• Energy Research

The results of a study on incorporating solar-thermal collectors into a hybrid renewable energy system are reported. A photovoltaic–wind turbine–fuel cell–solar-thermal collector system is designed and an economic model is introduced for supplying the residential thermal and electrical loads via the grid-connected hybrid system. Since determining the optimal operation of a hybrid system such as a combined heat and power system constitutes a complex optimization problem requiring a sophisticated optimization method, a modified heuristic approach-based particle swarm optimization is proposed for solving the optimization problem. The results are compared with those obtained by an efficient metaheuristic optimization method, namely a genetic algorithm, in terms of accuracy and run time. The results show that, using the grid-connected hybrid combined heat and power system, among the cases considered, decreases the total cost of the system. The results also demonstrate that the reductions in daily cost relative to the base case by the modified particle swarm optimization algorithm for Cases 1–4 are 5.01%, 25.59%, 19.42%, and 22.19%, respectively. Finally, Case 2 is the most cost-effective and reliable. Moreover, the modified particle swarm optimization algorithm leads to better results than the genetic algorithm.

Abstract Cement production is considered as one of the most energy consuming industries. Any novel strategy to make cement plants more efficient would increase the annual revenue while significantly reducing local and global emissions. In this paper, an energy audit in a cement plant (Kerman, Iran) is presented in order to introduce more viable potential solutions to push this industry towards a more sustainable status. Various parameters, such as temperature and system dimensions, were measured, and raw materials and product analyses were conducted. Then, using the measured and analyzed data, an energy balance analysis was performed. A novel framework based on a well-known global optimizer algorithm and a multi-criteria decision method is presented for integrated energy optimization. This proposed framework is then tested for the audited cement plant at two major steps. First, an efficient metaheuristic optimization approach is employed to optimize the compounds of clinker to reduce the consumption of energy in the cement industry. Second, a decision-making procedure, namely analytic hierarchy process, is used to make right decisions to reduce energy losses after completing an energy audit program. Results of the present study indicate the effectiveness of the proposed framework based on metaheuristic algorithm and decision-making approach.

Increment in energy demand, limitation of fossil fuels and fluctuations in their price, in addition to their pollution, necessitate development of renewable energy systems. Regarding the considerable potential of solar energy in Iran, this type of renewable energy has developed more compared with other renewable energies. Hybrid technologies consisting of photovoltaic (PV) cells, diesel generator, and battery are one of the efficient solutions to resolve the issues related to the energy supply of rural areas. In this study, a hybrid PV/diesel/battery system composed of the mentioned components is applied to supply the off-grid power with capacity of 233.10 kWh/day with peak load of 38.38 kW in a rural region in South Khorasan, Iran. The purpose of this study is to reduce the net present cost (NPC), levelized cost of energy (LCOE), CO2 reduction, renewable fraction (RF) enhancement and increase reliability. In order to improve the performance of the system, different tracking system, including fixed system, horizontal axis with monthly and continuous adjustment, vertical axis with continuous adjustment and two-axis tracker, are analyzed and assessed. The results indicate that the vertical axis with continuous adjustment tracker is the most suitable option in terms of economic and technical requirements. In this work, a sensitivity analysis is performed on different parameters such as PV cost, interest rate, diesel generator cost, battery cost, and price of fuel, and the outcomes reveal that the hybrid system with vertical axis continuous adjustment is very sensitive to costs of fuel and the battery, i.e., NPC decreases by 5% in case of 20% variations in costs of battery and fuel. In addition, it is found that diesel generator and inverter costs significantly influence NPC of the system.

Abstract Two heuristic approaches based on particle swarm optimization (PSO), i.e., a PSO algorithm with adaptive inertia weight (PSOAIW) and a PSO algorithm with a constriction factor (PSOCF), are applied to the optimization of a hybrid system consisting of photovoltaic panels, a fuel cell, natural gas and the electrical grid to supply residential thermal and electrical loads. An economic model is developed and an economic analysis carried out for the grid-connected hybrid solar–hydrogen combined heat and power systems. The optimization seeks to achieve the minimum cost of the system with relevant constraints for residential applications. The optimization process is implemented and tested using actual data from northeastern Iran. Three other well-known meta-heuristic optimization techniques, namely the imperialist competition algorithm, genetic algorithm, and original particle swarm optimization, are applied to solve the problem and the results are compared with those obtained by the two heuristic approaches. The results show that the proposed PSOAIW and PSOCF algorithms achieve better results than other algorithms, and the hybrid solar-hydrogen system is the most cost-effective and reliable for satisfying residential energy demands in the near future.

Hybrid systems composed of solar photovoltaic (PV) and battery storage units are reliable and clean technologies for utilization in off-grid cases. Optimal sizing of these systems results in more cost-effective units, which is the main subject of various researches. The current work focuses on the assessment of an optimization approach for finding the optimum size of PV/battery hybrid unit in order to provide the required electricity of the case study and reach the minimum Total Life Cycle Cost (TLCC). In this regard, the components of the designed systems are modelled; afterwards, the objective function is established on the basis of TLCC. In the optimization procedure, a constraint is considered for the highest possible loss of power in order to have a system with acceptable reliability. In addition, Improved Harmony Search (IHS) algorithm is applied to determine the variables with optimal quantities for satisfying the required electricity in the most cost-effective condition. The calculated results are compared with harmony search and simulated annealing algorithms to evaluate the reliability of the applied method. The outcomes show that employing IHS leads to more promising results and it has higher robustness compared with the HS and simulated annealing algorithms. Moreover, the number of units in the hybrid system reduces by increment in the loss of power supply probability.

Abstract Renewable energy technologies have been developed in recent years due to the limited sources of fossil fuels, the possibility of depletion of fossil fuels and the related environmental issues. In these types of systems, it is crucial to reach optimum sizing in order to have an affordable system based on solar and wind energy and energy storage. In this study, a powerful optimization scheme based on tabu search, called discrete tabu search, has been proposed for sizing three stand-alone solar/wind/energy storage (battery) hybrid systems. For validating the applied algorithm effectiveness, the results are compared with the results found by the discrete harmony search. The obtained outcomes are compared on the basis of total annual cost. The components of the scheme are analyzed in different operating conditions by applying meteorological data in addition to real time information from three typical regions of Iran. According to the obtained data, applying ‘discrete tabu search’ leads to better outputs on the basis of mean, standard deviation and worst indexes.

The increasing need for fossil fuels along with their limited resources and also environmental concerns caused by pollution from internal combustion engines have always led researchers to improve the technology. The use of homogeneous compression ignition combustion technology as well as alternative fuel sources such as biodiesel is proposed as a novel and promising solution to achieve goals including improving efficiency and reducing environmental pollutants specifically NOx and CO2 that are emerging in the industry. In this study, the effect of using different blends of diesel/biodiesel fuel on thermal performance along with output pollution of a homogeneous fuel compression ignition engine is investigated. The fuels range from B00 to B40, and parameters such as engine thermal efficiency, maximum cylinder pressure and exhaust emissions have been evaluated. The results show retarded and reduced maximum pressure with a decrease in engine thermal efficiency, a reduction in NOx and CO2 emissions and an increase in CO emissions, while the mass fraction of biodiesel in the fuel composition is increased. Moreover, in the investigated engine, the B20 is considered as the optimum composition with a reduction of 4.2% in maximum cylinder pressure, 5% in thermal efficiency and 26% in NOx emissions compared to B00.

Abstract Wind speed (WS) and solar radiation (SR) are innately uncertain and bring about more uncertainties in the power system. Therefore, due to the nonlinear nature of photovoltaic cells and wind turbines, the mean values of solar radiation and wind speed cannot be assuredly measured and a small change in these values alters the results of the study. Furthermore the mean values of WS and SR occur with a low degree of probability, that is, if the mean values one utilized in system design, it will mean that not all possible states have been considered. Therefore, in hybrid system analysis, it has been suggested that the degree of uncertainty be taken into calculation in order for all possibilities to be covered. For this purpose, the Monte Carlo Simulation Method and Particle Swarm Optimization Algorithm have been used in this article. The proposed methodology is applied to a real case study and the results are discussed. In this regard, an off-grid hybrid multisource system (photovoltaic–wind–battery) is considered, modeled, optimally sized, and compared of different seasons in terms of the total annual cost and uncertainty in WS, SR, and electricity demand.