. Demand response in smart electricity grids equipped with renewable energy sources: A review. Renew. Sust. Energy Rev. 18 (2013), 64--72. Google ScholarCross Ref
- Fereidoun Ahourai and Mohammad Abdullah Al Faruque. 2013. Grid impact analysis of a residential microgrid under various EV penetration rates in GridLAB-D. Center for Embedded Computer Systems, Irvine, CA.Google Scholar
- Advanced Integrated Cyber-Physical Systems Lab AICPS. 2015. GridMat. Retrieved from http://www.sourceforge.net/projects/gridmat.Google Scholar
- Mohammad Abdullah Al Faruque. 2014. RAMP: Impact of rule based aggregator business model for residential microgrid of prosumers including distributed energy resources. In Proceedings of the IEEE PES Innovative Smart Grid Technologies Conference (ISGT). 1--6. Google ScholarCross Ref
- Mohammad Abdullah Al Faruque and Fereidoun Ahourai. 2014a. A model-based design of cyber-physical energy systems. In Proceedings of the 19th Asia and South Pacific Design Automation Conference (ASP-DAC). 97--104. Google ScholarCross Ref
- Mohammad Abdullah Al Faruque and Fereidoun Ahourai. 2014b. GridMat: Matlab toolbox for gridLAB-D to analyze grid impact and validate residential microgrid level energy management algorithms. In Proceedings of the IEEE PES Innovative Smart Grid Technologies Conference (ISGT). 1--5. Google ScholarCross Ref
- Mohammad Abdullah Al Faruque and Arquimedes Canedo. 2012. Intelligent and collaborative embedded computing in automation engineering. In Proceedings of the Conference on Design, Automation and Test in Europe. 344--345. Google ScholarCross Ref
- Mohammad Abdullah Al Faruque, Livio Dalloro, Siyuan Zhou, Hartmut Ludwig, and George Lo. 2012. Managing residential-level EV charging using network-as-automation platform (NAP) technology. In International Electric Vehicle Conference (IEVC). 1--6. Google ScholarCross Ref
- Mohammad Abdullah Al Faruque and Korosh Vatanparvar. 2016. Energy management-as-a-service over fog computing platform. IEEE Internet Things J. 3, 2 (2016), 161--169. Google ScholarCross Ref
- ALCAN. 2015. Aluminum Conductor Steel Reinforced Cables. Retrieved from http://www.generalcable.com/.Google Scholar
- Italo Atzeni, Luis G. Ordóñez, Gesualdo Scutari, Daniel P. Palomar, and Javier Rodríguez Fonollosa. 2013. Demand-side management via distributed energy generation and storage optimization. IEEE Trans. Smart Grid 4, 2 (2013), 866--876. Google ScholarCross Ref
- Shaghayegh Bahramirad, Wanda Reder, and Amin Khodaei. 2012. Reliability-constrained optimal sizing of energy storage system in a microgrid. IEEE Trans. Smart Grid 3, 4 (2012), 2056--2062. Google ScholarCross Ref
- Berkeley. 2015. Microgrids at Berkeley Lab. Retrieved from https://building-microgrid.lbl.gov/.Google Scholar
- Holger Blume, H. Hubert, H. T. Feldkamper, and Tobias G Noll. 2002. Model-based exploration of the design space for heterogeneous systems on chip. In Proceedings of the IEEE International Conference on Application-Specific Systems, Architectures and Processors. 29--40. Google ScholarCross Ref
- Stabiloy Brand. 2015. Aluminum Conductor Steel Reinforced. Retrieved from http://www.stabiloy.com/CablePublic/en-US/Information+Center/Price+Sheets+Cut+Sheets+and+Brochures/Price+Sheets.Google Scholar
- Joseph Buck, Soonhoi Ha, Edward A. Lee, and David G. Messerschmitt. 1994. Ptolemy: A framework for simulating and prototyping heterogeneous systems.Google Scholar
- D. P. Chassin, K. Schneider, and C. Gerkensmeyer. 2008. GridLAB-D: An open-source power systems modeling and simulation environment. In Proceedings of the IEEE/PES Transmission and Distribution Conference and Exposition. 1--5. Google ScholarCross Ref
- Kwok Cheung. 2012. Challenges of generation dispatch for smart grid. IEEE Smart Grid Newsletter (2012).Google Scholar
- D. B. Crawley, C. O. Pedersen, and others. 2000. Energy plus: Energy simulation program. ASHRAE J. (2000), 49--56.Google Scholar
- Christopher K. Duffey and Ray P. Stratford. 1989. Update of harmonic standard IEEE-519 : IEEE recommended practices and requirements for harmonic control in electric power systems. IEEE Trans. Indust. Appl. 25, 6 (1989), 1025--1034. Google ScholarCross Ref
- Roger C. Dugan. 2012. Reference guide: The open distribution system simulator (openDSS). Electric Power Research Institute, Inc.Google Scholar
- Daniel D. Gajski, Frank Vahid, Sanjiv Narayan, and Jie Gong. 1994. Specification and design of embedded systems. Prentice Hall, Upper Saddle River, NJ.Google Scholar
- Marija D. Ilic, Le Xie, Usman A. Khan, and José M. F. Moura. 2010. Modeling of future cyber-physical energy systems for distributed sensing and control. IEEE Trans. Syst. Man Cybernet. A. 40, 4 (2010), 825--838. Google ScholarDigital Library
- InterPSS. 2015. InterPSS Community. Retrieved from http://www.interpss.org/.Google Scholar
- Mohsen Jafari. 2012. Optimal energy management in community micro-grids. IEEE PES Innovative Smart Grid Technologies (ISGT) (2012), 1--6.Google Scholar
- Jeff C. Jensen, Danica H. Chang, and Edward A. Lee. 2011. A model-based design methodology for cyber-physical systems. In Proceedings of the International Wireless Communications and Mobile Computing Conference. 1666--1671. Google ScholarCross Ref
- Iris Hui-Ru Jiang, Gi-Joon Nam, Hua-Yu Chang, Sani R Nassif, and Jerry Hayes. 2014. Smart grid load balancing techniques via simultaneous switch/tie-line/wire configurations. In Proceedings of the IEEE/ACM International Conference on Computer-Aided Design (ICCAD). 382--388.Google Scholar
- Stamatis Karnouskos. 2011. Cyber-physical systems in the smartgrid. In Proceedings of the IEEE International Conference on Industrial Informatics (INDIN). 20--23. Google ScholarCross Ref
- J. Kleissl and Y. Agarwal. 2010. Cyber-physical energy systems: Focus on smart buildings. In Proceedings of the 47th ACM/IEEE Design Automation Conference (DAC). Google ScholarDigital Library
- Luciano Lavagno, Grant Martin, and Louis Scheffer. 2006. Electronic design automation for integrated circuits handbook-2 volume set. CRC Press, Inc.Google Scholar
- MathWorks. 2015. MATLAB, Simulink. Retrieved from http://www.mathworks.com.Google Scholar
- A. P. Sakis Meliopoulos. 2002. Challenges in simulation and design of μGrids. In Proceedings of the IEEE Power Engineering Society Winter Meeting. 309--314.Google Scholar
- Javier Moreno Molina, Xiao Pan, Christoph Grimm, and Markus Damm. 2013. A framework for model-based design of embedded systems for energy management. In Proceedings of the Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES). 1--6. Google ScholarCross Ref
- Thomas H. Morris, Anurag K. Srivastava, and others. 2009. Engineering future cyber-physical energy systems: challenges, research needs, and roadmap. North American Power Symposium (NAPS), 1--6. Google ScholarCross Ref
- Sani Nassif, Gi-Joon Nam, Jerry Hayes, and Sani Fakhouri. 2014. Applying VLSI EDA to energy distribution system design. In Proceedings of the 19th Asia and South Pacific Design Automation Conference (ASP-DAC). 91--96. Google ScholarCross Ref
- HSVS Kumar Nunna and Suryanarayana Doolla. 2013. Multiagent-based distributed-energy-resource management for intelligent microgrids. IEEE Trans. Indust. Electron. 60, 4 (2013), 1678--1687.Google ScholarCross Ref
- Manisa Pipattanasomporn, Murat Kuzlu, and Saifur Rahman. 2012. An algorithm for intelligent home energy management and demand response analysis. IEEE Trans. Smart Grid 3, 4 (2012), 2166--2173. Google ScholarCross Ref
- Prashant Saxena, Noel Menezes, Pasquale Cocchini, and Desmond A. Kirkpatrick. 2003. The scaling challenge: Can correct-by-construction design help? In Proceedings of the 2003 International Symposium on Physical Design. ACM, 51--58. Google ScholarDigital Library
- R. R. Schaller. 1997. Moore’s law: Past, present and future. IEEE Spectrum 34 (1997). Google ScholarDigital Library
- Shengnan Shao, Manisa Pipattanasomporn, and Saifur Rahman. 2009. Challenges of PHEV penetration to the residential distribution network. IEEE Power and Energy Society General Meeting (2009).Google ScholarCross Ref
- Shengnan Shao, Tianshu Zhang, Manisa Pipattanasomporn, and Saifur Rahman. 2010. Impact of TOU rates on distribution load shapes in a smart grid with PHEV penetration. In Proceedings of the IEEE PES Transmission and Distribution Conference and Exposition: Smart Solutions for a Changing World. Google ScholarCross Ref
- Pierluigi Siano. 2014. Demand response and smart gridsA survey. Renew. Sust. Energy Rev. 30 (2014), 461--478. Google ScholarCross Ref
- Tiago Sousa, Hugo Morais, Zita Vale, Pedro Faria, and Joo Soares. 2012. Intelligent energy resource management considering vehicle-to-grid: A simulated annealing approach. IEEE Trans. Smart Grid 3, 1 (2012), 535--542. Google ScholarCross Ref
- Southwire. 2015. IEEE PES Test Feeders. Retrieved from http://www.southwire.com/.Google Scholar
- IEEE PES Distribution System Analysis Subcommittee. 2015. IEEE PES Test Feeders. Retrieved from http://ewh.ieee.org/soc/pes/dsacom/testfeeders.html.Google Scholar
- Korosh Vatanparvar and Mohammad Abdullah Al Faruque. 2015a. Demo abstract: Energy management as a service over fog computing platform. In Proceedings of the ACM/IEEE Sixth International Conference on Cyber-Physical Systems (ICCPS). 248--249. Google ScholarDigital Library
- Korosh Vatanparvar and Mohammad Abdullah Al Faruque. 2015b. Design space exploration for the profitability of a rule-based aggregator business model within a residential microgrid. IEEE Trans. Smart Grid 6, 3 (2015), 1167--1175. Google ScholarCross Ref
- Korosh Vatanparvar, Quan Chau, and Mohammad Abdullah Al Faruque. 2015a. Home energy management as a service over networking platforms. In Proceedings of the IEEE PES Conference on Innovative Smart Grid Technologies (ISGT). Google ScholarCross Ref
- Korosh Vatanparvar, Jiang Wan, and Mohammad Abdullah Al Faruque. 2015b. Battery-aware energy-optimal electric vehicle driving management. In Proceedings of the International Symposium on Low Power Electronics and Design (ISLPED). 353--358. Google ScholarCross Ref
- Laung-Terng Wang, Yao-Wen Chang, and Kwang-Ting Tim Cheng. 2009. Electronic design automation: Synthesis, verification, and test. Morgan Kaufmann.Google Scholar
- Shouxiang Wang, Zhixin Li, Lei Wu, Mohammad Shahidehpour, and Zuyi Li. 2013. New metrics for assessing the reliability and economics of microgrids in distribution system. IEEE Trans. Power Syst. 28, 3 (2013), 2852--2861. Google ScholarCross Ref
- Thomas Weng and Yuvraj Agarwal. 2012. From buildings to smart buildings-sensing and actuation to improve energy efficiency. IEEE Des. Test Comput. (2012), 36--44. Google ScholarDigital Library
- Michael Wetter. 2011. Co-simulation of building energy and control systems with the building controls virtual test bed. J. Build. Perf. Simul. (2011), 185--203.Google Scholar
- R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas. 2011. MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Trans. Power Syst. (2011), 12--19. Google ScholarCross Ref
Index Terms
- Application-Specific Residential Microgrid Design Methodology
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