| 1. |
- Ferrari, M. L., et al.
(författare)
-
Pressurized SOFC system fuelled by biogas : Control approaches and degradation impact
- 2020
-
Ingår i: Proceedings of the ASME Turbo Expo. - : American Society of Mechanical Engineers (ASME). - 9780791884140
-
Konferensbidrag (refereegranskat)abstract
- This paper shows control approaches for managing a pressurized Solid Oxide Fuel Cell (SOFC) system fuelled by biogas. This is an advanced solution to integrate the high efficiency benefits of a pressurized SOFC with a renewable source. The operative conditions of these analyses are based on the matching with an emulator rig including a T100 machine for tests in cyber-physical mode (a real-time model including components emulated in the rig, operating in parallel with the experimental facility and used to manage some properties in the plant, such as the turbine outlet temperature set-point and the air flow injected in the anodic circuit). The T100 machine is a microturbine able to produce a nominal electric power output of 100 kW. So, the paper presents a real-time model including the fuel cell, the off-gas burner, and the recirculation lines. Although the microturbine components are planned to be evaluated with the hardware devices, the model includes also the T100 expander for machine control reasons, as detailed presented in the devoted section. The simulations shown in this paper regard the assessment of an innovative control tool based on the Model Predictive Control (MPC) technology. This controller and an additional tool based on the coupling of MPC and PID approaches were assessed against the application of Proportional Integral Derivative (PID) controllers. The control targets consider both steady-state (e.g. high efficiency solutions) and dynamic aspects (stress smoothing in the cell). Moreover, different control solutions are presented to operate the system during fuel cell degradation. The results include the system response to load variations, and SOFC voltage decrease. Special attention is devoted to the fuel cell system constraints, such as temperature and time-dependent thermal gradient. Considering the simulations including SOFC degradation, the MPC was able to decrease the thermal stress, but it was not able to compensate the degradation. On the other hand, the tool based on the coupling of the MPC and the PID approaches produced the best results in terms of set-point matching, and SOFC thermal stress containment.
|
|
| 2. |
- Ferrari, M. L., et al.
(författare)
-
Pressurized SOFC System Fuelled by Biogas : Control Approaches and Degradation Impact
- 2021
-
Ingår i: Journal of engineering for gas turbines and power. - : American Society of Mechanical Engineers (ASME). - 0742-4795 .- 1528-8919. ; 143:6
-
Tidskriftsartikel (refereegranskat)abstract
- This paper shows control approaches for managing a pressurized solid oxide fuel cell (SOFC) system fuelled by biogas. This is an advanced solution to integrate the high efficiency benefits of a pressurized SOFC with a renewable source. The operative conditions of these analyses are based on the matching with an emulator rig including a T100 machine for tests in cyber-physical mode. So, this paper presents a real-time model including the fuel cell, the off-gas burner (OFB), and the recirculation lines. Although the microturbine components are planned to be evaluated with the hardware devices, the model includes also the T100 expander for machine control reasons. The simulations shown in this paper regard the assessment of an innovative control tool based on the model predictive control (MPC) technology. This controller and an additional tool based on the coupling of MPC and proportional integral derivative (PID) approaches were assessed against the application of PID controllers. The control targets consider both steady-state and dynamic aspects. Moreover, different control solutions are presented to operate the system during fuel cell degradation. The results include the system response to load variations, and SOFC voltage decrease. Considering the simulations including SOFC degradation, the MPC was able to decrease the thermal stress, but it was not able to compensate the degradation. On the other hand, the tool based on the coupling of the MPC and the PID approaches produced the best results in terms of set-point matching, and SOFC thermal stress containment.
|
|
| 3. |
- Mantelli, L., et al.
(författare)
-
A degradation diagnosis method for gas turbine - fuel cell hybrid systems using Bayesian networks
- 2020
-
Ingår i: Proceedings of the ASME Turbo Expo. - : American Society of Mechanical Engineers (ASME). - 9780791884140
-
Konferensbidrag (refereegranskat)abstract
- During the last decades there has been a rise of awareness regarding the necessity to increase energy systems efficiency and reduce carbon emissions. These goals could be partially achieved through a greater use of gas turbine - solid oxide fuel cell hybrid systems to generate both electric power and heat. However, this kind of systems are known to be delicate, especially due to the fragility of the cell, which could be permanently damaged if its temperature and pressure levels exceed their operative limits. This could be caused by degradation of a component in the system (e.g. the turbomachinery), but also by some sensor fault which leads to a wrong control action. To be considered commercially competitive, these systems must guarantee high reliability and their maintenance costs must be minimized. Thus, it is necessary to integrate these plants with an automated diagnosis system capable to detect degradation levels of the many components (e.g. turbomachinery and fuel cell stack) in order to plan properly the maintenance operations, and also to recognize a sensor fault. This task can be very challenging due to the high complexity of the system and the interactions between its components. Another difficulty is related to the lack of sensors, which is common on commercial power plants, and makes harder the identification of faults in the system. This paper aims to develop and test Bayesian belief network based diagnosis methods, which can be used to predict the most likely degradation levels of turbine, compressor and fuel cell in a hybrid system on the basis of different sensors measurements. The capability of the diagnosis systems to understand if an abnormal measurement is caused by a component degradation or by a sensor fault is also investigated. The data used both to train and to test the networks is generated from a deterministic model and later modified to consider noise or bias in the sensors. The application of Bayesian belief networks to fuel cell - gas turbine hybrid systems is novel, thus the results obtained from this analysis could be a significant starting point to understand their potential. The diagnosis systems developed for this work provide essential information regarding levels of degradation and presence of faults in gas turbine, fuel cell and sensors in a fuel cell - gas turbine hybrid system. The Bayesian belief networks proved to have a good level of accuracy for all the scenarios considered, regarding both steady state and transient operations. This analysis also suggests that in the future a Bayesian belief network could be integrated with the control system to achieve safer and more efficient operations of these plants.
|
|
| 4. |
- Marzi, E., et al.
(författare)
-
Power-to-Gas for energy system flexibility under uncertainty in demand, production and price
- 2023
-
Ingår i: Energy. - : Elsevier Ltd. - 0360-5442 .- 1873-6785. ; 284
-
Tidskriftsartikel (refereegranskat)abstract
- The growing penetration of non-programmable renewable energy sources and the consequent fluctuations in energy prices and availability lead to the need to enhance energy system flexibility and synergies between different energy vectors. This can be reached through sector integration. Among the most relevant technologies used for this purpose, Power-to-Gas systems allow excess renewable electricity to be converted directly into fuels that can be then stored or used. A smart energy system, however, which includes these innovative solutions, requires intelligent management methods to optimize its operation. This work investigates the operational strategy of energy systems integrated with Power-to-Gas solutions for seasonal storage, by developing an optimization model for the system, formulated as Mixed-Integer Linear Programming problem. The algorithm tackles the uncertain nature of future disturbances, such as energy needs, generation and price using two-stage stochastic programming. The algorithm is tested on grid-connected and 100% renewable energy supply case studies. The novel stochastic algorithm allows a more robust optimization compared to a deterministic optimization, and system management is ensured under several future disturbances realization. Furthermore, the integration of Power-to-Gas solutions warrants the energy security of the energy systems and acts as a buffer to forestall unpredictable behavior of the disturbances.
|
|
