Afzal Siddiqui

Afzal Siddiqui


Visa sidan på svenska
Works at Department of Computer and Systems Sciences
Telephone 08-16 13 85
Visiting address Nodhuset, Borgarfjordsgatan 12
Postal address Institutionen för data- och systemvetenskap 164 07 Kista

About me

I am a Professor in the Department of Computer and Systems Sciences at Stockholm University. I am also an Adjunct Professor in the Department of Mathematics and Systems Analysis at Aalto University. Previously, I was Professor of Energy Economics in the Department of Statistical Science at University College London and a Visiting Professor in the Department of Decision Sciences at HEC Montréal. My research interests are in the application of operational research methods to analyse decision making under uncertainty and competition in the energy sector. Besides participation in and coordination of several research projects, I have also served as a consultant to the Energy Technologies Area of the Ernest Orlando Lawrence Berkeley National Laboratory and the Directorate‑General for Communications Networks, Content and Technology (DG CONNECT) of the European Commission.


•   University of California, Berkeley, CA, USA
-Ph.D. (2002) Industrial Engineering and Operations Research
-Minor Fields: Economics, Energy and Resources
-Dissertation Title: Equilibrium Analysis of Forward Markets for Electricity and Reserves
-Advisor: Shmuel Oren
-Committee: Chris Marnay, Sheldon Ross, and Catherine Wolfram

•   University of California, Berkeley, CA, USA
-M.S. (1998) Industrial Engineering and Operations Research

•   Columbia University, New York, NY, USA
-B.S. (1997) Industrial Engineeringacademic honours with distinction


Full-Time Employment

•   Stockholm University, Stockholm, Sweden
-Professor, Department of Computer and Systems Sciences (2011-present)

•   University College London, London, United Kingdom
-Professor of Energy Economics, Department of Statistical Science (2017-2020)
-Senior Lecturer, Department of Statistical Science (2010-2017)
-Lecturer, Department of Statistical Science (2005-2010)

•   University College Dublin, Dublin, Republic of Ireland
-College Lecturer, Department of Banking and Finance (2003-2005)

•  Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA, USA
-Visiting Post-Doctoral Researcher, Environmental Energy Technologies Division (2002-2003)

Other Appointments

•   University College London, London, United Kingdom
-Honorary Professor, Department of Statistical Science (2020-present)

•   Stockholm University, Stockholm, Sweden
-Research Engineer (20% appointment), Department of Computer and Systems Sciences (2008-2011)

•   Aalto University, Helsinki, Finland
-Adjunct Professor, Department of Mathematics and Systems Analysis (2019-present)
-Visiting Professor, Department of Mathematics and Systems Analysis (2012-2018)

•   Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, CA, USA
-Guest Senior Scientist and Consultant, Environmental Energy Technologies Division (2004-2013)

•   HEC Montréal, Montréal, Québec, Canada
-Affiliate Professor, Department of Decision Sciences (2017-present)
-Visiting Professor, Department of Decision Sciences (2016-2017)
-Instructor, Projets Internationaux (2009-2013)

•   Norwegian University of Science and Technology, Trondheim, Norway
-Visiting Researcher (sabbatical leave), Department of Industrial Economics and Technology Management (2008)

•   Asia Pacific Management Institute, Singapore
-Lecturer, Business Finance (2004-2005)

•   University of California, Berkeley, CA, USA
-Visiting Assistant Professor, Department of Industrial Engineering and Operations Research (2002)


During the 2020-2021 academic year, I will teach Business Analytics and Methodology of Decision Analysis with Advanced Applications.


My research interests are in energy economics, specifically the application of operational research methods for analysing investment incentives and energy policy. Currently, I am using game theory to assess the strategic use of Nordic hydro reservoirs in the STRING project. This work bears synergies with the PlanFES project at Aalto University in which I am also involved. 

Between 2009 and 2011, I was part of the ELDEV project in Trondheim, Norway, which sought to foster closer cooperation between industry and academia, namely in the area of improved business practices for the electric power industry. I have also contributed to the development of a deterministic optimisation tool, DER-CAM, that solves a microgrid's distributed-generation investment and operational problem in detail. In the context of sustainable development, I had the chance to be exposed to challenges facing hydropower as part of the FutureDAMS project. Concluded work includes a UK Energy Research Centre project on the use of real options in energy-sector decarbonisation as well as coordination of an EU FP7 project, EnRiMa, which aimed to use stochastic optimisation in order to improve energy efficiency and to manage risk in European public buildings. Finally, I have served as an international collaborator for the STRIDES, STEEM, and RISKY-RES projects in Canada, Finland, and Norway, respectively.

Current and Former Doctoral Students

  • Michail ChronopoulosInvestment Decision Making under Uncertainty: The Impact of Risk Aversion, Operational Flexibility, and Competition (2007-2011 at UCL)
  • Lucas Dias CondeixaOptimization of Generation and Transmission Expansion Planning: the Impacts of Emerging Technologies (2018-present at Aalto University; instructor)
  • Mingzhi Dong: Robust Localised Learning for Classification (2013-2018 at UCL; subsidiary supervisor)
  • Xiaojia GuoDecision Analysis and Time-Series Models (2013-2020 at UCL; subsidiary supervisor)
  • Farzad Hassanzadeh Moghimi: Storage, Transmission, and Renewable Interactions in the Nordic Grid (2020-present at Stockholm University)
  • Somayeh HeydariTime-Series and Real Options Analysis of Energy Markets (2006-2010 at UCL)
  • Lajos Maurovich-Horvat: Decision Making under Uncertainty and Competition for Sustainable Energy Technologies (2009-2014 at UCL)
  • Tuomas RintamäkiStudies on Flexible Integration of Renewable Energy (2015-present at Aalto University; instructor)
  • Maria Toomik: Regularization Approaches to Hyperspectral Unmixing (2014-2020 at UCL; subsidiary supervisor)
  • Vilma VirasjokiDecision Models of Energy Markets for Investment and Policy Analysis (2015-2019 at Aalto University; instructor)
  • Verena ViškovićEquilibrium Analysis of Carbon Emissions Caps in Regional Electricity Markets (2013-2018 at UCL)

Current and Former Post-Doctoral Researchers

  • Michail ChronopoulosAn Options Approach to UK Energy Futures (2011-2012 at UCL; subsidiary supervisor)
  • Sébastien DebiaStrategic Transmission and Renewable Investment in a Decentralised Electricity Sector (2017-2020 at HEC Montréal; subsidiary supervisor)
  • Somayeh HeydariEnergy Efficiency and Risk Management in Public Buildings (2011-2012 at UCL)
  • Paula RochaEnergy Efficiency and Risk Management in Public Buildings (2013-2014 at UCL)

Articles in Refereed Journals

  1. Tanaka, Makoto, Yihsu Chen, and Afzal S. Siddiqui (2021), "Regulatory Jurisdiction and Policy Coordination: A Bi-Level Modeling Approach for Performance-Based Environmental Policy," Journal of the Operational Research Society, forthcoming.
  2. Debia, Sébastien, Pierre-Olivier Pineau, and Afzal S. Siddiqui (2021), "Strategic Storage Use in a Hydro-Thermal Power System with Carbon Constraints," Energy Economics 98: 105261.
  3. Višković, Verena, Yihsu Chen, Afzal S. Siddiqui, and Makoto Tanaka (2021), "Economic and Environmental Consequences of Market Power in the South-East Europe Regional Electricity Market," The Energy Journal 42(6): 121-145.
  4. Gonzalez, Jose M., James E. Tomlinson, Julien J. Harou, Eduardo A. Martínez Ceseña, Mathaios Panteli, Andrea Bottacin-Busolin, Anthony Hurford, Marcelo A. Olivares, M.A., Afzal S. Siddiqui, Tohid Erfani, Kenneth M. Strzepek, Pierluigi Mancarella, Joseph Mutale, Emmanuel Obuobie, Abdulkarim H. Seid, and Aung Ze Ya (2020), "Spatial and Sectoral Benefit Distribution in Water-Energy System Design," Applied Energy 269: 114794.
  5. Virasjoki, Vilma, Afzal S. Siddiqui, Fabricio Oliveira, and Ahti Salo (2020), "Utility-Scale Energy Storage in an Imperfectly Competitive Power Sector," Energy Economics 88: 104716.
  6. Rintamäki, Tuomas, Afzal S. Siddiqui, and Ahti Salo (2020), "Strategic Offering of a Flexible Producer in Day-Ahead and Intraday Power Markets," European Journal of Operational Research 284(3): 1136-1153.
  7. Tejada-Arango, Diego A., Sonja Wogrin, Afzal S. Siddiqui, and Efraim Centeno (2019), "Opportunity Cost Including Short-Term Energy Storage in Hydrothermal Dispatch Models Using a Linked Representative Periods Approach," Energy 188: 116079.
  8. Višković, Verena, Yihsu Chen, Afzal S. Siddiqui, and Makoto Tanaka (2019), "Regional Carbon Policies in an Interconnected Power System: How Expanded Coverage Could Exacerbate Emission Leakage," Energy Policy 134: 110914.
  9. Tejada-Arango, Diego A., Afzal S. Siddiqui, Sonja Wogrin, and Efraim Centeno (2019), "A Review of Energy Storage System Legislation in the US and the European Union," Current Sustainable/Renewable Energy Reports 6(1): 22-28.
  10. Siddiqui, Afzal S., Ramteen Sioshansi, and Antonio J. Conejo (2019), "Merchant Storage Investment in a Restructured Electricity Industry," The Energy Journal 40(4): 129-163.
  11. Debia, Sébastien, Pierre-Olivier Pineau, and Afzal S. Siddiqui (2019), "Strategic Use of Storage: The Impact of Carbon Policy, Resource Availability, and Technology Efficiency on a Renewable-Thermal Power System," Energy Economics 80: 100-120.
  12. Siddiqui, Afzal S., Makoto Tanaka, and Yihsu Chen (2019), "Sustainable Transmission Planning in Imperfectly Competitive Electricity Industries: Balancing Economic and Environmental Outcomes," European Journal of Operational Research 275(1): 208-223.
  13. Chen, Yihsu, Makoto Tanaka, and Afzal S. Siddiqui (2018), "Market Power with Tradable Performance-Based CO₂ Emission Standards in the Electricity Sector," The Energy Journal 39(6): 95-119.
  14. Virasjoki, Vilma, Afzal S. Siddiqui, Behnam Zakeri, and Ahti Salo (2018), "Market Power with Combined Heat and Power Production in the Nordic Energy System," IEEE Transactions on Power Systems 33(5): 5263 - 5275.
  15. Kauppinen, Lauri, Afzal S. Siddiqui, and Ahti Salo (2018), "Investing in Time-to-Build Projects With Uncertain Revenues and Costs: A Real Options Approach," IEEE Transactions on Engineering Management 65(3): 448 - 459.
  16. Reichenberg, Lina, Afzal S. Siddiqui, and Sonja Wogrin (2018), "Policy Implications of Downscaling the Time Dimension in Power System Planning Models to Represent Variability in Renewable Output," Energy 159: 870-877.
  17. Boonman, Hettie J. and Afzal S. Siddiqui (2017), "Capacity Optimization under Uncertainty: The Impact of Operational Time Lags," European Journal of Operational Research 262(2): 660-672.
  18. Višković, Verena, Yihsu Chen, and Afzal S. Siddiqui (2017), "Implications of the EU Emissions Trading System for the South-East Europe Regional Electricity Market," Energy Economics 65: 251-261.
  19. Rintamäki, Tuomas, Afzal S. Siddiqui, and Ahti Salo (2017), "Does Renewable Energy Generation Decrease the Volatility of Electricity Prices? An Analysis of Denmark and Germany," Energy Economics 62: 272-282.
  20. Rintamäki, Tuomas, Afzal S. Siddiqui, and Ahti Salo (2016), "How Much Is Enough? Optimal Support Payments in a Renewable-Rich Power System," Energy 117: 300-313.
  21. Maurovich-Horvat, Lajos, Bert De Reyck, Paula Rocha, and Afzal S. Siddiqui (2016), "Optimal Selection of Distributed Energy Resources under Uncertainty and Risk Aversion," IEEE Transactions on Engineering Management 63(4): 462-474.
  22. Virasjoki, Vilma, Paula Rocha, Afzal S. Siddiqui, and Ahti Salo (2016), "Market Impacts of Energy Storage in a Transmission-Constrained Power System," IEEE Transactions on Power Systems 31(5): 4108-4117.
  23. Rocha, Paula, Michal Kaut, and Afzal S. Siddiqui (2016), "Energy-Efficient Building Retrofits: An Assessment of Regulatory Proposals under Uncertainty," Energy 101: 278-287.
  24. Guo, Xiaojia, Alexandros Beskos, and Afzal S. Siddiqui (2016), "The Natural Hedge of a Gas-Fired Power Plant," Computational Management Science 13(1): 63-86.
  25. Maurovich-Horvat, Lajos, Paula Rocha, and Afzal S. Siddiqui (2016), "Optimal Operation of Combined Heat and Power under Uncertainty and Risk Aversion," Energy and Buildings 110: 415-425.
  26. Siddiqui, Afzal S., Makoto Tanaka, and Yihsu Chen (2016), "Are Targets for Renewable Portfolio Standards Too Low? The Impact of Market Structure on Energy Policy," European Journal of Operational Research 250(1): 328-341.
  27. Chronopoulos, Michail and Afzal S. Siddiqui (2015), "When is it Better to Wait for a New Version? Optimal Replacement of an Emerging Technology under Uncertainty," Annals of Operations Research 235(1): 177-201.
  28. Vilkkumaa, Eeva, Juuso Liesiö, Ahti Salo, and Afzal S. Siddiqui (2015), "Fostering Breakthrough Technologies How Do Optimal Funding Decisions Depend on Evaluation Accuracy?," Technological Forecasting and Social Change 96: 173-190.
  29. Maurovich-Horvat, Lajos, Trine K. Boomsma, and Afzal S. Siddiqui (2015), "Transmission and Wind Investment in a Deregulated Electricity Industry," IEEE Transactions on Power Systems 30(3): 1633-1643.
  30. Rocha, Paula, Afzal S. Siddiqui, and Michael Stadler (2015), "Improving Energy Efficiency via Smart Building Energy Management Systems: A Comparison with Policy Measures," Energy and Buildings 88: 203-213.
  31. Momber, Ilan, Afzal S. Siddiqui, Tomás Gómez, and Lennart Söder (2015), "Risk Averse Scheduling by a PEV Aggregator under Uncertainty," IEEE Transactions on Power Systems 30(2): 882-891.
  32. Chronopoulos, Michail, Derek Bunn, and Afzal S. Siddiqui (2014), "Optionality and Policymaking in Re-Transforming the British Power Market," Economics of Energy & Environmental Policy 3(2): 79-100.
  33. Chronopoulos, Michail, Bert De Reyck, and Afzal S. Siddiqui (2014), "Duopolistic Competition under Risk Aversion and Uncertainty," European Journal of Operational Research 236(2): 643-656.
  34. Chin, Dorea and Afzal S. Siddiqui (2014), "Capacity Expansion and Forward Contracting in a Duopolistic Power Sector," Computational Management Science 11(1-2): 57-86.
  35. Groissböck, Markus, Somayeh Heydari, Ana Mera, Eugenio Perea, Afzal S. Siddiqui, and Michael Stadler (2014), "Optimizing Building Energy Operations via Dynamic Zonal Temperature Settings," Journal of Energy Engineering 140(1): 04013008.
  36. Cardoso, Gonçalo, Michael Stadler, Afzal S. Siddiqui, Chris Marnay, Nicholas DeForest, Ana Barbosa-Póvoa, and Paulo Ferrão (2013), "Microgrid Reliability Modeling and Battery Scheduling using Stochastic Linear Programming," Electric Power Systems Research 103: 61-69.
  37. Chronopoulos, Michail, Bert De Reyck, and Afzal S. Siddiqui (2013), "The Value of Capacity Sizing under Risk Aversion and Operational Flexibility," IEEE Transactions on Engineering Management 60(2): 272-288.
  38. Marnay, Chris, Michael Stadler, Afzal S. Siddiqui, Nicholas DeForest, Jon Donadee, Prajesh Bhattacharya, and Judy Lai (2013), "Applications of Optimal Building Energy System Selection and Operation," Journal of Power and Energy 227(1): 82-93.
  39. Siddiqui, Afzal S. and Ryuta Takashima (2012), "Capacity Switching Options under Rivalry and Uncertainty," European Journal of Operational Research 222(3): 583-595.
  40. Stadler, Michael, Chris Marnay, Maximilian Kloess, Gonçalo Cardoso, Gonçalo Mendes, Afzal S. Siddiqui, Ratnesh Sharma, Olivier Mégel, and Judy Lai (2012), "Optimal Planning and Operation of Smart Grids with Electric Vehicle Interconnection," Journal of Energy Engineering 138(2): 95-108.
  41. Heydari, Somayeh, Nick Ovenden, and Afzal S. Siddiqui (2012), "Real Options Analysis of Investment in Carbon Capture and Sequestration Technology," Computational Management Science 9(1): 109-138.
  42. Fleten, Stein-Erik, Ane Marte Heggedal, and Afzal S. Siddiqui (2011), "Transmission Capacity between Norway and Germany a Real Options Analysis," Journal of Energy Markets 4(1): 121-147.
  43. Coville, Aidan, Afzal S. Siddiqui, and Klaus-Ole Vogstad (2011), "The Effect of Missing Data on Wind Resource Estimation," Energy 36(7): 4505-4517.
  44. Chronopoulos, Michail, Bert De Reyck, and Afzal S. Siddiqui (2011), "Optimal Investment under Operational Flexibility, Risk Aversion, and Uncertainty," European Journal of Operational Research 213(1): 221-237.
  45. Stadler, Michael, Afzal S. Siddiqui, Chris Marnay, Hirohisa Aki, and Judy Lai (2011), "Control of Greenhouse Gas Emissions by Optimal DER Technology Investment and Energy Management in Zero-Net-Energy Buildings," European Transactions on Electrical Power 21(2): 1291-1309.
  46. Siddiqui, Afzal S. and Stein-Erik Fleten (2010), "How to Proceed with Competing Alternative Energy Technologies: a Real Options Analysis," Energy Economics 32(4): 817-830.
  47. Heydari, Somayeh and Afzal S. Siddiqui (2010), "Valuing a Gas-Fired Power Plant: a Comparison of Ordinary Linear Models, Regime-Switching Approaches, and Models with Stochastic Volatility," Energy Economics 32(3): 709-725.
  48. Siddiqui, Afzal S. and Karl M. Maribu (2009), "Investment and Upgrade in Distributed Generation under Uncertainty," Energy Economics 31(1): 25-37.
  49. Siddiqui, Afzal S. and Chris Marnay (2008), "Distributed Generation Investment by a Microgrid under Uncertainty," Energy 33(12): 1729-1737.
  50. Marnay, Chris, Giri Venkatarmanan, Michael Stadler, Afzal S. Siddiqui, Ryan M. Firestone, and Bala Chandran (2008), "Optimal Technology Selection and Operation of Commercial-Building Microgrids," IEEE Transactions on Power Systems 23(3): 975-982.
  51. Siddiqui, Afzal S. and Chris Marnay (2007), "Operation of Distributed Generation under Stochastic Prices," Pacific Journal of Optimization 3(3): 439-458.
  52. Maribu, Karl M., Ryan M. Firestone, Chris Marnay, and Afzal S. Siddiqui (2007), "Distributed Energy Resources Market Diffusion Model," Energy Policy 35(9): 4471-4484.
  53. Siddiqui, Afzal S., Chris Marnay, Ryan M. Firestone, and Nan Zhou (2007), "Distributed Generation with Heat Recovery and Storage," Journal of Energy Engineering 133(3): 181-210.
  54. Illindala, Mahesh, Afzal S. Siddiqui, Giri Venkataramanan, and Chris Marnay (2007), "Localized Aggregation of Diverse Energy Sources for Rural Electrification Using Microgrids," Journal of Energy Engineering 133(3): 121-131.
  55. Siddiqui, Afzal S., Chris Marnay, and Ryan H. Wiser (2007), "Real Options Valuation of US Federal Energy Efficiency and Renewable Energy Research, Development, Demonstration and Deployment," Energy Policy 35(1): 265-279.
  56. Siddiqui, Afzal S., Emily Bartholomew, and Chris Marnay (2005), "Empirical Analysis of the Spot Market Implications of Price-Responsive Demand," Energy Studies Review 14(1): 136-155.
  57. Siddiqui, Afzal S., Emily Bartholomew, Chris Marnay, and Shmuel S. Oren (2005), "Efficiency of the New York Independent System Operator Market for Transmission Congestion Contracts," Managerial Finance 31(6): 1-45.
  58. Siddiqui, Afzal S., Chris Marnay, Jennifer L. Edwards, Ryan M. Firestone, Srijay Ghosh, and Michael Stadler (2005), "Effects of Carbon Tax on Combined Heat and Power Adoption by a Microgrid," Journal of Energy Engineering 131(1): 2-25.
  59. Siddiqui, Afzal S., Chris Marnay, Owen Bailey, and Kristina Hamachi LaCommare (2005), "Optimal Selection of On-Site Power Generation with Combined Heat and Power Applications," International Journal of Distributed Energy Resources 1(1): 33-62.
  60. Bartholomew, Emily, Afzal S. Siddiqui, Chris Marnay, and Shmuel S. Oren (2003), "The New York Transmission Congestion Contract Market: Is It Truly Working Efficiently?The Electricity Journal 16(9): 14-24.
  61. Siddiqui, Afzal S. (2003), "Managing Electricity Reliability Risk Through the Forward Markets," Networks and Spatial Economics 3(2): 225-263.
  62. Siddiqui, Afzal S., Chris Marnay, and Mark Khavkin (2000), "Excessive Price Volatility in the California Ancillary Services Markets: Causes, Effects, and Solutions," The Electricity Journal 13(6): 58-68.

Books, Book Chapters, and Conference Proceedings

  1. Chen, Yihsu, Afzal S. Siddiqui, and Makoto Tanaka (2020), Analysis of Environmental Policy in the Power Sector: Equilibrium Methods and Bi-Level Modeling, Springer International Publishing, ISBN: 978-3-030-44865-3.
  2. Siddiqui, Afzal S. and R. Takashima (2017), "Bouncing Back: Assessing the Resilience of Infrastructure Projects and the Use of Average Outage Factors," in 21st Annual International Real Options Conference, Boston, MA, USA (29 June-1 July 2017).
  3. Conejo, Antonio J., Luis Baringo, S. Jalal Kazempour, and Afzal S. Siddiqui (2016), Investment in Electricity Generation and Transmission: Decision Making under Uncertainty, Springer International Publishing, ISBN: 9783319294995.
  4. Chen, Yihsu, Makoto Tanaka, and Afzal S. Siddiqui, "Tradable Performance-Based CO₂ Emissions Standards: Walking on Thin Ice?," in 39th International Conference Proceedings of the International Association for Energy Economics, Bergen, Norway (19-22 June 2016).
  5. Maurovich-Horvat, Lajos, Trine K. Boomsma, Stein-Erik Fleten, and Afzal S. Siddiqui, "Transmission and Wind Investment in a Deregulated Electricity Industry," in 10th International Conference on the European Energy Market, Stockholm, Sweden (27-31 May 2013).
  6. Takashima, Ryuta, Afzal S. Siddiqui, and Shoji Nakada, "Investment Timing, Capacity Sizing, and Technology Choice of Power Plants," in Handbook of Networks in Power Systems (A. Sorokin, S. Rebennack, N. Iliadis, M. Pereira, and P. Pardalos, eds.), Springer (2012): 303-322.
  7. Siddiqui, Afzal S., Michael Stadler, Chris Marnay, and Judy Lai, "Optimal Control of Distributed Energy Resources and Demand Response under Uncertainty," in 33rd International Conference Proceedings of the International Association for Energy Economics, Rio de Janeiro, Brazil (6-9 June 2010).
  8. Heydari, Somayeh and Afzal S. Siddiqui, "Real Options Analysis of Multiple-Exercise Interruptible Load Contracts," in 5th Conference on Energy Economics and Technology, Dresden, Germany (16 April 2010).
  9. Marnay, Chris, Judy Lai, Michael Stadler, and Afzal S. Siddiqui, "Added Value of Reliability to a Microgrid: Simulations of Three California Buildings," in Cigré-PES Symposium on Integration of Wide-Scale Renewable Resources into the Power Delivery System General Meeting PES, Calgary, AB, Canada (29-31 July 2009).
  10. Chronopoulos, Michail, Bert De Reyck, and Afzal S. Siddiqui, "Optimal Investment and Operational Decision-Making under Risk Aversion and Uncertainty," in INFORMS MSOM and SIG Meetings, Cambridge, MA, USA (28-30 June 2009).
  11. Coville, Aidan, Afzal S. Siddiqui, and Klaus-Ole Vogstad, "The Effect of Missing Data on Wind Resource Estimation," in 32nd International Conference Proceedings of the International Association for Energy Economics, San Francisco, CA, USA (21-24 June 2009).
  12. Stadler, Michael, Chris Marnay, Afzal S. Siddiqui, Judy Lai, and Hirohisa Aki, "Integrated Building Energy Systems Design Considering Storage Technologies," in Proceedings of the 2009 European Council for an Energy Efficient Economy (ECEEE) Summer Study, paper #7301, La Colle sur Loup, France (1-6 June 2009).
  13. Stadler, Michael, Hirohisa Aki, Ryan Firestone, Judy Lai, Chris Marnay, and Afzal S. Siddiqui, "Distributed Energy Resources On-Site Optimization for Commercial Buildings with Electric and Thermal Storage Technologies," in Proceedings of the 2008 American Council for an Energy Efficient Economy Summer Study, Pacific Grove, CA, USA (17-22 August 2008).
  14. Siddiqui, Afzal S. and Stein-Erik Fleten, "How to Proceed with the Thorium Nuclear Technology: a Real Options Analysis," in 3rd Conference on Energy Economics and Technology, Dresden, Germany (11 April 2008).
  15. Siddiqui, Afzal S., Emily Bartholomew, Chris Marnay, and Shmuel S. Oren, "Risk Aversion and the New York Independent System Operators Market for Transmission Congestion Contracts," in 6th European Conference Proceedings of the International Association for Energy Economics, Zurich, Switzerland (2-3 September 2004).
  16. Wiser, Ryan, Steven Pickle, and Afzal S. Siddiqui, "The California Power Sector," in Principles of Electricity Economics: Regulation and Deregulation (T. Gómez and G. Rothwell, eds.), Wiley-IEEE Press (2003): 129-160.
  17. Marnay, Chris, Kristina Hamachi, Mark Khavkin, and Afzal S. Siddiqui, "Direct Participation of Electrical Loads in the California Independent System Operator Markets During the Summer of 2000," in Proceedings of the 2001 European Council for an Energy Efficient Economy (ECEEE) Summer Study, paper #5162, Mandelieu, France (11-16 June 2001).
  18. Siddiqui, Afzal S., Chris Marnay, Kristina Hamachi, and F. Javier Rubio, "Customer Adoption of Small-Scale On-Site Power Generation," in Proceedings of the 2001 European Council for an Energy Efficient Economy (ECEEE) Summer Study, paper #2163, Mandelieu, France (11-16 June 2001).
  19. Siddiqui, Afzal S., Chris Marnay, and Mark Khavkin, "Spot Pricing of Electricity and Ancillary Services in a Competitive California Market," in Proceedings of the 34th Hawaii International Conference on Systems Sciences, Maui, HI, USA (3-6 January 2001).
  20. Siddiqui, Afzal S., Chris Marnay, and Mark Khavkin, "Competitive Ancillary Service Procurement in California," in Energieinnovation im liberalisierten markt (K. Friedrich and W. Wallner, eds.), Graz, Austria (1-2 February 2000).

Technical Reports

  1. Siddiqui, Afzal S. and Chris Marnay, "Addressing an Uncertain Future Using Scenario Analysis," Lawrence Berkeley National Laboratory Report, Berkeley, CA, USA (January 2007).
  2. Siddiqui, Afzal S., Equilibrium Analysis of Forward Markets for Electricity and Reserves, PhD thesis, Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA, USA (May 2002).
  3. Edwards, Jennifer L., Chris Marnay, Emily Bartholomew, Boubékeur Ouaglal, Afzal S. Siddiqui, and Kristina LaCommare, "Assessment of mGrid Distributed Energy Resource Potential Using DER-CAM and GIS", Lawrence Berkeley National Laboratory Report LBNL-50132, Berkeley, CA, USA (January 2002).
  4. Siddiqui, Afzal S., Anibal de Almeida, Chris Marnay, and F. Javier Rubio, "The Implications of Carbon Taxation on Microgrid Adoption of Small-Scale On-Site Power Generation Using a Multi-Criteria Approach," Lawrence Berkeley National Laboratory Report LBNL-49309, Berkeley, CA, USA (December 2001).
  5. Marnay, Chris, Joseph Chard, Kristina Hamachi, Timothy Lipman, Mithra Moezzi, Boubékeur Ouaglal, and Afzal S. Siddiqui, "Modeling of Customer Adoption of Distributed Energy Resources," Lawrence Berkeley National Laboratory Report LBNL-49582, Berkeley, CA, USA (August 2001).
  6. Rubio, F. Javier, Afzal S. Siddiqui, Chris Marnay, and Kristina Hamachi, "CERTS Customer Adoption Model," Lawrence Berkeley National Laboratory Report LBNL-47772, Berkeley, CA, USA (March 2001).
  7. Siddiqui, Afzal S., Chris Marnay and Mark Khavkin, "Design of a CERTS Database," Lawrence Berkeley National Laboratory Report LBNL-45821, Berkeley, CA, USA (April 2000).
  8. Gómez, Tomás, Chris Marnay, Afzal S. Siddiqui, Lucy Liew, and Mark Khavkin, "Ancillary Services Markets in California," Lawrence Berkeley National Laboratory Report LBNL-43986, Berkeley, CA, USA (July 1999).

Editorials, Letters, and Other Non-Refereed Publications

  1. Siddiqui, Afzal S. (2021), "The Real Lesson of Sweden’s Laissez-Faire COVID-19 Response," IEEE Spectrum, Tech Talk, 26 February.
  2. Siddiqui, Afzal S., Ramteen Sioshansi, and Antonio J. Conejo (2018), "Merchant Storage Investment in a Restructured Electricity Industry," IAEE Energy Forum, Special Issue: 15-16.
  3. Fleten, Stein-Erik, Daniel Kuhn, and Afzal S. Siddiqui (2016), "Computational Management Science Special Issue on Optimisation Methods and Applications in the Energy Sector," Computational Management Science 13(1): 1-3.
  4. Siddiqui, Afzal S. (2015), "Smiling Buddha," Foreign Affairs, November/December issue.
  5. Groissböck, Markus, Emilio López, Eugenio Perea, Afzal S. Siddiqui, and Adrian Werner (2013), "Improving Energy Efficiency and Risk Management in EU Public Buildings," IAEE Energy Forum, Second Quarter: 1720.
  6. Green, Richard J., Benjamin F. Hobbs, Shmuel S. Oren, and Afzal S. Siddiqui (2010), "Policymaking Benefits and Limitations from Using Financial Methods and Modelling in Electricity Markets," Energy Economics 32(4): 749-750.
  7. Siddiqui, Afzal S. and Chung-Li Tseng (2007), "E-Business Applications in the Energy Sector," International Journal of Electronic Business Management 5(4): 319.


A selection from Stockholm University publication database
  • 2020. Jose M. Gonzalez (et al.). Applied Energy 269

    The design of water and energy systems has traditionally been done independently or considering simplified interdependencies between the two systems. This potentially misses valuable synergies between them and does not consider in detail the distribution of benefits between different sectors or regions. This paper presents a framework to couple integrated water-power network simulators with multi-objective optimisation under uncertainty to explore the implications of explicitly including spatial topology and interdependencies in the design of multi-sector integrated systems. A synthetic case study that incorporates sectoral dependencies in resource allocation, operation of multi-purpose reservoirs and spatially distributed infrastructure selection in both systems is used. The importance of explicitly modelling the distribution of benefits across different sectors and regions is explored by comparing different spatially aggregated and disaggregated multi-objective optimisation formulations. The results show the disaggregated formulation identifies a diverse set of non-dominated portfolios that enables addressing the spatial and sectoral distribution of benefits, whilst the aggregated formulations arbitrarily induce unintended biases. The proposed disaggregated approach allows for detailed spatial design of interlinked water and energy systems considering their complex regional and sectoral trade-offs. The framework is intended to assist planners in real resource systems where diverse stakeholder groups are mindful of receiving their fair share of development benefits.

  • 2020. Tuomas Rintamäki, Afzal S. Siddiqui, Ahti Salo. European Journal of Operational Research 284 (3), 1136-1153

    The increase in intraday electricity market volumes due to intermittent renewable generation may give a strategic producer an opportunity to exert market power. We study offering strategies of a flexible producer in day-ahead and intraday markets using a bi-level model in which the upper level represents the profit-maximization problem of the producer and the lower-level problems clear the day-ahead and intraday markets sequentially. Using a three-node network, we first demonstrate that a flexible producer with perfect forecasts can increase its profit in both markets by coordinating its offer so as to cause transmission grid congestion or lack of competitive generation capacity. Moreover, we show that strategic behavior is possible even when the day-ahead and intraday markets are cleared simultaneously to lower balancing costs. We next assess these market designs in a Nordic test network and offer an explanation for high Nordic intraday prices. Finally, via an annual simulation using the Nordic market data, we verify that strategic offering in day-ahead and intraday markets under imperfect forecasts leads to increased profits vis-a-vis perfect competition but are mitigated through simultaneous market clearing.

  • 2020. Vilma Virasjoki (et al.). Energy Economics 88

    Interest in sustainabiity has increased the share of variable renewable energy sources (VRES) in power generation. Energy storage systems' potential to mitigate intermittencies from non-dispatchable VRES has enhanced their appeal. However, the impacts of storage vary based on the owner and market conditions. We examine the policy implications of investments in utility-scale battery storage via a bi-level optimization model. The lower level depicts power system operations, modeled as either perfect competition or Coumot oligopoly to allow for the assessment of producer market power. The upper-level investor is either a welfare-maximizer or a profit-maximizing standalone merchant to reflect either welfare enhancement or arbitrage, respectively. We implement a realistic case study for Western Europe based on all possible size-location storage investment combinations. We find that market competition affects investment sizes, locations, and their profitability more than the investor's objectives. A welfare-maximizer under perfect competition invests the most in storage capacity. Consumers typically gain most from storage investments in all cases, exceeding the gains for the investors. Specifically, our results show that storage investments may either not occur or be located differently than at social optimum, if market power is exerted. Thus, policy makers need to anticipate producer market power when setting regulation.

  • 2019. Diego A. Tejada-Arango (et al.).

    Purpose of Review

    This paper focuses on the current possibilities for energy storage systems (ESS) to participate in different power system services. ESS can provide multiple services such as spinning reserve, deferral upgrades, and energy management. However, this versatility of ESS poses a challenge for regulators in designing markets where ESS have prominent roles. We assess recent regulatory proposals in the US and the EU in order to understand their implications for ESS.

    Recent Findings

    These proposals attempt to improve the current rules for efficient ESS deployment. Nevertheless, they have different approaches to the same problem. We discuss these differences in an attempt to shed light on the regulatory debate about ESS ownership and market design.


    The successful integration of ESS will depend on proper incentives to provide multiple services without hampering the current market structure. New asset definitions could help to define the roles of ESS as either a generation or a transmission asset.

  • 2019. Afzal Siddiqui, Ramteen Sioshansi, Antonio J. Conejo. Energy Journal 40 (4), 129-163

    Restructuring and liberalisation of the electricity industry creates opportunities for investment in energy storage, which could be undertaken by a profit-maximising merchant storage operator. Because such a firm is concerned solely with maximising its own profit, the resulting storage-investment decision may be socially suboptimal (or detrimental). This paper develops a bi-level model of an imperfectly competitive electricity market. The modelling framework assumes electricity-generation and storage-operations decisions at the lower level and storage investment at the upper level. Our analytical results demonstrate that a relatively high (low) amount of market power in the generation sector leads to low (high) storage-capacity investment by the profit-maximising storage operator relative to a welfare maximiser. This can result in net social welfare losses with a profit-maximising storage operator compared to a no-storage case. Moreover, there are guaranteed to be net social welfare losses with a profit-maximising storage operator if the generation sector is sufficiently competitive. Using a charge on generation ramping between off- and on-peak periods, we induce the profit-maximising storage operator to invest in the same level of storage capacity as the welfare-maximising firm. Such a ramping charge can increase social welfare above the levels that are attained with a welfare-maximising storage operator.

  • 2019. Diego A. Tejada-Arango (et al.). Energy 188

    Short-term energy storage systems, e.g., batteries, are becoming one promising option to deal with flexibility requirements in power systems due to the accommodation of renewable energy sources. Previous works using medium- and long-term planning tools have modeled the interaction between short-term energy storage systems and seasonal storage (e.g., hydro reservoirs) but despite these developments, opportunity costs considering the impact of short-term energy storage systems in stochastic hydrothermal dispatch models have not been analyzed. This paper proposes a novel formulation to include short-term energy storage systems operational decisions in a stochastic hydrothermal dispatch model, which is based on a Linked Representative Periods approach. The Linked Representative Periods approach disposes of both intra- and inter-period storage constraints, which in turn allow to adequately represent both short- and long-term storage at the same time. Apart from the novelty of the model formulation itself, one of the main contributions of this research stems from the underlying economic information that can be extracted from the dual variables of the intra- and inter-period constraints, which allows to derive an hourly opportunity cost of storage. Such a detailed hourly economic value of storage has not been proposed before in the literature and is not possible in a classic Load Duration Curve model that does not adequately capture short-term operation. This advantage is reflected in the case study results. For instance, the model proposed in this paper and based on Linked Representative Periods obtains operating decisions of short-term energy storage systems with errors between 5% and 10%, while the classic Load Duration Curve approach fails by an error greater than 100%. Moreover, the Load Duration Curve model cannot determine opportunity costs on an hourly basis and underestimates these opportunity costs of hydro (also known as water value) by 6%–24% for seasonal hydro reservoirs. The proposed Linked Representative Periods model produces an error on the opportunity cost of hydro units lower than 3%. Hourly opportunity costs for short-term battery energy storage systems using dual variables from both intra- and inter-period storage balance equations in the proposed model are also presented and analyzed. The case study shows that the proposed approach successfully internalizes both short- and long-term opportunity costs of energy storage systems. These results are useful for planning and policy analysis, as well as for bidding strategies of ESS owners in day-ahead markets and not taking them into account may lead to infeasible operation or to suboptimal planning.

  • 2019. Verena Višković (et al.). Energy Policy 134

    Interconnected regional electricity markets are often subject to asymmetric carbon policies with partial coverage for CO2 emissions. While the resulting problem of carbon leakage has been well studied, its mitigation has received relatively less attention. We devise a proactive carbon policy via a bi-level modelling approach by considering the impact of an emission cap that limits the cost of damage from a regional power market. In particular, a welfare-maximising policymaker sets the cap when facing profit-maximising producers and the damage costs from their emissions at two nodes. A partial-coverage policy could degrade maximised social welfare and increase total regional CO2 emissions with potential for carbon leakage due to a higher nodal price difference. A modified carbon policy that considers CO2 emissions from both nodes tightens the cap, which increases maximised social welfare and decreases total CO2 emissions vis-a-vis the partial-coverage policy, albeit at the cost of greater scope for carbon leakage as it causes nodal prices to diverge. As a compromise, an import-coverage policy, implemented by California, that counts only domestic and imported CO2 emissions could alleviate carbon leakage at the cost of lower maximised social welfare with higher total emissions vis-a-vis the modified-coverage policy.

  • 2019. Sébastien Debia, Pierre-Olivier Pineau, Afzal S. Siddiqui. Energy Economics 80, 100-122

    Concerns about climate change have spurred governments to reduce carbon emissions by supporting adoption of renewable energy (RE) technologies. Due to the intermittent and location-specific nature of RE technologies, energy storage has become important because it could be used to smooth out temporal disparities in residual demand. Thus, carbon policy has made storage-enabled RE generation more critical to the power sector, and this enhanced position could be exploited by firms to exert market power. Using an equilibrium model, we examine the implications of policy interventions and technological change on the marginal value of energy storage in a power market with RE and thermal generation. In particular, we specify the market conditions under which RE producers with storage strategically shift deployment of their resource to the off-peak period and outline its implications for the marginal value of RE storage. Moreover, we find that even price-taking RE producers may actually increase off-peak RE production as storage efficiency increases. Consequently, the RE producer's profit decreases with storage efficiency, which conflicts with the social objective of improving storage efficiency. These private and social incentives can be better aligned via a carbon tax, however. Hence, our results may inform the regulatory process governing market design of a power sector with increasing capacities of RE generation and storage.

  • 2019. Afzal S. Siddiqui, Makoto Tanaka, Yihsu Chen. European Journal of Operational Research 275 (1), 208-223

    We explore the role of a transmission system operator (TSO) that builds a transmission line to accommodate renewable energy in order to lower emissions as required by government policy. In contrast to central planning, a TSO in a deregulated electricity industry can only indirectly influence outcomes through its choice of the transmission line capacity. Via a bi-level model, we show that this results in less transmission capacity and with limited emissions control in a perfectly competitive industry vis-a-vis a benchmark centrally planned system. A carbon charge on industry that fully accounts for the cost of pollution damage leads to a perfect alignment of incentives and maximised social welfare only under perfect competition. By contrast, a carbon charge may actually lower social welfare under a Cournot oligopoly as the resulting reduction in consumption facilitates the further exercise of market power.

  • 2018. Lauri Kauppinen, Afzal S. Siddiqui, Ahti Salo. IEEE transactions on engineering management 65 (3), 448-459

    Lagging public-sector investment in infrastructure and the deregulation of many industries mean that the private sector has to make decisions under multiple sources of uncertainty. We analyze such investment decisions by accounting for both multiple sources of uncertainty and the time-to-build aspect. The latter feature arises in the energy and transportation sectors, because investors can decide the rate at which the project is completed. Furthermore, two explicit sources of uncertainty represent the discounted cash inflows and outflows of the completed project. We use a finite-difference scheme to solve numerically the option value and the optimal investment threshold. Somewhat counterintuitively, with a relatively long time to build, a reduction in the growth rate of the discounted operating cost may actually lower the investment threshold. This is contrary to the outcome when the stepwise aspect is ignored in a model with uncertain price and cost. Hence, research and development efforts to enhance emerging technologies may be more relevant for infrastructure projects with long lead times.

  • 2018. Vilma Virasjoki (et al.). IEEE Transactions on Power Systems 33 (5), 5263-5275

    The trend toward increasing energy efficiency and variable renewable energy (VRE) production has implications for combined heat and power (CHP) plants, which operate in both the price-driven power market and the district heating (DH) sector. Since CHP will be important in VRE integration, we develop a complementarity model to analyze CHP producers' roles in integrated markets. We use a Nordic case study to gain insights into (i) the effect of the link between CHP and DH on market power and (ii) market power's impact on operations in the DH sector. The results indicate that (i) the link of CHP to DH supply can increase market power and (ii) market power can induce shifts in DH production from heat-only to CHP.

  • 2018. Yihsu Chen, Makoto Tanaka, Afzal S. Siddiqui. Energy Journal 39 (6), 121-145

    The U.S. Clean Power Plan stipulates a state-specific performance-based CO2 emission standard, delegating states with considerable flexibility for using either a tradable performance-based or a mass-based permit program. This paper analyzes these two standards under imperfect competitive. We limit our attention to (1) short-run analyses and (2) a situation in which all states are subject to the same type of standard. We show that while the cross-subsidy inherent in the performance-based standard might effectively reduce power prices, it could also inflate energy consumption. A dominant firm with a relatively clean endowment under the performance-based standard would be able to manipulate the electricity market as well as to elevate permit prices, which might worsen market outcomes compared to its mass-based counterpart. On the other hand, the "cross-subsidy" could be the dominant force leading to a higher social welfare if the leader has a relatively dirty endowment.

  • 2018. Lina Reichenberg, Afzal S. Siddiqui, Sonja Wogrin. Energy 159, 870-877

    Due to computational constraints, power system planning models are typically unable to incorporate full annual temporal resolution. In order to represent the increased variability induced by large amounts of variable renewable energy sources, two methods are investigated to reduce the time dimension: the integral approach (using typical hours based on demand and renewable output) and the representative days method (using typical days to capture annual variability). These two approaches are tested with a benchmark implementation that incorporates full time representation in order identify their suitability for assessing power systems with high renewable penetration. The integral method predicts renewable capacities within a 10% error margin, this paper's main performance metric, using just 32 time steps, while the representative days approach needs 160–200 time steps before providing similarly accurate renewable capacity estimates. Since the integral method generally cannot handle variation management, such as trade and storage, without enhancing the state-space representation, it may be more applicable to one-node models, while the representative days method is suitable for multi-regional models. In order to assess power systems with increasing renewable policy targets, models should be designed to handle at least the 160 time steps needed to provide results that do not systematically overestimate the renewable capacity share.

  • Conference Bouncing Back
    2017. Afzal S. Siddiqui, Ryuta Takashima. Proceedings of the 21st Annual International Real Options Conference

    Motivated by the need to bolster the resilience of infrastructure, such as bridges, nuclear power plants, and ports, in face of extreme weather events, we consider two types of projects: “riskier” and “safer.” Each type of project, once constructed, earns identical instantaneous cash flows and is subject to the same risk of outage, which causes its cash flows to diminish. The only difference between the two projects is that the repair rate of the “safer” project is greater than that of the “riskier” one. Naturally, the “safer” project is more valuable to an investor due to its greater resilience. However, how much extra would an investor be willing to pay for this resilience? Under which circumstances would it be reasonable to replace the outage and repair rates with average outage factors? Using a real options approach, we show that even though the proportions of up- and down-times remain fixed, changes in the transition rates affect the willingness to pay for resilience. This implies that use of average outage factors will incorrectly inflate the resilience premium. In fact, only in the limit when transitions occur at infinitely high rates does the use of average outage factors accurately reflect the investor’s willingness to pay for resilience. Somewhat paradoxically, very frequent transitions reflect a situation in which average outage factors may be used.

  • 2017. Hettie J. Boonman, Afzal S. Siddiqui. European Journal of Operational Research 262 (2), 660-672

    Time lags in switching operational modes are typical in the manufacturing and power sectors but are not treated in most real options models. In this paper, we consider a firm that has the opportunity to suspend and to resume production infinitely many times subject to a time lag after each startup decision. We contribute to the literature by allowing the firm to determine its level of installed capacity in conjunction with its optimal investment timing. We find that an increase in the length of the time lag results in an increase in the optimal capacity level. Capacity optimization also interacts with the length of the time lag to affect investment timing and the triggers to suspend and resume production, thereby weakening the result about hysteresis from a standard real options model. Under the assumption of a fixed level of capacity, a longer lag speeds up the decision to resume operations due to a positive upside to the revenue but delays the suspension of operations. By contrast, with capacity optimization, a longer time lag results in a larger capacity choice, which can indirectly delay the investment decision and the timing to resume operations. This indirect effect dominates when the level of market uncertainty is low and the time lag is initially small.

  • 2017. Tuomas Rintamäki, Afzal S. Siddiqui, Ahti Salo. Energy Economics 62, 270-282

    Although variable renewable energy (VRE) technologies with zero marginal costs decrease electricity prices, the literature is inconclusive about how the resulting shift in the supply curves impacts price volatility. Because the flexibility to respond to high peak and low off-peak prices is crucial for demand-response applications and may compensate for the losses of conventional generators caused by lower average prices, there is a need to understand how the penetration of VRE affects volatility. In this paper, we build distributed lag models with Danish and German data to estimate the impact of VRE generation on electricity price volatility. We find that in Denmark wind power decreases the daily volatility of prices by flattening the hourly price profile, but in Germany it increases the volatility because it has a stronger impact on off-peak prices. Our analysis suggests that access to flexible generation capacity and wind power generation patterns contribute to these differing impacts. Meanwhile, solar power decreases price volatility in Germany. By contrast, the weekly volatility of prices increases in both areas due to the intermittency of VRE. Thus, policy measures for facilitating the integration of VRE should be tailored to such region-specific patterns.

  • 2017. Verena Višković, Yihsu Chen, Afzal S. Siddiqui. Energy Economics 65, 251-261

    As part of its climate policy, the European Union (EU) aims to reduce greenhouse gas (GHG) emissions levels by 20% by the year 2020 compared to 1990 levels. Although the EU is projected to reach this goal, its achievement of objectives under its Emissions Trading System (ETS) may be delayed by carbon leakage, which is defined as a situation in which the reduction in emissions in the ETS region is partially offset by an increase in carbon emissions in the non-ETS regions. We study the interaction between emissions and hydropower availability in order to estimate the magnitude of carbon leakage in the South-East Europe Regional Electricity Market (SEE-REM) via a bottom-up partial equilibrium framework. We find that 6.3% to 40.5% of the emissions reduction achieved in the ETS part of SEE-REM could be leaked to the non-ETS part depending on the price of allowances. Somewhat surprisingly, greater hydropower availability may increase emissions in the ETS part of SEE-REM. However, carbon leakage might be limited by demand response to higher electricity prices in the non-ETS area of SEE-REM. Such carbon leakage can affect both the competitiveness of producers in ETS member countries on the periphery of the ETS and the achievement of EU targets for CO2 emissions reduction. Meanwhile, higher non-ETS electricity prices imply that the current policy can have undesirable outcomes for consumers in non-ETS countries, while non-ETS producers would experience an increase in their profits due to higher power prices as well as exports. The presence of carbon leakage in SEE-REM suggests that current EU policy might become more effective when it is expanded to cover more countries in the future.

  • 2016. Afzal S. Siddiqui, Makoto Tanaka, Yihsu Chen. European Journal of Operational Research 250 (1), 328-341

    In order to limit climate change from greenhouse gas emissions, governments have introduced renewable portfolio standards (RPS) to incentivise renewable energy production. While the response of industry to exogenous RPS targets has been addressed in the literature, setting RPS targets from a policymaker's perspective has remained an open question. Using a bi-level model, we prove that the optimal RPS target for a perfectly competitive electricity industry is higher than that for a benchmark centrally planned one. Allowing for market power by the non-renewable energy sector within a deregulated industry lowers the RPS target vis-a-vis perfect competition. Moreover, to our surprise, social welfare under perfect competition with RPS is lower than that when the non-renewable energy sector exercises market power. In effect, by subsidising renewable energy and taxing the non-renewable sector, RPS represents an economic distortion that over-compensates damage from emissions. Thus, perfect competition with RPS results in "too much" renewable energy output, whereas the market power of the non-renewable energy sector mitigates this distortion, albeit at the cost of lower consumer surplus and higher emissions. Hence, ignoring the interaction between RPS requirements and the market structure could lead to sub-optimal RPS targets and substantial welfare losses.

  • 2016. Paula Rocha, Michal Kaut, Afzal S. Siddiqui. Energy 101, 278-287

    Improving energy efficiency in European Union buildings will require retrofitting much of the existing stock due to limited new construction opportunities. Given uncertainty in energy prices and technology costs stemming from deregulation, a stochastic optimisation framework is desirable for long-term decision support. We synthesise treatment of uncertainty and risk management to obtain insights about the impact of retrofits on energy consumption, costs, CO2 emissions, and risk at real buildings in Austria and Spain. The optimal strategy for the Spanish site is to invest in photovoltaic and solar thermal technologies. This lowers expected costs by 8.5% and reduces expected primary energy consumption and CO2 emissions by 20% relative to using existing equipment. By limiting exposure to volatile energy prices, the strategy also yields a nearly 10% reduction in risk. We obtain similar results also for the Austrian site. Via this framework, tradeoffs among competing objectives and the effectiveness of proposed regulation may be assessed. Specifically, we find that more stringent restrictions on energy efficiency are economically viable if regulation also facilitates enhanced operational decision support for buildings. Indeed, primary energy consumption can be lowered only through more on-site generation such as combined heat and power, which is more complex for building managers to deploy.

  • 2016. Tuomas Rintamäki, Afzal S. Siddiqui, Ahti Salo. Energy 117 (1), 300-313

    The large-scale deployment of intermittent renewable energy sources may cause substantial power imbalance. Together with the transmission grid congestion caused by the remoteness of these sources from load centers, this creates a need for fast-adjusting conventional capacity such as gas-fired plants. However, these plants have become unprofitable because of lower power prices due to the zero marginal costs of renewables. Consequently, policymakers are proposing new measures for mitigating balancing costs and securing supply. In this paper, we take the perspective of the regulator to assess the effectiveness of support payments to flexible generators. Using data on the German power system, we implement a bi-level programming model, which shows that such payments for gas-fired plants in southern Germany reduce balancing costs and can be used as part of policy to integrate renewable energy.

  • 2016. Antonio J. Conejo (et al.).
  • 2016. Vilma Virasjoki (et al.). IEEE Transactions on Power Systems 31 (5), 4108-4117

    Environmental concerns have motivated governments in the European Union and elsewhere to set ambitious targets for generation from renewable energy (RE) technologies and to offer subsidies for their adoption along with priority grid access. However, because RE technologies like solar and wind power are intermittent, their penetration places greater strain on existing conventional power plants that need to ramp up more often. In turn, energy storage technologies, e.g., pumped hydro storage or compressed air storage, are proposed to offset the intermittency of RE technologies and to facilitate their integration into the grid. We assess the economic and environmental consequences of storage via a complementarity model of a stylized Western European power system with market power, representation of the transmission grid, and uncertainty in RE output. Although storage helps to reduce congestion and ramping costs, it may actually increase greenhouse gas emissions from conventional power plants in a perfectly competitive setting. Conversely, strategic use of storage by producers renders it less effective at curbing both congestion and ramping costs, while having no net overall impact on emissions.

  • 2016. Lajos Maurovich-Horvat, Paula Rocha, Afzal S. Siddiqui. Energy and Buildings 110, 415-425

    Despite the proven benefits of combined heat and power (CHP) and recently introduced subsidies to support it, CHP adoption has not met its targets. One of the possible reasons for this is risk from uncertain electricity and gas prices. To gain insights into the risk management of a CHP unit, we develop a multi-stage stochastic mean-risk optimisation model for the medium-term management of a distributed generation system with a gas-fired microturbine with heat recovery and a boiler. The model adopts the perspective of a large consumer that procures gas (for on-site generation) and electricity (for consumption) on the spot and futures markets. The consumer's risk aversion is incorporated into the model through the conditional value-at-risk (CVaR) measure. We show that CHP not only decreases the consumer's expected cost and risk exposure by 10% each but also improves expected energy efficiency by 4 percentage points and decreases expected CO2 emissions by 16%. The risk exposure can be further mitigated through the use of financial contracts.

  • 2016. Lajos Maurovich-Horvat (et al.). IEEE transactions on engineering management 63 (4), 462-474

    The adoption of small-scale electricity generation has been hindered by uncertain electricity and gas prices. In order to overcome this barrier to investment, we develop a mean-risk optimization model for the long-term risk management problem of an energy consumer using stochastic programming. The consumer can invest in a number of generation technologies, and also has access to electricity and gas futures to reduce its risk. We examine the role of on-site generation in the consumer's riskmanagement strategy, as well as interactions between on-site generation and financial hedges. Our study shows that by swapping electricity (with high price volatility) for gas (with low price volatility), even relatively inefficient technologies reduce risk exposure and CO2 emissions. The capability of on-site generation is enhanced through the use of combined heat and power (CHP) applications. In essence, by investing in a CHP unit, a consumer obtains the option to use on-site generation whenever the electricity price peaks, thereby reducing its financial risk. Finally, in contrast to the extant literature, we demonstrate that on-site generation affects the consumer's decision to purchase financial hedges. In particular, while on-site generation and electricity futures may act as substitutes, on-site generation and gas futures can function as complements.

  • 2016. Xiaojia Guo, Alexandros Beskos, Afzal Siddiqui. Computational Management Science 13 (1), 63-86

    Electricity industries worldwide have been restructured in order to introduce competition. As a result, decision makers are exposed to volatile electricity prices, which are positively correlated with those of natural gas in markets with price-setting gas-fired power plants. Consequently, gas-fired plants are said to enjoy a “natural hedge.” We explore the properties of such a built-in hedge for a gas-fired power plant via a stochastic programming approach, which enables characterisation of uncertainty in both electricity and gas prices in deriving optimal hedging and generation decisions. The producer engages in financial hedging by signing forward contracts at the beginning of the month while anticipating uncertainty in spot prices. Using UK energy price data from 2006 to 2011 and daily aggregated dispatch decisions of a typical gas-fired power plant, we find that such a producer does, in fact, enjoy a natural hedge, i.e., it is better off facing uncertain spot prices rather than locking in its generation cost. However, the natural hedge is not a perfect hedge, i.e., even modest risk aversion makes it optimal to use gas forwards partially. Furthermore, greater operational flexibility enhances this natural hedge as generation decisions provide a countervailing response to uncertainty. Conversely, higher energy-conversion efficiency reduces the natural hedge by decreasing the importance of natural gas price volatility and, thus, its correlation with the electricity price.

  • 2016. Yihsu Chen, Makoto Tanaka, Afzal S. Siddiqui. Energy: Expectations and Uncertainty, 1-28

    Climate policy, like climate change itself, is subject to debate. Partially due to the political deadlock in Washington, DC, US climate policy, historically, has been driven mainly by state or regional effort until the recently introduced federal Clean Power Plan (CPP). Instead of a traditional mass-based standard, the US CPP stipulates a state-specific performance-based CO2 emission standard and delegates considerable flexibility to the states in achieving the standard. Typically, there are two sets of policy tools available: a tradable performance-based and a mass-based permit program. We analyze these two related but distinct standards when they are subject to imperfect competition in the product and/or permit markets. Stylized models are developed to produce general conclusions. Detailed models that account for heterogenous technologies and the transmission network are developed to evaluate policy efficiency. Depending on the scenarios under consideration, the resulting problem could be either a complementarity problem or a Stackelberg leaderfollower game, which is implemented as a mathematical program with equilibrium constraints (MPEC). We overcome the nonconvexity of MPECs by reformulating them as mixed integer problems. We show that while the cross-subsidy inherent in the performance-based standard that might effectively reduce power prices, it could inflate energy demand, thereby rendering permits scarce. When the leader in a Stackelberg formulation has a relatively clean endowment under the performancebased standard, its ability to manipulate the electricity market as well as to lower permit prices might worsen the market outcomes compared to its mass-based counterpart. On the other hand, when the leader has a relatively dirty endowment, the "cross-subsidy" could be the dominant force leading to a higher social welfare compared to the mass-based program. This paper contributes to the current policy debates in regulating emissions from the US power sector and highlights different incentives created by the mass- and performance-based standards.

  • 2015. Eeva Vilkkumaa (et al.). Technological forecasting & social change 96, 173-190

    There is a growing interest in fostering breakthrough technologies that offer exceptionally high value to society. However, when starting technology projects, it is impossible to know which of them have the potential to lead to breakthroughs. Therefore, organizations have adopted funding policies in which on-going projects are subjected to interim evaluations based on which some projects may be abandoned to release resources for seizing new opportunities. In this paper, we study which funding policies are optimal when the objective is either (i) to maximize the expected value of the project portfolio, or (ii) to maximize the expected number of exceptionally excellent projects that may lead to breakthrough technologies. We show that the optimal policy for funding exceptionally excellent projects is to start a large number of projects and abandon a high proportion of them later, whereas the optimal policy for maximizing the expected value of the project portfolio is to grant long-term funding to a smaller set of projects based on initial evaluation. Furthermore, we show how the trade-off between these two objectives depends on the initial project evaluation accuracy and the rate at which this accuracy improves. Our results suggest that this trade-off is particularly significant when the initial project evaluations are very uncertain but become more accurate soon after the projects have been launched. In such a setting, policies that seek to maximize the expected portfolio value may fail to promote breakthrough technologies.

  • 2015. Lajos Maurovich-Horvat, Trine Krogh Boomsma, Afzal Siddiqui. IEEE Transactions on Power Systems 30 (3), 1633-1643

    Adoption of dispersed renewable energy technologies requires transmission network expansion. Besides the transmission system operator (TSO), restructuring of electricity industries has introduced a merchant investor (MI), who earns congestion rents from constructing new lines. We compare these two market designs via a stochastic bi-level programming model that has either the MI or the TSO making transmission investment decisions at the upper level and power producers determining generation investment and operation at the lower level while facing wind power variability. We find that social welfare is always higher under the TSO because the MI has incentive to boost congestion rents by restricting capacities of transmission lines. Such strategic behavior also limits investment in wind power by producers. However, regardless of the market design (MI or TSO), when producers behave a la Cournot, a higher proportion of energy is produced by wind. In effect, withholding of generation capacity by producers prompts more transmission investment since the TSO aims to increase welfare by subsidizing wind and the MI creates more flow to maximize profit.

  • 2015. Michail Chronopoulos, Afzal Siddiqui. Annals of Operations Research 235 (1), 177-201

    Firms that use an emerging technology often face uncertainty in both the arrival of new versions and the revenue that may be earned from their deployment. Via a sequential decision-making framework, we determine the value of the investment opportunity and the optimal replacement rule under three different strategies: compulsive, laggard, and leapfrog. In the first one, a firm invests sequentially in every version that becomes available, whereas in the second and third ones, it first waits for a new version to arrive and then either invests in the older or the newer version, respectively. We show that, under a compulsive strategy, technological uncertainty has a non-monotonic impact on the optimal investment decision. In fact, uncertainty regarding the availability of future versions may actually hasten investment. By comparing the relative values of the three strategies, we find that, under a low output price the compulsive strategy always dominates, whereas, at a high output price, the incentive to wait for a new version and adopt either a leapfrog or a laggard strategy increases as the rate of innovation increases. By contrast, high price uncertainty mitigates this effect, thereby increasing the relative attraction of a compulsive strategy.

  • 2014. Chin Dorea, Afzal Siddiqui. Computational Management Science 11 (1-2), 57-86

    The surge in demand for electricity in recent years requires that power companies expand generation capacity sufficiently. Yet, at the same time, energy demand is subject to seasonal variations and peak-hour factors that cause it to be extremely volatile and unpredictable, thereby complicating the decision-making process. We investigate how power companies can optimise their capacity-expansion decisions while facing uncertainty and examine how expansion and forward contracts can be used as suitable tools for hedging against risk under market power. The problem is solved through a mixed-complementarity approach. Scenario-specific numerical results are analysed, and conclusions are drawn on how risk aversion, competition, and uncertainty interact in hedging, generation, and expansion decisions of a power company. We find that forward markets not only provide an effective means of risk hedging but also improve market efficiency with higher power output and lower prices. Power producers with higher levels of risk aversion tend to engage less in capacity expansion with the result that together with the option to sell in forward markets, very risk-averse producers generate at a level that hardly varies with scenarios.

  • 2014. Michail Chronopoulos, Bert De Reyck, Afzal Siddiqui. European Journal of Operational Research 236 (2), 643-656

    A monopolist typically defers entry into an industry as both price uncertainty and the level of risk aversion increase. By contrast, the presence of a rival typically hastens entry under risk neutrality. Here, we examine these two opposing effects in a duopoly setting. We demonstrate that the value of a firm and its entry decision behave differently with risk aversion and uncertainty depending on the type of competition. Interestingly, if the leader's role is defined endogenously, then higher uncertainty makes her relatively better off, whereas with the roles exogenously defined, the impact of uncertainty is ambiguous.

  • 2014. Paula Rocha, Afzal Siddiqui, Michael Stadler. Energy and Buildings 88, 203-213

    To foster the transition to more sustainable energy systems, policymakers have been approving measures to improve energy efficiency as well as promoting smart grids. In this setting, building managers are encouraged to adapt their energy operations to real-time market and weather conditions. Yet, most fail to do so as they rely on conventional building energy management systems (BEMS) that have static temperature set points for heating and cooling equipment. In this paper, we investigate how effective policy measures are at improving building-level energy efficiency compared to a smart BEMS with dynamic temperature set points. To this end, we present an integrated optimisation model mimicking the smart BEMS that combines decisions on heating and cooling systems operations with decisions on energy sourcing. Using data from an Austrian and a Spanish building, we find that the smart BEMS results in greater reduction in energy consumption than a conventional BEMS with policy measures. 

  • 2014. Markus Groissböck (et al.). Journal of energy engineering 140 (1)

    Deregulation of the energy sector has created new markets for producers as well as opportunities for consumers to meet their needs in a more customized way. Yet, traditional building energy management systems operate statically by adjusting air or water flow in heating and cooling systems in response to predetermined triggers, in relation to large deviations in the zone temperature from the equipment’s set-point temperature. The writers provide decision support to managers of buildings through dynamic control of the installed equipment that seeks to minimize energy costs. Assuming that the building’s occupants have comfort preferences expressed by upper and lower limits for the temperature, the writers model the effect of active equipment control (through changes to either the set point or valve flow) on the zone temperature, taking into account the external temperature, solar gains, building’s shell, and internal loads. The energy required to change the zone temperature in each time period is then used to calculate the energy cost in the objective function of an optimization problem. By implementing the model for actual public buildings, the writers demonstrate the advantages of more active equipment-management in terms of lower costs and energy consumption. 

  • 2014. Michail Chronopoulos, Derek Bunn, Afzal Siddiqui. Economics of Energy & Environmental Policy 3 (2), 79-100

    Conventional models to support policymaking for the energy sector have been largely based on deterministic or static settings that focus on planning welfare-maximising investment pathways. But, in a liberalised market, since investments are made by competitive, profit-maximising companies, the increased intervention of government policy in the trading arrangements creates uncertain responses to incentives. Industry may perceive policy risks to be high, and major companies may choose to act more cautiously than governments expect. This presents a modelling challenge, and we propose an extension to the use of real options in this context. We model several features of the low-carbon investment context, viz., irreversibility, delay, and competition, which impinge upon the radical policy imperatives for structural change in electricity markets to meet ambitious sustainability targets.

  • 2014. Ilan Momber (et al.). IEEE Transactions on Power Systems 30 (2), 882-891

    Research on electric power systems has considered the impact of foreseeable plug-in electric vehicle (PEV) penetration on its regulation, planning, and operation. Indeed, detailed treatment of PEV charging is necessary for efficient allocation of resources. It is envisaged that a coordinator of charging schedules, i.e., a PEV aggregator, could exercise some form of load control according to electricity market prices and network charges. In this context, it is important to consider the effects of uncertainty of key input parameters to optimization algorithms for PEV charging schedules. However, the modeling of the PEV aggregator's exposure to profit volatility has received less attention in detail. Hence, this paper develops a methodology to maximize PEV aggregator profits taking decisions in day-ahead and balancing markets while considering risk aversion. Under uncertain market prices and fleet mobility, the proposed two-stage linear stochastic program finds optimal PEV charging schedules at the vehicle level. A case study highlights the effects of including the conditional value-at-risk (CVaR) term in the objective function and calculates two metrics referred to as the expected value of aggregation and flexibility.

  • 2013. G. Cardoso (et al.). Electric power systems research 103, 61-69

    This paper describes the introduction of stochastic linear programming into Operations DER-CAM, a tool used to obtain optimal operating schedules for a given microgrid under local economic and environmental conditions. This application follows previous work on optimal scheduling of a lithium-iron-phosphate battery given the output uncertainty of a 1 MW molten carbonate fuel cell. Both are in the Santa Rita Jail microgrid, located in Dublin, California. This fuel cell has proven unreliable, partially justifying the consideration of storage options. Several stochastic DER-CAM runs are executed to compare different scenarios to values obtained by a deterministic approach. Results indicate that using a stochastic approach provides a conservative yet more lucrative battery schedule. Lower expected energy bills result, given fuel cell outages, in potential savings exceeding 6%.

  • 2013. M. Groissböck (et al.).

    Building managers and operators as at Campus Pinkafeld are interested in a cost optimal fulfilment of their energy needs. From a strategic point of view they are interested in optimal investments and upgrades. From an operative point of view they are interested in an optimal use of all available resources. This paper shows how the decision support system (DSS) of the project Energy Efficiency and Risk Management in Public Buildings (EnRiMa) will help with this challenges and the integration of the DSS with the existing energy management system (EMS) is one of the key issues for a successful project. The strategic DSS will inform the building owner about possible new technologies that might reduce the total building energy costs or environmental impact. The benefit of an operational DSS is to enable the building operator to use already adopted energy efficiency improving technologies as pre-cooling, pre-heating or any other demand response related tasks to decrease costs and emissions caused by the heating and cooling system of the building. Assuming an upper and lower limit for the room temperature, we model the effect of active equipment control (via changes to either the set point or the valve flow) on the zone temperature taking into account the external temperature, solar gains, the building shell, and internal loads. The energy required to change the zone temperature in each time period is then used to calculate the energy cost or efficiency in the objective function of an optimization problem. This paper reports on example results for Campus Pinkafeld, shows the technical approach, and that such a flexible approach can save 10% costs only on an operational level.

  • 2013. Michail Chronoopoulos, Bert De Reyck, Afzal Siddiqui. IEEE transactions on engineering management 60 (2), 272-288

    Risk aversion typically erodes the value of an investment opportunity, often increasing the incentive to delay investment. Although this may be true when the decision maker has discretion only over the timing of investment, any additional discretion over the capacity of a project may lead to different results. In this paper, we extend the traditional real options approach by allowing for discretion over capacity while incorporating risk aversion and operational flexibility in the form of suspension and resumption options. In contrast to a project without scalable capacity, we find that increased risk aversion may actually facilitate investment because it decreases the optimal capacity of the project. Finally, we illustrate how the relative loss in the value of the investment opportunity due to an incorrect capacity choice may become less pronounced with increasing risk aversion and uncertainty.

  • 2013. Lajos Maurovich-Horvat (et al.). 10th International Conference on the European Energy Market, 1-7

    The transition to a more sustainable energy system requires investment in renewable energy technologies such as wind. Due to the dispersed nature of sites for wind farms, concomitant expansionof the transmission network is also necessary. While the two objectives could be reconciled within the auspices of a regulated welfare-maximising planner, recent restructuring of electricity industries has introduced a merchant model for transmission investment, which provides congestion rents from construction of a new line. Thus, the merchant investor's incentives are different from those of producers carrying out investment in wind farms. In this paper, we analyse the interaction between the two conflicting objectives under various assumptions about the electricity market structure and the degree of producers' market power. Via a three-node illustrative example, we show that a merchant investor typically builds less transmission capacity than a welfare-maximising transmission system operator or central planner. Although social welfare is lower and nodal prices are generally higher with a merchant investor and when producers are assumed to behave à la Cournot, the effect of lower price response at the dominant demand node is to increase concentration of generation capacity. Hence, the distributional effects of transmission expansion depend on the relative supply-demand balance throughout the network.

  • 2012. Afzal Siddiqui, Ryuta Takashima. European Journal of Operational Research 222 (3), 583-595

    Deregulated infrastructure industries exhibit stiff competition for market share. Firms may be able to limit the effects of competition by launching new projects in stages. Using a two-stage real options model, we explore the value of such flexibility. We first demonstrate that the value of investing in a sequential manner for a monopolist is positive but decreases with uncertainty. Next, we find that a typical duopoly firm's value relative to a monopolist's decreases with uncertainty as long as the loss in market share is high. Intriguingly, this result is reversed for a low loss in market share. We finally show that this loss in value is reduced if a firm invests in a sequential manner and specify the conditions under which sequential capacity expansion is more valuable for a duopolist firm than for a monopolist.

  • 2012. M. Stadler (et al.). Journal of energy engineering 138 (2), 95-108

    Connection of electric storage technologies to smart grids will have substantial implications for building energy systems. Local storage will enable demand response. When connected to buildings, mobile storage devices, such as electric vehicles (EVs), are in competition with conventional stationary sources at the building. These EVs can change the financial and environmental attractiveness of on-site generation [e. g., photovoltaic (PV) or fuel cells (FCs)]. To examine the effect of EVs on building energy costs and carbon dioxide (CO2) emissions, a distributed-energy resources adoption problem is formulated as a mixed-integer linear program with minimization of annual building energy costs or CO2 emissions and solved for 2020 technology assumptions. The mixed-integer linear program is applied to a set of 139 different commercial buildings in California, and example results and the aggregated economic and environmental benefits are reported. Special constraints for the available PV, solar thermal, and EV parking lots at the commercial buildings are considered. The research shows that EV batteries can be used to reduce utility-related energy costs at the smart grid or commercial building due to arbitrage of energy between buildings with different tariffs. However, putting more emphasis on CO2 emissions makes stationary storage more attractive, and stationary storage capacities increase, whereas the attractiveness of EVs decreases. The limited availability of EVs at the commercial building decreases the attractiveness of EVs, and if PV is chosen by the optimization, then it is mostly used to charge the stationary storage at the commercial building and not the EVs connected to the building. DOI: 10.1061/(ASCE)EY.1943-7897.0000070.

  • 2012. Somayeh Heydari, Nick Ovenden, Afzal Siddiqui. Computational Management Science 9 (1), 109-138

    Among a comprehensive scope of mitigation measures for climate change, CO2 capture and sequestration (CCS) plays a potentially significant role in industrialised countries. In this paper, we develop an analytical real options model that values the choice between two emissions-reduction technologies available to a coal-fired power plant. Specifically, the plant owner may decide to invest in either full CCS (FCCS) or partial CCS (PCCS) retrofits given uncertain electricity, CO2, and coal prices. We first assess the opportunity to upgrade to each technology independently by determining the option value of installing a CCS unit as a function of CO2 and fuel prices. Next, we value the option of investing in either FCCS or PCCS technology. If the volatilities of the prices are low enough, then the investment region is dichotomous, which implies that for a given fuel price, retrofitting to the FCCS (PCCS) technology is optimal if the CO2 price increases (decreases) sufficiently. The numerical examples provided in this paper using current market data suggest that neither retrofit is optimal immediately. Finally, we observe that the optimal stopping boundaries are highly sensitive to CO2 price volatility.

  • 2011. Michael Stadler (et al.). European transactions on electrical power 21 (2), 1291-1309

    The U.S. Department of Energy has launched the commercial building initiative (CBI) in pursuit of its research goal of achieving zero-net-energy commercial buildings (ZNEB), i.e., ones that produce as much energy as they use. Its objective is to make these buildings marketable by 2025 such that they minimize their energy use through cutting-edge, energy-efficiency technologies and meet their remaining energy needs through on-site renewable energy generation. This paper examines how such buildings may be implemented within the context of a cost-or CO(2)-minimizing microgrid that is able to adopt and operate various technologies: photovoltaic (PV) modules and other on-site generation, heat exchangers, solar thermal collectors, absorption chillers, and passive/demand-response technologies. A mixed-integer linear program (MILP) that has a multi-criteria objective function is used. The objective is minimization of a weighted average of the building's annual energy costs and CO(2) emissions. The MILP's constraints ensure energy balance and capacity limits. In addition, constraining the building's energy consumed to equal its energy exports enables us to explore how energy sales and demand-response measures may enable compliance with the ZNEB objective. Using a commercial test site in northern California with existing tariff rates and technology data, we find that a ZNEB requires ample PV capacity installed to ensure electricity sales during the day. This is complemented by investment in energy-efficient combined heat and power (CHP) equipment, while occasional demand response saves energy consumption. A large amount of storage is also adopted, which may be impractical. Nevertheless, it shows the nature of the solutions and costs necessary to achieve a ZNEB. Additionally, the ZNEB approach does not necessary lead to zero-carbon (ZC) buildings as is frequently argued. We also show a multi-objective frontier for the CA example, which allows us to estimate the needed technologies and costs for achieving a ZC building or microgrid.

  • 2011. Michail Chronopoulos, Bert De Reyck, Afzal Siddiqui. European Journal of Operational Research 213 (1), 221-237

    Traditional real options analysis addresses the problem of investment under uncertainty assuming a risk-neutral decision maker and complete markets. In reality, however, decision makers are often risk averse and markets are incomplete. We confirm that risk aversion lowers the probability of investment and demonstrate how this effect can be mitigated by incorporating operational flexibility in the form of embedded suspension and resumption options. Although such options facilitate investment, we find that the likelihood of investing is still lower compared to the risk-neutral case. Risk aversion also increases the likelihood that the project will be abandoned, although this effect is less pronounced. Finally, we illustrate the impact of risk aversion on the optimal suspension and resumption thresholds and the interaction among risk aversion, volatility, and optimal decision thresholds under complete operational flexibility.

  • 2011. Aidan Coville, Afzal Siddiqui, Klaus-Ole Vogstad. Energy 36 (7), 4505-4517

    Investment in renewable energy sources requires reliable data. However, meteorological datasets are often plagued by missing data, which can bias energy resource estimates if the missingness is systematic. We address this issue by considering the influence of missing data due to icing of equipment during the winter on the wind resource estimation for a potential wind turbine site in Norway. Using a mean-reverting jump-diffusion (MRJD) process to model electricity prices, we also account for the impact on the expected revenue from a wind turbine constructed at the site. While missing data due to icing significantly bias the wind resource estimate downwards, their impact on revenue estimates is dampened because of volatile electricity spot prices. By contrast, with low-volatility electricity prices, the effect of missing data on revenue estimates is highly significant. The seasonality-based method we develop removes most of the bias in wind resource and revenue estimation caused by missing data.

  • 2011. Stein-Erik Fleten, Heggedal Ane Marte, Afzal Siddiqui. Journal of Energy Markets 4 (1), 121-147

    Interconnection of two electricity markets provides revenues to the owner of the line. In this paper we study the alternatives open to an investor holding a unique right to construct transmission capacity between Norway and Germany. The alternatives are either constructing a 700 MW cable with a subsequent expansion option, or to construct a 1400 MW cable. We use a real options valuation (ROV) framework to decide which capacity should be chosen, and when the investment should be carried through. Our scientific contribution is to apply the ROV framework where sequential investment is allowed on transmission capacity investment. Further, we combine information from a bottom-up model, which estimates the reduction in average price differences due to the investment itself, and a top-down model for finding the values and optimal decisions.

Show all publications by Afzal Siddiqui at Stockholm University

Last updated: April 28, 2021

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