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[en] Environmental concerns besides fuel costs are the predominant reasons for unprecedented escalating integration of wind turbine on power systems. Operation and planning of power systems are affected by this type of energy due to the intermittent nature of wind speed inputs with high uncertainty in the optimization output variables. Consequently, in order to model this high inherent uncertainty, a PRPO (probabilistic reactive power optimization) framework should be devised. Although MC (Monte-Carlo) techniques can solve the PRPO with high precision, PEMs (point estimate methods) can preserve the accuracy to attain reasonable results when diminishing the computational effort. Also, this paper introduces a methodology for optimally dispatching the reactive power in the transmission system, while minimizing the active power losses. The optimization problem is formulated as a LFP (linear fuzzy programing). The core of the problem lay on generation of 2m + 1 point estimates for solving PRPO, where n is the number of input stochastic variables. The proposed methodology is investigated using the IEEE-14 bus test system equipped with HVDC (high voltage direct current), UPFC (unified power flow controller) and DFIG (doubly fed induction generator) devices. The accuracy of the method is demonstrated in the case study. - Highlights: • This paper uses stochastic loads in optimization process. • AC–DC load flow is modified to use some advantages of DC part in optimization process. • UPFC and DFIG are simulated in a way that could be effective in optimization process. • Fuzzy set has been used as an uncertainty analysis tool in the optimization
[en] Highlights: • A new approach to the problem of optimal reactive power control variables planning is proposed. • The energy loss minimization problem has been formulated by modeling the load of system as a Load Duration Curve. • To solving the energy loss problem, the classic methods and the evolutionary methods are used. • A new proposed fuzzy teaching–learning based algorithm is applied to energy loss problem. • Simulations are done to show the effectiveness and superiority of the proposed algorithm compared with other methods. - Abstract: This paper offers a new approach to the problem of optimal reactive power control variables planning (ORPVCP). The basic idea is division of Load Duration Curve (LDC) into several time intervals with constant active power demand in each interval and then solving the energy loss minimization (ELM) problem to obtain an optimal initial set of control variables of the system so that is valid for all time intervals and can be used as an initial operating condition of the system. In this paper, the ELM problem has been solved by the linear programming (LP) and fuzzy linear programming (Fuzzy-LP) and evolutionary algorithms i.e. MHBMO and TLBO and the results are compared with the proposed Fuzzy-TLBO method. In the proposed method both objective function and constraints are evaluated by membership functions. The inequality constraints are embedded into the fitness function by the membership function of the fuzzy decision and the problem is modeled by fuzzy set theory. The proposed Fuzzy-TLBO method is performed on the IEEE 30 bus test system by considering two different LDC; and it is shown that using this method has further minimized objective function than original TLBO and other optimization techniques and confirms its potential to solve the ORPCVP problem with considering ELM as the objective function
[en] Highlights: • In this paper an expert energy management system (EEMS) is presented. • A power forecasting module for wind generation capacity is presented. • The objective functions that must be minimized are operating cost and net emission. • A smart energy storage system (EES) for electrochemical batteries is presented. • A new modified Bacterial Foraging Optimization (MBFO) algorithm is presented. - Abstract: Recently, the use of wind generation has rapidly increased in micro-grids. Due to the fluctuation of wind power, it is difficult to schedule wind turbines (WTs) with other distributed energy resources (DERs). In this paper, we propose an expert energy management system (EEMS) for optimal operation of WTs and other DERs in an interconnected micro-grid. The main purpose of the proposed EEMS is to find the optimal set points of DERs and storage devices, in such a way that the total operation cost and the net emission are simultaneously minimized. The EEMS consists of wind power forecasting module, smart energy storage system (ESS) module and optimization module. For optimal scheduling of WTs, the power forecasting module determines the possible available capacity of wind generation in the micro-grid. To do this, first, an artificial neural network (ANN) is used to forecast wind speed. Then, the obtaining results are used considering forecasting uncertainty by the probabilistic concept of confidence interval. To reduce the fluctuations of wind power generation and improve the micro-grid performances, a smart energy storage system (ESS) module is used. For optimal management of the ESS, the comprehensive mathematical model with practical constraints is extracted. Finally, an efficient modified Bacterial Foraging Optimization (MBFO) module is proposed to solve the multi-objective problem. An interactive fuzzy satisfying method is also used to simulate the trade-off between the conflicting objectives (cost and emission). To evaluate the proposed algorithm, the EEMS is applied to a typical micro-grid which consists of various DERs, smart ESS and electrical loads. The results show that the EEMS can effectively coordinate the power generation of DERs and ESS with respect to economic and environmental considerations
[en] Highlights: • Integration of electrical, natural gas, and district heating networks is studied. • Part-load performances of units are considered in modeling. • A modified teaching–learning based optimization is used to solve the problem. • Results show the advantages of the integrated optimization approach. - Abstract: In this paper, an integrated approach to optimize electrical, natural gas, and district heating networks simultaneously is studied. Several interdependencies between these infrastructures are considered in details including a nonlinear part-load performance for boilers and CHPs besides the valve-point effect for generators. A novel approach based on selecting an appropriate set of state-variables for the problem is proposed that eliminates the addition of any new variable to convert irregular equations into a regular set while the optimization problem is still solvable. As a large optimization problem, the optimal solution cannot be achieved by conventional mathematical techniques. Hence, it is better to use evolutionary algorithms instead. In this paper, the well-known modified teaching–learning based optimization algorithm is utilized to solve the multi-period optimal power flow problem of multi-carrier energy networks. The proposed scheme is implemented and applied to a typical multi-carrier energy network. Results are compared with some other conventional heuristic algorithms and the applicability and superiority of the proposed methodology is verified
[en] Highlights: • This paper focused on the expansion planning optimization of energy systems. • Employing two form of energy: the expansion of electrical and thermal energies. • The main objective is to minimize the costs. • A new Modified Honey Bee Mating Optimization (MHBMO) algorithm is applied. - Abstract: This study focused on the expansion planning optimization of energy systems employing two forms of energy: the expansion of electrical and thermal energies simultaneously. The main objective of this investigation is confirming network adequacy by adding new equipment to the network, over a given planning horizon. The main objective of the energy expansion planning (EEP) is to minimize the real energy loss, voltage deviation and the total cost of installation equipments. Since the objectives are different and incommensurable, it is difficult to solve the problem by the conventional approaches that may optimize a single objective. So, the meta-heuristic algorithm is applied to this problem. Here, Honey Bee Mating Optimization algorithm (HBMO) as a new evolutionary optimization algorithm is utilized. In order to improve the total ability of HBMO for the global search and exploration, a new modification process is suggested such a way that the algorithm will search the total search space globally. Also, regarding the uncertainties of the new complicated energy systems, in this paper for the first time, the EEP problem is investigated in a stochastic environment by the use of probabilistic load flow technique based on Point Estimate Method (PEM). In order to evaluate the feasibility and effectiveness of the proposed algorithm, two modified test systems are used as case studies
[en] Highlights: • A new modified teaching–learning based algorithm is proposed. • A self-adaptive wavelet mutation strategy is used to enhance the performance. • To avoid reaching a large repository size, a fuzzy clustering technique is used. • An efficiently smart population selection is utilized. • Simulations show the superiority of this algorithm compared with other ones. - Abstract: In this paper, a modified teaching–learning based optimization algorithm is analyzed to solve the multi-objective optimal power flow problem considering the total fuel cost and total emission of the units. The modified phase of the optimization algorithm utilizes a self-adapting wavelet mutation strategy. Moreover, a fuzzy clustering technique is proposed to avoid extremely large repository size besides a smart population selection for the next iteration. These techniques make the algorithm searching a larger space to find the optimal solutions while speed of the convergence remains good. The IEEE 30-Bus and 57-Bus systems are used to illustrate performance of the proposed algorithm and results are compared with those in literatures. It is verified that the proposed approach has better performance over other techniques
[en] Highlights: • Convection of H2O/MWCNT through a backward-facing contracting channel was studied. • Effects of expansion ratio, nanoparticle and Reynolds number were investigated. • Thermal parameters enhances by Re or nanoparticles weight percentage increment. • Higher Re, vortexes will be located closer to the outlet of channel. - Abstract: In recent years, the study of rheological behavior and heat transfer of nanofluids in the industrial equipment has become widespread among the researchers and their results have led to great advancements in this field. In present study, the laminar flow and heat transfer of water/functional multi-walled carbon nanotube nanofluid have been numerically investigated in weight percentages of 0.00, 0.12 and 0.25 and Reynolds numbers of 1–150 by using finite volume method (FVM). The analyzed geometry is a two-dimensional backward-facing contracting channel and the effects of various weight percentages and Reynolds numbers have been studied in the supposed geometry. The results have been interpreted as the figures of Nusselt number, friction coefficient, pressure drop, velocity contours and static temperature. The results of this research indicate that, the enhancement of Reynolds number or weight percentage of nanoparticles causes the reduction of surface temperature and the enhancement of heat transfer coefficient. By increasing Reynolds number, the axial velocity enhances, causing the enhancement of momentum. By increasing fluid momentum at the beginning of channel, especially in areas close to the upper wall, the axial velocity reduces and the possibility of vortex generation increases. The mentioned behavior causes a great enhancement in velocity gradients and pressure drop at the inlet of channel. Also, in these areas, Nusselt number and local friction coefficient figures have a relative decline, which is due to the sudden reduction of velocity. In general, by increasing the mass fraction of solid nanoparticles, the average Nusselt number increases and in Reynolds number of 150, the enhancement of pumping power and pressure drop does not cause any significant changes. This behavior is an important advantage of choosing nanofluid which causes the enhancement of thermal efficiency.
[en] Highlights: • Dual and quadruple external wind breakers next to the cooling tower were studied. • Effects of wind velocity, number and place of wind brokers were investigated. • Using wind breakers, the heat loss retrieves, but maximun for quadruple wind breaker. • Condenser vacuum has a direct relation with the outlet water temperature of cooling tower. • Using wind breakers, outlet water temperature variation of cooling tower reduces. - Abstract: The dry cooling towers (HELLER) are one of the most current cooling towers used in steam power plants. These cooling towers are mostly used in dry areas. The environmental effects are one of the important problems on the performance of these kinds of cooling towers and the most challenging problem is the velocity of wind blow inside and around the cooling tower. In present research, finite volume method and k-ε model have been used for 3-D modeling. In this study, the dual and quadruple external breakers next to the cooling tower in different wind blow velocities have been examined. By using wind breakers, the heat loss retrieves. However, this enhancement is more for quadruple wind breaker, compared to the dual wind breaker. Condenser vacuum has a direct relation with the outlet water temperature of cooling tower. By using wind breakers, outlet water temperature increment of cooling tower reduces.