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Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model

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  • Lombardi, Francesco
  • Balderrama, Sergio
  • Quoilin, Sylvain
  • Colombo, Emanuela

Abstract

Energy access projects in remote off-grid areas would benefit from the adoption of a multi-energy system perspective, addressing all energy needs – not only lighting and power appliances, but also water-heating and cooking – by means of a mix of energy vectors. However, multi-energy analyses in remote areas are hindered by a lack of models allowing for the generation of multi-energy load profiles based on interview-based information characterised by high uncertainty. This study proposes a novel open-source bottom-up stochastic model specifically conceived for the generation of multi-energy loads for systems located in remote areas. The model is tested and validated against data obtained from a real system, showing a very good approximation of measured profiles, with percentage errors consistently below 2% for all the selected indicators, and an improved accuracy compared to existing approaches. In particular, some innovative features – such as the possibility to define and modulate throughout the day appliances’ duty cycles – seem to be determinant in marking a difference with previous approaches. This might arguably be even more beneficial for case studies characterised by a larger penetration of appliances that are subject to complex and unpredictable duty cycle behaviour.

Suggested Citation

  • Lombardi, Francesco & Balderrama, Sergio & Quoilin, Sylvain & Colombo, Emanuela, 2019. "Generating high-resolution multi-energy load profiles for remote areas with an open-source stochastic model," Energy, Elsevier, vol. 177(C), pages 433-444.
    • Handle: RePEc:eee:energy:v:177:y:2019:i:c:p:433-444
    DOI: 10.1016/j.energy.2019.04.097
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    Cited by:

    1. Giacomo Falchetta & Nicolò Stevanato & Magda Moner-Girona & Davide Mazzoni & Emanuela Colombo & Manfred Hafner, 2020. "M-LED: Multi-sectoral Latent Electricity Demand Assessment for Energy Access Planning," Working Papers 2020.09, Fondazione Eni Enrico Mattei.
    2. Mangipinto, Andrea & Lombardi, Francesco & Sanvito, Francesco Davide & Pavičević, Matija & Quoilin, Sylvain & Colombo, Emanuela, 2022. "Impact of mass-scale deployment of electric vehicles and benefits of smart charging across all European countries," Applied Energy, Elsevier, vol. 312(C).
    3. Marlon Schlemminger & Raphael Niepelt & Rolf Brendel, 2021. "A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles," Energies, MDPI, vol. 14(8), pages 1-24, April.
    4. Balderrama, Sergio & Lombardi, Francesco & Riva, Fabio & Canedo, Walter & Colombo, Emanuela & Quoilin, Sylvain, 2019. "A two-stage linear programming optimization framework for isolated hybrid microgrids in a rural context: The case study of the “El Espino” community," Energy, Elsevier, vol. 188(C).
    5. Matthew Leach & Chris Mullen & Jacquetta Lee & Bartosz Soltowski & Neal Wade & Stuart Galloway & William Coley & Shafiqa Keddar & Nigel Scott & Simon Batchelor, 2021. "Modelling the Costs and Benefits of Modern Energy Cooking Services—Methods and Case Studies," Energies, MDPI, vol. 14(12), pages 1-28, June.
    6. Weinand, Jann Michael & Scheller, Fabian & McKenna, Russell, 2020. "Reviewing energy system modelling of decentralized energy autonomy," Energy, Elsevier, vol. 203(C).
    7. Laha, Priyanka & Chakraborty, Basab, 2021. "Low carbon electricity system for India in 2030 based on multi-objective multi-criteria assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    8. Maria Christina Gudrun Hart & Michael Hans Breitner, 2022. "Fostering Energy Resilience in the Rural Thai Power System—A Case Study in Nakhon Phanom," Energies, MDPI, vol. 15(19), pages 1-20, October.
    9. Mattia Dallapiccola & Grazia Barchi & Jennifer Adami & David Moser, 2021. "The Role of Flexibility in Photovoltaic and Battery Optimal Sizing towards a Decarbonized Residential Sector," Energies, MDPI, vol. 14(8), pages 1-18, April.
    10. Falchetta, Giacomo & Stevanato, Nicolò & Moner-Girona, Magda & Mazzoni, Davide & Colombo, Emanuela & Hafner, Manfred, 2020. "M-LED: Multi-sectoral Latent Electricity Demand Assessment for Energy Access Planning," FEP: Future Energy Program 305213, Fondazione Eni Enrico Mattei (FEEM) > FEP: Future Energy Program.
    11. Balderrama, Sergio & Lombardi, Francesco & Stevanato, Nicolo & Peña, Gabriela & Colombo, Emanuela & Quoilin, Sylvain, 2021. "Surrogate models for rural energy planning: Application to Bolivian lowlands isolated communities," Energy, Elsevier, vol. 232(C).
    12. Totaro, Simone & Boukas, Ioannis & Jonsson, Anders & Cornélusse, Bertrand, 2021. "Lifelong control of off-grid microgrid with model-based reinforcement learning," Energy, Elsevier, vol. 232(C).
    13. William Clements & Surendra Pandit & Prashanna Bajracharya & Joe Butchers & Sam Williamson & Biraj Gautam & Paul Harper, 2021. "Techno-Economic Modelling of Micro-Hydropower Mini-Grids in Nepal to Improve Financial Sustainability and Enable Electric Cooking," Energies, MDPI, vol. 14(14), pages 1-23, July.

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