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The value of electricity and reserve services in low carbon electricity systems

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  • Vijay, Avinash
  • Fouquet, Nicolas
  • Staffell, Iain
  • Hawkes, Adam

Abstract

Decarbonising electricity systems is essential for mitigating climate change. Future systems will likely incorporate higher penetrations of intermittent renewable and inflexible nuclear power. This will significantly impact on system operations, particularly the requirements for flexibility in terms of reserves and the cost of such services. This paper estimates the interrelated changes in wholesale electricity and reserve prices using two novel methods. Firstly, it simulates the short run marginal cost of generation using a unit commitment model with post-processing to achieve realistic prices. It also introduces a new reserve price model, which mimics actual operation by first calculating the day ahead schedules and then letting deviations from schedule drive reserve prices. The UK is used as a case study to compare these models with traditional methods from the literature. The model gives good agreement with and historic prices in 2015. In a 2035 scenario, increased renewables penetration reduces mean electricity prices, and leads to price spikes due to expensive plants being brought online briefly to cope with net load variations. Contrary to views previously held in literature, a renewable intensive scenario does not lead to a higher reserve price than a fossil fuel intensive scenario. Demand response technology is shown to offer sizeable economic benefits when maintaining system balance. More broadly, this framework can be used to evaluate the economics of providing reserve services by aggregating decentralised energy resources such as heat pumps, micro-CHP and electric vehicles.

Suggested Citation

  • Vijay, Avinash & Fouquet, Nicolas & Staffell, Iain & Hawkes, Adam, 2017. "The value of electricity and reserve services in low carbon electricity systems," Applied Energy, Elsevier, vol. 201(C), pages 111-123.
  • Handle: RePEc:eee:appene:v:201:y:2017:i:c:p:111-123
    DOI: 10.1016/j.apenergy.2017.05.094
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    as
    1. Ederer, Nikolaus, 2015. "The market value and impact of offshore wind on the electricity spot market: Evidence from Germany," Applied Energy, Elsevier, vol. 154(C), pages 805-814.
    2. Watson, Jim & Gross, Rob & Ketsopoulou, Ioanna & Winskel, Mark, 2015. "The impact of uncertainties on the UK's medium-term climate change targets," Energy Policy, Elsevier, vol. 87(C), pages 685-695.
    3. Cludius, Johanna & Hermann, Hauke & Matthes, Felix Chr. & Graichen, Verena, 2014. "The merit order effect of wind and photovoltaic electricity generation in Germany 2008–2016: Estimation and distributional implications," Energy Economics, Elsevier, vol. 44(C), pages 302-313.
    4. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
    5. Kannan, R., 2009. "Uncertainties in key low carbon power generation technologies - Implication for UK decarbonisation targets," Applied Energy, Elsevier, vol. 86(10), pages 1873-1886, October.
    6. Patteeuw, Dieter & Henze, Gregor P. & Helsen, Lieve, 2016. "Comparison of load shifting incentives for low-energy buildings with heat pumps to attain grid flexibility benefits," Applied Energy, Elsevier, vol. 167(C), pages 80-92.
    7. Gunnar Luderer & Valentina Bosetti & Michael Jakob & Marian Leimbach & Jan Steckel & Henri Waisman & Ottmar Edenhofer, 2012. "The economics of decarbonizing the energy system—results and insights from the RECIPE model intercomparison," Climatic Change, Springer, vol. 114(1), pages 9-37, September.
    8. Würzburg, Klaas & Labandeira, Xavier & Linares, Pedro, 2013. "Renewable generation and electricity prices: Taking stock and new evidence for Germany and Austria," Energy Economics, Elsevier, vol. 40(S1), pages 159-171.
    9. Kazagic, Anes & Merzic, Ajla & Redzic, Elma & Music, Mustafa, 2014. "Power utility generation portfolio optimization as function of specific RES and decarbonisation targets – EPBiH case study," Applied Energy, Elsevier, vol. 135(C), pages 694-703.
    10. Hirth, Lion & Ueckerdt, Falko & Edenhofer, Ottmar, 2015. "Integration costs revisited – An economic framework for wind and solar variability," Renewable Energy, Elsevier, vol. 74(C), pages 925-939.
    11. Hübler, Michael & Löschel, Andreas, 2013. "The EU Decarbonisation Roadmap 2050—What way to walk?," Energy Policy, Elsevier, vol. 55(C), pages 190-207.
    12. Simoes, Sofia & Nijs, Wouter & Ruiz, Pablo & Sgobbi, Alessandra & Thiel, Christian, 2017. "Comparing policy routes for low-carbon power technology deployment in EU – an energy system analysis," Energy Policy, Elsevier, vol. 101(C), pages 353-365.
    13. Florian Ziel, 2015. "Forecasting Electricity Spot Prices using Lasso: On Capturing the Autoregressive Intraday Structure," Papers 1509.01966, arXiv.org, revised Jan 2016.
    14. Gelabert, Liliana & Labandeira, Xavier & Linares, Pedro, 2011. "An ex-post analysis of the effect of renewables and cogeneration on Spanish electricity prices," Energy Economics, Elsevier, vol. 33(S1), pages 59-65.
    15. Nowotarski, Jakub & Raviv, Eran & Trück, Stefan & Weron, Rafał, 2014. "An empirical comparison of alternative schemes for combining electricity spot price forecasts," Energy Economics, Elsevier, vol. 46(C), pages 395-412.
    16. Adam Misiorek & Rafal Weron, 2006. "Interval forecasting of spot electricity prices," HSC Research Reports HSC/06/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    17. Kristiansen, Tarjei, 2012. "Forecasting Nord Pool day-ahead prices with an autoregressive model," Energy Policy, Elsevier, vol. 49(C), pages 328-332.
    18. Tan, Zhongfu & Zhang, Jinliang & Wang, Jianhui & Xu, Jun, 2010. "Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models," Applied Energy, Elsevier, vol. 87(11), pages 3606-3610, November.
    19. Just, Sebastian & Weber, Christoph, 2008. "Pricing of reserves: Valuing system reserve capacity against spot prices in electricity markets," Energy Economics, Elsevier, vol. 30(6), pages 3198-3221, November.
    20. Aghaei, J. & Shayanfar, H.A. & Amjady, N., 2009. "Joint market clearing in a stochastic framework considering power system security," Applied Energy, Elsevier, vol. 86(9), pages 1675-1682, September.
    21. Clò, Stefano & Cataldi, Alessandra & Zoppoli, Pietro, 2015. "The merit-order effect in the Italian power market: The impact of solar and wind generation on national wholesale electricity prices," Energy Policy, Elsevier, vol. 77(C), pages 79-88.
    22. Pfenninger, Stefan & Keirstead, James, 2015. "Renewables, nuclear, or fossil fuels? Scenarios for Great Britain’s power system considering costs, emissions and energy security," Applied Energy, Elsevier, vol. 152(C), pages 83-93.
    23. Jónsson, Tryggvi & Pinson, Pierre & Madsen, Henrik, 2010. "On the market impact of wind energy forecasts," Energy Economics, Elsevier, vol. 32(2), pages 313-320, March.
    24. Maciejowska, Katarzyna & Nowotarski, Jakub & Weron, Rafał, 2016. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," International Journal of Forecasting, Elsevier, vol. 32(3), pages 957-965.
    25. Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.
    26. Barteczko-Hibbert, Christian & Bonis, Ioannis & Binns, Michael & Theodoropoulos, Constantinos & Azapagic, Adisa, 2014. "A multi-period mixed-integer linear optimisation of future electricity supply considering life cycle costs and environmental impacts," Applied Energy, Elsevier, vol. 133(C), pages 317-334.
    27. Newbery, David M., 2016. "Towards a green energy economy? The EU Energy Union’s transition to a low-carbon zero subsidy electricity system – Lessons from the UK’s Electricity Market Reform," Applied Energy, Elsevier, vol. 179(C), pages 1321-1330.
    28. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, vol. 21(3), pages 435-462.
    29. Dupont, B. & De Jonghe, C. & Olmos, L. & Belmans, R., 2014. "Demand response with locational dynamic pricing to support the integration of renewables," Energy Policy, Elsevier, vol. 67(C), pages 344-354.
    30. Staffell, Iain, 2017. "Measuring the progress and impacts of decarbonising British electricity," Energy Policy, Elsevier, vol. 102(C), pages 463-475.
    31. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    32. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    33. Gaillard, Pierre & Goude, Yannig & Nedellec, Raphaël, 2016. "Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1038-1050.
    34. Weigt, Hannes, 2009. "Germany's wind energy: The potential for fossil capacity replacement and cost saving," Applied Energy, Elsevier, vol. 86(10), pages 1857-1863, October.
    35. Nistor, Silviu & Wu, Jianzhong & Sooriyabandara, Mahesh & Ekanayake, Janaka, 2015. "Capability of smart appliances to provide reserve services," Applied Energy, Elsevier, vol. 138(C), pages 590-597.
    36. Poncelet, Kris & Delarue, Erik & Six, Daan & Duerinck, Jan & D’haeseleer, William, 2016. "Impact of the level of temporal and operational detail in energy-system planning models," Applied Energy, Elsevier, vol. 162(C), pages 631-643.
    37. Ries, Jan & Gaudard, Ludovic & Romerio, Franco, 2016. "Interconnecting an isolated electricity system to the European market: The case of Malta," Utilities Policy, Elsevier, vol. 40(C), pages 1-14.
    38. Nowotarski, Jakub & Weron, Rafał, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 57(C), pages 228-235.
    39. Sensfuß, Frank & Ragwitz, Mario & Genoese, Massimo, 2008. "The merit-order effect: A detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany," Energy Policy, Elsevier, vol. 36(8), pages 3076-3084, August.
    40. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
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