Bibliography¶
This section provides the full bibliography for the ESFEX documentation. References are numbered and cited throughout the formulation, user guide, and tutorial documents using the notation [N]. Click any citation number in the documentation to jump directly to the corresponding entry below. The citation style follows Applied Energy (Elsevier): Author(s), Title, Journal/Publisher, Volume(Issue), Pages, Year.
DC Power Flow and Network Analysis¶
[1] Stott B, Jardim J, Alsaç O. DC Power Flow Revisited. IEEE Transactions on Power Systems 2009;24(3):1290–1300. doi:10.1109/TPWRS.2009.2021235
[42] Van Hertem D, Verboomen J, Belmans R, Kling WL. Transmission Line Outage Distribution Factors (LODFs) and their application in security analysis. International Journal of Electrical Power & Energy Systems 2006;28(6):379–384. doi:10.1016/j.ijepes.2006.01.005
[43] Guler T, Gross G, Liu M. Generalized Line Outage Distribution Factors. IEEE Transactions on Power Systems 2007;22(2):879–881. doi:10.1109/TPWRS.2007.894867
Optimal Power Flow¶
[2] Jabr RA. Radial Distribution Load Flow Using Conic Programming. IEEE Transactions on Power Systems 2006;21(3):1458–1459. doi:10.1109/TPWRS.2006.879234
[3] Coffrin C, Hijazi H, Van Hentenryck P. The QC Relaxation: A Theoretical and Computational Study on Optimal Power Flow. IEEE Transactions on Power Systems 2016;31(4):3008–3018. doi:10.1109/TPWRS.2015.2463111
[27] Low SH. Convex Relaxation of Optimal Power Flow — Part I: Formulations and Equivalence. IEEE Transactions on Control of Network Systems 2014;1(1):15–27. doi:10.1109/TCNS.2014.2309732
[56] Cain MB, O'Neill RP, Castillo A. History of Optimal Power Flow and Formulations. Federal Energy Regulatory Commission, Staff Technical Paper; 2012. Available: ferc.gov
[57] Bai X, Wei H, Fujisawa K, Wang Y. Semidefinite Programming for Optimal Power Flow Problems. International Journal of Electrical Power & Energy Systems 2008;30(6–7):383–392. doi:10.1016/j.ijepes.2007.12.003
[58] Molzahn DK, Hiskens IA. A Survey of Relaxations and Approximations of the Power Flow Equations. Foundations and Trends in Electric Energy Systems 2019;4(1–2):1–221. doi:10.1561/3100000012
Software and Solvers¶
[4] Wächter A, Biegler LT. On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming 2006;106(1):25–57. doi:10.1007/s10107-004-0559-y. Repository: github.com/coin-or/Ipopt
[20] Dunning I, Huchette J, Lubin M. JuMP: A Modeling Language for Mathematical Optimization. SIAM Review 2017;59(2):295–320. doi:10.1137/15M1020575. Repository: github.com/jump-dev/JuMP.jl
[21] Huangfu Q, Hall JAJ. Parallelizing the dual revised simplex method. Mathematical Programming Computation 2018;10(1):119–142. doi:10.1007/s12532-017-0130-5. Repository: github.com/ERGO-Code/HiGHS
[46] Zimmerman RD, Murillo-Sánchez CE, Thomas RJ. MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education. IEEE Transactions on Power Systems 2011;26(1):12–19. doi:10.1109/TPWRS.2010.2051168. Repository: github.com/MATPOWER/matpower
[47] Thurner L, Scheidler A, Schäfer F, Menke JH, Dollichon J, Meier F, Meinecke S, Braun M. pandapower — an Open-Source Python Tool for Convenient Modeling, Analysis, and Optimization of Electric Power Systems. IEEE Transactions on Power Systems 2018;33(6):6510–6521. doi:10.1109/TPWRS.2018.2829021. Repository: github.com/e2nIEE/pandapower
Power Systems Analysis and Optimization¶
[5] Kundur P. Power System Stability and Control. New York: McGraw-Hill; 1994.
[31] Wood AJ, Wollenberg BF, Sheblé GB. Power Generation, Operation, and Control. 3rd ed. Hoboken: Wiley; 2014.
[41] Glover JD, Sarma MS, Overbye TJ. Power Systems Analysis and Design. 6th ed. Boston: Cengage Learning; 2017.
[62] Bergen AR, Vittal V. Power Systems Analysis. 2nd ed. Upper Saddle River: Prentice Hall; 2000.
Frequency Stability¶
[6] Anderson PM, Fouad AA. Power System Control and Stability. 2nd ed. Hoboken: Wiley-IEEE Press; 2003.
[59] ENTSO-E. Frequency Stability Evaluation Criteria for the Synchronous Area of Continental Europe. Brussels: ENTSO-E; 2017. Available: entsoe.eu
[60] Ela E, Gevorgian V, Scholbrook A, Fleming M, Zhang Y, Cochran J, Mackay D. Effective Inertia Constant and Its Impact on Power System Frequency. NREL Technical Report NREL/TP-5500-55503; 2012. Available: nrel.gov
[61] Ulbig A, Borsche TS, Andersson G. Impact of Low Rotational Inertia on Power System Stability and Operation. IFAC Proceedings Volumes 2014;47(3):7290–7297. doi:10.3182/20140824-6-ZA-1003.02615
MGA/SPORES (Near-Optimal Alternatives)¶
[7] Lombardi F, Pickering B, Colombo E, Pfenninger S. Policy decision support for renewables deployment through spatially explicit practically optimal alternatives. Joule 2020;4(10):2185–2207. doi:10.1016/j.joule.2020.08.002
[8] DeCarolis JF. Using modeling to generate alternatives (MGA) to expand our thinking on energy futures. Energy Economics 2011;33(2):145–152. doi:10.1016/j.eneco.2010.05.002
[38] Neumann F, Brown T. The near-optimal feasible space of a renewable power system model. Electric Power Systems Research 2021;190:106690. doi:10.1016/j.epsr.2020.106690
Stochastic Programming in Energy Systems¶
[9] Birge JR, Louveaux F. Introduction to Stochastic Programming. 2nd ed. New York: Springer; 2011. doi:10.1007/978-1-4614-0237-4
[10] Conejo AJ, Carrión M, Morales JM. Decision Making Under Uncertainty in Electricity Markets. New York: Springer; 2010. doi:10.1007/978-1-4419-7421-1
Sensitivity Analysis¶
[11] Sobol IM. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Mathematics and Computers in Simulation 2001;55(1–3):271–280. doi:10.1016/S0378-4754(00)00270-6
[12] Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S. Global Sensitivity Analysis: The Primer. Chichester: Wiley; 2008. doi:10.1002/9780470725184
[19] Herman J, Usher W. SALib: An open-source Python library for Sensitivity Analysis. Journal of Open Source Software 2017;2(9):97. doi:10.21105/joss.00097
Comparable Energy System Models¶
[13] Brown T, Hörsch J, Schlachtberger D. PyPSA: Python for Power System Analysis. Journal of Open Research Software 2018;6(4). doi:10.5334/jors.188. Repository: github.com/PyPSA/PyPSA
[14] Jenkins JD, Sepulveda NA. Enhanced Decision Support for a Changing Electricity Landscape: The GenX Configurable Electricity Resource Capacity Expansion Model. MIT Energy Initiative Working Paper; 2017. Repository: github.com/GenXProject/GenX
[15] Pfenninger S, Pickering B. Calliope: a multi-scale energy systems modelling framework. Journal of Open Source Software 2018;3(29):825. doi:10.21105/joss.00825. Repository: github.com/calliope-project/calliope
[16] Loulou R, Goldstein G, Kanudia A, Lettila A, Remne U. Documentation for the TIMES Model. IEA-ETSAP; 2016. Available: iea-etsap.org
[17] IRENA. Planning for the Renewable Future: Long-term Modelling and Tools to Expand Variable Renewable Power in Emerging Economies. Abu Dhabi: International Renewable Energy Agency; 2017. Available: irena.org
[18] Howells M, Rogner H, Strachan N, Heaps C, Huntington H, Kypreos S, Hughes A, Silveira S, DeCarolis J, Bazillian M, Roehrl A. OSeMOSYS: The Open Source Energy Modeling System. Energy Policy 2011;39(10):5850–5870. doi:10.1016/j.enpol.2011.06.033. Repository: github.com/OSeMOSYS/OSeMOSYS
[36] Helistö N, Kiviluoma J, Holttinen H, Lara JD, Hodge BM. Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches. WIREs Energy and Environment 2019;8(5):e341. doi:10.1002/wene.341
Time Series Aggregation¶
[22] Kotzur L, Markewitz P, Robinius M, Stolten D. Impact of different time series aggregation methods on optimal energy system design. Renewable Energy 2018;117:474–487. doi:10.1016/j.renene.2017.10.017
[23] Hoffmann M, Kotzur L, Stolten D, Robinius M. A Review on Time Series Aggregation Methods for Energy System Models. Energies 2020;13(3):641. doi:10.3390/en13030641
[37] Nahmmacher P, Schmid E, Hirth L, Knopf B. Carpe diem: A novel approach to select representative days for long-term power system planning. Energy 2016;112:430–442. doi:10.1016/j.energy.2016.06.081
Capacity Expansion Planning¶
[24] Koltsaklis NE, Dagoumas AS. State-of-the-art generation expansion planning: A review. Applied Energy 2018;230:563–589. doi:10.1016/j.apenergy.2018.08.087
[25] Palmintier BS, Webster MD. Impact of Operational Flexibility on Electricity Generation Planning with Renewable and Carbon Targets. IEEE Transactions on Sustainable Energy 2016;7(2):672–684. doi:10.1109/TSTE.2015.2497482
[26] Poncelet K, Delarue E, Six D, Duerinck J, D'haeseleer W. Impact of the level of temporal and operational detail in energy-system planning models. Applied Energy 2016;162:631–643. doi:10.1016/j.apenergy.2015.10.100
[35] Schwele A, Kazempour J, Pinson P. Do unit commitment constraints affect generation expansion planning? A scalable stochastic model. Energy Systems 2020;11:247–282. doi:10.1007/s12667-018-00321-z
Flexibility in Power Systems¶
[28] Denholm P, Hand M. Grid flexibility and storage required to achieve very high penetration of variable renewable electricity. Energy Policy 2011;39(3):1817–1830. doi:10.1016/j.enpol.2011.01.019
[29] IEA. Status of Power System Transformation 2019: Power System Flexibility. Paris: International Energy Agency; 2019. Available: iea.org
[30] Kondziella H, Bruckner T. Flexibility requirements of renewable energy based electricity systems — a review of research results and methodologies. Renewable and Sustainable Energy Reviews 2016;53:10–22. doi:10.1016/j.rser.2015.07.199
Battery Storage Modeling¶
[32] Xu B, Oudalov A, Ulbig A, Andersson G, Kirschen DS. Modeling of Lithium-Ion Battery Degradation for Cell Life Assessment. IEEE Transactions on Smart Grid 2018;9(2):1131–1140. doi:10.1109/TSG.2016.2578950
[63] Diaz-Gonzalez F, Sumper A, Gomis-Bellmunt O, Villafafila-Robles R. A review of energy storage technologies for wind power applications. Renewable and Sustainable Energy Reviews 2012;16(4):2154–2171. doi:10.1016/j.rser.2012.01.029
Value of Lost Load and Reliability¶
[33] Schröder T, Kuckshinrichs W. Value of Lost Load: An Efficient Economic Indicator for Power Supply Security? A Literature Review. Frontiers in Energy Research 2015;3:55. doi:10.3389/fenrg.2015.00055
[67] ACER/CEER. Annual Report on the Results of Monitoring the Internal Electricity and Gas Markets. European Union Agency for the Cooperation of Energy Regulators; 2022. Available: acer.europa.eu
N-1 Security and Contingency Analysis¶
[34] Capitanescu F, Martinez Ramos JL, Panciatici P, Kirschen D, Marano Marcolini A, Platbrood L, Wehenkel L. State-of-the-art, challenges, and future trends in security constrained optimal power flow. Electric Power Systems Research 2011;81(8):1731–1741. doi:10.1016/j.epsr.2011.04.003
[44] Monticelli A, Pereira MVF, Granville S. Security-Constrained Optimal Power Flow with Post-Contingency Corrective Rescheduling. IEEE Transactions on Power Systems 1987;2(1):175–180. doi:10.1109/TPWRS.1987.4335095
[45] Ejebe GC, Wollenberg BF. Automatic Contingency Selection. IEEE Transactions on Power Apparatus and Systems 1979;PAS-98(1):97–109. doi:10.1109/TPAS.1979.319517
LCOE and System Cost Metrics¶
[48] IRENA. Renewable Power Generation Costs in 2023. Abu Dhabi: International Renewable Energy Agency; 2024. Available: irena.org
[64] Ueckerdt F, Hirth L, Luderer G, Edenhofer O. System LCOE: What are the costs of variable renewables? Energy 2013;63:61–75. doi:10.1016/j.energy.2013.10.072
Hydrogen and Power-to-X¶
[49] Buttler A, Spliethoff H. Current status of water electrolysis for energy storage, grid balancing and sector coupling via power-to-gas and power-to-liquids: A review. Renewable and Sustainable Energy Reviews 2018;82:2440–2454. doi:10.1016/j.rser.2017.09.003
[50] Glenk G, Reichelstein S. Economics of converting renewable power to hydrogen. Nature Energy 2019;4:216–222. doi:10.1038/s41560-019-0326-1
Electric Vehicle Integration¶
[51] Kempton W, Tomić J. Vehicle-to-grid power fundamentals: Calculating capacity and net revenue. Journal of Power Sources 2005;144(1):268–279. doi:10.1016/j.jpowsour.2004.12.025
[52] Lopes JAP, Soares FJ, Almeida PMR. Integration of Electric Vehicles in the Electric Power System. Proceedings of the IEEE 2011;99(1):168–183. doi:10.1109/JPROC.2010.2066250
[53] Bass FM. A New Product Growth for Model Consumer Durables. Management Science 1969;15(5):215–227. doi:10.1287/mnsc.15.5.215
Rooftop Solar and Distributed Generation¶
[54] Sigrin B, Gleason M, Preus R, Baring-Gould I, Margolis R. The Distributed Generation Market Demand Model (dGen): Documentation. NREL Technical Report NREL/TP-6A20-65231; 2016. doi:10.2172/1239054
[55] Melius J, Margolis R, Ong S. Estimating Rooftop Suitability for PV: A Review of Methods, Patents, and Validation Techniques. NREL Technical Report NREL/TP-6A20-63983; 2013. Available: nrel.gov
Project Finance and Investment Analysis¶
[68] Brealey RA, Myers SC, Allen F. Principles of Corporate Finance. 14th ed. New York: McGraw-Hill; 2022.
[69] Kierulff H. MIRR: A better measure. Business Horizons 2008;51(4):321–329. doi:10.1016/j.bushor.2008.02.005
[70] Vose D. Risk Analysis: A Quantitative Guide. 3rd ed. Chichester: Wiley; 2008. doi:10.1002/9780470512210
Risk, Resilience, and Climate Adaptation¶
[71] Rockafellar RT, Uryasev S. Optimization of conditional value-at-risk. Journal of Risk 2000;2:21–41. doi:10.26509/frbc-wp-199918
[72] Birge JR, Louveaux F. Introduction to Stochastic Programming. 2nd ed. New York: Springer; 2011. doi:10.1007/978-1-4614-0237-4
[73] Panteli M, Mancarella P. Metrics and quantification of operational and infrastructure resilience in power systems. IEEE Transactions on Power Systems 2017;32(6):4732–4742. doi:10.1109/TPWRS.2017.2664141
[74] Panteli M, Mancarella P. The grid: Stronger, bigger, smarter? Presenting a conceptual framework of power system resilience. IEEE Power and Energy Magazine 2015;13(3):58–66. doi:10.1109/MPE.2015.2397334
[75] Ben-Tal A, El Ghaoui L, Nemirovski A. Robust Optimization. Princeton: Princeton University Press; 2009. doi:10.1515/9781400831050
[76] Munoz FD, Hobbs BF, Watson JP. New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints. European Journal of Operational Research 2014;236(3):831–845. doi:10.1016/j.ejor.2013.12.028
[77] Nirandjan S, Koks EE, Ward PJ, Aerts JCJH. A comprehensive database of physical vulnerability functions for natural hazard risk assessment of critical infrastructure. Natural Hazards and Earth System Sciences 2024;24:4341–4366. doi:10.5194/nhess-24-4341-2024
[78] PNNL-33587. Fragility Functions Resource Report: Documented Sources for Fragility Functions for Electricity and Water Infrastructure Assets. Pacific Northwest National Laboratory; 2023. Available: pnnl.gov
[79] Watson M, Etemadi AH. An assessment methodology for multi-hazard threat and the corresponding infrastructure failure probability. Journal of Critical Infrastructure Policy 2020;1(1):69–92. doi:10.18278/jcip.1.1.5
[80] Bloemendaal N, Haigh ID, de Moel H, Muis S, Haarsma RJ, Aerts JCJH. Generation of a global synthetic tropical cyclone hazard dataset using STORM. Scientific Data 2020;7:40. doi:10.1038/s41597-020-0381-2
[81] Heitsch H, Römisch W. Scenario tree modeling for multistage stochastic programs. Mathematical Programming 2009;118:371–406. doi:10.1007/s10107-007-0197-2
[82] Esfahani PM, Kuhn D. Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations. Mathematical Programming 2018;171:115–166. doi:10.1007/s10107-017-1172-1
[83] Wilson T, Stewart C, Sword-Daniels V, Leonard G, Johnston DM, Cole JW, Wardman J, Wilson G, Barnard S. Volcanic ash impacts to critical infrastructure: A review. Journal of Volcanology and Geothermal Research 2014;286:148–182. doi:10.1016/j.jvolgeores.2014.08.030
[84] Brattle Group. Value of Lost Load Study for the ERCOT Region. Prepared for the Public Utility Commission of Texas; 2024. Available: brattle.com
Standards¶
[65] IEC 60909-0:2016. Short-circuit currents in three-phase a.c. systems — Part 0: Calculation of currents. International Electrotechnical Commission; 2016. Available: iec.ch
[66] IEEE Std 1547-2018. IEEE Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces. IEEE; 2018. doi:10.1109/IEEESTD.2018.8332112