Machine learning approach for smart operation of PV-battery systems at Cranfield University Campus

Pedraza Alonso, David Segundo (2020). Machine learning approach for smart operation of PV-battery systems at Cranfield University Campus. Thesis (Master thesis), E.T.S.I. Industriales (UPM).

Description

Title: Machine learning approach for smart operation of PV-battery systems at Cranfield University Campus
Author/s:
  • Pedraza Alonso, David Segundo
Contributor/s:
  • Long, Chao
  • Migoya Valor, Emilio
Item Type: Thesis (Master thesis)
Masters title: Ingeniería Industrial
Date: 7 September 2020
Subjects:
Freetext Keywords: Renewable Energy; PV Plant; Batteries; Energy Storage; Dispatching Strategy; Machine Learning; Neural Networks.
Faculty: E.T.S.I. Industriales (UPM)
Department: Ingeniería Mecánica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (19MB) | Preview

Abstract

The rapid growth of large-scale PV plants is increasing the need for energy storage, being batteries the most promising technology in the short-term. Research focused on improving the feasibility of PV-battery systems could help to encourage their development. This study contributes to this process, studying how optimising the operation could make these systems more cost-effective. The optimal operation can be obtained with an optimisation algorithm, that minimises the daily cost of electricity. Conventional methods use this approach, but need to forecast PV generation, electricity price and demand in real-time, with the problems that it entails. A novel method based on Machine Learning (ML) is proposed in this thesis. Past data is used to train the model, that learns to predict the optimal operation. This method is assessed for the case of Cranfield University, where a new PV-battery plant has been proposed. Conventional and ML methods are compared, analysing their performance and calculating their potential savings. After this analysis, ML outperformed the conventional operation, obtaining 13% more accuracy and 5% more savings. For Cranfield University, the cost-benefit analysis showed that including a PV-battery system would be beneficial, saving more than £300k per year. The benefits from using ML operation are high, particularly when increasing PV or battery capacity. Therefore, this methodology could have a great impact on large-scale PV-battery applications.

More information

Item ID: 65519
DC Identifier: https://oa.upm.es/65519/
OAI Identifier: oai:oa.upm.es:65519
Deposited by: David S. Pedraza Alonso
Deposited on: 24 Nov 2020 08:42
Last Modified: 24 Nov 2020 08:42
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM