نشریه انرژی ایران
سال بیست و پنجم شماره 2 (پیاپی 95، تابستان 1401)

In Iran, due to the high rate of economic growth and development activities, the annual demand for various energies, including electrical energy, has increased, as a result of which the construction of new power plants has become an undeniable necessity. Considering the evolution of energy in the current century and the need for renewable energies, the use of wind energy and the establishment of wind power plants are increasingly developing. One of the most important issues in the use of wind energy is determining the location of its use, which has a great impact on the efficiency of wind power generation equipment and devices. Determining the right place for the construction of wind power plants requires attention to various criteria and factors. The purpose of this research is to determine the role of climate, geography, economic-social, environmental and geological factors in determining the suitable location for the construction of wind power plants in Khuzestan province. Based on the different roles and effects of these factors, a limitation map and a map of effective factors were prepared. The weights of criteria, sub-criteria and options were determined by Analytical Hierarchy Process (AHP) in EC2000 software environment and GIS software environment was used for spatial modeling and analysis and information integration. The results show that the southern and southwestern regions of Khuzestan province are the best places to build wind power plants.

Today, issues such as restructuring, environmental issues, problems and limitations in transmission and distribution lines have led to the increasing use of distributed generation (DG) systems. DG units, according to the characteristics, technology and location of connection to the network, can cause positive effects such as improving the voltage profile, reducing power losses and reliability in distribution networks; Therefore, with the increase in the use of scattered production resources as well as the technical and financial issues of these technologies, new issues such as determining the capacity and location of connecting these equipments to the network have been investigated. The problem of rearranging the distribution network is another optimization problem in distribution networks, so that it optimizes the network for the intended purpose without the need for new equipment. In this article, using an improved model of the learning and teaching-based optimization algorithm (TLBO), simultaneous simulation of distributed generation location problem and distribution network reconfiguration problem, in order to optimize the voltage profile, increase the voltage stability index and reducing network power losses. The proposed method has been implemented with different scenarios on radial networks of 33 buses and 69 buses, and the results obtained after comparing with other methods show the efficiency of the proposed method.

Nowadays importance of energy consumption behavior studies has been increased due to energy demand expanded and governance of sustainable development. This article reinvestigated the factors affecting the behavior of electricity consumption by adopting a qualitative approach and conducting 60 semi-structured interviews with citizens in different regions of the country and conducting a qualitative content analysis (including 3 main categories and 10 subcategories). We have achieved an analytical model starts from individual layer of behavior to social layer. The findings indicate energy consumption behavior in the country is the result of man-made factors as well as natural factors, and only paying attention to individual dimensions will not be effective in the policy of energy consumption behavior. it is necessary to create intervention policies that combine all dimensions. Based on the findings, 8 theoretical propositions has been formulated regarding the factors affecting the energy consumption in the country.

The combination of smart homes with renewable energy under the management of the smart distribution network has led to the emergence of smart networks. Therefore, in recent years, attention has been paid to smart networks in order to optimally use the distribution network, manage the consumption side, increase the reliability of the network, and as a result, intelligent control has increased. The maximum energy consumption of household appliances under load does not need to be done at specific times, but rather during the desired period of time. If these types of tasks can create harmony among multiple houses, So that all of them do not occur at the same time, At the same time, satisfying the duration of the demand of silent customers, the cost of energy and the maximum power demand can be reduced. In this research, the optimal planning of smart home energy consumption has been studied using the proposed linear programming method. It is assumed that new energies are used to supply energy locally, which includes some generators, steam boilers, wind generators, thermal storage and electrical storage. It also has a grid connection to obtain electricity during peak power demand hours or sell electricity to the grid when there is excess electricity generation. In order to realize smart homes, a method for optimal exploitation of renewable energy units (such as wind and sun), heat, batteries and controlled loads is proposed in this article.

In this paper, the optimal design of a hybrid photovoltaic-wind system with battery storage for a remote area in Tehran province has been done by a proposed modified version of the Bald Eagle algorithm (MBES). An innovative objective function is proposed which is a combination of the investment cost, maintenance cost and fines for energy not supply. Also, the loss of power supply probability (LPSP) reliability index is also considered as a main optimization constraint. The simulations are done in two scenarios with LPSP of 2% and 5% and the obtained results from the optimization by the proposed MBES algorithm are compared with the discrete simulated annealing (DSA), discrete harmony search (DHS) and also the initial version of the bald eagle search (BES). The simulation results showed that the system cost at LPSP=2% is about 19% higher than LPSP=5%. The results also proved that the proposed MBES performed better than the other three algorithms in both scenarios and its designing cost is lower than other.