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Abstract In the current study, an integrated renewable based energy system consisting of a solar flat plate collector is employed to generate electricity while providing cooling load and hydrogen. A parametric study is carried out in order to determine the main design parameters and their effects on the objective functions of the system. The outlet temperature of generator, inlet temperature to organic Rankine cycle turbine, solar irradiation intensity ( I ) , collector mass flow rate ( m ˙ c o l ) and flat plate collector area ( A P ) are considered as five decision variables. The results of parametric study show that the variation of collector mass flow rate between 3 kg/s and 8 kg/s has different effects on exergy efficiency and total cost rate of the system. In addition, the result shows that increment of inlet temperature to the ORC evaporator has a negative effect on cooling capacity of the system. It can lead to a decrease the cooling capacity from 44.29 kW to 22.6 kW, while the electricity generation and hydrogen production rate of the system increase. Therefore, a multi objective optimization is performed in order to introduce the optimal design conditions based on an evolutionary genetic algorithm. Optimization results show that exergy efficiency of the system can be enhanced from 1.72% to 3.2% and simultaneously the cost of the system can increase from 19.59 $/h to 22.28 $/h in optimal states.

Abstract A three dimensional numerical model of the northwest (NW) Sabalan geothermal system was developed on the basis of the designed conceptual model from available field data. A numerical model of the reservoir was expressed with a grid system of a rectangular prism of 12 km × 8 km with 4.6 km height, giving a total area of 96 km2. The model has 14 horizontal layers ranging in thickness between 100 m to 1000 m extending from a maximum of 3600 to −1000 m a.s.l. Fifteen rock types were used in the model to assign different horizontal permeabilities from 5.0 × 10−18 to 4.0 × 10−13 m2 based on the conceptual model. Natural state modeling of the reservoir was performed, and the results indicated good agreements with measured temperature and pressure in wells. Numerical simulations were conducted for predicting reservoir performances by allocating production and reinjection wells at specified locations. Three different exploitation scenarios were examined for sustainability of reservoir for the next 30 years. Effects of reinjection location and required number of makeup wells to maintain the specified fluid production were evaluated. The results showed that reinjecting at Site B, immediate adjacent to production zone, is most effective for pressure maintenance of the system. On the base of existing data and assumptions the reservoir can sustain producing fluid equivalent to 50 MWe of electricity for more than 30 years. The reservoir can produce the maximum amount of fluids equivalent to 90–100 MWe for only 5 years, but the production capacity decreases to 50 MWe after 20 years of operation because of pressure and enthalpy drop. The reservoir can sustain 50 MWe over 100 years that can be defined as a sustainable production level of the field.

Abstract In the present study, enhancing the heat transfer is experimentally investigated by the electro-hydrodynamics (EHD) through a single-pass air-cooled PV/T (Photovoltaic/Thermal System). The corona wind increases the heat transfer coefficient by producing a secondary flow and vortex, and consequently, increases the PV/T system efficiency. The effects of the corona wind are studied by changing the voltage values and the flow rates in the air channel. The results show that the corona wind is effective on enhancing the system performance; so that the heat transfer coefficient increases by 65% in natural flow regime by applying 11 kV voltage in the pilot setup. Totally, the thermal efficiency of the PV/T system increases up to 28.9%. Also, the effects of corona wind are studied by changing the amount of the applied high voltages and flow rates.

With the spotlight on smart grid development around the world, it is critical to recognize the key factors contributing to changing power system characteristics. This is more apparent in distribution systems with the integration of renewable energy sources, energy storage, and microgrid development. Utilities are also focusing on the reliability and resiliency of the grid. These activities require distribution automation (DA) strategies that take advantage of available technologies while promoting newer solutions. It is necessary to create a roadmap for holistic DA strategies in a smarter grid. Sustainable and resilient grid development is a paradigm shift requiring a new line of thinking in the engineering, operation, and maintenance of the power system. International perspectives on DA are also addressed, with the understanding that one solution will not fit all. Integrating technical, business, and policy decisions into the challenges will generate the development of technologies, standards, and implementation of the overall solution. The challenges in the development of industry standards are also discussed. This paper explores the challenges and opportunities in the changing landscape of the distribution systems. Evolution of technologies and the business case for infrastructure investment in distribution systems are covered in another paper by the same authors.

Refrigerators have considerable share of residential consumption. They can be, however, flexible loads because their operating time and consumption patterns can be changed to some extent. Accordingly, they can be selected as a target for the study of Demand Side Management plans. In this paper, two experimental models for a refrigerator are derived. In obtaining the first model, following assumptions are made: the ambient temperature of refrigerator is assumed to be constant and the refrigerator door is remained closed. However, in the second model the variation of ambient temperature and door-opening effects are considered according to some general patterns. Further, two strategies are proposed to reduce the annual electricity cost and electric power consumption at peak-load times. These strategies together with the aforementioned models form an optimization problem which is, then, solved by Particle Swarm Optimization algorithm. Simulation results indicate a reduction of more than 28.61% in the annual cost. Also, the annual electricity consumption has decreased more than 20.46% and load shifting from the peak periods has achieved about 40%. In addition, these approaches are implemented in laboratory and their performance is confirmed by experimental results.

In this study, nitrogen oxides emission reduction is investigated with humid combustion in a non-premixed combustion chamber of a gas turbine that burns natural gas using computational fluid dynamics. For humidification of the combustion chamber, two methods are studied: direct water injection into the combustion chamber and increasing the inlet air humidity. The mathematical model of combustion, governing equations, numerical method, and the combustion chamber properties are extensively discussed. The mathematical model is first validated. The two approaches are then simulated and compared for low water and steam flow rates. The results show that at the water to fuel ratio of about 1, direct water injection is 1.69 times more efficient than increasing inlet air humidity in reducing nitrogen oxide emission. Finally, it is concluded that direct water injection is appropriate at low flow rates (up to the fuel flow rate) and for situations where reducing nitrogen oxide emission is highly relevant. Furthermore, increasing the humidity of the inlet air up to 25% is suitable and increases the combustion efficiency and reduces pollutants, while the outlet temperature is not decreased too much.

Using hybrid renewable energy technology is an efficient method for greenhouse gas mitigation caused by fossil fuel combustion. However, these renewable microgrids are not free from environmental damages, especially during the lifetime of hybrid renewable energy systems (HRES). The main objective of this study is to assess the environmental impacts of three optimized HRES for the Sea Water Reverse Osmosis Desalination (SWROD) plant. An objective optimization was developed using the division algorithm, and the environmental impacts of the optimized HRES were investigated by the life cycle assessment approach. The results showed that producing 1 m3 freshwater by an optimal size SWROD integrated with wind turbine/battery is responsible for 3.56E - 07 disability-adjusted life year (DALY). It is significantly less than 1 m3 freshwater production by an optimal size SWROD integrated with solar PV/battery (5.88E - 07 DALY) and solar PV/wind turbine/battery (5.13E - 07 DALY) energy systems. Moreover, 1 m3 freshwater by a SWROD integrated with proposed microgrids in this study led to a damage of 0.089 to 0.193 potentially disappeared fraction of species (PDF)*m2*yr to ecosystem quality. It also results in an emission of 0.143 to 0.339 kg CO2 eq per 1 m3 freshwater. Furthermore, resources for 1 m3 freshwater production by a SWROD are calculated at 2.77 to 4.806 MJ primary. Freshwater production by an optimal size SWROD integrated with solar wind/battery compared with solar PV/battery and solar PV/wind turbine/battery had less damage to ecosystem quality, climate, and resources. The results showed reductions of 91.23% in human health, 73.51% in an ecosystem quality, 92.43% in climate change, and 90.08% in resources for producing 1 m3 of freshwater using SWROD integrated with wind turbine/battery bank compared to fossil-based desalination. Finally, the result showed that solving the optimization problem using the division algorithm compared to other algorithms leads to less environmental damage in freshwater production.

Based on the algorithm structure, each metaheuristic algorithm may have its pros and cons, which may result in high performance in some problems and low functionality in some others. The idea is to hybridize two or more algorithms to cover each other’s weaknesses. In this study, particle swarm optimization (PSO), simulated annealing (SA) and differential evolution (DE) are combined to develop a more powerful search algorithm. First, the temperature concept of SA is applied to balance the exploration/exploitation capability of the hybridized algorithm. Then, the DE’s mutation operator is used to improve the exploration capability of the algorithm to escape the local minimums. Next, DE’s mutation operator has been modified so that past experiences can be used for smarter mutations. Finally, the PSO particles’ tendency to their local optimums or the global optimum, which balances the algorithm’s random and greedy search, is affected by the temperature. The temperature influences the algorithm’s behavior so that the random search is more significant at the beginning, and the greedy search becomes more important as the temperature is reduced. The results are compared with the basic PSO, SA, DE, cuckoo search (CS), and hybridized CS-PSO algorithm on 20 benchmark problems. The comparison reveals that, in most cases, the new algorithm outperforms others.

Abstract A Solar Chimney (SC) is a passive system harnessing solar energy to ventilate inner spaces and improve occupants’ thermal comfort. The purpose of this study is to determine the time periods of a year most suitable for the use of a SC in terms of thermal comfort under the different climates of Bandar-Abbas (hot and humid), Yazd (hot and arid), Paris (mild and humid) and Toronto (cold and humid). To this aim, a thermosyphonic model – validated against published experimental data – was employed for running dynamic thermal simulations of a residential building using the EnergyPlus software. The optimal time periods of operation of the SC, driven by the values of the Predicted Mean Vote (PMV) between −0.7 and +0.7 and Predicted Percentage of Dissatisfied (PPD) below 15%, are found to be November and January to April in Bandar-Abbas (2265 h), while for the city of Yazd it comprises the months of April and May together with September and October (2145 h). As for Paris and Toronto, the period spans from June to August, for a total of 1065 and 1620 h respectively. Correlation coefficients disclosing the impact of the increased air mass flow rate revealed a positive impact everywhere during nighttime in terms of indoor air temperatures (from 0.02 in Bandar-Abbas to 0.77 in Yazd). On the other hand, the impact on indoor relative humidity is always negatively correlated, with a peak of −0.73 in Yazd. Year-long comfort conditions are finally displayed in psychometric charts against ASHRAE 55 Standard boundaries.