Abstract
Renewable energy (e.g., solar energy) is an attractive option to provide green energy to homes. Unfortunately, the intermittent nature of renewable energy results in a mismatch between when these sources generate energy and when homes demand it. This mismatch reduces the efficiency of using harvested energy by either (i) requiring batteries to store surplus energy, which typically incurs ∼ 20% energy conversion losses, or (ii) using net metering to transmit surplus energy via the electric grid’s AC lines, which severely limits the maximum percentage of renewable penetration possible. In this article, we propose an alternative structure where nearby homes explicitly share energy with each other to balance local energy harvesting and demand in microgrids. We develop a novel energy sharing approach to determine which homes should share energy, and when to minimize system-wide energy transmission losses in the microgrid. We evaluate our approach in simulation using real traces of solar energy harvesting and home consumption data from a deployment in Amherst, MA. We show that our system (i) reduces the energy loss on the AC line by 64% without requiring large batteries, (ii) performance scales up with larger battery capacities, and (iii) is robust to different energy consumption patterns and energy prediction accuracy in the microgrid.
- A. Anil and C. Tomlin. 2011. Reducing transient and steady state electricity consumption in HVAC using learning-based model predictive control. Proceedings of IEEE 100, 1 (2011), 240--253.Google Scholar
- A. Chakrabortty. 2012. Wide-area control of large power systems using dynamic clustering and TCSC-based redesigns. IEEE Transactions on Smart Grid 3, 3, 1503--1514. Google ScholarCross Ref
- A. Chakrabortty and A. Salazar. 2011. Building a dynamic electro-mechanical model for the Pacific AC intertie using distributed synchrophasor measurements. European Transactions on Electric Power: Special Issue on PMU Applications. 21, 4, 1657--1672. Google ScholarCross Ref
- A. Chakrabortty, J. H. Chow, and A. Salazar. 2011. A measurement-based framework for dynamic equivalencing of power systems using wide-area phasor measurements. IEEE Transactions on Smart Grid 1, 2, 68--81. Google ScholarCross Ref
- A. Thatte and L. Xie. 2012. Towards a unified operational value index of energy storage in smart grid environment. 3, 3, 1418--1426.Google Scholar
- A. Aswani, N. Master, J. Taneja, A. Krioukov, D. Culler, and C. Tomlin. 2012. Energy-efficient building HVAC control using hybrid system LBMPC. In Proceedings of IFAC Conference on Nonlinear Model Predictive Control. Google ScholarCross Ref
- R. Basmadjian, H. De Meer, R. Lent, and G. Giuliani. 2012. Cloud computing and its interest in saving energy: The use case of a private cloud. Journal of Cloud Computing: Advances, Systems and Applications 1, 5, 1--25.Google ScholarCross Ref
- M. Behl, M. Aneja, H. Jain, and R. Mangharam. 2011. EnRoute: An energy router for energy-efficient buildings. In IPSN.Google Scholar
- J. Crowcroft. 2012. Cutting the energy cost of TV content distribution by 5, by understanding the popularity of the top ten programs. In e-Energy. Google ScholarDigital Library
- DOE. 2014. VEHICLE TECHNOLOGIES OFFICE: BATTERIES. Retrieved from http://www.energy.gov/eere/transportation/vehicles.Google Scholar
- DSIRE. 2010. Database of State Incentives for Renewables and Efficiency. (2010). http://www.dsireusa.org.Google Scholar
- F. J. Jin and K. G. Shin. 2012. Pack sizing and reconfiguration for management of large-scale batteries. In Proceedings of International Conference on Cyber-Physical Systems. Google ScholarDigital Library
- Xiaofan Jiang, Minh Van Ly, Jay Taneja, Prabal Dutta, and David Culler. 2009. Experiences with a high-fidelity wireless building energy auditing network. In SenSys. Google ScholarDigital Library
- Aman Kansal, Jason Hsu, Sadaf Zahedi, and Mani B. Srivastava. 2007. Power management in energy harvesting sensor networks. ACM Transction on Embedded Computer Systems 6, 4 (2007), 1539--9087. Google ScholarDigital Library
- L. Xie, Y. Gu, A. Eskandari, and M. Ehsani. 2012. Fast MPC-based coordination of wind power and battery energy storage systems. Journal of Energy Engineering 138, 2, 43--53. Google ScholarCross Ref
- D. M. Larruskain, I. Zamora, A. J. Mazn, O. Abarrategui, and J. Monasterio. 2005. Transmission and distribution networks: AC versus DC. In 9th Spanish-Portuguese Congress on Electrical Engineering.Google Scholar
- Vijay Mann, Avinash Kumar, Partha Dutta, and Shivkumar Kalyanaraman. 2011. VMFlow: Leveraging VM mobility to reduce network power costs in data centers. In NETWORKING 2011. Springer, 198--211. Google ScholarDigital Library
- M. D. Ilic, L. Xie, and J. Joo. 2011a. Efficient coordination of wind power and price-responsive demand Part I: Theoretical foundations. IEEE Transactions on Power Systems 26, 4, 1875--1884. Google ScholarCross Ref
- M. D. Ilic, L. Xie, and J. Joo. 2011b. Efficient coordination of wind power and price-responsive demand PartII: Case studies. IEEE Transactions on Power Systems 26, 4, 1885--1893. Google ScholarCross Ref
- A. Mishra, D. Irwin, P. Shenoy, J. Kurose, and T. Zhu. 2012. SmartCharge: Cutting the electricity bill in smart homes with energy storage. In e-Energy. Google ScholarDigital Library
- State of California. 2009. State of California Executive Order S-21-09. Retrieved from https://www.gov.ca.gov/news.php?id=13269.Google Scholar
- Lei Rao, Xue Liu, M. D. Ilic, and Jie Liu. 2012. Distributed coordination of internet data centers under multiregional electricity markets. Proceedings of the IEEE 100, 1 (2012), 269--282. Google ScholarCross Ref
- Lei Rao, Xue Liu, Le Xie, and Wenyu Liu. 2010. Minimizing electricity cost: Optimization of distributed internet data centers in a multi-electricity-market environment. In INFOCOM. Google ScholarDigital Library
- Lei Rao, Xue Liu, Le Xie, and Wenyu Liu. 2012. Coordinated energy cost management of distributed internet data centers in smart grid. IEEE Transactions on Smart Grid 3, 1 (2012), 50--58. Google ScholarCross Ref
- James Rose and Shaun Chapman. 2009. Freeing the Grid: Best and Worst Practices in State Net Metering Policies and Interconnection Procedures. Retrieved from http://www.newenergychoices.org/uploads/FreeingTheGrid2009.pdf.Google Scholar
- S. M. Schoenung. 2011. Energy storage systems cost update {A study for the DOE energy storage systems program}. Tech. Rep. SAND2011-2730, Sandia National Laboratories.Google ScholarCross Ref
- N. Sharma, J. Gummeson, D. Irwin, and P. Shenoy. 2010. Cloudy computing: Leveraging weather forecasts in energy harvesting sensor systems. In SECON.Google Scholar
- S. Sojoudi and S. H. Low. 2011. Optimal charging of plug-in hybrid electric vehicles in smart grids. In Proceedings of the 2011 IEEE Power and Energy Society General Meeting. Google ScholarCross Ref
- SERC: State Environmental Resource Center. 2011. ISSUE: NET METERING. Retrieved from http://www.serconline.org/netmetering/stateactivity.html.Google Scholar
- T. Zhu, A. Mishra, D. Irwin, N. Sharma, P. Shenoy, and D. Towsley. 2011. The case for efficient renewable energy management for smart homes. In ACM BuildSys. Google ScholarDigital Library
- Narseo Vallina-Rodriguez, Pan Hui, Jon Crowcroft, and Andrew Rice. 2010. Exhausting battery statistics: Understanding the energy demands on mobile handsets. In Proceedings of the 2nd ACM SIGCOMM Workshop on Networking, Systems, and Applications on Mobile Handhelds. Google ScholarDigital Library
- Jianguo Yao, Xue Liu, Wenbo He, and Ashikur Rahman. 2012. Dynamic control of electricity cost with power demand smoothing and peak shaving for distributed internet data centers. In ICDCS. Google ScholarDigital Library
Index Terms
- Minimizing Transmission Loss in Smart Microgrids by Sharing Renewable Energy
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