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DC Field | Value | Language |
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dc.contributor.author | Mesa-Jiménez, JJ | - |
dc.contributor.author | Tzianoumis, AL | - |
dc.contributor.author | Stokes, L | - |
dc.contributor.author | Yang, Q | - |
dc.contributor.author | Livina, VN | - |
dc.date.accessioned | 2022-12-06T11:41:50Z | - |
dc.date.available | 2022-12-06T11:41:50Z | - |
dc.date.issued | 2022-12-08 | - |
dc.identifier | ORCiD: J.J. Mesa-Jiménez https://orcid.org/0000-0003-0822-2700 | - |
dc.identifier | ORCiD: L. Stokes https://orcid.org/0000-0003-0702-6070 | - |
dc.identifier | ORCiD: Qingping Yang https://orcid.org/0000-0002-2557-8752 | - |
dc.identifier | ORCiD: V.N. Livina https://orcid.org/0000-0003-3759-9013 | - |
dc.identifier.citation | Mesa-Jiménez, J.J. et al. (2022) 'Long-term wind and solar energy generation forecasts, and optimisation of Power Purchase Agreements', Energy Reports, 9, pp. 292 - 302. doi: 10.1016/j.egyr.2022.11.175. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/25590 | - |
dc.description | Data availability: The authors do not have permission to share data. | - |
dc.description.abstract | Due to more affordable solar and wind power, and the European Union regulations for decarbonisation of the economy, more than 40% of the Fortune 500 companies have targets related to green energy. This is one of the main reasons why multi-technology Power-Purchase Agreements (PPAs) are becoming increasingly important. However, there are risks associated with the uncertainty and variable generation patterns in wind speed and solar radiation. Moreover, there are challenges to predict intermittent wind and solar generation for the forecasting horizon required by PPAs, which is usually of several years. We propose a long-term wind and solar energy generation forecasts suitable for PPAs with cost optimisation in energy generation scenarios. We use Markov Chain Monte Carlo simulations with suitable models of wind and solar generation and optimise long-term energy contracts with purchase of renewable energy. | - |
dc.description.sponsorship | Department for Business, Energy and Industrial Strategy of the United Kingdom; Brunel University London. | en_US |
dc.description.sponsorship | Department for Business, Energy and Industrial Strategy of the United Kingdom; Engineering and Physical Sciences Research Council DTP of Brunel University London; National Physical Laboratory; Mitie. | - |
dc.format.extent | 292 - 302 | - |
dc.format.medium | Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Copyright © 2022 The Authors. Published by Elsevier Ltd. under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | power purchase agreements | en_US |
dc.subject | Markov Chain Monte Carlo | en_US |
dc.subject | stochastic forecast | en_US |
dc.subject | renewable energy optimisation | en_US |
dc.title | Long-term wind and solar energy generation forecasts, and optimisation of Power Purchase Agreements | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.egyr.2022.11.175 | - |
dc.relation.isPartOf | Energy Reports | - |
pubs.publication-status | Published | - |
pubs.volume | 9 | - |
dc.identifier.eissn | 2352-4847 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The Authors | - |
Appears in Collections: | Dept of Mechanical and Aerospace Engineering Research Papers |
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FullText.pdf | Copyright © 2022 The Authors. Published by Elsevier Ltd. under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/). | 2.46 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License