AI Summary & Key Takeaways
AI InsightBy considering this, yet the results show that, the total benefit in presented scheduling is higher than homogeneous works.
📌 3 Key Takeaways
Abstract
In recent years, because of increasing interest to integrate energy storage devices as one of the main goals into the power system smart grid, penetration of battery energy storage and distribution generation in distribution networks is increased. The integration of battery energy storages and distribution generations are improve the reliability and satisfactory operation ofpower system. The deployment of distributed generations and battery energy storages has led to revolution in the use of distribution systems to improve many concepts of smart grid. This paper prepared to optimal palacement and optimal operation of distributed generations and battery energy storages to improved net present value reduction related to power losses as one ofthe main factors in smart grids. This placement is evaluated based on genetic algorithm in order to achieve the best operation duringfaced different percentage ofload levels with specrfic electricity price for each level. In this paper in spite of most of researches which are proposed in literatures in this fields, the placement problem is done simultaneously for distribution generation and battery energy storages, in addition, the load characteristic is considered multi-level to approach realty in optimal scheduling. In this paper to show the superiority of using distribution generation and battery energy storages, cost benefit of energy storage installation respected to the energy losses cost is calculated toward optimal costs. This is while, in this paper, arbitrage benefits of this installation did not considered. By considering this, yet the results show that, the total benefit in presented scheduling is higher than homogeneous works. During this scheduling, comprehensive investigation is done to describe of combination structure of distribution generation and battery energy storage systems. To evaluate this optimization challenge, IEEE 33 bus standard distribution network test is chosen to implement presented scheme. To validation ofefliciency ofpresented scheme, results are compared with previous similar works.