2015, Volume 1, Issue 4
Engineering >> 2015, Volume 1, Issue 4 doi: 10.15302/J-ENG-2015109
Agent-Based Simulation for Interconnection-Scale Renewable Integration and Demand Response Studies
1 Department of Mechanical Engineering, University of Victoria, Victoria, BC V8W 2Y2, Canada
2 Institute for Integrated Energy Systems, University of Victoria, Victoria, BC V8W 2Y2, Canada
3 Pacific Northwest National Laboratory, Richland, WA 99352, USA
4 Renewable Energy Research Group, King Abdulaziz University, Jeddah, Makkah 21589, Saudi Arabia
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Abstract
This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnection-scale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council (WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methods presented.
Keywords
interconnection studies ; demand response ; load control ; renewable integration ; agent-based simulation ; electricity markets
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References
[ 1 ] Y. V. Makarov, C. Loutan, J. Ma, P. de Mello. Operational impacts of wind generation on California power systems. IEEE Trans. Power Syst., 2009, 24(2): 1039–1050
[ 2 ] L. Kane, G. Ault, S. Gill. An assessment of principles of access for wind generation curtailment in active network management schemes. In: Proceedings of the 22nd International Conference and Exhibition on Electricity Distribution. Stockholm, Sweden, 2013: 0237
[ 3 ] F. C. Schweppe, R. D. Tabors, J. L. Kirtley, H. R. Outhred, F. H. Pickel, A. J. Cox. Homeostatic utility control. IEEE Trans. Power App. Syst., 1980, PAS-99(3): 1151–1163 link1
[ 4 ] D. Trudnowski, M. Donnelly, E. Lightner. Power-system frequency and stability control using decentralized intelligent loads. In: Proceedings of 2005/2006 IEEE Power Engineering Society Transmission and Distribution Conference and Exhibition. Dallas, TX, USA, 2006: 1453–1459
[ 5 ] D. J. Hammerstrom, Pacific Northwest GridWise™ Testbed Demonstration Projects: Part II. Grid Friendly™ Appliance Project, PNNL-17079. Richland: Pacific Northwest National Laboratory, 2007
[ 6 ] J. Kondoh, N. Lu, D. J. Hammerstrom. An evaluation of the water heater load potential for providing regulation service. IEEE Trans. Power Syst., 2011, 26(3): 1309–1316
[ 7 ] P. Kundur. Power System Stability and Control. New York: McGraw Hill, Inc., 1994
[ 8 ] D. S. Callaway. Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy. Energy Convers. Manage., 2009, 50(5): 1389–1400 link1
[ 9 ] R. G. Pratt, C. C. Conner, B. A. Cooke, E. E. Richman. Metered end-use consumption and load shapes from the ELCAP residential sample of existing homes in the Pacific Northwest. Energ. Buildings, 1993, 19(3): 179–193 link1
[10] D. Baylon, P. Storm, B. Hannas, K. Geraghty, V. Mudford. Residential building stock assessment: Multifamily characteristics and energy use, 13−263. Portland: Northwest Energy Efficiency Alliance, 2013
[11] F. Rahimi, A. Ipakchi. Demand response as a market resource under the smart grid paradigm. IEEE Trans. Smart Grid, 2010, 1(1): 82–88 link1
[12] J. Ma, Y. V. Makarov, C. Loutan, Z. Xie. Impact of wind and solar generation on the California ISO’s intra-hour balancing needs. In: Proceedings of 2011 IEEE Power and Energy Society General Meeting. San Diego, CA, USA, 2011: 1–6
[13] A. J. Conejo, J. M. Morales, L. Baringo. Real-time demand response model. IEEE Trans. Smart Grid, 2010, 1(3): 236–242 link1
[14] M. D. Ilic, Y. Makarov, D. Hawkins. Operations of electric power systems with high penetration of wind power: Risks and possible solutions. In: Proceedings of 2007 IEEE Power Engineering Society General Meeting. Tampa, FL, USA, 2007: 1–4
[15] N. Lu, P. Du, Y. V. Makarov. The potential of thermostatically controlled appliances for intra-hour energy storage applications. In: Proceedings of 2012 IEEE Power and Energy Society General Meeting. San Diego, CA, USA, 2012: 1–6
[16] D. J. Hammerstrom, Pacific Northwest GridWise™ Testbed Demonstration Projects: Part I. Olympic Peninsula Project, PNNL-17167. Richland: Pacific Northwest National Laboratory, 2007
[17] A. Faruqui, R. Hledik, J. Tsoukalis. The power of dynamic pricing. Electr. J., 2009, 22(3): 42–56
[18] S. E. Widergren, AEP Ohio gridSMART® Demonstration Project Real-Time Pricing demonstration analysis, PNNL-23192. Richland: Pacific Northwest National Laboratory, 2014
[19] K. Subbarao, J. Fuller, K. Kalsi, R. Pratt, S. Widergren, D. Chassin. Transactive control and coordination of distributed assets for ancillary services, PNNL-22942. Richland: Pacific Northwest National Laboratory, 2013
[20] D. Fabozzi, T. Van Cutsem. Simplified time-domain simulation of detailed long-term dynamic models. In: Proceedings of 2009 IEEE Power and Energy Society General Meeting. Calgary, AB, Canada, 2009: 1–8
[21] D. P. Chassin, J. C. Fuller, N. Djilali. GridLAB-D: An agent-based simulation framework for smart grids. J. Appl. Math., 2014(2014): 492320
[22] S. Stoft. Power System Economics: Designing Markets for Electricity. Piscataway: Wiley-IEEE Press, 2002
[23] B. C. Ummels, M. Gibescu, E. Pelgrum, W. L. Kling, A. J. Brand. Impacts of wind power on thermal generation unit commitment and dispatch. IEEE Trans. Energ. Convers., 2007, 22(1): 44–51 link1
[24] R. Billinton, G. Bai. Generating capacity adequacy associated with wind energy. IEEE Trans. Energ. Convers., 2004, 19(3): 641–646 link1
[25] L. Soder. Reserve margin planning in a wind-hydro-thermal power system. IEEE Trans. Power Syst., 1993, 8(2): 564–571 link1
[26] A. Fabbri, T. G. S. Roma?n, J. R. Abbad, V. H. M. Quezada. Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market. IEEE Trans. Power Syst., 2005, 20(3): 1440–1446
[27] M. Govardhan, F. Master, R. Roy. Economic analysis of different demand response programs on unit commitment. In: Proceedings of 2014 IEEE Region 10 Conference. Bangkok, Thailand, 2014: 1–6
[28] C. Zhao, J. Wang, J. P. Watson, Y. Guan. Multi-stage robust unit commitment considering wind and demand response uncertainties. IEEE Trans. Power Syst., 2013, 28(3): 2708–2717
[29] M. Kia, M. M. R. Sahebi, E. A. Duki, S. H. Hosseini. Simultaneous implementation of optimal demand response and security constrained unit commitment. In: Proceedings of the 16th IEEE Conference on Electrical Power Distribution Networks. Bandar Abbas, Iran, 2011: 1–5
[30] A. Papavasiliou, S. S. Oren. A stochastic unit commitment model for integrating renewable supply and demand response. In: Proceedings of 2012 IEEE Power and Energy Society General Meeting. San Diego, CA, USA, 2012: 1–6
[31] E. Bonabeau. Agent-based modeling: Methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. U.S.A., 2002, 99(Suppl 3): 7280–7287 link1
[32] B. L. Heath, R. R. Hill, F. W. Ciarallo. A survey of agent-based modeling practices (January 1998 to July 2008). JASSS-J. Artif. Soc. S., 2009, 12(4): 9
[33] F. Klügl. A validation methodology for agent-based simulations. In: Proceedings of the 2008 ACM symposium on Applied Computing. Fortaleza, Ceara, Brazil, 2008: 39–43
[34] S. Behboodi, D. P. Chassin, C. Crawford, N. Djilali. Renewable resources portfolio optimization in the presence of demand response. Appl. Energ., 2016, 162: 139–148 link1
[35] D. Kosterev, Development and implementation of composite load model in WECC. In: Proceedings of CIGRE 2015 Grid of the Future Symposium. Philadelphia, PA, USA, 2015
[36] W. Zhang, J. Lian, C. Y. Chang, K. Kalsi. Aggregated modeling and control of air conditioning loads for demand response. IEEE Trans. Power Syst., 2013, 28(4): 4655–4664 link1
[37] D. P. Chassin. New residential thermostat for transactive systems (Master’s thesis). Greater Victoria: University of Victoria, 2014
[38] D. McFadden. Quantal choice analysis: A survey. Ann. Econ. Soc. Meas., 1976, 5(4): 363–390
[39] K. G. Pillai, C. Hofacker. Calibration of consumer knowledge of the web. Int. J. Res. Mark., 2007, 24(3): 254–267
[40] P. Jazayeri, A survey of load control programs for price and system stability. IEEE Trans. Power Syst., 2005, 20(3): 1504–1509
[41] N. Lu, D. J. Hammerstrom. Design considerations for frequency responsive Grid FriendlyTM appliances. In: Proceedings of 2005/2006 IEEE Power Engineering Society Transmission and Distribution Conference and Exhibition. Dallas, TX, USA, 2006: 647–652
[42] J. Xie, C. C. Liu, M. Sforna. Distributed underfrequency load shedding using a multi-agent system. In: Proceedings of 2015 IEEE Eindhoven PowerTech. Eindhoven, the Netherlands, 2015: 1–6
[43] B. P. Zeigler, H. Praehofer, T. G. Kim. Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems. 2nd ed. San Diego: Academic Press, 2000
[44] K. M. Carley, Validating computational models. Pittsburgh: Carnegie Mellon University, 1996
[45] M. Richiardi, R. Leombruni, N. Saam, M. Sonnessa. A common protocol for agent-based social simulation. JASSS-J. Artif. Soc. S., 2006, 9(1): 15
[46] P. Windrum, G. Fagiolo, A. Moneta. Empirical validation of agent-based models: Alternatives and prospects. JASSS-J. Artif. Soc. S., 2007, 10(2): 8
[47] C. Werker, T. Brenner. Empirical calibration of simulation models, Papers on Economics and Evolution # 0410. Jena: Max Planck Institute for Research into Economic Systems, 2004
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