Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Optimal Discrete Net-Load Balancing in Smart Grids with High PV Penetration

Published: 27 November 2018 Publication History

Abstract

Mitigating supply-demand mismatch is critical for smooth power grid operation. Traditionally, load curtailment techniques such as demand response have been used for this purpose. However, these cannot be the only component of a net-load balancing framework for smart grids with high PV penetration. These grids sometimes exhibit supply surplus, causing overvoltages. Currently, these are mitigated using voltage manipulation techniques such as Volt-Var Optimizations, which are computationally expensive, thereby increasing the complexity of grid operations. Taking advantage of recent technological developments that enable rapid selective connection of PV modules of an installation to the grid, we develop a unified net-load balancing framework that performs both load and solar curtailment. We show that when the available curtailment values are discrete, this problem is NP-hard and we develop bounded approximation algorithms. Our algorithms produce fast solutions, given the tight timing constraints required for grid operation, while ensuring that practical constraints such as fairness, network capacity limits, and so forth are satisfied. We also develop an online algorithm that performs net-load balancing using only data available for the current interval. Using both theoretical analysis and practical evaluations, we show that our net-load balancing algorithms provide solutions that are close to optimal in a small amount of time.

References

[1]
Mohamed H. Albadi and E. F. El-Saadany. 2007. Demand response in electricity markets: An overview. In IEEE Power Engineering Society General Meeting, Vol. 2007. IEEE, 1--5.
[2]
Mohamed H. Albadi and E. F. El-Saadany. 2008. A summary of demand response in electricity markets. Electric Power Systems Research 78, 11 (2008), 1989--1996.
[3]
Saima Aman, Charalampos Chelmis, and Viktor Prasanna. 2016. Learning to REDUCE: A reduced electricity consumption prediction ensemble. In Workshops at the 30th AAAI Conference on Artificial Intelligence. AAAI.
[4]
Saima Aman, Marc Frincu, Charalampos Chelmis, Muhammad Noor, Yogesh Simmhan, and Viktor K. Prasanna. 2015. Prediction models for dynamic demand response. In IEEE International Conference on Smart Grid Communications (SmartGridComm’15): Data Management, Grid Analytics, and Dynamic Pricing. IEEE, 338--343.
[5]
Antimo Barbato, Cristiana Bolchini, Maurizio Delfanti, Angela Geronazzo, Giovanni Accetta, Alessio Dede, Giovanni Massa, and Massimo Trioni. 2015. An energy management framework for optimal demand response in a smart campus. ICGREEN 11.
[6]
Prasenjit Basak, S. Chowdhury, S. Halder nee Dey, and S. P. Chowdhury. 2012. A literature review on integration of distributed energy resources in the perspective of control, protection and stability of microgrid. Renewable and Sustainable Energy Reviews 16, 8 (2012), 5545--5556.
[7]
Mahdi Behrangrad, Hideharu Sugihara, and Tsuyoshi Funaki. 2010. Analyzing the system effects of optimal demand response utilization for reserve procurement and peak clipping. In 2010 IEEE Power and Energy Society General Meeting. IEEE, 1--7.
[8]
Stephen Boyd and Lieven Vandenberghe. 2004. Convex Optimization. Cambridge University Press.
[9]
Zhi Chen, Lei Wu, and Yong Fu. 2012. Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization. IEEE Transactions on Smart Grid 3, 4 (2012), 1822--1831.
[10]
Soojeong Choi, Sunju Park, Dong-Joo Kang, Seung-jae Han, and Hak-Man Kim. 2011. A microgrid energy management system for inducing optimal demand response. In 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm’11). IEEE, 19--24.
[11]
Chris Deline. 2010. Partially shaded operation of multi-string photovoltaic systems. In 2010 35th IEEE Photovoltaic Specialists Conference (PVSC’10). IEEE, 000394--000399.
[12]
Yue Min Ding, Seung Ho Hong, and Xiao Hui Li. 2014. A demand response energy management scheme for industrial facilities in smart grid. IEEE Transactions on Industrial Informatics 10, 4 (2014), 2257--2269.
[13]
Johan Driesen and Farid Katiraei. 2008. Design for distributed energy resources. IEEE Power and Energy Magazine 6, 3 (2008), 30--40.
[14]
Ahmed T. Elsayed, Christopher R. Lashway, and Osama A. Mohammed. 2016. Advanced battery management and diagnostic system for smart grid infrastructure. IEEE Transactions on Smart Grid 7, 2 (2016), 897--905.
[15]
Hassan Farhangi. 2009. The path of the smart grid. IEEE Power and Energy Magazine 8 (2009), 18--28.
[16]
Ognjen Gagrica, Phuong H. Nguyen, Wil L. Kling, and Tadeusz Uhl. 2015. Microinverter curtailment strategy for increasing photovoltaic penetration in low-voltage networks. IEEE Transactions on Sustainable Energy 6, 2 (2015), 369--379.
[17]
Nikolaos Gatsis and Georgios B. Giannakis. 2011. Cooperative multi-residence demand response scheduling. In 2011 45th Annual Conference on Information Sciences and Systems (CISS’11). IEEE, 1--6.
[18]
Yuanxiong Guo, Miao Pan, and Yuguang Fang. 2012. Optimal power management of residential customers in the smart grid. IEEE Transactions on Parallel and Distributed Systems 23, 9 (2012), 1593--1606.
[19]
IBM. 2017. ILOG CPLEX Optimization Studio. Retrieved from http://www-01.ibm.com/support/knowledgecenter/SSSA5P/welcome.
[20]
Sanmukh R. Kuppannagari, Rajgopal Kannan, Charalampos Chelmis, and Viktor K. Prasanna. 2016. Implementation of learning-based dynamic demand response on a campus micro-grid. In The 25th International Joint Conference on Artificial Intelligence. AAAI, 4250--4251.
[21]
Sanmukh R. Kuppannagari, Rajgopal Kannan, Charalampos Chelmis, Arash S. Tehrani, and Viktor K. Prasanna. 2016. Optimal customer targeting for sustainable demand response in smart grids. Procedia Computer Science 80 (2016), 324--334.
[22]
Sanmukh R. Kuppannagari, Rajgopal Kannan, and Viktor K. Prasanna. 2015. An ILP based algorithm for optimal customer selection for demand response in smartgrids. In 2015 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 300--305.
[23]
Sanmukh R. Kuppannagari, Rajgopal Kannan, and Viktor K. Prasanna. 2017. Optimal net-load balancing in smart grids with high PV penetration. arXiv Preprint arXiv:1709.00644 (2017).
[24]
Jungsuk Kwac and Ram Rajagopal. 2013. Demand response targeting using big data analytics. In 2013 IEEE International Conference on Big Data. IEEE, 683--690.
[25]
Robert H. Lasseter and Paolo Paigi. 2004. Microgrid: A conceptual solution. In 2004 IEEE 35th Annual Power Electronics Specialists Conference, 2004 (PESC’04). Vol. 6. IEEE, 4285--4290.
[26]
Stephen Lee, Srinivasan Iyengar, David Irwin, and Prashant Shenoy. 2017. Distributed rate control for smart solar arrays. In Proceedings of the 8th International Conference on Future Energy Systems. ACM, 34--44.
[27]
Thillainathan Logenthiran, Dipti Srinivasan, and Tan Zong Shun. 2012. Demand side management in smart grid using heuristic optimization. IEEE Transactions on Smart Grid 3, 3 (2012), 1244--1252.
[28]
Yuri V. Makarov, Clyde Loutan, Jian Ma, and Phillip De Mello. 2009. Operational impacts of wind generation on California power systems. IEEE Transactions on Power Systems 24, 2 (2009), 1039--1050.
[29]
Mathworks. 2017. MATLAB-MathWorks. Retrieved from https://www.mathworks.com/products/matlab.html.
[30]
Albert Molderink, Vincent Bakker, Maurice G. C. Bosman, Johann L. Hurink, and Gerard J. M. Smit. 2009. Domestic energy management methodology for optimizing efficiency in smart grids. In 2009 IEEE Bucharest PowerTech. IEEE, 1--7.
[31]
Khosrow Moslehi and Ranjit Kumar. 2010. A reliability perspective of the smart grid. IEEE Transactions on Smart Grid 1, 1 (2010), 57--64.
[32]
NREL. 2010. National Solar Radiation Data Base. Retrieved from http://rredc.nrel.gov/solar/old_data/nsrdb/1991-2010/hourly/list_by_state.html.
[33]
NREL. 2013. Photovoltaic (PV) Pricing Trends: Historical, Recent, and Near-Term Projections. Retrieved from http://www.nrel.gov/docs/fy13osti/56776.pdf.
[34]
Photovoltaic-software.com. 2013. How to Calculate the Annual Solar Energy Output of a Photovoltaic System? Retrieved from http://photovoltaic-software.com/PV-solar-energy-calculation.php.
[35]
Viktor Prasanna. 2018. DEEP SOLAR. Retrieved from http://deepsolar.usc.edu/.
[36]
Saaed Rahimi, Mattia Marinelli, and Federico Silvestro. 2012. Evaluation of requirements for volt/var control and optimization function in distribution management systems. In 2012 IEEE International Energy Conference and Exhibition (ENERGYCON’12). IEEE, 331--336.
[37]
Pamela Ramsami and Vishwamitra Oree. 2015. A hybrid method for forecasting the energy output of photovoltaic systems. Energy Conversion and Management 95 (2015), 406--413.
[38]
Subendhu Rongali, Tanuja Ganuy, Manikandan Padmanabhan, Vijay Arya, Shivkumar Kalyanaraman, and Mohamad Iskandar Petra. 2016. iPlug: Decentralised dispatch of distributed generation. In 2016 8th International Conference on Communication Systems and Networks (COMSNETS’16). IEEE, 1--8.
[39]
Tamal Roy, Avijit Das, and Zhen Ni. 2017. Optimization in load scheduling of a residential community using dynamic pricing. In 2017 IEEE Innovative Smart Grid Technologies Conference (ISGT’17). IEEE, 1--5.
[40]
Nerea Ruiz, Iñigo Cobelo, and José Oyarzabal. 2009. A direct load control model for virtual power plant management. IEEE Transactions on Power Systems 24, 2 (2009), 959--966.
[41]
Sartaj Sahni. 1975. Approximate algorithms for the 0/1 knapsack problem. Journal of the ACM (JACM) 22, 1 (1975), 115--124.
[42]
Akansha Singh, Stephen Lee, David Irwin, and Prashant Shenoy. 2017. SunShade: Enabling software-defined solar-powered systems. In Proceedings of the 8th International Conference on Cyber-Physical Systems. ACM, 61--70.
[43]
Stanford. 2010. Car Battery Efficiencies. Retrieved from http://large.stanford.edu/courses/2010/ph240/sun1/.
[44]
Han-I Su and Abbas El Gamal. 2011. Modeling and analysis of the role of fast-response energy storage in the smart grid. In 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton’11). IEEE, 719--726.
[45]
SunShot. 2012. SunShot Vision Study A Comprehensive Analysis of the Potential for U.S. Solar Electricity Generation. Retrieved from http://www.nrel.gov/docs/fy12osti/54294.pdf.
[46]
Chee-Wooi Ten, Chen-Ching Liu, and Govindarasu Manimaran. 2008. Vulnerability assessment of cybersecurity for SCADA systems. IEEE Transactions on Power Systems 23, 4 (2008), 1836--1846.
[47]
Reinaldo Tonkoski, Luiz A. C. Lopes, and Tarek H. M. El-Fouly. 2011. Coordinated active power curtailment of grid connected PV inverters for overvoltage prevention. IEEE Transactions on Sustainable Energy 2, 2 (2011), 139--147.
[48]
John S. Vardakas, Nizar Zorba, and Christos V. Verikoukis. 2015. A survey on demand response programs in smart grids: Pricing methods and optimization algorithms. IEEE Communications Surveys 8 Tutorials 17, 1 (2015), 152--178.
[49]
Ziming Zhu, Jie Tang, Sangarapillai Lambotharan, Woon Hau Chin, and Zhong Fan. 2012. An integer linear programming based optimization for home demand-side management in smart grid. In 2012 IEEE PES Innovative Smart Grid Technologies (ISGT’12). IEEE, 1--5.
[50]
Vasileios Zois, Marc Frincu, Charalampos Chelmis, Muhammad Rizwan Saeed, and Viktor Prasanna. 2014. Efficient customer selection for sustainable demand response in smart grids. In 2014 International Green Computing Conference (IGCC'14). IEEE, 1--6.

Cited By

View all
  • (2024)Flexible energy utilization potential of demand response oriented photovoltaic direct-driven air-conditioning system with energy storageEnergy and Buildings10.1016/j.enbuild.2024.114818(114818)Online publication date: Sep-2024
  • (2023)Distributed rate control of smart solar arrays with batteriesFrontiers in the Internet of Things10.3389/friot.2023.11293672Online publication date: 28-Jun-2023
  • (2023)Behind-the-Meter Solar Generation Disaggregation at Varying Aggregation Levels Using Consumer Mixture ModelsIEEE Transactions on Sustainable Computing10.1109/TSUSC.2022.31924568:1(43-55)Online publication date: 1-Jan-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks  Volume 14, Issue 3-4
Special Issue on BuildSys'17
November 2018
392 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/3294070
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

Publication History

Published: 27 November 2018
Accepted: 01 May 2018
Revised: 01 April 2018
Received: 01 January 2018
Published in TOSN Volume 14, Issue 3-4

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Net-load balancing
  2. approximation algorithms
  3. discrete curtailment
  4. smart grid

Qualifiers

  • Research-article
  • Research
  • Refereed

Funding Sources

  • Department of Energy (DoE)
  • US National Science Foundation

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)9
  • Downloads (Last 6 weeks)2
Reflects downloads up to 16 Oct 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Flexible energy utilization potential of demand response oriented photovoltaic direct-driven air-conditioning system with energy storageEnergy and Buildings10.1016/j.enbuild.2024.114818(114818)Online publication date: Sep-2024
  • (2023)Distributed rate control of smart solar arrays with batteriesFrontiers in the Internet of Things10.3389/friot.2023.11293672Online publication date: 28-Jun-2023
  • (2023)Behind-the-Meter Solar Generation Disaggregation at Varying Aggregation Levels Using Consumer Mixture ModelsIEEE Transactions on Sustainable Computing10.1109/TSUSC.2022.31924568:1(43-55)Online publication date: 1-Jan-2023
  • (2020)Disaggregation of Behind-the-Meter Solar Generation in Presence of Energy Storage Resources2020 IEEE Conference on Technologies for Sustainability (SusTech)10.1109/SusTech47890.2020.9150506(1-7)Online publication date: Apr-2020
  • (2019)An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural NetworkApplied Sciences10.3390/app90714879:7(1487)Online publication date: 9-Apr-2019
  • (2019)Approximate Scheduling of DERs with Discrete Complex InjectionsProceedings of the Tenth ACM International Conference on Future Energy Systems10.1145/3307772.3328311(204-214)Online publication date: 15-Jun-2019

View Options

Get Access

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media