This paper proposes an energy management system based on an Artificial Neural Network (ANN) to be integrated with the standard ISO 50001 and aims to describe the definition and the enhancement of the energy baselines by means of artificial intelligence techniques applied and tested on the real electrical absorption data of the auxiliary units of different thermal power plants in Italy. Power plant optimized operations are important both for cost and energy performance reasons with related effects on the environment in the next future energy transition scenario. The improvement of the energy baselines consists in determining more accurate consumption monitoring models that are able to track inefficiencies and absorption drifts through data analytics and Artificial Intelligence. Starting from the analysis of the energy vectors at the production site level, we performed a multi-scale analysis to define the consumption at macro areas level and finally find the most relevant consumption units within the plants. A comparison of different ANNs applied to several real power plant data was performed to model complex plant architecture and optimize energy savings with respect to pre-set thresholds according to the ISO 50001 standard procedure. The energy baselines are determined through the analysis of the data available in the power plants' Distributed Control System (DCS), and we can identify the consumption derived from the unit's proper operation. Based on the reported numerical simulations, improved baselines have been reached up to a 5% threshold for different plant sub-units, thus representing a relevant overall saving in terms of alert threshold definition and related control efficiency: a potential saving of about 140 MWh throughout the considered three-year dataset was obtained taking into account a cooling tower sub-unit, representing a considerable economic benefit. The results obtained highlight the neural technique efficiency in defining more accurate energy baselines and represents a valuable tool for large energy plant asset management to face relevant energy drifts observed in the last years of plant operation.
ISO 50001 Data Driven Methods for Energy Efficiency Analysis of Thermal Power Plants
Grimaccia F.;Niccolai A.;Mussetta M.;
2023-01-01
Abstract
This paper proposes an energy management system based on an Artificial Neural Network (ANN) to be integrated with the standard ISO 50001 and aims to describe the definition and the enhancement of the energy baselines by means of artificial intelligence techniques applied and tested on the real electrical absorption data of the auxiliary units of different thermal power plants in Italy. Power plant optimized operations are important both for cost and energy performance reasons with related effects on the environment in the next future energy transition scenario. The improvement of the energy baselines consists in determining more accurate consumption monitoring models that are able to track inefficiencies and absorption drifts through data analytics and Artificial Intelligence. Starting from the analysis of the energy vectors at the production site level, we performed a multi-scale analysis to define the consumption at macro areas level and finally find the most relevant consumption units within the plants. A comparison of different ANNs applied to several real power plant data was performed to model complex plant architecture and optimize energy savings with respect to pre-set thresholds according to the ISO 50001 standard procedure. The energy baselines are determined through the analysis of the data available in the power plants' Distributed Control System (DCS), and we can identify the consumption derived from the unit's proper operation. Based on the reported numerical simulations, improved baselines have been reached up to a 5% threshold for different plant sub-units, thus representing a relevant overall saving in terms of alert threshold definition and related control efficiency: a potential saving of about 140 MWh throughout the considered three-year dataset was obtained taking into account a cooling tower sub-unit, representing a considerable economic benefit. The results obtained highlight the neural technique efficiency in defining more accurate energy baselines and represents a valuable tool for large energy plant asset management to face relevant energy drifts observed in the last years of plant operation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
