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Abstract The paper presents a model-based approach to supporting battery selection for a fuel cell (FC)-based auxiliary power unit (APU). It is introduced to a case study of electrical power production and consumption management in a truck anti-idling application of a diesel-powered FC-based APU, a system under development in FCGEN, a FCH JU European project of the FP7 program. With fuel cell and related technologies increasingly competing with others in the market, they need to form complete systems with matching and well-balanced components to enable using the technology to its best. Within the whole system, the battery, serving as an energy buffer, represents a medium-cost element, but it affects the operating parameters importantly. Within the scope of this study, a purpose-oriented model of the diesel powered FC-based system is developed together with a realistic load scenario for the comparison of three batteries. The battery size and type are investigated and discussed in the light of the simulation results.

Abstract A promising and shortly emerging energy supply chain network based on residential-scale microgeneration through micro combined heat and power systems is proposed, modeled and optimized in this work. Interchange of electrical energy can take place among the members of this domestic microgrid, which is connected to the main electrical grid for potential power interchange with it. A mathematical programming framework is developed for the operational planning of such energy supply chain networks. The minimization of total costs (including microgeneration system’s startup and operating costs as well as electricity production revenue, sales, and purchases), under full heat demand satisfaction, constitutes the objective function in this study. Additionally, an alternative microgrid structure that allows the heat interchange within subgroups of the overall microgrid is proposed, and the initial mathematical programming formulation is extended to deal with this new aspect. An illustrative example is presented in order to highlight the particular significance of selecting a proper optimization goal that thoroughly takes into account the major operational, technical and economic driven factors of the problem in question. Also, a number of real-world size case studies are used to illustrate the efficiency, applicability and the potential benefits of the microgeneration energy supply chain networks suggested in this study. Finally, some concluding remarks are drawn and potential future research directions are identified.

Due to growing interest in wind energy harvesting offshore as well as in the urban environment, vertical axis wind turbines (VAWTs) have recently received renewed interest. Their omni-directional capability makes them a very interesting option for use with the frequently varying wind directions typically encountered in the built environment while their scalability and low installation costs make them highly suitable for offshore wind farms. However, they require further performance optimization to become competitive with horizontal axis wind turbines (HAWTs) as they currently have a lower power coefficient (CP). This can be attributed both to the complexity of the flow around VAWTs and the significantly smaller amount of research they have received. The pitch angle is a potential parameter to enhance the performance of VAWTs. The current study investigates the variations in loads and moments on the turbine as well as the experienced angle of attack, shed vorticity and boundary layer events (leading edge and trailing edge separation, laminar-to-turbulent transition) as a function of pitch angle using Computational Fluid Dynamics (CFD) calculations. Pitch angles of −7° to +3° are investigated using Unsteady Reynolds-Averaged Navier-Stokes (URANS) calculations while turbulence is modeled with the 4-equation transition SST model. The results show that a 6.6% increase in CP can be achieved using a pitch angle of −2° at a tip speed ratio of 4. Additionally, it is found that a change in pitch angle shifts instantaneous loads and moments between upwind and downwind halves of the turbine. The shift in instantaneous moment during the revolution for various pitch angles suggests that dynamic pitching might be a very promising approach for further performance optimization.

There is an intrinsic value in higher integration of multi-carrier energy systems (especially gas and electricity), to increase operational flexibility in the electricity system and to improve allocation of resources in gas and electricity networks. The integration of different energy carrier markets is challenging due to the existence of physical and economic dependencies between the different energy carriers. We propose in this paper an integrated day-ahead multi-carrier gas, electricity and heat market clearing which includes new types of orders and constraints on these orders to represent techno-economic constraints of conversion and storage technologies. We prove that the proposed market clearing gives rise to competitive equilibria. In addition, we propose two decentralised clearing algorithms which differ in how the decomposition of the underlying centralised clearing optimisation problem is performed. This has implications in terms of the involved agents and their mutual information exchange. It is proven that they yield solutions equivalent to the centralised market clearing under a mild assumption of sufficient number of iterations. We argue that such an integrated multi-carrier energy market mitigates (spot) market risks faced by market participants and enables better spot pricing of the different energy carriers. The results show that conversion/storage technology owners would suffer from losses and/or opportunity costs, if they were obliged to only use elementary orders. For the test cases considered in this article, sum of losses and opportunity costs could reach up to 13,000 EUR/day and 9,000 EUR/day respectively, compared with the case where conversion and storage orders are used.

Abstract We propose a multi-time scale energy management framework for a smart photovoltaic (PV) system that can calculate optimized schedules for battery operation, power purchases, and appliance usage. A smart PV system is a local energy community that includes several buildings and households equipped with PV panels and batteries. However, due to the unpredictability and fast variation of PV generation, maintaining energy balance and reducing electricity costs in the system is challenging. Our proposed framework employs a model predictive control approach with a physics-based PV forecasting model and an accurately parameterized battery model. We also introduce a multi-time scale structure composed of two-time scales: a longer coarse-grained time scale for daily horizon with 15-minutes resolution and a shorter fine-grained time scale for 15-minutes horizon with 1-second resolution. In contrast to the current single-time scale approaches, this alternative structure enables the management of a necessary mix of fast and slow system dynamics with reasonable computational times while maintaining high accuracy. Simulation results show that the proposed framework reduces electricity costs up 48.1% compared with baseline methods. The necessity of a multi-time scale and the impact on accurate system modeling in terms of PV forecasting and batteries are also demonstrated.

Abstract The feasibility of pressure-retarded osmosis (PRO) for power generation was evaluated with consideration of the energy inputs and losses in the process. The effects of the concentration polarization, reverse salt diffusion, and external resistance at the membrane porous layer were quantified, for the first time, along the membrane module to determine their contributions to the energy loss in the PRO process. Concentration polarization was responsible for up to 40% of the energy loss during the PRO process. However, increasing the PRO membrane modules from 1 to 4 resulted in a variable increase of the energy output depending on the salinity gradient. The energy requirements for draw and feed solution pretreatment were estimated to be over 38% of the total energy inputs. Results showed that coupling seawater (SW) with river water (RW) was unable to generate sufficient energy to compensate for the energy inputs and losses during the PRO process. With 0.39 kwh/m 3 maximum specific energy in the PRO process, the energy yield of reverse osmosis brine (ROB)-wastewater (WW) salinity gradient was slightly greater than the total energy inputs, although using Dead Sea-SW/ROB salinity gradient was more promising. Overall, the primary current limitation is the lack of suitable PRO membranes that can withstand a high hydraulic pressure.

Abstract This paper presents theoretical investigations into a hybrid ejector and CO2 vapour compression (VC) system for road transport cooling. The purpose is to utilise the waste heat from exhaust gas and the VC sub-system to drive the ejector system, whose cooling effect will be employed to subcool the VC sub-system. Exploitation of the energy consumption ratio between ejector sub-system and CO2 VC sub-system indicated that the more energy obtained from exhausted gas, the better system performance could be achieved for CO2 VC sub-system, and hence higher cooling capacity of the VC sub-system at the same compression power. Thermodynamic simulations of two sub-systems and the hybrid system were presented. The results indicated that, at boiler temperature of 120 °C, evaporator temperature of 10 °C, a COP of 0.584 was achieved for hybrid system, with 22% improvement over a single ejector cycle. Preliminary experimental studies were carried out on a single ejector cycle, with boiler temperatures between 115 °C and 130 °C, and evaporator temperatures between 5 °C and 10 °C. The effects of various operation conditions on the overall ejector operation were coherently analysed. The COP of the ejector sub-system from experimental results was approximately 85% compared with simulation results, which showed a good agreement between theoretical analysis and experimental results.