We build elements of a pervasive computing enabled modeling environment for integrated national energy systems to support policy and decision-making as it pertains to co-evolving socio-energy systems. The built system aims to provide public policy makers as well as private stakeholders entirely new ways to design and architect next-generation energy systems.

Highlights

  • In the absence of public data on the US electrical grid, there is a need to infer the structure of the electric grid. We use first-principles based, generative models to build an approximate synthetic grid. A large number of publicly available datasets and expert knowledge have enabled us to build representations of electrical networks that are statistically similar to the real electrical networks.

  • We use mobility model, location choice, demographics and activity schedules of individuals to build highly disaggregated and resolved spatio-temporal energy demand profiles.

  • New technologies, smart grid and cyber-based systems have resulted in close coupling of the communication, electrical, and other societal infrastructures. This has made the infrastructures more robust and efficient but at the same time more vulnerable to cascading failures in them.

Overview

The energy systems team has been engaged in energy research for the past 15 years. An agent based, integrated system has been developed for representing and analyzing a wide array of topics:  

  1. Interdependencies of infrastructures: transportation, communication, electrical, societal and health
  2. Demand side energy mangement through generation of detailed spatio-temporal demand profiles based on daily activities, locations and demographics of  individuals
  3. Development of computational methods and underlying theory to build a synthetic electrical grid for the US
  4. Reliability and vulnerabilities in the electrical grid caused by its structural attributes, human interaction, renewable sources (two-way flow of power), and natural disasters.
  5. Synchrophasor measurement based monitoring, protection and control of modern power networks
  6. Software: Framework and Models.

Reliability and Vulnerability of the Grid

From safety and security point of view, it is extremely important to understand the structure of the grid, identify potential points of vulnerabilities and build redundancies around those vulnerabilities to make the electrical infrastructure more robust. We perform structural analysis and flow analysis of the electrical network to identify points of vulnerabilities.

Table Top Exercises

  1. A Table Top Exercise Involving Multiple Localized Targeted Insults to the Power Network. Barrett, S. Eubank, A. Marathe, M. Marathe, A. Phadke, J. Thorp, A. Vullikanti 2011. Effects of Multiple Local Network Insults: Vulnerabilities, Analysis and Recommendations. NDSSL Technical Report TR: 11-001
  2. National Planning Scenario 1: Effects of Hypothetical Improvised Nuclear Detonation on the Electrical Infrastructure. C. Barrett, S. Eubank, C. Y. Evrenosoglu, A. Marathe, M. Marathe, A. Phadke, J. Thorp, A. Vullikanti, 2013. Effects of Hypothetical Improvised Nuclear Detonation on the Electrical Infrastructure. Keynote Presentation. Invited paper for a Keynote address at the International ETG-Conference, Berlin, November 5-6.

Synchrophasor measurement based monitoring, protection and control

Synchrophasor measurements obtained from phasor measurement units (PMUs) have been extensively used in the monitoring, protection, and control of modern power systems. Some PMU-based applied research includes:

  1. Fault classification using only the voltage phasor of a PMU-only state estimator
  2. A methodology to validate the quality of PMU-data to be used by downstream applications
  3. Robust damping of inter-area oscillations using PMU data, linear matrix inequalities and decision trees

Other exploratory work using PMUs are:

  1. Creation of a partitioned linear state estimator that makes possible the integration of results obtained from independent linear state estimators
  2. Estimation of three-phase line parameters using a Kalman filter-based recursive regression
  3. Analyzing effects of different load models on a quadratic prediction algorithm
  4. A PMU placement scheme that integrates other placement algorithms
  5. Estimation of power system stress using metrics developed from PMU-data

Software Framework and Models

We are building a pervasive computing enabled modeling environment for integrated national energy systems to support policy and decision making as it pertains to co-evolving socio-energy systems. Decision support systems built using this software will provide public policy makers as well as private stakeholders entirely new ways to design and architect next-generation energy systems. It will also help evaluate new ways to invest in renewable energy sources and assess the reliability and security of the emerging grid architectures. It is based on recent computational advances for modeling extremely large, complex, multi-scale socio-technical systems.

Publications

2015

  1. A. Pal, M. Youssef, A. Vullikanti, A. Marathe, S. Eubank, M. Marathe, C. Barrett, J. S. Thorp, A. G. Phadke, V. Centeno. Role of power system relays in a large scale physical attack. 6th Int. Conf. Liberalization & Modernization of Power Systems and CRIS Problems of Critical Infrastructures, 2015. (PDF)
  2. A. Pal, P. Chatterjee, J. S. Thorp, and V. A. Centeno, “On-line calibration of voltage transformers using synchrophasor measurements,” accepted for publication in IEEE Transactions on Power Delivery. (PDF)
  3. C. Mishra, K. D. Jones, A. Pal, and V. A. Centeno, “A binary PSO-based optimal substation coverage algorithm for PMU installations in practical systems,” accepted for publication in IET Generation, Transmission & Distribution. (PDF)
  4. T. Wang, A. Pal, J. S. Thorp, Z. Wang, J. Liu, and Y. Yang, “Multi-polytope based adaptive robust damping control in power systems using CART,” IEEE Trans. Power Syst., vol. 30, no. 4, pp. 2063-2072, Jul. 2015. (PDF)
  5. K. Amare, V. A. Centeno, and A. Pal, “Unified PMU placement algorithm for power systems,” accepted for publication in 2015 IEEE North American Power Symposium. (PDF)
  6. C. Mishra, A. Pal, and V. A. Centeno, “Kalman-filter based recursive regression for three-phase line parameter estimation using phasor measurements,” in Proc. IEEE Power Eng. Soc. General Meeting, Denver, CO, pp. 1-5, 26-30 Jul. 2015. (PDF)
  7. K. D. Jones, A. Pal, and J. S. Thorp, “Methodology for performing synchrophasor data conditioning and validation,” IEEE Trans. Power Syst., vol. 30, no. 3, pp. 1121-1130, May 2015. (PDF)
  8. P. Chatterjee, A. Pal, J. S. Thorp, and J. De La Ree, “Partitioned linear state estimation,” in Proc. IEEE Power Eng. Soc. Conf. Innovative Smart Grid Technol., Washington D.C, pp. 1-5, 18-20 Feb. 2015. (PDF)
  9. A. Pal, “Effect of different load models on the three-sample based quadratic prediction algorithm,” in Proc. IEEE Power Eng. Soc. Conf. Innovative Smart Grid Technol., Washington D.C, pp. 1-5, 18-20 Feb. 2015. (PDF)
  10. F. Gao, J. S. Thorp, S. Gao, A. Pal, and K. A. Vance, “A voltage phasor based fault classification method for PMU only state estimator output,” Elect. Power Compon. Syst., vol. 43, no. 1, pp. 22-31, Jan. 2015. (PDF)

2014

  1. C. Barrett, V. Centeno, S. Eubank, C. Y. Evrenosoglu, A. Marathe, M. Marathe, C. Mishra, H. Mortveit, A. Pal, A. Phadke, J. Thorp, A. Vullikanti, M. Youssef, 2014. Impact of a surface nuclear blast on the transient stability of the power system. Proceedings of the 9th Int. Conf. Critical Information Infrastructures Security, Limassol, Cyprus, pp. 1-10, 13-15 Oct. 2014. (PDF)
  2. A. Pal, I. Singh, and B. Bhargava, “Stress assessment in power systems and its visualization using synchrophasor based metrics,” in Proc. IEEE 2014 North American Power Symposium (NAPS), Pullman, WA, pp. 1-6, 7-9 Sep. 2014. (PDF)

2013

  1. C. Barrett, K. Bisset, S. Chandan, J. Chen, Y. Chungbaek, S. Eubank, Y. Evrenosoglu, B. Lewis, K. Lum, A. Marathe, M. Marathe, H. Mortveit, N. Parikh, A. Phadke, J. Reed, C. Rivers, S. Saha, P. Stretz, S. Swarup, J. Thorp, A. Vullikanti, D. Xie, 2013. Planning and Response in the Aftermath of a Large Crisis: An Agent-based Informatics Framework. Proceedings of the Winter Simulation Conference, Washington, D.C. Edited by R. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, and M. E. Kuhl. (PDF)
  2. C. Barrett, S. Eubank, C. Y. Evrenosoglu, A. Marathe, M. Marathe, A. Phadke, J. Thorp, A. Vullikanti, 2013. Effects of Hypothetical Improvised Nuclear Detonation on the Electrical Infrastructure. Keynote Presentation. Invited paper for a Keynote address at the International ETG-Conference, Berlin, November 5-6. (PDF)
  3. R. Subbiah, K. Lum, A. Marathe, M. Marathe, 2013. Activity Based Energy Demand Modeling for Residential Buildings. Proceedings of IEEE PES Innovative Smart Grid Technologies Conference (ISGT), Washington DC, February, pages 198-203. (PDF)
  4. R. Subbiah, K. Lum, A. Marathe, M. Marathe, 2013. A High Resolution Energy Demand Model for Commercial Buildings. International ETG-Conference, Berlin, November 5-6. (PDF)

2012

  1. C. Barrett, K. Channakeshava, F. Huang, J. Kim, A. Kumar, A. Marathe, M. Marathe, G. Pei, S. Saha, B.S.P. Subbiah, and V.S.A. Kumar, 2012. Human Initiated Cascading Failures in Societal Infrastructures. PLoS ONE, vol. 7, no. 10, October. (PDF)

2011

  1. P. Mozumder, W. Mazariegos, A. Marathe, 2011. Consumers’ Preference for Renewable Energy in the Southwest USA. Energy Economics, vol 33, Issue 6, November, pages 1119- 1126. (PDF)
  2. A. Marathe, M. Marathe and A. Vullikanti, 2011. Towards a Pervasive Computing Enabled Modeling Environment for Integrated Coevolving Energy, EPU-CRIS International Conference on Science and Technology, November 16, Hanoi, Vietnam. (PDF)

2009

  1. K. Atkins, C. Barrett, A. Marathe, 2009. A Web Based Artificial Market. Proceedings of the Winter Simulation Conference, Austin TX, December 2009, pages 3047-3054. (PDF)
  2. J. Chen, M. Macauley and A. Marathe. Network Topology and Locational Market Power. Computational Economics, volume 34, Number 1, August, 2009, pages 21‐35. (PDF)
  3. K. Atkins, J. Chen, V.S.A. Kumar, M. Macauley and A. Marathe. Locational Market Power in Network Constrained Markets. Journal of Economic Behavior and Organization, Vol. 70, issues 1‐2, pages 416‐430, May 2009. (PDF)

2007

  1. K. Atkins, A. Marathe and C. Barrett, 2007. A Computational Approach to Modeling Commodity Markets. Computational Economics, vol 30, no.2, September, pages 125-142. (PDF)
  2. P. Mozumder and A. Marathe, 2007. Causality Relationship Between Electricity Consumption and GDP in Bangladesh. Energy Policy, vol. 35, January, pages 395-402. (PDF)

2006

  1. K. Atkins, J. Chen, A. Kumar, and A. Marathe, 2006. Structural Analysis of Electrical Networks. International Conference on Critical Infrastructures, September 25-27, pages 1-7, Alexandria, Virginia. (PDF)
  2. K. Atkins, J. Chen, A. Kumar, A. Marathe, 2006. Model Based Spatial Data Mining for Power Markets. Spatial Data Mining conference (SIAM-DM), Bethesda, Maryland, April 22. (PDF)

2005

  1. I. Arciniegas, A. Marathe, 2005. Important Variables in Explaining Real-Time Peak Price in the Independent Power Market of Ontario. Utilities Policy, vol.13, issue 1, pages 27-39. (PDF)

2004

  1. L. Hadsell, A. Marathe and H. Shawky, 2004. Estimating the Volatility of Wholesale Electricity Spot Prices in the US. The Energy Journal, vol. 25, no.4, October, pages 23-40. (PDF)
  2. P. Mozumder and A. Marathe, 2004. Implications of an Integrated Market for Tradable Renewable Energy Contracts. Ecological Economics, vol. 49, issue 3, July, pages 259-272. (PDF)
  3. K. Atkins, C. Homan, A. Marathe, 2004. Physical Clearing Mechanisms in Power Industry. IEEE Power Systems Conference and Exposition, New York City, New York, October. (PDF)

2003

  1. I. Arciniegas, C. Barrett, A. Marathe, 2003. Assessing the Efficiency of US Electricity Markets. Utilities Policy, vol. 11, issue 2, June, pages 75-86. (PDF)
  2. C. Barrett, A. Marathe, M. Marathe, D. Cook, G. Hicks, V. Faber, A. Srinivasan, Y. J. Sussmann, H. Thornquist, 2003. Statistical Analysis of Algorithms: A Case Study of Market-Clearing Mechanisms in the Power Industry. Journal of Graph Algorithms and Applications, vol. 7, no. 1, pages 3-31. (PDF)
  3. H. Shawky, A. Marathe and C. Barrett, 2003. A first look at the empirical relation between spot and futures electricity prices in the United States. Journal of Futures Market, vol. 23, issue 10, October, pages 931-955. (PDF)

2001

  1. C. Barrett, D. Cook, V. Faber, G. Hicks, A. Marathe, M. Marathe, A. Srinivasan, Y. J. Sussmann, H. Thornquist, 2001. Experimental Analysis of Algorithms for Bilateral-Contract Clearing Mechanisms Arising in Deregulated Power Industry. Proceedings of 5th Workshop in Algorithm Engineering (WAE2001), Springer’s Lecture Notes in Computer Science series, U. Aarhus, Denmark, August, pages 172-184. (PDF)

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