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The Proton Exchange Membrane Fuel Cell is a promising energy converter for various fields of application: stationary, portable and mobile. However durability avoids its widespread deployment. Deterioration mechanisms are not all fully understood and that is the reason why the prognostic of such device is gaining attention. This helps determine the present and future state of health of Fuel Cell, to deduce the remaining life in order to take corrective actions. The work presented in this paper attempts to address this issue by proposing a method based on a degradation model. An observer, based on an Extended Kalman Filter, estimates the state of health and the dynamic of the degradations. This result is extrapolated until a threshold is reached and the residual life is deduced. This method allows estimating the lifespan with a single model, robust to uncertainties, whatever the operating conditions are. Simulations are conducted to validate the method. Finally, this framework is used on a set of experimental data from long term test on a 5-cell stack operated under a constant current solicitation.

Electric Vehicles (EVs) may significantly affect the stability of microgrids. To incorporate EVs with Vehicle-to-Grid (V2G) capability into the frequency control, existing approaches have primarily focused on the charge level of batteries to derive the participation level of individual EVs. However, examining only the State of Charge (SoC) does not provide an effective evaluation measure to distinguish between EVs with varying owner expectations. Furthermore, with the proliferation of renewable energies, EVs will be required to participate in voltage and frequency control consistently. In light of these, it is imperative to effectively consider the technical limitations of EVs when providing ancillary services in a microgrid. Thus, to fully realize the potential benefits of EVs in frequency regulation, the control scheme should consider both the microgrid-side and the EV-side requirements, including the rate at which the frequency changes, the operating conditions of EVs, and the satisfaction of the owners. This paper proposes a novel decentralized adaptive control scheme to regulate the contribution of EVs to primary frequency control in an islanded microgrid. The proposed framework adapts the droop parameter to address the concerns of both the microgrid and the EV. As the EV charger continuously monitors the frequency, it responds to any changes in the load-generation balance and adjusts its contribution accordingly. Moreover, to reflect the EV-side requirements and the expectations of owners in the control process, new indices are introduced to determine the charge and discharge capabilities of EVs in real-time, considering the operating boundaries associated with the V2G application. The efficacy of the proposed method has been demonstrated through simulations of various case studies in MATLAB/Simulink. ; Full Text

Low temperature (<25 °C) Aquifer Thermal Energy Storage (ATES) systems have a world-wide potential to provide low-carbon space heating and cooling for buildings by using heat pumps combined with the seasonal subsurface storage and recovery of heated and cooled groundwater. ATES systems increasingly utilize aquifer space, decreasing the overall primary energy use for heating and cooling for an urban area. However, subsurface interaction may negatively affect the energy performance of individual buildings with existing ATES systems. In this study, it is investigated how aquifer utilization levels, obtained by varying well placement policies, affect subsurface interaction between ATES systems and how this in turn affects individual primary energy use. To this end, a building climate installation model is developed and integrated with a MODFLOW-MT3DMS thermal groundwater model. For the spatial distribution and thermal requirements of 26 unique buildings as present in the city centre of Utrecht, the placement of ATES wells is varied using an agent-based modelling approach applying dense and spacious placement restrictions. Within these simulations ATES adoption order and well placement location is randomized. Well placement density is varied for 9 scenarios by changing the distance between wells of the same and the opposite type. The results of this study show that the applied dense well placement policies lead to a 30% increase of ATES adoption and hence overall GHG emission reduction improved with maximum 60% compared to conventional heating and cooling. The primary energy use of individual ATES systems is affected at varying well placement policies by two mechanisms. Firstly, at denser well placement, ATES systems are able to place more wells, which increases the capacity of their ATES system, thereby decreasing their electricity and gas use. Secondly, aquifer utilization increases with denser well placement policies and thus interaction between individual ATES increases. At subsurface utilization up to 80%, ...

Abstract Hybrid energy systems (HES) have been proposed to be an important element to enable increasing penetration of clean energy. This paper proposes a methodology for operations optimization to maximize their economic value based on predicted renewable generation and market information. A multi-environment computational platform for performing such operations optimization is also developed. To compensate for prediction error, a control strategy is accordingly designed to operate a standby energy storage element (ESE) to avoid energy imbalance within HES. The proposed operations optimizer allows systematic control of energy conversion for maximal economic value. Simulation results of two specific HES configurations illustrate the proposed methodology and computational capability. Economic advantages of such operations optimizer and associated flexible operations are demonstrated by comparing the economic performance of flexible operations with that of constant operations. Sensitivity analysis with respect to market variability and prediction error are also performed.

Abstract Decentralized peer-to-peer energy sharing techniques are highly promising to become the next-generation regime for smart building energy management, which can impulse the realization of nearly net-zero energy buildings. In this context, this paper proposes a comprehensive energy sharing framework for smart buildings in considering multiple dynamic components covering heating, ventilation, air conditioning (HVAC), battery energy storage systems (BESS) and electric vehicles (EVs). Specifically, both the power loss and shadow price of shared energy are explicitly modeled in a combined optimization framework, which is aimed to maximize the social welfare through peer-to-peer energy cooperation. Moreover, the role of agents acting as producers or consumers can be endogenously determined in the proposed model. In addition, distinguished with the classical distributed algorithms, we develop a fully decentralized algorithm based on dual-consensus version of alternating direction method of multipliers (DC-ADMM). The proposed algorithm avoids the need of coordinators at both the primal and dual variable updates in the iteration process, which suggests distinctive merits on high-level privacy protection as compared to most of the distributed optimization-based methods. Extensive case studies based on a smart building community demonstrate that the proposed peer-to-peer transactive framework can admirably improve the overall welfare for the involved smart buildings.

Due to limited fossil fuel resources, a growing increase in energy demand and the need to maintain positive environmental effects, concentrating solar power (CSP) plant as a promising technology has driven the world to find new sustainable and competitive methods for energy production. The scheduling capability of a CSP plant equipped with thermal energy storage (TES) surpasses a photovoltaic (PV) unit and augments the sustainability of energy system performance. However, restricting CSP plant application compared to a PV plant due to its high investment is a challenging issue. This paper presents a model to assemble a combined heat and power (CHP) with a CSP plant for enhancing heat utilization and reduce the overall cost of the plant, thus, the CSP benefits proved by researches can be implemented more economically. Moreover, the compressed air energy storage (CAES) is used with a CSP-TES-CHP plant in order that the thermoelectric decoupling of the CHP be facilitated. Therefore, the virtual power plant (VPP) created is a suitable design for large power grids, which can trade heat and electricity in response to the market without restraint by thermoelectric constraint. Furthermore, the day-ahead offering strategy of the VPP is modeled as a mixed integer linear programming (MILP) problem with the goal of maximizing the profit in the market. The simulation results prove the efficiency of the proposed model. The proposed VPP has a 2% increase in profit and a maximum 6% increase in the market electricity price per day compared to the system without CAES.

Studies analysing the resource use of industrial production are often performed at highly aggregated levels, e.g. yearly across industry sectors. Conversely, the remit of work performed at the operational level is limited to the management of energy or concerned with aspects such as safety or reliability, both of which fail to consider material efficiency options at that scale. This gap is filled by applying the concept of exergy to the disaggregated time-scales and scopes typical of real-time operations. Our tool measures the resource efficiency of processes and visually traces the use of both energy and materials from available control data. This is exemplified through the case study of a Tata Steel basic oxygen steelmaking plant, where resource flows are visualised using Sankey diagrams. An analysis of the resource efficiency variations across batches and days for a period of 30 days - over 900 batches - show the plant's inefficiencies primarily arise from the converter process, the resource efficiency of which varies from 87.4% to 93.7%. By recovering material and energy by-products, and reducing fuel inputs we estimate that 7% of the total exergy input can be saved or further utilised. About 60% of these improvements arise from energy-related measures. The remaining 40% emanates from reductions in material use, a contribution which would be missed if using conventional energy metrics. This approach makes three contributions. First, it gives industry a single metric of resource efficiency that can jointly measure the system-level performance of material and energy transformations. Second, it provides a new picture of the plant's operational resource use. Third, it allows managers to have more detailed information on resource flows and thus helps place material-efficiency improvements on an equal footing to energy efficiency. This, therefore, provides a clearer picture of where interventions can deliver the greatest efficiency gains.