• Energy Research

The dynamic modeling of energy systems can be used for different purposes, obtaining important information both for the design phase and control system strategies, increasing the confidence during experimental phase. Such analysis in dynamic conditions is generally performed considering fixed values for both geometrical and operational parameters such as volumes, orifices, but also initial temperatures, pressure. However, such characteristics are often subject to uncertainty, either because they are not known accurately or because they may depend on the operating conditions at the beginning of the relevant transient. With focus on a gas turbine fuel cell hybrid system (HS), compressor surge may or may not occur during transients, depending on the aforementioned cycle characteristics; hence, compressor surge events are affected by uncertainty. In this paper, a stochastic analysis was performed taking into account an emergency shut-down (ESD) in a fuel cell gas turbine HS, modeled with TRANSEO, a deterministic tool for the dynamic simulations. The aim of the paper is to identify the main parameters that impact on compressor surge margin. The stochastic analysis was performed through the response sensitivity analysis (RSA) method, a sensitivity-based approximation approach that overcomes the computational burden of sampling methods. The results show that the minimum surge margin occurs in two different ranges of rotational speed: a high-speed range and a low-speed range. The temperature and geometrical characteristics of the pressure vessel, where the fuel cell is installed, are the two main parameters that affect the surge margin during an emergency shut down.

There are several small energy sources that can be exploited to provide useful energy: small temperature differences, mechanical vibrations, flow variations, latent exhausts are just some examples. The recovery of such common and small energy sources, usually wasted, for example with the conversion into useful amounts of electrical energy, is called energy harvesting. Energy harvesting allows low-power embedded devices to be powered from naturally-occurring or unwanted environmental energy (e.g. pressure or temperature difference). The main aim in the last years of researches in such field, was the increasing of the efficiency of such components, with a higher power output and a smaller size. At present, a wide range of systems incorporating energy harvesters are now available commercially, all of them specific to certain types of energy source. Energy harvesting from dissipation processes such as fluid lamination is a challenge for many different applications. In addition, control valves to dissipate overpressures are common usage of many plants and systems. This paper surveys the market opportunities of such harvesting systems, considering the trade-offs affecting their efficiency, their applicability, and ease of deployment. Particular attention will be devoted to small energy harvesters than can exploit small expansions, such as from lamination valves or to systems that can feed mini sensors from small pressure drops, promising compactness, efficiency and cost effectiveness.

Abstract Nowadays research in energy field is focused on conversion technologies which could achieve higher efficiencies and lower environmental impact. In such a context, fuel cells in general and pressurized solid oxide fuel cell (SOFC) hybrid systems are attractive for their high electric efficiency, potential for cogeneration applications, low carbon emissions and high performance at part-load. The aim of this work is to perform the design under uncertainty of an innovative turbocharged hybrid system, where a turbocharger is used to pressurize the fuel cell, featuring better cost effectiveness than a microturbine-based hybrid system at small scales (

Abstract The current study aims to perform a stochastic analysis on microturbine compact recuperators to evaluate the impact of uncertainties in design parameters on their cost and volume, by using two different probabilistic approaches: Monte Carlo (MC) and Response Sensitivity Analysis (RSA). These two methods have been developed in Matlab® and then coupled with CHEOPE (Compact Heat Exchanger Optimisation and Performance Evaluation) software, which allows to analyze two different types of recuperators, used in microturbine applications: the furnace-brazed plate-fin type and the welded primary surface type. This paper focuses on an analysis of plate-fin type recuperators, for which the cost function adopted was tuned and verified in a previous study. Three main parameters of the recuperator have been considered as uncertain: effectiveness, hot side and cold side pressure drops. The uncertainties associated with these three parameters are based on industrial knowledge. The aforementioned stochastic methods have been used to propagate such uncertainties on the relevant outputs, such as cost and volume, allowing us to evaluate the least expensive and the most compact recuperator among those analysed.

Abstract This paper proposes a time-dependent, thermo-economic hierarchical approach for the analysis of energy districts and smart poly-generation microgrids, in order to determine the optimal size of different prime movers, required to meet the energy demand of a generic user. This approach allows for determining the optimal size for each component of the energy district, as well as defining its most efficient operation management for the entire year, taking into proper account the time-dependent nature of the electrical, thermal and cooling demands, which are the main constraints of the optimisation problem. Additionally, the proposed method takes into consideration both energy performance and operation costs. A specific case study is developed around the smart poly-generation microgrid at the University of Genoa, Savona Campus (Italy), which has been operational since 2013. In the original design, the microgrid includes different co-generative prime movers, renewable generators and a thermal storage system. In a second design an absorption chiller is included to supply the campus' energy cooling demand. Obtained results allowed identifying the best operation configuration, from a thermo-economic standpoint, for the considered scenario. The proposed method can be easily replicated in different applications and configurations of different smart poly-generative grids.

The cluster on Enabling Technologies in the framework of the Fast Track on Energy Communities workshop held in Rome during the Sustainable Places 2021 conference presented a series of H2020 projects and their innovative technological solutions to facilitate the uptake of energy communities, demand-response and energy efficiency projects in Europe.

The analysis of different energy systems has shown various sources of variability and uncertainty; hence the necessity to quantify and take these into account is becoming more and more important. In this paper, a steady state, off-design model of a solid oxide fuel cell and turbocharger hybrid system with recuperator has been developed. Performances of such stiff systems are affected significantly by uncertainties both in component performance and operating parameters. This work started with the application of Monte Carlo Simulation method, as a reference sampling method, and then compared it with two different approximated methods. The first one is the Response Sensitivity Analysis, based on Taylor series expansion, and the latter is the Polynomial Chaos, based on a linear combination of different polynomials. These are non-intrusive methods, thus the model is treated as a black-box, with the uncertainty propagation method staying at an upper level. The work is focused on the application on highly non-linear complex systems, such as the hybrid systems, without any optimization process included. Hence, only the uncertainty propagation is considered. Uncertainties in the fuel utilization, ohmic resistance of the fuel cell, and efficiency of the recuperator are taken into account. In particular, their effects on fuel cell lifetime and some simple economic parameters are evaluated. The analysis distinguishes the specific features of each approach and identifies the strongest influencing inputs to the monitored output. Both approximated methods allow an important reduction in the number of evaluations while maintaining a good accuracy compared to Monte Carlo Simulation.

Abstract The performance of a solid oxide fuel cell (SOFC) is subject to inherent uncertainty in operational and geometrical parameters, which can cause performance variability and affect system reliability. Operating conditions such as current demand, cell temperature and fuel utilization play an important role on the degradation mechanisms, which affect typical SOFCs. In previous work, a deterministic empirical degradation model of a SOFC was developed as a function of such operating conditions. By the nature of experimental data and regression fitting, this model was not deterministic. The aim of this work is to evaluate the impact of the uncertainties in the degradation model through a stochastic analysis. In particular, the Response Sensitivity Analysis (RSA), an approximate stochastic method based on Taylor series expansion, is applied to a standalone SOFC model and a fuel cell hybrid system model both subjected to cell degradation. The attention is principally focused on the impact on the fuel cell lifetime. To provide an indication of degradation effect and resulting lifetime uncertainty on economic performance, a cursory economic analysis is performed.

Abstract The growing need of dispatchable units, capable to balance the variable renewable energy electrical production leads to the development of strategies aimed at increasing power plants operational flexibility and global efficiency in part-load operation. A highly efficient heat pump (integrated in a conventional natural gas combined cycle is here proposed as a flexibility enhancement solution. Such concept, applied to power oriented combined cycle, allows to modify the compressor intake temperature with a consequent increase of the power production. While this operation for open cycle gas turbine is beneficial also to electric efficiency, combined cycles’ efficiency is less sensitive to the temperature variation and thus more influenced by the auxiliary consumption. The selection of the proper heat pump size for the proposed layout was based on an optimization process considering both combined cycle and heat pump off-design performance. After a statistical analysis of climatic data and their correlations with energy market condition for the six Italian price zones, the models developed were applied to assess the thermoeconomic potential of the proposed layout. This work highlights how a proper optimization process influences both revenues and size optimization and to highlight how such integrated system can be selected at its best considering typical market and climatic frames. The ratio between the air-cooled heat pump electrical consumption and the electrical combined cycle capacity that maximize power production increase was found to be 1/100. This finding can be extended to the others world Humid subtropical climate and Mediterranean hot summer climates zones. It is underlined how electrical market conditions could jeopardize the installation profits even under favourable climatic potential reducing the optimal economic heat pump size. Using off-design curves and optimization algorithm in performing coupling analysis appears to be more effective, with respect to simplified calculations under unfavourable economic and climate conditions.