Energy Speaker Series - Module 3: Energy Storage and Power System Control with AI
Dr. Pietro Raboni and Guido Recalcati, NHOA
Prof. Francisco Gonzalez-Longatt, UniversitetetiSørøst-Norge
Dr. Francisco Sánchez, Loughborough University
Matteo Galgani and Marco Magrini, ENEL
Session 3.2: Development of State-of-the-art Power Plant Controllers for Energy Storage Applications,
Dr. Pietro Raboni and Guido Recalcati, NHOA
NHOA, formerly Electro Power Systems - Engie EPS, is one of the top global players in energy storage and e-mobility with the aim of enabling the paradigm shift in the global energy system towards clean energy and sustainable mobility.
This presentation aims to showcase how MATLAB®, Simulink® and Simulink PLC Coder™ sped up the development of the EEPS’s proprietary Power Plant Controller (PPC). The speech introduces the PPC as the key brick for any utility scale renewable, storage and Vehicle-to-Grid plant. Indeed, these applications have in common the need of coordinating fast acting power electronics converters alongside with TSO requests and the longer-term decisions of an Energy Management System or a Market Aggregator. Moreover, the PPC plays a vital role for large scale plants in terms of grid code compliance, as well as for preserving and optimally managing the batteries in BESS and V2G applications.
In the last 3 years EEPS upgraded its PPC development approach embracing Model Based Design and using Simulink. This eased the design of more complex control structures, their testing and integration with the rest of our library. Moreover, the integration of Git with Simulink makes easier tracking the versioning and code development in a growing company. The Simulink projects are then automatically converted to different industrial hardware control platforms on a project base, leveraging on Simulink PLC Coder™ and a set of in-house scripts. This revolution accelerated the whole PPC design and turned out a key-advantage during the COVID-19 months for the remote commissioning of the plants. The presentation is based on simulation and field recordings, collected during design, commissioning and operation of our BESS and V2G projects.
Session 3.3: Deep Reinforcement Learning-Based Controller for SOC Management of Multi-Electrical Energy Storage System,
Prof. Francisco Gonzalez-Longatt - UniversitetetiSørøst-Norge and Dr. Francisco Sanchez, Researcher - Loughborough University
Electrical energy storage systems (EESSs) have become increasingly attractive to provide fast frequency response services due to their response times. However, proper management of their finite energy reserves is required to ensure timely and secure operation. This presentation shows the implementation of a deep reinforcement learning (DRL) based controller to manage the state of charge (SOC) of a Multi-EESS (M-EESS) and provide frequency response services to the power grid.
The DRL based controller decides when to charge or discharge the M-EESS to provide the frequency service while simultaneously controlling the SOC of the M-EESS to reach the desired level. The DRL agent is trained using an actor-critic method called Deep Deterministic Policy Gradients (DDPG) that allows for continuous action and smoother SOC control of the M-EESS. MATLAB and Simulink are used as the modelling and simulation framework for the controller. Battery energy storage, flywheel and ultra-capacitor energy storage models have been implemented using Simulink together with the environment used to define observation and actions; also, the agent has been developed in Simulink taking advantage of the Reinforcement Learning Toolbox (RLT). The training process was implemented using MATLAB live script, making it easy to understand and use the RLT. The proposed controller is compared to benchmark DRL methods and other control techniques, i.e., Fuzzy Logic and a traditional PID control. Simulation results show the effectiveness of the proposed approach.
Session 3.4: Diagnosis and control of geothermal plants with MATLAB
Matteo Galgani and Marco Magrini, ENEL Italy
Enel Green Power GEO deals with the production of electricity from geothermal sources. The fleet currently has 35 plants for a total of 750MW of installed power. This presentation focuses on the calculation architecture adopted by Enel Green Power for the execution of algorithms for both diagnostic and control purposes.
The calculation system is composed of two parts: the first part is the execution of monitoring and diagnostic algorithms, residing in the central Mainframe; the second part for the execution of control algorithms, residing in the PLC of the production.
The heart of the system is MATLAB, which is used for the development, testing and execution of the developed algorithms. The data is acquired from the production plants with an average sampling time of 5 seconds and stored in the central Mainframe. This data stream is accessible by MATLAB for the development of algorithms based on historical data. Through the use of the MATLAB Production Server we are also able to execute the algorithms developed in real time directly on the data acquired by the plants.
In this presentation, we will show plant signature algorithms we used for monitoring & diagnostics and control algorithms we developed and implemented on a PLC to allow a direct and much faster action directly on a centrifugal compressor.
About the Presenters
Dr. Pietro Raboni received his B.S. and M.S. degrees in Electrical Engineering from Università degli Studi di Pavia, Italy, in 2008 and 2011, and the Ph.D. in Energy Technology from Aalborg University, Denmark, in 2016. He is currently Head of System R&D at ENGIE-Eps, Milan, Italy. From 2014 to 2017 he was inverter and plant modelling specialist with ABB, either for PV or BESS products. He is currently member of CEI CT-316 and industrial lecturer at Politecnico di Milano, Italy. He is an expert in modelling and control of large-scale PV and BESS plants, as well as microgrids. His research interests span from inverter to renewable power plant controllers and include EMS for microgrids and V2G applications.
Guido Recalcati received the M.S degree in Electrical Engineering from Politecnico di Milano in 2018. Since then, he has been working with Engie EPS as part of the R&D team. Recently he has joined Free2Move eSolutions, a JV between Stellantis and ENGIE Eps. His main activities include simulation and development of control systems for energy storage and V2G plants.
Prof. Francisco Gonzalez-Longatt is a full professor in electrical power engineering at Instituttfor elektro, IT ogkybernetikk, UniversitetetiSørøst-Norge, Norway. His research interest includes innovative (operation/control) schemes to optimize the performance of future energy systems.
Dr. Francisco Sánchez received his BS degree in Electrical Engineering from Simon Bolivar University, Caracas, Venezuela, in 2011 and master’s degree in Renewable Energy Technologies from Polytechnic University in Madrid, Spain, in 2013. He recently completed his PhD. degree in Electrical Engineering at Loughborough University in the UK. His research focuses on the development of artificial intelligence techniques for power system analysis and energy management applications.
Matteo Galgani received his BS degree in computer engineering and specialized in robotics and artificial intelligence. Mr. Galgani has been with ENEL for six year, where he focuses on technology innovations and data analysis.
Marco Magrini received his BS degree in Information Technology from University of Pisa and he also holds a MBA from the Politecnico di Milano. Mr. Magrini has been with ENEL since 1991, where he focuses on Green Power such as the operation and maintenance of geothermal power plants. In the past, Mr. Magrini worked on physical models of the geothermal reservoir, implementation of relational databases, SAP customizations, remote control and monitoring of power plants, development of computer networks and advanced data analysis related projects.
Recorded: 1 Dec 2021
También puede seleccionar uno de estos países/idiomas:
Cómo obtener el mejor rendimiento
Seleccione China (en idioma chino o inglés) para obtener el mejor rendimiento. Los sitios web de otros países no están optimizados para ser accedidos desde su ubicación geográfica.