فصلنامه مهندسی و مدیریت انرژی
سال هفتم شماره 3 (پیاپی 25، پاییز 1396)

By the recent growing of global trend for decentralized power generation via natural gas, the interest in joint operation of several energy carriers has substantially increased. The most significant concept in this regard is the “Energy Hub”, which in this paper is employed to supply electrical and heat demands via electricity and natural gas carriers. Transformers, furnaces, combined heat and power (CHP) units, and electrical and heat storage units are utilized for this purpose. Unconventionally, CHP units are not modeled as constant electrical and heat efficiencies, and thorough operational characteristics of these units are employed in the problem formulation, which consists of feasible operating region (FOR), and nonlinear input-output relationship. In order to formulate the problem as mixed-integer linear programming, a piecewise linear approximation of CHP units’ operating region is presented to linearize their nonlinear operation. Additionally, the reliability index is added to the problem formulation, containing a model for changing the output point of CHP units in contingencies. The problem is aimed at choosing the best combination of candidate devices for decreasing total costs and increasing the reliability of supply in a long-term planning period. Simulations are presented for a case study, investigating impacts of applying energy price uncertainty on the results.

This paper presents a lifetime efficiency index model and a developed crow search algorithm (DCSA) in order to achieve optimal maintenance for the power transformer. Not doing maintenance on the power transformer, resulting in instability of the transmission system and not to supply consistently electric energy in the load points. In other words, deterioration of the transmission system effects on the status of distribution system and the consumers of electrical energy may face blackouts. Therefore, a maintenance strategy is required in order to reduce the failure rate and increase the reliability of power transformer. The maintenance strategy for power transformer consists of preventive and corrective maintenance. In this paper, the failure rate of power transformer is modeled by an appropriate Weibull distribution and the lifetime of power transformer is modeled using the Taylor series in Weibull distribution. The maintenance strategy should be optimized for the minimum maintenance cost under the required reliability condition. In order to solve this objective problem, a DCSA is used and results are evaluated with other methods. The results show that the proposed DCSA was indeed capable of obtaining higher quality solutions than other methods for the optimal maintenance problem.

In past, the grounding systems usually were modeled by an individual Resistor. This estimation is useful only at low frequencies, while in high frequencies, the inductance of grounding system plays the main role to identify the phase and amplitude of that. In recent years, as wind farms grow rapidly throughout the world, the grounding system and ground impedance of wind turbine towers under transients have got more important. As a matter of fact, an exact model to testify dynamic properties of grounding system should represent nonlinear effects of soil ionization and be frequency dependent. Some new researches conducted in this issue, all are based circuit theory which does not allow for accurate analysis of aforementioned behaviors. This paper has come to express a new approach to model any grounding system only by few RLC branches in wide frequency range. A new model and some simple formula that is recommended in this paper, represent all effects of transient phenomena of lightning strikes and all analysis have got more easier than other approach. Then the outputs or the RLC branches are implemented in EMTP and get analyzed. So it’s possible to evaluate power systems under lightning strikes with presence of precise behavior of grounding systems.

In this study, effect of press pressure used in the production of silver electrodes on the life of zinc - silver oxide batteries was investigated. For this purpose, initially four Ag electrodes (positive plate) with composition of 95 wt% silver oxide, 4.9 wt% carbon powder and 0.1 wt% resin prepared by powder metallurgy method and the press pressures of 40, 60, 80 and 100 bar was applied. Then, four silver electrodes were sintered for 13 minutes at temperature 500°C. Electrical discharge method were used in the 1.4% KOH electrolysis to evaluate the effects of press pressure used in the production of silver electrodes on the life of zinc - silver oxide batteries. Scanning electron microscopy was used to examine the microstructures of electrodes and point analysis was accomplished by EDX method. Electrical discharge tests results showed that press pressure of 60 bar is the most optimized pressure for increase of the life of zinc - silver oxide batteries. SEM images were showed that with increasing press pressure, the amount and size of apparent pores reduced in the Ag electrodes. Also, point analysis results has implies on reduction of electrodes oxygen, with the increasing of press pressure.

Biofuel is known as one of the best gasoline substitutes in the transportation industry. Designing an optimal supply chain is an essential requirement for the commercialization of biofuel production. This paper presents a mixed integer linear programming model to design a biofuel supply chain in which biofuel demand is under autoregressive moving average time series models. It is studied how the autoregressive moving average time series structure of biofuel demand affects the given supply chain design. Also, a numerical example demonstrates application of the proposed model used to design the most optimal biofuel supply chain. In addition, to provide insights for efficiently designing the biofuel supply chain, the autoregressive moving average models of biofuel demand are used to forecast biofuel supply chain design for an 8-year time horizon.

As an environment-friendly and renewable energy source, biomethane plays a significant role in the supply of sustainable energy. To determine location of hubs, reactors and condensers and allocate feedstocks, biomethane and liquefied biomethane to these facilities in a biomethane production system by minimizing the supply chain cost, a mathematical model is developed in this article. Constraints, such as the limited workforce, the reactors’ demand on the residues, and the deterioration of the residues in the hubs are considered. Genetic algorithm for solving mixed integer nonlinear programming model is proposed. Genetic algorithm finds better solutions than Lingo when applied to the given problems but the speed of obtaining the solution is slower. Biomethane supply chain has been designed in Khorasan Razavi using the developed mathematical model and genetic algorithm.