Impacts of Heat Decarbonisation on System Adequacy considering Increased Meteorological Sensitivity

Abstract This paper explores the impacts of decarbonization of heat on demand and subsequently on the generation capacity required to secure against system adequacy standards. Gas demand is explored as a proxy variable for modelling the electrification of heating demand in existing housing stock, with a focus on impacts on timescales of capacity markets (up to four years ahead). The work considers the systemic changes that electrification of heating could introduce, including biases that could be introduced if legacy modelling approaches continue to prevail. Covariates from gas and electrical regression models are combined to form a novel, time-collapsed system model, with demand-weather sensitivities determined using lasso-regularized linear regression. It is shown, using a Great Britain-based case study with one million domestic heat pump installations per year, that the sensitivity of electrical system demand to temperature (and subsequently sensitivities to cold/warm winter seasons) could increase by 50% following four years of heat demand electrification. A central estimate of 1.75 kW additional peak demand per heat pump is estimated, with variability across three published heat demand profiles leading to a range of more than 14 GW in the most extreme cases. It is shown that the legacy approach of scaling historic demand, as compared to the explicit modelling of heat, could lead to over-procurement of 0.79 GW due to bias in estimates of additional capacity to secure. Failure to address this issue could lead to £100m overspend on capacity over ten years.

[1]  Hans C. van Houwelingen,et al.  The Elements of Statistical Learning, Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer, New York, 2001. No. of pages: xvi+533. ISBN 0‐387‐95284‐5 , 2004 .

[2]  Hazel E. Thornton,et al.  The changing sensitivity of power systems to meteorological drivers: a case study of Great Britain , 2018 .

[3]  R. Gross,et al.  Path dependency in provision of domestic heating , 2019, Nature Energy.

[4]  Matthias C. M. Troffaes,et al.  Assessing the contribution of nightly rechargeable grid-scale storage to generation capacity adequacy , 2017 .

[5]  Muireann Á. Lynch,et al.  The impacts of demand response participation in capacity markets , 2019, Applied Energy.

[6]  Adam A. Scaife,et al.  The role of temperature in the variability and extremes of electricity and gas demand in Great Britain , 2016 .

[7]  D. Newbery.  Missing Money and Missing Markets: Reliability, Capacity Auctions and Interconnectors , 2015 .

[8]  S. Pfenninger,et al.  Using bias-corrected reanalysis to simulate current and future wind power output , 2016 .

[9]  P. Cramton,et al.  A capacity market that makes sense , 2005, IEEE Power Engineering Society General Meeting, 2005.

[10]  Alastair R. Buckley,et al.  A review of energy systems models in the UK: Prevalent usage and categorisation , 2016 .

[11]  K. Lomas,et al.  Decarbonising domestic heating: What is the peak GB demand? , 2019, Energy Policy.

[12]  Matthew Deakin,et al.  Calculations of System Adequacy Considering Heat Transition Pathways , 2020, 2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[13]  Stan Zachary,et al.  Estimation of Joint Distribution of Demand and Available Renewables for Generation Adequacy Assessment , 2014 .

[14]  Swasti R. Khuntia,et al.  Forecasting the load of electrical power systems in mid- and long-term horizons : A review , 2016 .

[15]  Pierluigi Mancarella,et al.  Assessment of the impact of heating on integrated gas and electrical network flexibility , 2016, 2016 Power Systems Computation Conference (PSCC).

[16]  Robert Lowe,et al.  Heat Decarbonisation Modelling Approaches in the UK: An Energy System Architecture Perspective , 2020, Energies.

[17]  P. Cramton.  Electricity market design , 2017 .

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

[19]  Matthias C. M. Troffaes,et al.  Impact of high wind penetration on variability of unserved energy in power system adequacy , 2016, 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[20]  Lennart Söder,et al.  Review of wind generation within adequacy calculations and capacity markets for different power systems , 2020, Renewable and Sustainable Energy Reviews.

[21]  Remco Verzijlbergh,et al.  How do demand response and electrical energy storage affect (the need for) a capacity market , 2018 .

[22]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[23]  Geoffrey P. Hammond,et al.  Detailed simulation of electrical demands due to nationwide adoption of heat pumps, taking account of renewable generation and mitigation , 2016 .

[24]  Yun Li,et al.  The Use of Extreme Value Theory for Forecasting Long-Term Substation Maximum Electricity Demand , 2020, IEEE Transactions on Power Systems.

[25]  Bin Zhao,et al.  The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). , 2017, Journal of climate.

[26]  L. Shaffrey,et al.  Quantifying the increasing sensitivity of power systems to climate variability , 2016 .

[27]  P. Mancarella,et al.  Integrated electricity-heat-gas modelling and assessment, with applications to the Great Britain system. Part II: Transmission network analysis and low carbon technology and resilience case studies , 2018, Energy.

[28]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[29]  Alex Summerfield,et al.  The addition of heat pump electricity load profiles to GB electricity demand: Evidence from a heat pump field trial , 2017 .

[31]  Rob J Hyndman,et al.  Density Forecasting for Long-Term Peak Electricity Demand , 2010, IEEE Transactions on Power Systems.

[32]  Jonathan L. Ho,et al.  Considerations for maintaining resource adequacy of electricity systems with high penetrations of PV and storage , 2020 .

[33]  Adam Hawkes,et al.  A review of domestic heat pumps , 2012 .

[34]  Lars Nolting,et al.  Can we phase-out all of them? Probabilistic assessments of security of electricity supply for the German case , 2020 .

[35]  Jim W. Hall,et al.  A high-resolution spatio-temporal energy demand simulation to explore the potential of heating demand side management with large-scale heat pump diffusion , 2019, Applied Energy.

[36]  A. Charlton-Perez,et al.  Characterizing the winter meteorological drivers of the European electricity system using targeted circulation types , 2019, Meteorological Applications.

[37]  Rob J Hyndman,et al.  Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond , 2016 .

[38]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[39]  Stan Zachary,et al.  Use of meteorological data for improved estimation of risk in capacity adequacy studies , 2018, 2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[40]  Lion Hirth,et al.  Time series of heat demand and heat pump efficiency for energy system modeling , 2019, Scientific Data.

[41]  Pranab J. Baruah,et al.  Uncertainties in future energy demand in UK residential heating , 2015 .

[42]  Iain Staffell,et al.  The increasing impact of weather on electricity supply and demand , 2018 .

[43]  J. Hall,et al.  How weather affects energy demand variability in the transition towards sustainable heating , 2020, Energy.

[44]  Matteo De Felice,et al.  Seasonal climate forecasts for medium-term electricity demand forecasting , 2015 .

[45]  Dirk J. Cannon,et al.  Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain , 2015 .

[46]  R. Sioshansi,et al.  Capacity value of solar power: Report of the IEEE PES task force on capacity value of solar power , 2016, 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[47]  Axel Gandy,et al.  Importance subsampling: improving power system planning under climate-based uncertainty , 2019, Applied Energy.

[48]  Stan Zachary,et al.  The Integration of Variable Generation and Storage into Electricity Capacity Markets , 2019, 1907.05973.

[49]  Nicolas Kelly,et al.  Historical daily gas and electrical energy flows through Great Britain's transmission networks and the decarbonisation of domestic heat $ , 2013 .