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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 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.

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.

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 In recent years, many different concepts to manage smart distributed systems were proposed and solutions developed. Smart grids and the increasing influence of renewable sources on energy production lead to concerns about grid stability and load balance. Combined Heat and Power (CHP) generators coupled with solar or other renewable sources offer the opportunity to satisfy both electric and thermal power economically. Both electric and thermal demand and supply change continuously, and sources such as solar and wind are not dispatchable or accurately predictable. At the same time, it is essential to use the most efficient and cost effective sources to satisfy the demand. This problem has been studied at the University of Genoa (UNIGE), Italy, using different generators and energy storage device that can supply both electric and thermal energy to consumer buildings. Here the problem is formulated as a constrained Multi-Input Multi-Output (MIMO) problem with sometimes conflicting requests that must be satisfied. The results come from experiments carried out on the test rig located at the Innovative Energy System Laboratories (IESL) of the Thermochemical Power Group (TPG) of UNIGE. This paper compares three different control approaches to manage the distributed generation system: Simplified Management Control (SMC), Model Predictive Control (MPC), and Multi-Commodity Matcher (MCM). Control systems and their control actions are evaluated through economic and performance key indicators.

Nowadays the research in energy field is focused on conversion technologies which could achieve higher efficiencies and lower environmental impact. Among these, fuel cells are considered an extremely promising technology and pressurized solid oxide fuel cell (SOFC) systems are particularly attractive for their high electric efficiency, potential for cogeneration applications, low carbon emissions and high performance at part-load. This paper aims to perform a robust design of an innovative turbocharged hybrid system model, featuring components validated with industrial data, where a turbocharger is used to pressurize the fuel cell, promising better cost effectiveness than a microturbine-based hybrid system, at small scales. This study will evaluate the impact of the main operating parameters (fuel cell area, stack current density and recuperator surface) on the plant performance, considering uncertainties in the system and creating a response surface of the model to perform the study. Finally, a study of the operating costs of such plant is performed to evaluate its profitability in the Italian market scenario.