• Energy Research
• 7. Clean energy close
• 12. Responsible consumption close
• 3. Good health close
• IR close
• UNSW Sydney close

In the present work, an ice thermal energy storage (ITES) system incorporating a phase change material (PCM) as the partial cold storage was modeled for air-conditioning (A/C) applications. The system was analyzed from energy, exergy, economic, and environmental aspects (4E analysis) for charging and discharging processes. Utilizing genetic algorithm optimization technique, multi-objective optimization of the system was performed and the optimal values of system design parameters were obtained. The exergy efficiency and total cost rate were considered as objective functions. The performance of the hybrid ITES system (with PCM) was compared with a simple ITES (without PCM) system and also was compared with a conventional air-conditioning system. The results indicated that the electricity consumption of hybrid system was 6.7% and 17.1% lower than that of the simple ITES and the conventional systems respectively. Furthermore, the amount of CO2 emission produced in hybrid system decreased by 7.2% and 17.5% relative to simple ITES and conventional systems respectively. Finally, the extra capital cost associated with using PCM with ITES (hybrid) system was paid back with savings in electricity in 3.97 years, while the payback period for simple ITES system (without PCM) was found to be 3.39 years.

Abstract Due to high energy demand and adverse effect of global warming, improving the performance of solar thermal systems is of great importance. In this respect, the transient flow and thermal behavior of turbulent naturally ventilated airflow in a flat plate solar air heater (SAH) are investigated numerically. To achieve this aim, conservation of mass, momentum, and energy equations are solved for the turbulent airflow concurrently with the conduction equation inside the solid element using finite element method (FEM). The low Reynolds turbulence model is applied in the calculation of turbulent stress and heat flux, and the Boussinesq approximation is applied for computing buoyancy forces associated with the density gradient. The surface radiation is also considered explicitly in the calculations to obtain more accurate and reliable results. It is found that although based on the considered location the maximum incident radiation occurs at the optimum angle of 30°, the maximum flow rate reach to its peak when the inclination angle is 60°, in which the total increase of incident radiation is nearly 17%. It is also found that the rate of heat transfer is a decreasing function of the inclination angle ( 30 ⩽ θ ⩽ 90 ), such that the overall rate of heat transfer reduction is around 50%. Further, investigation of the impacts of solar radiation and air duct width on thermal performance showed that by increasing both parameter the rate of heat transfer and air flow increase by about 35% and 100% respectively. The exclusive analysis of the effects of sudden climate change on the SAH’s behavior revealed that a short time climate change leads to zero incident radiation, and considerable fluctuations in outlet temperature and flow rate.

Abstract This paper investigates the short-term environmental/economic hydrothermal scheduling problem. The multi-objective optimization framework is proposed to model the Short-term Hydro Thermal Scheduling (SHTS) problem, while two competing objective functions are supposed to be minimized, simultaneously. The first objective function is to minimize the cost and the second one is to minimize the emissions caused by thermal units. In order to solve the presented multi-objective optimization problem and generate the Pareto optimal front, lexicographic optimization and Normal Boundary Intersection (NBI) method are employed in this paper. The main positive point with this approach is that it avoids the selection of arbitrary parameters and produces a set of evenly distributed points regardless of the objectives’ scales. Afterwards, the most preferred solution among all Pareto solutions is selected utilizing a fuzzy satisfying method. The proposed model is implemented on a sample test system comprising four cascaded hydro generating units as well as three thermal units. Furthermore, the proposed method is implemented on IEEE 118 bus test system. The obtained results show the efficiency of the proposed multi-objective method to solve the SHTS problem compared to other methods recently employed.

Abstract Renewable energies and electric vehicles are introduced as promising solutions to save energy costs and reduce environmental impacts in microgrid systems. However, the uncertainty of such resources would necessitate the development of advanced management models for optimal operation of microgrids. To address this issue, this paper proposes a new model for energy and reserve management of microgrids in the presence of electric vehicles. To effectively cope with uncertainties, a robust optimization methodology is proposed and applied to handle the uncertain parameters. Furthermore, the optimization problem is transferred into a mixed-integer linear programming model to ensure achieving near-global and tractable solutions. The proposed model aims to coordinate energy serving entities a way that the microgrid social welfare is optimized while at the same time driving requirements of the electric vehicle owners satisfied reliably. The methodology is implemented to a microgrid and solved over a day-ahead scheduling time horizon. The trends of techno-economic-environmental indices confronting to the increasing level of uncertainty control parameter are evaluated thoroughly in four case-studies. A robust multi-objective model is developed to trade-off between social welfare and emission. The numerical results are verified through a Monte-Carlo Simulation strategy to demonstrate the impressiveness of the proposed methodology.

Abstract Water vapor is vital both as an energy carrier and as an intermediary state for removing impurities from water. In nature, transpiration occurs when water is transported (against gravity) from the roots to the underside of leaves where it evaporates. Using this process, one large tree can pump and purify 400 L of water each day. Based on trunk cross-sectional area, this corresponds to a water flux range of ∼100–1000 kg/m2day, but based on evaporation area it only corresponds to a rate of ∼0.1 kg/m2day. Compared to industrial mechanisms of producing water vapor (i.e. typical thermal-driven systems have a flux of ∼4000 kg/m2day), natural wood has a relatively low flux. In an effort to boost the flux of sustainable, natural wood, we investigated wood surface modifications, laser carbonization and deposition of gold nanolayers, which achieved an instantaneous evaporation rate of ∼4 kg/m2h—under 3 kW/m2 light intensity, exceeding all previous studies of synthetic materials (including 3.8 kg/m2h reported by Zhou et al. in a 2016 Nature Photonics article) for solar steam generation applications. The cost analysis of different natural and synthetic material-based techniques for solar steam generation indicated that the carbonization and laser treatments are very cost-effective and even the gold coating was comparable to previously reported synthetic materials. Based on these results, we suggest that natural, surface-modified poplar wood could represent a viable alternative to synthetic materials for liquid/vapor separation.

Abstract The present study conducts a comprehensive comparative techno-economic analysis of some near-term sensible thermal energy storage (TES) alternatives to the ‘standard’ two-tank molten salt system for concentrated solar power (CSP) plants. As such, we conducted detailed, relative annual transient simulations for single-medium thermocline (SMT), dual-media thermocline (DMT), and shell-and-tube (ST) systems. To be consistent with recent literature, the DMT and ST systems use concrete with a porosity of 0.2 (e.g. where concrete occupies 80% of the system) as their low cost filler material. The systems were integrated into a validated 19.9 MWe Gemasolar CSP model, which has a solar multiple of 2.5. For a relative analysis, the storage capacity of each TES alternative was fixed at 722 MWhth (15 h storage) for all TES alternatives. Based on this capacity, a geometric optimization was performed on DMT and ST systems to maximize the discharged power and minimize the pressure drop. Using the optimum designs, it was found that a CSP plant with a two-tank molten salt system enables the highest amount of electricity generation in a year followed by the SMT and DMT systems, which resulted in 7% and 9% less electricity generation, respectively. As the worst performer, a CSP plant integrated with a ST system generates 20% less electricity over a year. This implies that despite having the same theoretical capacity, the real performance is not same for the alternatives. While these losses may seem egregious at first, large TES cost reductions are made possible in these alternatives due to the fact that a single tank or concrete can be used (noting that concrete is

Abstract One of methods for loss reduction and reliability improvement of radial distribution system is using of renewable energy generation. In this paper, a new optimal placement and sizing of renewable energy sources based on photovoltaic panels (PVs) and wind turbines (WTs) in the distribution network is presented with the objective of loss reduction and reliability improvement based on energy not-supplied (ENS). A multi-objective evolutionary algorithm based on fuzzy decision-making method, called the Multi-Objective Hybrid Teaching–Learning Based Optimization-Grey Wolf Optimizer (MOHTLBOGWO) is proposed to solve the optimization problem. The proposed hybrid method has a high convergence speed and not trapped at all in local optimal. The proposed method is implemented in the form of single-objective and multi-objective on 33 and 69 bus IEEE radial distribution networks. The simulation results clear that the multi-objective optimization is a more precise approach to network utilization taking into account all objective indices than the single objective method. The results show that the proposed method has better convergence speed and less convergence tolerance in achieving to best solution in comparison with TLBO and GWO methods in loss reduction, reliability improvement and increasing the net saving and also in comparison with last studies. Moreover, the results show that dispersion of the size and location of distributed renewable generation leads to a further reduction in losses and a better improvement of the reliability criterion.

Gamma alumina is one of the widely used supports in catalyst preparation, possessing a high specific surface area and good thermal stability. Spray drying is an efficient way to produce narrow particle size distribution and spherical shape powders. In this study, spray drying method has been implemented to prepare microspherical nanoporous gamma alumina with a high specific surface area. The nanoporous gamma alumina support was utilized in the preparation of various heterogeneous base catalysts. The highest biodiesel yield of 99% was obtained at 6 wt% loading of K/γ-Al2O3 catalyst, using waste cooking oil as feedstock. The obtained results revealed the great potential of the synthesized nanoporous gamma alumina as an effective support for heterogeneous base catalysts preparation in the transesterification reaction.

Due to influences by power system restructuring, fuel price uncertainties, future demand forecasting, and utilities and transmission lines availability, demand response (DR) programs for consumers have gained more attention. One important DR scheme is the emergency demand response program (EDRP). This paper focuses on simultaneous implementation of security-constraint unit commitment (SCUC) and EDRP by using an economic model. Moreover, a stochastic optimization method is employed for realistic modelling. Since the combined implementation of SCUC and EDRP results in a complex nonlinear optimization problem, a linearization method to ensure computational efficiency is used. The proposed model is formulated as two-stage Stochastic Mixed-Integer Programming (SMIP) model implemented using GAMS. The implemented model is tested on three case studies using the IEEE 24-bus system. Results are analyzed with a focus on the impact of demand elasticity and electricity prices.