Publications
9 results found
DAeth J, Ghosal S, Grimm F, et al., 2023, Optimal hospital care scheduling during the SARS-CoV-2 pandemic, Management Science, Vol: 69, Pages: 5923-5947, ISSN: 0025-1909
The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity so as to reduce the backlog of non-COVID patients whilst maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nation-wide prioritization scheme that models each individual patient as a dynamic program whose states encode the patient’s health and treatment condition, whose actions describe the available treatment options, whose transition probabilities characterize the stochastic evolution of the patient’s health and whose rewards encode the contribution to the overall objectives of the health system. The individual patients’ dynamic programs are coupled through constraints on the available resources, such as hospital beds, doctors and nurses. We show that the overall problem can be modeled as a grouped weakly coupled dynamic program for which we determine near-optimal solutions through a fluid approximation. Our case study for the National Health Service in England shows how years of life can be gained by prioritizing specific disease types over COVID patients, such as injury & poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system and cancer.
Yliruka MI, Moret S, Jalil-Vega F, et al., 2022, The Trade-Off between Spatial Resolution and Uncertainty in Energy System Modelling, Computer Aided Chemical Engineering, Pages: 2035-2040
In energy system models, computational tractability is often maintained by adopting a simplified temporal and spatial representation in a deterministic model formulation i.e., neglecting uncertainty. However, such simplifications have been shown to impact the optimal result. To address the question of how to prioritize the limited computational resources, the trade-off between spatial resolution and uncertainty is assessed by applying a novel method based on global sensitivity analysis to a peer-reviewed heat decarbonization model. For all output variables apart from the total system and fuel cost, spatial resolution is ranks amongst the five most important model inputs. It is the most relevant factor for investment decisions on network capacities. For the total fuel consumption and emissions, spatial resolution turns out to be more relevant than the fuel prices themselves. Compared across all outputs, the analysis suggests the impact of spatial resolution is comparable the impact of heat demand levels and the discount rate.
Borasio M, Moret S, 2022, Deep decarbonisation of regional energy systems: A novel modelling approach and its application to the Italian energy transition, Renewable and Sustainable Energy Reviews, Vol: 153, Pages: 1-19, ISSN: 1364-0321
Deep decarbonisation – i.e. the transition towards net-zero emissions energy systems – will be enabled by a high penetration of intermittent renewables, storage and sector-coupling technologies. In this paper, we present a novel modelling approach to capture the increasing complexity of such future energy systems and help policy makers choose among the different possible transition scenarios. Salient features of our model, consisting of an extended and regionalised version of EnergyScope (Limpens et al., 2019 [1]), are a low computational time and a concise formulation which make it suitable for uncertainty and what-if analyses. As a case study, the model is applied to devise scenarios for the Italian energy transition. Specifically, we develop the first open-source whole-energy system model of Italy and assess the feasibility of its decarbonisation strategy with respect to uncertainties in the deployment of carbon capture and storage (CCS) and renewable technologies. Results show that emissions can be cut by 79%–97% vs. 1990 levels thanks to a radical electrification of the energy system coupled to a wide deployment of renewables and efficient energy conversion technologies. Finally, we discuss the synergies, advantages and disadvantages of our proposed approach with respect to alternative modelling approaches used across 88 recent deep decarbonisation studies. The analysis suggests that our model, thanks to its computational efficiency and a snapshot approach (i.e., modelling a target-year in the future), can complement more detailed and established energy models optimising the energy transition pathway (i.e., modelling the pathway from today to the target year).
DAeth J, Ghosal S, Grimm F, et al., 2021, Optimal national prioritization policies for hospital care during the SARS-CoV-2 pandemic, Nature Computational Science, Vol: 1, Pages: 521-531, ISSN: 2662-8457
In response to unprecedent surges in the demand for hospital care during the SARS-CoV-2 pandemic, health systems have prioritized COVID patients to life-saving hospital care to the detriment of other patients. In contrast to these ad hoc policies, we develop a linear programming framework to optimally schedule elective procedures and allocate hospital beds among all planned and emergency patients to minimize years of life lost. Leveraging a large dataset of administrative patient medical records, we apply our framework to the National Health System in England and show that an extra 50,750-5,891,608 years of life can be gained in comparison to prioritization policies that reflect those implemented during the pandemic. Significant health gains are observed for neoplasms, diseases of the digestive system, and injuries & poisoning. Our open-source framework provides a computationally efficient approximation of a large-scale discrete optimization problem that can be applied globally to support national-level care prioritization policies.
Moret S, Limpens G, Dommisse J, 2021, glimpens/EnergyScope: Improved model of EnergyScope TD
This version of the code is related to G.Limpens PhD thesis (2021). Compare to previous version (v2.0), this version:includes the cost of mobility technologiesincludes a panel of technologies to produce synthetic fuelsincludes renewable fuels that can be importedhas minor changes in the model formulation
D'Aeth J, Ghosal S, Grimm F, et al., 2020, Report 40: Optimal scheduling rules for elective care to minimize years of life lost during the SARS-CoV-2 pandemic: an application to England
SummaryCountries have deployed a wide range of policies to prioritize patients to hospital care to address unprecedent surges in demand during the course of the pandemic. Those policies included postponing planned hospital care for non-emergency cases and rationing critical care.We develop a model to optimally schedule elective hospitalizations and allocate hospital general and critical care beds to planned and emergency patients in England during the pandemic. We apply the model to NHS England data and show that optimized scheduling leads to lower years of life lost and costs than policies that reflect those implemented in England during the pandemic. Overall across all disease areas the model enables an extra 50,750 - 5,891,608 years of life gained when compared to standard policies, depending on the scenarios. Especially large gains in years of life are seen for neoplasms, diseases of the digestive system, and injuries & poisoning.
Contino F, Moret S, Limpens G, et al., 2020, Whole-energy system models: The advisors for the energy transition, PROGRESS IN ENERGY AND COMBUSTION SCIENCE, Vol: 81, ISSN: 0360-1285
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- Citations: 13
Guevara E, Babonneau F, Homem-de-Mello T, et al., 2020, A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty, Applied Energy, Vol: 271, Pages: 1-18, ISSN: 0306-2619
This paper investigates how the choice of stochastic approaches and distribution assumptions impacts strategic investment decisions in energy planning problems. We formulate a two-stage stochastic programming model assuming different distributions for the input parameters and show that there is significant discrepancy among the associated stochastic solutions and other robust solutions published in the literature. To remedy this sensitivity issue, we propose a combined machine learning and distributionally robust optimization (DRO) approach which produces more robust and stable strategic investment decisions with respect to uncertainty assumptions. DRO is applied to deal with ambiguous probability distributions and Machine Learning is used to restrict the DRO model to a subset of important uncertain parameters ensuring computational tractability. Finally, we perform an out-of-sample simulation process to evaluate solutions performances. The Swiss energy system is used as a case study all along the paper to validate the approach.
Baldi F, Moret S, Tammi K, et al., 2020, The role of solid oxide fuel cells in future ship energy systems, ENERGY, Vol: 194, ISSN: 0360-5442
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- Citations: 64
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