Solar energy forecasting and optimization system for efficient renewable energy integration

Diana Manjarres*, Ricardo Alonso, Sergio Gil-Lopez, Itziar Landa-Torres

*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

Solar energy forecasting represents a key issue in order to efficiently manage the supply-demand balance and promote an effective renewable energy integration. In this regard, an accurate solar energy forecast is of utmoss importance for avoiding large voltage variations into the electricity network and providing the system with mechanisms for managing the produced energy in an optimal way. This paper presents a novel solar energy forecasting and optimization approach called SUNSET which efficiently determines the optimal energy management for the next 24 h in terms of: self-consumption, energy purchase and battery energy storage for later consumption. The proposed SUNSET approach has been tested in a real solar PV system plant installed in Zamudio (Spain) and compared towards a Real-Time (RT) strategy in terms of price and energy savings obtaining attractive results.

Idioma originalInglés
Título de la publicación alojadaData Analytics for Renewable Energy Integration
Subtítulo de la publicación alojadaInforming the Generation and Distribution of Renewable Energy - 5th ECML PKDD Workshop, DARE 2017, Revised Selected Papers
EditoresOliver Kramer, Stuart Madnick, Wei Lee Woon, Zeyar Aung
EditorialSpringer Verlag
Páginas1-12
Número de páginas12
ISBN (versión impresa)9783319716428
DOI
EstadoPublicada - 2017
Evento5th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2017 - Skopje, Antigua República Yugoslava de Macedonia
Duración: 22 sept 201722 sept 2017

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10691 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia5th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2017
País/TerritorioAntigua República Yugoslava de Macedonia
CiudadSkopje
Período22/09/1722/09/17

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