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Hilbers AP, Brayshaw DJ, Gandy A, 2023, Reducing climate risk in energy system planning: A posteriori time series aggregation for models with storage, APPLIED ENERGY, Vol: 334, ISSN: 0306-2619
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Hilbers AP, Brayshaw DJ, Gandy A, 2021, Efficient quantification of the impact of demand and weather uncertainty in power system models, IEEE Transactions on Power Systems, Vol: 36, Pages: 1771-1779, ISSN: 0885-8950
This paper introduces a novel approach to quantify the effect of forwardpropagated demand and weather uncertainty on power system planning andoperation model outputs. Recent studies indicate that such samplinguncertainty, originating from demand and weather time series inputs, should notbe ignored. However, established uncertainty quantification approaches fail inthis context due to the computational resources and additional data requiredfor Monte Carlo-based analysis. The method introduced here quantifiesuncertainty on model outputs using a bootstrap scheme with shorter time seriesthan the original, enhancing computational efficiency and avoiding the need forany additional data. It both quantifies output uncertainty and determines thesample length required for desired confidence levels. Simulations performed ontwo generation and transmission expansion planning models and one unitcommitment and economic dispatch model illustrate the method's efficacy. A testis introduced allowing users to determine whether estimated uncertainty boundsare valid. The models, data and code applying the method are provided asopen-source software.
Hilbers A, Brayshaw D, Gandy A, 2020, Importance subsampling for power system planning under multi-year demand and weather uncertainty, PMAPS 2020 (the 16th International Conference on Probabilistic Methods Applied to Power Systems), Publisher: IEEE, Pages: 1-6
This paper introduces a generalised version ofimportance subsamplingfor time series reduction/aggregation inoptimisation-based power system planning models. Recent studiesindicate that reliably determining optimal electricity (investment)strategy under climate variability requires the consideration ofmultiple years of demand and weather data. However, solvingplanning models over long simulation lengths is typically com-putationally unfeasible, and established time series reductionapproaches induce significant errors. Theimportance subsamplingmethod reliably estimates long-term planning model outputs atgreatly reduced computational cost, allowing the considerationof multi-decadal samples. The key innovation is a systematicidentification and preservation of relevant extreme events inmodeling subsamples. Simulation studies on generation andtransmission expansion planning models illustrate the method’senhanced performance over established “representative days”clustering approaches. The models, data and sample code aremade available as open-source software.
Hilbers A, Brayshaw D, Gandy A, 2019, Importance subsampling: Improving power system planning under climate-based uncertainty, Applied Energy, Vol: 251, Pages: 1-12, ISSN: 0306-2619
Recent studies indicate that the effects of inter-annual climate-based variability in power system planning are significant and that long samples of demand & weather data (spanning multiple decades) should be considered. At the same time, modelling renewable generation such as solar and wind requires high temporal resolution to capture fluctuations in output levels. In many realistic power system models, using long samples at high temporal resolution is computationally unfeasible. This paper introduces a novel subsampling approach, referred to as importance subsampling, allowing the use of multiple decades of demand & weather data in power system planning models at reduced computational cost. The methodology can be applied in a wide class of optimisation-based power system simulations. A test case is performed on a model of the United Kingdom created using the open-source modelling framework Calliope and 36 years of hourly demand and wind data. Standard data reduction approaches such as using individual years or clustering into representative days lead to significant errors in estimates of optimal system design. Furthermore, the resultant power systems lead to supply capacity shortages, raising questions of generation capacity adequacy. In contrast, importance subsampling leads to accurate estimates of optimal system design at greatly reduced computational cost, with resultant power systems able to meet demand across all 36 years of demand & weather scenarios.
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