Periodic Reporting for period 1 - THINKPV (Forecasting Tool for supporting of grid operations with HIgh INtegration of distributed PV generation)
Reporting period: 2018-09-10 to 2020-09-09
The European Union (EU) policy for climate and energy imposes significant targets for a high integration of renewable energy sources (RES) in the period from 2020 to 2030. More flexibility of the power system is required to respond to variability and uncertainty of the renewable generation without compromise the network reliability and security. The European promotion for a fast deployment of Smart Grids will allow accommodating high intermittent generation and providing the flexibility requirements. Distribution System Operators (DSOs) are facing numerous challenges of the existing infrastructures and they need to know in advance the amount of variable resources available within operations. Solar power generation forecasts are important tools for power grid operations. The solar power generation forecasting has also a significant influence on grid operation costs, considering that forecast uncertainty increases further the overall costs for utilities, customers, system operators and electricity market participants. Therefore the knowledge in advance of the solar generation and high forecast accuracy can lead to more efficient management of distribution systems in a cost-effective way, enabling a large-scale deployment of variable distributed resources.
The state-of-the-art for photovoltaic (PV) power forecasts deals with various forecasting techniques, several temporal and spatial horizons and different approaches. The solar power forecasting is traditionally performed by using statistical methods which use past observations of weather variables and solar power as input. Recently, Artificial Intelligence techniques are introduced for predicting the PV power output, extracting relations on past data to predict the PV power output without any information from system.
Forecasts can be classified is according to the time horizons, impacting on different aspects of grid operation. The intraday forecasts ensure the grid quality and stability, the one-day forecasts are normally used for planning, unit commitment and energy market. PV forecast can be performed for a single plant or at regional scale (spatial horizon of forecasts). Considering that grid operators deal with balancing between demand and supply in the electric system, the regional forecasts are more attractive than point forecasts. Depending on the availability of data regional forecasts can be approached by several ways. Generally, only PV power from some plants is known, so the up-scaling method represents the most suitable solution, where a set of PV plants, selected by correlation parameters and smoothing effect, became representative of the power output of the whole ensemble.
The overall objectives of this project is to improve the forecast accuracy for PV generation by the development of a novel spatial-temporal forecasting method. A forecasting method on a regional scale and at multi-time horizon for
PV generation has been developed using machine learning techniques applied to huge amount of power generation data collected from solar PV plants in the smart grids environment. Such data collection has supported the investigation of the exiting correlations between the different PV plants in order to improve the predictions accuracy with respect to time-domain forecasting models.
Further goals of the project were:
- To address reliability issues of PV systems in operational condition by using Artificial intelligence techniques, to identify and characterize failures and successively support the predictive maintenance strategies.
- To transfer of knowledge on Artificial intelligence techniques for PV generation. The findings of the project were disseminated in research, academia and the industry sectors.
- To foster the development of the individual researcher.
The state-of-the-art for photovoltaic (PV) power forecasts deals with various forecasting techniques, several temporal and spatial horizons and different approaches. The solar power forecasting is traditionally performed by using statistical methods which use past observations of weather variables and solar power as input. Recently, Artificial Intelligence techniques are introduced for predicting the PV power output, extracting relations on past data to predict the PV power output without any information from system.
Forecasts can be classified is according to the time horizons, impacting on different aspects of grid operation. The intraday forecasts ensure the grid quality and stability, the one-day forecasts are normally used for planning, unit commitment and energy market. PV forecast can be performed for a single plant or at regional scale (spatial horizon of forecasts). Considering that grid operators deal with balancing between demand and supply in the electric system, the regional forecasts are more attractive than point forecasts. Depending on the availability of data regional forecasts can be approached by several ways. Generally, only PV power from some plants is known, so the up-scaling method represents the most suitable solution, where a set of PV plants, selected by correlation parameters and smoothing effect, became representative of the power output of the whole ensemble.
The overall objectives of this project is to improve the forecast accuracy for PV generation by the development of a novel spatial-temporal forecasting method. A forecasting method on a regional scale and at multi-time horizon for
PV generation has been developed using machine learning techniques applied to huge amount of power generation data collected from solar PV plants in the smart grids environment. Such data collection has supported the investigation of the exiting correlations between the different PV plants in order to improve the predictions accuracy with respect to time-domain forecasting models.
Further goals of the project were:
- To address reliability issues of PV systems in operational condition by using Artificial intelligence techniques, to identify and characterize failures and successively support the predictive maintenance strategies.
- To transfer of knowledge on Artificial intelligence techniques for PV generation. The findings of the project were disseminated in research, academia and the industry sectors.
- To foster the development of the individual researcher.
The objectives have been addressed through five specific work packages (WP): (1) Project management; (2) Training and Career Development; (3) Development of the forecasting model of the solar generation in a smart grid environment; (4) Application of the Artificial intelligence techniques for predictive maintenance of grid-connected photovoltaic systems; (5) Dissemination and Public Engagement.
* WP1 Project Management
The intention of WP1 was to provide essential information to the European Commission regarding the progress of the grant. The present final report is the deliverable of WP1.
* WP2 Training and Career Development
The aim of WP2 was to increase the research and complementary skills of the Researcher. During the fellowship, the researcher gained new knowledge and skills, by pursuing the following objectives:
• To acquire specialist new knowledge in power systems
• To reinforce the teaching and mentoring skills;
• To increase the research track record;
• To improve the presentation and communication skills;
• To develop a solid collaboration network and contacts.
* WP3 - Development of the forecasting model of the solar generation in a smart grid environment
The aim of WP3 was to develop a short-term forecasting method on a regional scale and at multi-time horizon for PV generation with high accuracy by using measurements spread in the whole distribution network. A novel spatio-temporal forecasting model was developed to get high accurate generation forecasts for individual PV power plants, leading higher performance than time-domain models.
* WP 4 Application of Artificial intelligence techniques for predictive maintenance of grid-connected photovoltaic systems
WP4 included the secondment of 6 months at an Operation and Maintenance (O&M) company for PV systems in order to maximize the energy performance of PV systems by using artificial intelligence technologies and to transfer new knowledge to the partner to improve the reliability of PV plants. This was achieved by addressing issues on how typical failures were associated with the various components of PV systems, by the development of technical functionalities of the failure diagnostic to implement in the existing monitoring system.
* WP5 Dissemination and Public Engagement
The WP5 was specifically dedicated to the scientific dissemination via international conferences and research articles in international peer-reviewed journals as well as the Outreach Activities for the involvement of no scientific public.
Further details related to the results of each WP can be found in the Technical Report (Part B).
* WP1 Project Management
The intention of WP1 was to provide essential information to the European Commission regarding the progress of the grant. The present final report is the deliverable of WP1.
* WP2 Training and Career Development
The aim of WP2 was to increase the research and complementary skills of the Researcher. During the fellowship, the researcher gained new knowledge and skills, by pursuing the following objectives:
• To acquire specialist new knowledge in power systems
• To reinforce the teaching and mentoring skills;
• To increase the research track record;
• To improve the presentation and communication skills;
• To develop a solid collaboration network and contacts.
* WP3 - Development of the forecasting model of the solar generation in a smart grid environment
The aim of WP3 was to develop a short-term forecasting method on a regional scale and at multi-time horizon for PV generation with high accuracy by using measurements spread in the whole distribution network. A novel spatio-temporal forecasting model was developed to get high accurate generation forecasts for individual PV power plants, leading higher performance than time-domain models.
* WP 4 Application of Artificial intelligence techniques for predictive maintenance of grid-connected photovoltaic systems
WP4 included the secondment of 6 months at an Operation and Maintenance (O&M) company for PV systems in order to maximize the energy performance of PV systems by using artificial intelligence technologies and to transfer new knowledge to the partner to improve the reliability of PV plants. This was achieved by addressing issues on how typical failures were associated with the various components of PV systems, by the development of technical functionalities of the failure diagnostic to implement in the existing monitoring system.
* WP5 Dissemination and Public Engagement
The WP5 was specifically dedicated to the scientific dissemination via international conferences and research articles in international peer-reviewed journals as well as the Outreach Activities for the involvement of no scientific public.
Further details related to the results of each WP can be found in the Technical Report (Part B).
The project contributed in the spatio-temporal forecasting framework through the understanding of how a collection of distributed PV installations can improve forecasting accuracy of individual PV power plants. The research goes beyond traditional approaches to forecast the Photovoltaic power generation by using Artificial intelligence techniques to forecast the PV solar generation as well as for predictive maintenance of grid-connected photovoltaic systems.
On the researcher’s side, the career was enhanced by increase of the research track record, by improving the presentation and communication skills, by getting acquainted with advanced smart grids European laboratories and by the development of a solid collaboration network and contacts with national and international academics and PV related industries and utilities.
A final overarching impact is enhanced public perception of solar generation as well as the potential of the Artificial Intelligence techniques.
On the researcher’s side, the career was enhanced by increase of the research track record, by improving the presentation and communication skills, by getting acquainted with advanced smart grids European laboratories and by the development of a solid collaboration network and contacts with national and international academics and PV related industries and utilities.
A final overarching impact is enhanced public perception of solar generation as well as the potential of the Artificial Intelligence techniques.