فصلنامه مهندسی و مدیریت انرژی
سال دوازدهم شماره 1 (پیاپی 43، بهار 1401)

In this study, a new multi-objective method is proposed to optimal manage the local energy resources and customers’ load in a microgrid in the presence of demand response programs. The wind turbine and photovoltaic panel are the renewable energy sources of the microgrid. The time of use and critical peak pricing programs are also used to improve the consumption pattern of customers. Moreover, consumers try to use electrical energy when renewable sources are available. Maximizing the profit of microgrid and minimizing the pollutant gases of the microgrid are the objective functions of the demand side management problem. The multi-objective ant lion optimizer algorithm is used to optimize the indices of the microgrid and create the Pareto front. Then, the fuzzy method is utilized to select the best particle equal to the optimal management plan of the microgrid. Ultimately, the proposed method is evaluated in a sample microgrid. The results demonstrate the high efficiency of the proposed method in improving the performance of the microgrid by optimal managing the local energy sources and customers’ load. The proposed method of energy management increases the profitability of the distribution company and decreases the environmental pollution of the microgrid.

One of the most important objectives of smart distribution networks (SDNs) is to achieve a secure and reliable network. This can be realized by optimal operation in the presence of active power sources such as the distributed generations (DGs), reactive power sources such as switched capacitor bank (SCB), active loads such as the demand response program (DRP), and various network management strategies such as network reconfiguration In this paper, the planning and operation of the active and reactive sources in the smart distribution network (SDN) including reconfigurable capability and demand response programming (DRP) considering the voltage security is presented. Hence, the scheme to considering simultaneous modeling of the economic, operation and security of SDN is expressed as three-objective optimization problem, where objective functions minimize the annual costs of the sources planning and operation of network and sources, minimize the voltage deviation function, maximize the voltage security index, respectively. Problem constraints contain AC optimal power flow and network reconfiguration equations, operation and planning model of active and reactive sources, DRP formulation, and voltage security limit. In the following, the proposed multi-objective problem converts to single-objective formulation using Pareto optimization technique based on weighted functions summation method. Then, the hybrid solver of the Krill Herd Optimization (KHO) and Crow Search Algorithm (CSA) uses to achieve the reliable optimal solution including low standard deviation. Finally, the proposed scheme is implemented on the 69-bus radial SDN. Results show a low standard deviation of 0.94%, verifying its ability in achieving unique response conditions. Additionally, SCBs are generally placed at the feeder end buses to improve VDF and VSI while injecting reactive power equal to their maximum capacity into the SDN. DGs are also installed at either the beginning or end buses of the feeders to improve the Cost, VDF, and VSI functions. DRPs operate in charging (discharging) mode during off-peak (off-peak and middle-load) hours to enhance operating costs. Accordingly, the proposed strategy improves the economic, operation, and VSI indices by approximately 20%, 46%, and 11.5%, respectively. It can also reduce annual energy losses by 40% and maximum voltage drops by about 52% but will lead to a slight overvoltage in the SDN. The obtained numerical results confirm the capabilities of this scheme in the improving of the economic, operation and security situation of the SDN.

This paper proposes a new voltage stability assessment method based on vector analysis method. This index extraction process relies on measurements of active and reactive powers from connected buses to the generator buses. Therefore, a limit for beginning of voltage collapse is determined based on the maximum power transfer theory. On the other hand, when the system requires load shedding, a new voltage-frequency load shedding has been employed. This load shedding makes voltage and frequency stabilities be guaranteed via clustering of all loads as allowable and unallowable, and prioritizing allowable cases. Simulation results in DIGSILENT Power Factory software on IEEE-39 bus dynamic test system demonstrate the efficiency of proposed approach with respect to other methods.

The purpose of this paper is to design and evaluate the construction of a lightweight solar aircraft that is capable of flying continuously for 24 hours relying solely on solar energy. The required energy is absorbed during the day by solar panels, some is used directly for flight, and the excess energy is stored in the aircraft battery for overnight flight. The aircraft will then be used for missions that require long flight duration. For this purpose, a fire detection and notification system to the ground station has been designed and arrangements have been made to install it on the aircraft. In this paper, an analytical method for forming an aircraft mass prediction model is presented, which is based on the involvement of all electrical and mechanical devices in order to achieve an optimal design model. This analytical method is based on the balance of mass and energy in different stages of flight. By involving about 30 different parameters in this modeling, finally, according to the set goals, the desired design characteristics such as optimal aircraft wing length, battery capacity, flight altitude, transmitter power level, etc. can be achieved. An electrical simulation framework was introduced and implemented in MATLAB Simulink software in real-time, the results of which are presented. To ensure the performance of the simulated model, a laboratory sample is made and all flight, electrical and mechanical parameters are tested on it.

Due to the wind speed fluctuations in wind energy conversion systems (WECSs) and because of problems of the fixed speed wind turbine-generators, the variable speed wind turbines (VSWTs) are preferred to them. Most of the common types of VSWTs are WTs based on the doubly-fed induction generators (DFIG) and permanent magnet synchronous generator (PMSG). In DFIG wind turbines, the partial rated converters are used with lower cost, whereas, due to existence of brushes and slip rings they have less reliability and have higher maintenance cost. WTs based on PMSG have advantages as high efficiency and power density as well as brushless structure of rotor; however, they suffer from the high price of rare permanent magnet materials and the property reduction of these materials over time. Furthermore, the high cost of full power electronic converters and the infeasibility control of the excitation is the other disadvantages. To take benefits of both DFIG and PMSG, the dual stator winding induction generator (DSWIG) for VSWT applications has been recently proposed. This type of generator in comparison to DFIG due to the elimination of the brushes and slip rings has a robust structure and less maintenance cost. The rotor type in DSWIG is squirrel cage, therefore, in comparison to the nested-loop rotor type in the brushless doubly fed induction generator (BDFIG), the DSWIG has a simpler structure. On the other hand, in comparison to the PMSG, in this generator, the rare permanent magnet material does not exist and the excitation is controllable. The stator of DSWIG has two distributed three phase windings that share common pole pairs in air-gap. In this paper, DSWIG based WT equipped with related converters and battery is used to supply an independent AC load. In the proposed power topology, each of the DSWIG stator winding is connected to a voltage source converter that is called static excitation converter (SEC). The output active powers of both stator windings are transmitted to the load through load side inverter. 

In the proposed topology, the DC link voltage of three voltage source converters is common and in order to provide a balance between the generated active power by DSWIG and consumed active power by the AC load, the battery energy storage with bidirectional DC/DC converter is used. In this paper, for each converter, an appropriate control strategy comprising inner and outer control loops are used. In the proposed control strategy, the WT output power is equally shared between the two stator windings. The outer control loop of SECs is generator speed control and by implementing this control loop, the WT operates in maximum power point tracking (MPPT) mode. The inner control loops of SECs are extracted by using field oriented control (FOC) with indirect rotor flux orientation, in which, the amplitude and angle of rotor flux are estimated.

For examining the performance of the system under study and verifying the theoretical analyses, time domain simulations in the Matlab-Simulink environment are presented.

By proposed power topology and designed control system, with regulation the DC link voltage, once the WT power is more than the load power, the additional power is stored in the battery, and once the load demand is more than the WT power, the battery provides the power shortage, and thus, proper operation of voltage source converters is provided. Desired amplitude and frequency for the AC load is provided by the load side inverter in spite of load and wind speed variations. In a future study about the proposed power topology in this paper, the state of charge (SOC) of the battery as a control variable will be examined and the control loops with considering it will be designed.

In developed societies, residential customers use high-level appliances. The progress in the smart grids and the internet of things have eased the way for home energy management to schedule controllable appliances. Looking to demand increment, demand response strategies aiming at energy management, to achieve goals such as demand reduction and improving reliability, has received attention. A deep review of the existing literature shows the notable efforts put into optimizing the home energy management problem through classic and meta-heuristic optimization algorithms such as game theory, genetic algorithm, and PSO. But, it is worth saying that these algorithms are not pragmatic due to the inherent nature of the home energy management problem. To be more precise, as the environment of the problem changes continuously, these algorithms fail to solve the problem. Hence, some essential assumptions such as considering fixed scenarios are presumed in previous works to enable the conventional algorithm to solve the problem. This is while machine learning addresses this issue by extracting the main features from input data and constructing a general description of the environment. Implementation of machine learning-based algorithms to a home energy management problem requires smart appliances. Hence, in the case of having a smart home, taking the advantage of artificial intelligence for energy management would be feasible and useful. It should be noted that electricity cost reduction can make the demand response program inviting, where customer satisfaction is taken into consideration. Accordingly, customer satisfaction should be considered in the problem formulation. Regarding the mentioned issues, lately, with the remarkable progress in machine learning, novel algorithms evolved for solving optimal decision-making problems such as demand response. Machine learning can be categorized into three main categories, namely supervised learning, unsupervised learning, and reinforcement learning (RL). Among them, reinforcement learning has shown notable performance in decision-making problems. Q-Learning is a model-free RL algorithm that solves nonlinear problems through estimating and maximizing the cumulative reward, triggered by decided actions. The fundamental idea of this algorithm is to identify the best action in each situation. This paper aims to provide a day-ahead demand response program for a smart home. It is done by specifying the quantity of the energy consumption of each appliance, aiming to reduce the electricity cost and user dissatisfaction. In this respect, it is presumed that the smart home is equipped with smart appliances. Moreover, smart meters are installed on appliances to monitor the statuses and receive the command signals from the devices at each hour. These appliances can be divided into three categories, non-responsive, time-shiftable, and controllable loads. Dishwasher and washing machine as time-shiftable loads, EV, air conditioner, and lighting system as controllable loads, and TV and refrigerator as non-responsive loads are taken into account. All in all, we recommend an advanced home energy management system proposing the following contributions: i) Proposing a day-ahead multi-agent Q-Learning method to minimize the electricity cost. ii) Proposing a satisfaction-based framework, which employs a precise model of the customer dissatisfaction functions (i.e., thermal comfort, battery degradation, and desirable operation period). 

In this paper, a multi-agent Q-Learning approach is used to solve the home energy management for a smart home. Q-learning is a popular model-free algorithm among reinforcement learning algorithms, due to the fact that its convergence is proven, and it is feasible to implement, as well. In order to deploy Q-Learning on a home energy management system, first of all, smart home should be formed as a Markov decision process. A Markov decision process consists of four fundamental parameters namely, state, action, reward, and transition probability matrix. Afterward, an agent is trained through experiencing a specific state, taking an action, transition to a new state, and calculating the cumulative reward. By doing so, after visiting a considerable number of states and taking diverse decisions, it will learn gradually to select the optimum action whatever the state is.Another fundamental aspect of this paper is the proposed approach to take customer satisfaction into account. In this paper, a non-linear thermal comfort model, non-linear desirable operation period model, and linear battery degradation model are deployed to consider the customer dissatisfaction, precisely. It should be noted that all simulations have been implemented by python 3.6 programming language without making use of any commercial solver. 

Various case studies have been designed to verify the effectiveness of the proposed method. Scenario 1 is designed to simulate the behavior of a smart home associated with a random manner of energy usage. Scenario 2 is designed to verify the effectiveness of the proposed home energy management system, where Q-Learning is conducted. In this case, battery degradation is overlooked. Scenario 3 is similar to the previous one, where battery degradation is also taken into consideration. Comparing the obtained results indicates that the proposed algorithm has successfully reduced the electricity bill by 31.3% and 24.8% in scenarios 2 and 3, respectively. It is worth saying that customer satisfaction is not violated in mentioned scenarios. Furthermore, in order to evaluate the effect of thermal comfort on the electricity bill, another case study is deployed, where the thermal comfort coefficient is decreased to smaller magnitudes. As expected, the less thermal comfort coefficient, the less electricity bill. The reason behind this is that having a lower thermal comfort coefficient leads to less importance of temperature control compared to the electricity bill.

This paper proposed a method for home energy management, regarding minimizing the electricity bill and user discomfort. In this paper, a multi-agent reinforcement learning via Q-Learning is used to make optimal decisions for home appliances, which are categorized into non-shiftable loads, time-shiftable loads, and controllable loads. Comparing to classic optimization methods, the proposed approach in this paper is capable of modeling more appliances and solving complex problems, due to the inherent nature of the Q-Learning algorithm. Implementing the proposed method in the numerical study section led to a 24.8% electricity bill reduction. The numerical results prove the effectiveness of the proposed approach.

The growing penetration of renewable energy resources has posed serious technical and economic challenges to day-ahead generation scheduling in power system. Due to the stochastic nature in generation of these resources, providing the requirement flexibility to cover their uncertainty and variability has become an important issue. Among the resources to supplying this product are fast units such as gas units that using their non-spinning ramping capacity can reduce the need for spinning operation of expensive units, while providing the required flexibility of the power system. On the other hand, according to the acceptable approach of electricity markets, paying attention to maximizing social welfare in day-ahead scheduling is of great importance, which requires simultaneous energy and reserve clearing with ensuring resource adequacy in case of generation fluctuations of renewable units. Therefore, in this paper, adaptive robust optimization based on column-and-constraint generation method  has been used to solve day-ahead generation scheduling problem by exploiting the potential of fast units, under the high penetration of wind generation resources. Examination of the results on the  IEEE RTS24-bus test system indicates that utilizing the potential of fast resources, reduces operating costs by 0.85%. Also, using column and constraint method, has led to increasing the convergence speed  of the problem solving process and achieving the optimal solution in a maximum of three iterations.

Nowadays, biodiesel is considered as a suitable alternative for fossil fuels. According to the bioenvironmental importance of biodiesel, in the current study, ZrO2-NiO and ZrO2-CeO2 oxides with different molar ratios were synthesized using coprecipitation method and Pechini sol-gel and their catalytic activities were studied to produce biodiesel from corn oil and waste cooking oil. New catalysts were investigated in terms of morphology, crystallography, and chemicals with well-known techniques of X-ray diffraction, X-ray energy diffraction spectroscopy, field emission scanning electron microscopy, and Fourier transform infrared spectroscopy. Different factors and parameters affecting biodiesel production such as oil to methanol ratio, transesterification reaction temperature, and nanocatalyst synthesis method were optimized for the catalysts of interest. The empirical results showed that the use of zirconia with metal oxide as catalyst increases synthetic and decreases reaction time of biodiesel production. Also, the highest catalyst performance was observed in the moral ratio of oil to methanol (1/10) and molar ratios of Zr/Ce and Zr/Ni= 10, so that waste cooking oil transformation efficiency and corn oil to biodiesel on ZrO2-CeO2 catalysts were 92 and 83% with a satisfactory analytic accuracy (R.S.D.≤ 4.8 ٪) and with ZrO2-NiO catalyst, they were 83 and 78% with a satisfactory analytic accuracy (R.S.D.≤ 5.3 ٪).

The current trend of rising energy consumption in the world has hit mankind with two major crises: first, environmental pollution, and second, the acceleration of finishing energy supplies. Energy storage and reduction of pollutants in the city plays a crucial role in the process of maintaining existing energy. Meanwhile, the transportation sector needs attention due to its importance and position, and can annually bring significant amounts of economic savings to the people and governments by reducing energy consumption and adverse environmental effects, and reducing travel time and unwanted delays brought up. Therefore, in the present research, the most important parameters of environmental pollutant emissions in urban transport sector were determined and system dynamics model of Tehran's urban transport was developed. Based on quantitative analysis, six scenarios, Business As Usual, Priority to the Development of Public Transport, Technical Progress, Administrative Rules and Regulations Management, Travel Demand Management and comprehensive policy are evolved. According to the results, CP scenario has the best performance, and by simultaneously implementing the scenarios, each of them will play a role in improving the situation and significantly reducing energy consumption and CO2 emissions.

In this study, the performance of solar desalination by humidification-dehumidification of closed air and water in Ahvaz city was investigated. This desalination includes a collector, condenser, salty water, freshwater tanks, air blower, and water pump. The evaluation of the system was done at three levels of air velocity (3, 4, and 5 m/s) and at three pump discharge levels (2, 4, and 6 liters/min). The results showed that the lowest daily average of evaporative efficiency was about 56%, which obtained at air velocity of 3 m/s and water discharge of 2 liters/min, and the highest value of that was about 79%, which obtained at air velocity of 5 m/s and water discharge of 6 liters/min. Also, the highest daily average of condenser efficiency was 21.52%, which was obtained at the air velocity of 3 m/s and discharge water of 6 liters/min. The lowest freshwater was obtained at air velocity of 5 m/s and 2 liters/min, and the highest value was obtained at air velocity of 3 m/s and 6 liters/min.

In the present experimental investigation, the thermal and electrical performances of a photovoltaic/thermal system equipped with a sheet-and-grooved serpentine tube collector are investigated. The water-magnetite nanofluid is used as the heat transfer fluid. The effect of nanoparticle volume concentration (0-1%), nanofluid mass flow rate (10-40 kg/h) and groove pitch (0, 0.54 and 8 mm) on the temperature of photovoltaic panel, thermal efficiency, electrical efficiency and overall efficiency is examined. A review of the literature reveals that the present study is the first experimental study on the performance of photovoltaic/thermal systems with a sheet-and-grooved serpentine tube collector. 

In order to evaluate and compare the performance of the PVT systems studied in the present study, weather conditions such as ambient temperature and solar radiation intensity should be similar in different experiments. Therefore, it was decided to perform the experiments using a solar simulator capable of producing a uniform heat flux (1000 W/m2) at a constant ambient temperature (22 °C). The collector is connected to the bottom of the photovoltaic panel and the serpentine tube is welded to the bottom of the collector. In this way, the heat of the photovoltaic panel is transferred to the collector and from the collector to the serpentine tube, and finally, the nanofluid flowing in the tube receives heat from the tube wall and heated. After leaving the serpentine tube, the heated nanofluid enters a heat exchanger and transfers its heat to a coolant and, as a result, cools down. The grooves create secondary flow in the nanofluid that disrupts the thermal boundary layer and increases heat transfer from tube wall to the nanofluid. On the other hand, the grooves increase the pressure drop of the nanofluid, and thus increase the pumping power required to make the nanofluid flow in the serpentine tube, which is not desirable at all. 

The results showed that the thermal, electrical and overall efficiencies of the system with a sheet-and-plain serpentine tube collector are in the range of 30.89-41.09%, 11.89-11.99% and 62.19-72.63%, respectively. These values for the system equipped with a sheet-and-grooved serpentine collector having groove pitch of 8 mm are 34.57-46.81%, 12.06-12.15% and 66.30-78.78%, respectively, and for the system equipped with a sheet-and-grooved serpentine collector having groove pitch of 5.4 mm are 37.03-50.89%, 12.29-12.38% and 69.37-83.47%, respectively. Among the systems studied, the best thermal, electrical and overall performance belongs to the system having groove pitch of 5.4 mm, while the worst performance belongs to the system equipped with a sheet-and-plain serpentine tube collector. In addition, the results showed that increasing the nanoparticle concentration and nanofluid mass flow rate leads to improved thermal and electrical performance of all three systems studied in the present study.

Using a grooved serpentine tube instead of a plain serpentine tube causes the hot fluid near the tube wall to mix with the colder fluid passing through the central areas of the tube, and thus, the temperature distribution of the fluid becomes more uniform, which improves its cooling capability. This effect is enhanced by reducing the groove pitch. On the other hand, the larger the number of grooves, the greater the pressure drop across the nanofluid current in the serpentine tube, which in turn increases the power required to pump the nanofluid into the collector and, as a result, reduces the net electrical output of the system. Fortunately, the share of pumping power in the electrical power generated by the system is about 1%, which makes the positive effect of using a grooved serpentine tube more than its negative effect, and as a result, the performance of the photovoltaic/thermal system with a sheet-and-grooved serpentine tube having groove pitch of 5.4 mm is better than other systems. Also, because the nanofluid has a higher thermal conductivity than the pure water, it can receive more heat from the tube wall, which better cools the panel and, thus, improves its electrical efficiency. On the other hand, at the same mass flow rate, the velocity of the nanofluid in the tube is lower than that of water, which leads to a lower pressure drop of the nanofluid compared to water. Overall, the results showed that nanofluid is a better coolant than water for use in the photovoltaic/thermal systems studied in the present study. Increasing the mass flow rate also leads to an increase in the coolant velocity, which leads to a simultaneous increase in heat transfer and pressure drop, the former of which is desirable and the latter of which is undesirable. The results showed that the positive effect of increasing the mass flow rate outweighs the negative effect, and as a result, increasing the mass flow rate leads to improved thermal, electrical and overall performance of the studied photovoltaic/thermal systems.

This investigation evaluates the first and second laws of thermodynamics for a flat plate sheet and tube based solar collector located in Abu-Musa Island (in Persian Gulf) in Hormozgan province, Iran where a suspension of molybdenum disulfide (MoS2) nanoparticles with different shapes in water is the working fluid. The main aim of this study is to analyze morphology effects of MoS2 nanoparticles on heat transfer and entropy generation and due to fulfill this demand, analysis four different nanoparticles shapes (blades, platelets, bricks, and cylinders) are chosen. The results of Nusselt number, outlet fluid temperature, pressure drop, friction factor, performance evaluation criterion (PEC) and entropy generation are calculated and reported for different shapes of nanoparticles and also nanoparticles volume fractions up to 4% in turbulent flow with two different mass flow rates of 0.50 and 0.75 kg/s. According to obtained results, the PEC of model with ϕ=3% and bricks nanoparticle shape in ṁ=0.5 kg/s is found to be the best among all models and its value is around 1.269. But in case with ṁ=0.75 kg/s this value for nanofluid with ϕ=4% and bricks nanoparticle shape is found to be the best among all models and is about 1.182. It was also found that for mass flow rate of 0.50 kg/s, adding the bricks and blade shaped nanoparticles to base fluid is not advantageous from the second law viewpoint, whereas for mass flow rate of 0.75 kg/s it was seen that using volume fractions of 1% and 4% of bricks and blade can reduce the entropy generation rate compared to base fluid till 8%

Present work focuses on energy and exergy performance and parameter optimization of a solar air heater integrated with fins, baffles and external recycle. Theoretical analysis has been considered to study the effect of geometrical and operating parameters on effective and exergy efficiency of SAH. Energy and exergy balance equations have been solved by developed code using MATLAB. Further, Genetic algorithm has been invoked to optimize the design and operating parameters of SAH. The distance between the fins (number of fins), the width and the distance between the baffles and the recyle ratio are the parameters whose optimal values were obtained for different values of Reynolds numbers and solar radiation with the aim of achieving maximum energy and exergy efficiency. It was found that the use of fins and baffles under external recycle in a flat solar air heater improved thermal efficiency in all cases. However, at higher air flows, the additional power required to overcome the pressure drop may impair the performance of the SAH. The best effective and exergy efficiency for various Re are respectively 64.15% and 6.34% which obtaind for I=1200 W/m2. Simulations results of present model has been validated with models available in the literature and found to be in good agreement.

At a nuclear power plants a set of fuel rods held together by spacer grids form a fuel bundle. The coolant flow passing around the rods and the resulting flow turbulence, especially around the spacer grids, exerts transverse, vibrational and unsteady forces on the rods. The periodic tensions generated by these vibrations will result in fatigue and reduced mechanical strength of the rods as well as erosion corrosion of the rod clad. In this study, these forces are analyzed in terms of magnitude, amplitude and intensity of oscillations. To identify the effect of mixing vanes on the spacer grids, simulations were performed for spacer grids both with/without vanes. The obtained results are in good agreement with experimental results. Results showed a 17% increase in pressure drop due to the presence of mixing vanes. The forces exerted to the spacer in the downstream are 10% less than the forces in upstream. Analysis of the fluctuations of the forces applied to the fuel rod bundle showed the amplitude of the vibrations is relatively high at frequencies below 300 Hz.