فهرست مطالب فرخنده جباری

This paper presents a new approach for short-term forecasting of wind farm power generation using artificial neural networks under Python programming language. In this method, weather conditions such as wind speed, wind direction, temperature and air pressure are selected as key features affecting the power production of the wind farm. To achieve a relatively accurate estimate, the root mean squared error of the predicted values is calculated and minimized as the objective function. The speed and accuracy of the proposed algorithm have been evaluated by conducting a case study on a wind farm located in Manjil, Iran. The power production of the wind power plant is predicted for a time horizon of one week and hour by hour using the wind speed, wind direction, temperature and air pressure during 8592 hours (total hours of a year minus hours of a week). The root mean squared error, the highest relative error percentage, the time resolution of the forecasts and the calculation time of the proposed algorithm are compared with other algorithms published in recent years, which shows the effectiveness and high accuracy of the results in a short calculation time. The power production of the wind farm was predicted hour by hour during a week and 168 data points were obtained, the root mean squared error in the optimal scenario is equal to 0.010817. The calculation time of the forecasting algorithm is less than 1 minute, and the maximum relative error in the proposed method is 2.3%, which demonstrates that the uncertainties associated with the power production of the wind farm can be reduced by using this short-term forecasting approach.

Providing electricity to critical electrical loads in all conditions is one of the important goals facing designers and operators of power systems. On the other hand, power systems are always exposed to various events and disasters. The ability to face these events and disasters in power systems is brought up with the concept of resilience. In this article, improving the resilience of distribution networks is pursued. For this purpose, the expansion of fixed and portable energy storage systems in distribution networks has been carried out to keep distribution networks resilience. Due to the importance of providing the critical loads, meeting the critical loads is considered as the main resilience criterion. The proposed model is formulated as a mixed integer linear optimization problem. Minimization of costs is considered as the objective function and fulfillment of restrictions in normal and resilience conditions of the network are considered as the constraints of the problem. In this model, the distribution network is divided into several separate zones and the fulfillment of critical loads in the zones is followed by the available resources and energy storage systems. The results of studies on the test network show the ability of portable energy storage systems to meet the requirements of network resilience.

Currently, renewable energy is rapidly developing across the world in response to technical, economic, and environmental developments, as well as political and social initiatives. Moreover, the excessive penetration of distributed generation (DG) systems into electrical networks may lead to various problems and operational limit violations, such as over and under voltages, excessive line losses, overloading of transformers and feeders, protection failure and high harmonic distortion levels exceeding the limits of international standards. These problems occur when the system exceeds its Hosting Capacity (HC) limit. The HC is a transactive approach that provides a way for the distribution network to be integrated with different types of energy systems. Distributed Generation (DG) sources are one of the important componeents of novel distribution systems, among which renewable and clean energy sources have received more attention due to the important role of these sources in reducing greenhouse pollutants. The use of renewable sources such as wind and photovoltaic sources is expanding day by day with the increasing demand for electric energy supply. However, the limitations in the amount of the penetration of DG resources are one of the main challenges in the development of the use of these resources. This paper is looking for a method to improve the HC of the distribution network from DG sources by using reactive power compensation and the reconfiguration of distribution systems. The results of the simulation show the advantages of using the proposed method in increasing the HC and as a result the development of the use of renewable resources.