Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches

In the past, power system planning was based on meeting the load duration curve at minimum cost. The increasing share of variable generation (VG) makes operational constraints more important in the planning problem, and there is more and more interest in considering aspects such as sufficient ramping capability, sufficient reserve procurement, power system stability, storage behavior, and the integration of other energy sectors often through demand response assets. In VG integration studies, several methods have been applied to combine the planning and operational timescales. We present a four‐level categorization for the modeling methods, in order of increasing complexity: (1a) investment model only, (1b) operational model only, (2) unidirectionally soft‐linked investment and operational models, (3a) bidirectionally soft‐linked investment and operational models, (3b) operational model with an investment update algorithm, and (4) co‐optimization of investments and operation. The review shows that using a low temporal resolution or only few representative days will not suffice in order to determine the optimal generation portfolio. In addition, considering operational effects proves to be important in order to get a more optimal generation portfolio and more realistic estimations of system costs. However, operational details appear to be less significant than the temporal representation. Furthermore, the benefits and impacts of more advanced modeling techniques on the resulting generation capacity mix significantly depend on the system properties. Thus, the choice of the model should depend on the purpose of the study as well as on system characteristics.

[1]  Leo Schrattenholzer,et al.  The Energy Supply Model MESSAGE , 1981 .

[2]  M. V. F. Pereira,et al.  Multi-stage stochastic optimization applied to energy planning , 1991, Math. Program..

[3]  S. Messner,et al.  A stochastic version of the dynamic linear programming model MESSAGE III , 1996 .

[4]  Amit Kanudia,et al.  Robust responses to climate change via stochastic MARKAL: The case of Québec , 1996, Eur. J. Oper. Res..

[5]  Denmark Stockholm,et al.  Balmorel: A Model for Analyses of the Electricity and CHP Markets in the Baltic Sea Region , 2001 .

[6]  Helge V. Larsen,et al.  Balmorel: A model for analyses of the electricity and CHP markets in the Baltic Sea region , 2001 .

[7]  Carson W. Taylor,et al.  Definition and Classification of Power System Stability , 2004 .

[8]  P. Kundur,et al.  Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions , 2004, IEEE Transactions on Power Systems.

[9]  R. Loulou,et al.  Documentation for the TIMES Model PART I April 2005 , 2005 .

[10]  J. Rosen The future role of renewable energy sources in European electricity supply: A model-based analysis for the EU-15 , 2008 .

[11]  Ana Estanqueiro,et al.  Impacts of large amounts of wind power on design and operation of power systems, results of IEA collaboration , 2008 .

[12]  Brian Vad Mathiesen,et al.  Comparative analyses of seven technologies to facilitate the integration of fluctuating renewable energy sources , 2009 .

[13]  Mark O'Malley,et al.  Base-Load Cycling on a System With Significant Wind Penetration , 2010, IEEE Transactions on Power Systems.

[14]  Juha Kiviluoma,et al.  Influence of wind power, plug-in electric vehicles, and heat storages on power system investments , 2010 .

[15]  Marco Nicolosi,et al.  The Importance of High Temporal Resolution in Modeling Renewable Energy Penetration Scenarios , 2011 .

[16]  William D'haeseleer,et al.  Determining optimal electricity technology mix with high level of wind power penetration , 2011 .

[17]  Semida Silveira,et al.  OSeMOSYS: The Open Source Energy Modeling System: An introduction to its ethos, structure and development , 2011 .

[18]  Goran Strbac,et al.  Building a Resilient UK Energy System , 2011 .

[19]  M. O'Malley,et al.  Stochastic Optimization Model to Study the Operational Impacts of High Wind Penetrations in Ireland , 2011, IEEE Transactions on Power Systems.

[20]  Mark Z. Jacobson,et al.  A Monte Carlo approach to generator portfolio planning and carbon emissions assessments of systems with large penetrations of variable renewables. , 2011 .

[21]  Nate Blair,et al.  Regional Energy Deployment System (ReEDS) , 2011 .

[22]  A Keane,et al.  Capacity Value of Wind Power , 2011, IEEE Transactions on Power Systems.

[23]  Madeleine Gibescu,et al.  Short-Term Energy Balancing With Increasing Levels of Wind Energy , 2012, IEEE Transactions on Sustainable Energy.

[24]  Madeleine Gibescu,et al.  Short-Term Energy Balancing With Increasing Levels of Wind Energy , 2012 .

[25]  F. Leanez,et al.  Benefits of chronological optimization in capacity planning for electricity markets , 2012, 2012 IEEE International Conference on Power System Technology (POWERCON).

[26]  Brian Ó Gallachóir,et al.  Soft-linking of a power systems model to an energy systems model , 2012 .

[27]  Matthias Fripp,et al.  Switch: a planning tool for power systems with large shares of intermittent renewable energy. , 2012, Environmental science & technology.

[28]  R. Kannan,et al.  A Long-Term Electricity Dispatch Model with the TIMES Framework , 2013, Environmental Modeling & Assessment.

[29]  R. Wiser,et al.  Renewable Electricity Futures Study. Executive Summary , 2012 .

[30]  E. Ela,et al.  Studying the Variability and Uncertainty Impacts of Variable Generation at Multiple Timescales , 2012, IEEE Transactions on Power Systems.

[31]  Trieu Mai,et al.  Resource Planning Model: An Integrated Resource Planning and Dispatch Tool for Regional Electric Systems , 2013 .

[32]  D. Lew,et al.  The Western Wind and Solar Integration Study Phase 2 , 2013 .

[33]  Carlos Silva,et al.  High-resolution modeling framework for planning electricity systems with high penetration of renewables , 2013 .

[34]  M. O'Malley,et al.  Accommodating Variability in Generation Planning , 2013, IEEE Transactions on Power Systems.

[35]  A. Conejo,et al.  Risk-Constrained Multi-Stage Wind Power Investment , 2013 .

[36]  Luis F. Ochoa,et al.  Evaluating and planning flexibility in sustainable power systems , 2013, 2013 IEEE Power & Energy Society General Meeting.

[37]  Mort D. Webster,et al.  Optimal Selection of Sample Weeks for Approximating the Net Load in Generation Planning Problems , 2013 .

[38]  Andrew D. Mills,et al.  Changes in the Economic Value of Variable Generation at High Penetration Levels:A Pilot Case Study of California , 2014 .

[39]  Bryan Palmintier,et al.  Flexibility in generation planning: Identifying key operating constraints , 2014, 2014 Power Systems Computation Conference.

[40]  Fernando J. de Sisternes,et al.  Risk Implications of the Deployment of Renewables for Investments in Electricity Generation , 2014 .

[41]  Paul Smith,et al.  Studying the Maximum Instantaneous Non-Synchronous Generation in an Island System—Frequency Stability Challenges in Ireland , 2014, IEEE Transactions on Power Systems.

[42]  Brian Ó Gallachóir,et al.  The impact of sub-hourly modelling in power systems with significant levels of renewable generation , 2014 .

[43]  Sarah M. Ryan,et al.  Temporal Versus Stochastic Granularity in Thermal Generation Capacity Planning With Wind Power , 2014, IEEE Transactions on Power Systems.

[44]  H. Rogner,et al.  Incorporating flexibility requirements into long-term energy system models – A case study on high levels of renewable electricity penetration in Ireland , 2014 .

[45]  Adam Hawkes,et al.  Energy systems modeling for twenty-first century energy challenges , 2014 .

[46]  Goran Andersson,et al.  Analyzing operational flexibility of electric power systems , 2014 .

[47]  P. Denholm,et al.  Renewable Electricity Futures for the United States , 2014, IEEE Transactions on Sustainable Energy.

[48]  N. Amjady,et al.  Two-Stage Robust Generation Expansion Planning: A Mixed Integer Linear Programming Model , 2014, IEEE Transactions on Power Systems.

[49]  Marko Aunedi,et al.  Whole-Systems Assessment of the Value of Energy Storage in Low-Carbon Electricity Systems , 2014, IEEE Transactions on Smart Grid.

[50]  Ramesh C. Bansal,et al.  A review of key power system stability challenges for large-scale PV integration , 2015 .

[51]  Alexander Zerrahn,et al.  A Greenfield Model to Evaluate Long-Run Power Storage Requirements for High Shares of Renewables , 2015 .

[52]  Machteld van den Broek,et al.  Operational flexibility and economics of power plants in future low-carbon power systems , 2015 .

[53]  Nouredine Hadjsaid,et al.  Modelling the impacts of variable renewable sources on the power sector: Reconsidering the typology of energy modelling tools , 2015 .

[54]  Paul Denholm,et al.  Overgeneration from Solar Energy in California - A Field Guide to the Duck Chart , 2015 .

[55]  Nikolaos E. Koltsaklis,et al.  A multi-period, multi-regional generation expansion planning model incorporating unit commitment constraints , 2015 .

[56]  Ignacio J. Perez-Arriaga,et al.  The Impact of Bidding Rules on Electricity Markets With Intermittent Renewables , 2015, IEEE Transactions on Power Systems.

[57]  Analyzing major challenges of wind and solar variability in power systems , 2015 .

[58]  Wouter Nijs,et al.  Addressing flexibility in energy system models , 2015 .

[59]  I. Staffell,et al.  The Shape of Future Electricity Demand: Exploring Load Curves in 2050s Germany and Britain , 2015 .

[60]  Erik Delarue,et al.  Accounting for flexibility in power system planning with renewables , 2015 .

[61]  J. P. Deane,et al.  Assessing power system security. A framework and a multi model approach , 2015 .

[62]  Damian Flynn,et al.  Using Energy Storage to Manage High Net Load Variability at Sub-Hourly Time-Scales , 2015, IEEE Transactions on Power Systems.

[63]  Rodrigo Palma-Behnke,et al.  A column generation approach for solving generation expansion planning problems with high renewable energy penetration , 2016 .

[64]  Lion Hirth,et al.  Carpe diem: A novel approach to select representative days for long-term power system modeling , 2016 .

[65]  Goran Strbac,et al.  Co-Optimization of Generation Expansion Planning and Electric Vehicles Flexibility , 2016, IEEE Transactions on Smart Grid.

[66]  Alexander Shapiro,et al.  Risk neutral and risk averse approaches to multistage renewable investment planning under uncertainty , 2016, Eur. J. Oper. Res..

[67]  J. H. Nelson,et al.  Power system balancing for deep decarbonization of the electricity sector , 2016 .

[68]  Antonio J. Conejo,et al.  Investment in Electricity Generation and Transmission: Decision Making under Uncertainty , 2016 .

[69]  James Merrick On representation of temporal variability in electricity capacity planning models , 2016 .

[70]  Bryan Palmintier,et al.  Impact of operational flexibility on electricity generation planning with renewable and carbon targets , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[71]  Mark Z. Jacobson,et al.  Flexibility mechanisms and pathways to a highly renewable US electricity future , 2016 .

[72]  Wesley Cole,et al.  A view to the future of natural gas and electricity: An integrated modeling approach , 2016 .

[73]  Trine Krogh Boomsma,et al.  Impact of forecast errors on expansion planning of power systems with a renewables target , 2014, Eur. J. Oper. Res..

[74]  Audun Botterud,et al.  Capacity adequacy and revenue sufficiency in electricity markets with wind power , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[75]  Mark Z. Jacobson,et al.  Temporal and spatial tradeoffs in power system modeling with assumptions about storage: An application of the POWER model , 2016 .

[76]  Patrick Sullivan,et al.  System Integration of Wind and Solar Power in Integrated Assessment Models: A Cross-Model Evaluation of New Approaches , 2017 .

[77]  William D'haeseleer,et al.  Impact of the level of temporal and operational detail in energy-system planning models , 2016 .

[78]  Juan M. Morales,et al.  Capacity expansion of stochastic power generation under two-stage electricity markets , 2016, Comput. Oper. Res..

[79]  Erik Delarue,et al.  Integrating short term variations of the power system into integrated energy system models: A methodological review , 2017 .

[80]  Paula Varandas Ferreira,et al.  Generation expansion planning with high share of renewables of variable output , 2017 .

[81]  S. Simon,et al.  Carbon neutral archipelago – 100% renewable energy supply for the Canary Islands , 2017 .

[82]  Diego Luca de Tena,et al.  Integrated modelling of variable renewable energy-based power supply in Europe , 2017 .

[83]  Ronnie Belmans,et al.  Evaluating the Role of Electricity Storage by Considering Short-Term Operation in Long-Term Planning , 2016 .

[84]  Erik Delarue,et al.  Selecting Representative Days for Capturing the Implications of Integrating Intermittent Renewables in Generation Expansion Planning Problems , 2017, IEEE Transactions on Power Systems.

[85]  Stefan Pfenninger,et al.  Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability , 2017 .

[86]  Jie Zhang,et al.  Stochastic Multi-Timescale Power System Operations With Variable Wind Generation , 2017, IEEE Transactions on Power Systems.

[87]  Nikolaos E. Koltsaklis,et al.  A stochastic MILP energy planning model incorporating power market dynamics , 2017 .

[88]  R. Pietzcker,et al.  Application of a high-detail energy system model to derive power sector characteristics at high wind and solar shares , 2017 .

[89]  P. Fleming,et al.  Generation expansion planning optimisation with renewable energy integration: A review , 2017 .

[90]  Thomas Huld,et al.  Impact of different levels of geographical disaggregation of wind and PV electricity generation in large energy system models: A case study for Austria , 2017 .

[91]  Ye Wang,et al.  Technical impacts of high penetration levels of wind power on power system stability , 2017, Advances in Energy Systems.

[92]  J. P. Deane,et al.  Adding value to EU energy policy analysis using a multi-model approach with an EU-28 electricity dispatch model , 2017 .

[93]  Benjamin F. Hobbs,et al.  Hidden power system inflexibilities imposed by traditional unit commitment formulations , 2017 .

[94]  Casimir Lorenz,et al.  dynELMOD: A Dynamic Investment and Dispatch Model for the Future European Electricity Market , 2017 .

[95]  Juha Kiviluoma,et al.  Comparison of flexibility options to improve the value of variable power generation , 2018 .

[96]  Lennart Söder,et al.  Generation Adequacy Analysis of Multi-Area Power Systems With a High Share of Wind Power , 2018, IEEE Transactions on Power Systems.

[97]  James Price,et al.  The role of floating offshore wind in a renewable focused electricity system for Great Britain in 2050 , 2018, Energy Strategy Reviews.

[98]  Simo Rissanen,et al.  Effects of turbine technology and land use on wind power resource potential , 2018 .

[99]  Birgit Fais,et al.  Designing low-carbon power systems for Great Britain in 2050 that are robust to the spatiotemporal and inter-annual variability of weather , 2018 .

[100]  Hannele Holttinen,et al.  Long-term impact of variable generation and demand side flexibility on thermal power generation , 2018 .

[101]  Erik Ela,et al.  Long-Term Resource Adequacy, Long-Term Flexibility Requirements, and Revenue Sufficiency , 2018 .

[102]  Hannele Holttinen,et al.  Role of power to liquids and biomass to liquids in a nearly renewable energy system , 2019, IET Renewable Power Generation.

[103]  V. Weisskopf THE INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSIS , 2022 .