The increasing use of wind resources has led to new challenges in transmission network expansion planning (TNEP). Wind curtailment due to improper expansion of transmission lines prevents the optimal use of these renewable resources. In addition, the uncertainty of these resources puts expansion decisions at risk. In this paper, risk-constrained TNEP in electricity markets in the presence of wind resources is presented with the aim of increasing the utilization of these resources by considering the wind curtailment cost and reducing Expected energy not supplied (EENS). For this purpose, a risk-constrained bi-level stochastic model is used. At the upper level, transmission network expansion decisions are made by the independent system operator (ISO) with the aim of minimizing network investment cost and EENS cost. At the lower level, the market-clearing problems are solved maximizing social welfare. Using duality theory, the bi-level problem is converted into a mixed-integer linear programming (MILP) problem. The proposed framework is analyzed using Garver’s six-bus test system and the IEEE 24-bus reliability test system (RTS). The simulation results indicate that considering wind curtailment cost has increased the use of wind units and reduced the Expected energy produced by fossil units, which is important from an environmental point of view. However, considering the risk, due to the uncertainty of wind resources, the energy produced by these resources has decreased.

Background and Objectives: Distributed generations (DGs) based on renewable energy, such as PV units, are becoming more prevalent in distribution networks due to technical and environmental benefits. However, the intermittency and uncertainty of these sources lead to technical and operational challenges. energy storage application, uncertainty analysis, and network reconfiguration are apt therapies to resist these challenges. Methods: energy management of modern, smart, and renewable-penetrated distribution networks is tailored here considering the uncertainties correlations. Network operation costs including switching operations, the Expected energy not served (EENS) index as the reliability objective, and the node voltage deviation suppression as the technical objective are mathematically modeled. Multi-objective particle swarm optimization (MOPSO) is considered as the optimization engine. Scenario generation method and Nataf transformation are used in probabilistic evaluations of the problem. Moreover, the technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) is deployed to make a final balance between different objectives to yield a unified solution.Results: To show the effectiveness of the proposed approach, the IEEE 33-node distribution network is put under extensive simulations. Different cases are simulated and interrogated to assess the performance of the proposed model.Conclusion: For different objectives dealing with different aspects of the network, remarkable achievements are attained. In brief, the final solution shows 4.50% decrease in operation cost, 13.07% improvement in reliability index, and 18.85% reduction in voltage deviation compared to the initial conditions.

Today the application of Distributed energy Resources (DERs) in Distribution System expansion planning (DNEP) problems is more crucial than before. Despite of advantages, the presence of the resources considering Renewable energy Sources (RESs) and Dispatchable Generation (DG) units in the distribution System expansion planning problems, brings more challenges, especially in reliability characteristics. This paper proposes a new distribution network expansion planning model embedded with a novel reliability assessment approach for Active Distribution Networks (ADNs). The proposed method aims to determine the optimal location and capacity of the new generation and distribution assets, responsible for providing power, in both the normal operation and contingency conditions. The load forecast significantly affects the results of the distribution network expansion planning. The K-means clustering method is used to address the uncertainty of load growth in the planning horizon which is coordinated with a Mixed Integer Linear Programming (MILP) optimization model. The proposed model is applied to the IEEE 33 bus test case, to guarantee its technical and economical effectiveness. The results verify that this model is cost-effective and can increase the robustness of the distribution network compared with recent similar works.

This paper demonstrates a method to how reserve capacity and cost allocation could be determined in a pool-based and disaggregated market model. The method considers both the spinning reserve and interruptible loads as the operating reserve services. In the proposed market, generators and consumers (including participation of interruptible loads) submit offers and bids to the independent system operator. Firstly, the energy market is cleared according to GENCOs' offers and customers' energy requirements. To make, the more competitive market, interruptible customers participate in reserve market and supply operating reserve. It is assumed that the operating reserve market structure cleared in two stages. Based on the reliability evaluation of the generators, market operator (MO) clears the reserve market. According to the contribution of generation units to the system Expected energy not supplied, reserve cost of this level is allocated among them. In the second section of the reserve market clearing, customers can choose their desired reliability requirements. The independent system operator is cleared reserve market such that the required reliability levels of the customers are met. Reserve cost associated with this part is allocated among customers that are willing to have a higher reliability level than the standard level. To determine the share of each consumer from a shortage in the real time operation, Deficiency Factor is introduced. Finally, numerical results are presented to illustrate the impact of the reserve cost allocation and effectiveness of participations’ demand side on the operating reserve market.

This paper develops a new model for the optimal placement of switches (both manual and automatic ones) in distribution networks to simultaneously reduce energy loss and improve network security. Expected energy not supplied (EENS) is assumed as the security index, and a method is developed for more exact calculation of this index regarding drastic climatic changes along with global warming and the resultant effects on both power consumption patterns and power network occurrence. The objective function of the problem is minimizing investment and maintenance costs, the cost of energy loss, and EENS cost. The suggested model can locate optimal places for installing the switches and their seasonal closed and open states so that the total costs can be minimized. The model is implemented on two test networks and evaluated under different scenarios. According to the results, despite the higher costs of automatic switches, the application of automatic switches is more economical in low-security networks for improving network security.

This paper studies power management in a grid-tied hybrid energy system consisting of photovoltaic array, wind turbine, fuel cell, electrolyzer, hydrogen storage tank and a combinational heating system to supply the thermal and electrical demand of a building. Moreover, the hybrid system is capable of exchanging power with local grid. Thus, variable daily buying and selling tariffs are also taken into account so as to cover a wide range of operational conditions. The thermal demand is supplied by both electric heating system and the fuel cell exhaust heat. The paper formulates the matter in the form of a nonlinear constrained optimization problem and then evaluates several well-known heuristic optimization techniques. The performance of each of these algorithms is discussed in detail and the elite algorithm is introduced. Furthermore, the effect of the initial charge of the hydrogen storage tank on total operation cost and also charge remained are fully discussed. In order to fulfill this intention, a novel criterion is presented to determine the optimal initial charge. The validation of the results will be implemented based on simulations.

Cooperative sector is mentioned as one of the sectors of the economy in the Article 44 of the constitution of the Islamic Republic of Iran. Accordingly, many cooperatives are formed and operated in the country. However, cooperatives are faced with problems, some of which date back to the internal structure of the company. So the world today is witnessing the development of new types of cooperatives that are trying to solve some problems through changing their structure. Investor-Share Cooperation is one of the new models of this type. This thesis focuses on the issue that, how a cooperative can offer its shares in the stock market, but still adheres to cooperative principles. For doing this, we describe and analyze some different new models and we use the results to design the model. Our model has three important components: take holders, the rights and preferred stock. Members and investors (take holders) have one same voting right and they contribute to the ultimate benefit of its profitable activities. The share of this company will publish in market and the shareholders of the company are joint owners. According to the new legal relationship, "pseudo investor-share cooperation" for the company is more favorable. At the end, we checked out the model with Shi’a jurisprudence and in conclusion the application of this model was known permissible in Sharia.

This research focuses on the effect of passive methods in the field of energy storage, produce energy by using renewable energies, and also to achieve the construction patterns of residential buildings in the climate conditions of Shiraz, with the aim reducing energy consumption. Statistics show that in Iran, fuel consumption in residential and commercial buildings with an average annual growth of 7. It is about 2.37% of the country's total fuel consumption. One way to minimize energy consumption in this sector is to increase the role of zero-energy buildings. Zero-energy buildings provide technical ways to consume less energy in buildings. These buildings are designed and built so that all or a large part of these provide the energy they need through the combination of energy saving and the use of renewable energy technology. In this research, after studying the climate condition of the city of Shiraz, a building in the energy Plus software, the simulation and climatic data of the Shiraz station was introduced to the software. To achieve the most optimal conditions of energy consumption in the climatic conditions of Shiraz, each of the building elements Residential was optimized. The simulation shows that it is possible to reduce the energy consumption of the building by using passive building materials and designs, including double-glazed glass, Wall insulation, etc. On the other hand, by using solar energy, using pvsyst software, you can supply the energy needed by the building.