An Efficient Framework for Improving Microgrid Resilience against Islanding with Battery Swapping Stations

In this paper, an efficient bi-level framework is proposed to enhance the resilience of microgrids (MGs) against islanding due to low probability-high impact events by incorporating battery swapping stations (BSSs). In the emergency condition, MG solves the upper-level of the proposed model to report the desired energy transaction including surplus energy and unsupplied loads during the islanding period to the BSSs coordinator. The lower-level problem will be solved with an iterative algorithm by BSSs coordinator to report different plans of energy transactions and their prices to the MG during the emergency period. The price of each energy transaction plan is determined based on a bonus mechanism. Finally, MG will choose the best plan of energy trading considering a new proposed perspective of resilience improvement. Furthermore, a new formulation for BSS operation with fewer variables in comparison to the previous works is proposed in this paper. Simulations are carried out on an MG with two BSSs to verify the proposed model

An innovative two-stage machine learning-based adaptive robust unit commitment strategy for addressing uncertainty in renewable energy systems

Confronting the challenge of intermittent renewables, current unit commitment practices falter, urging the development of novel short-term generation scheduling techniques for enhanced microgrid stability. This study presents an adaptive robust unit commitment approach using machine learning techniques for renewable power systems, computing the Calinski-Harabasz index to identify prediction inaccuracies related to intermittent sources. The uncertainties are subsequently grouped together using the spatial clustering tool, and the average density of the K-means distribution is calculated. The clustering of points in space, considering noise, discrete uncertainty in renewable energy sources, and outliers within the comprehensive uncertainty set, is addressed via a nonparametric algorithm. The implementation of established methodologies and frameworks, in conjunction with density-based spatial clustering of applications with noise, introduces an innovative method for vulnerability clustering. This methodology guarantees that every cluster aligns with data pertaining to vulnerabilities of renewable energy sources. The performance of the suggested method is showcased by conducting experiments on modified IEEE 39-bus and 118-bus test systems that use intermittent wind power. The results demonstrate that the proposed framework may lower the cost of robustness by 8–48% compared to traditional robust optimization techniques. The results of stochastic programming showed that the optimized system with a stable economic organization would have 75 % faster calculations

Thermoelectric Generators as an Alternative Energy Source in Shipboard Microgrids

In recent years, the usage potential of alternative energy sources has been gaining importance to increase the efficiency of ships within the scope of the obligations brought by international maritime regulations. The possibility of using alternative energy sources such as solar energy, wind energy, fuel cells, and waste heat recovery technologies on ships has been evaluated in the literature. Today, ships also have waste heat recovery systems as standard equipment for this purpose, and this method is suitable for thermoelectric generators that generate electricity from temperature differences on shipboards. This article aims to review the thermal technologies for the power generation of shipboards. By conducting a case study, an energy efficiency increase was obtained when functional areas were selected on a practical ship, and the effect of this efficiency increase on emissions was examined. As a result of the research, it was discovered that thermoelectric generators increased onboard energy efficiency and have significant potential for sustainability in the maritime sector

Review of dynamic positioning control in maritime microgrid systems

For many offshore activities, including offshore oil and gas exploration and offshore wind farm construction, it is essential to keep the position and heading of the vessel stable. The dynamic positioning system is a progressive technology, which is extensively used in shipping and other maritime structures. To maintain the vessels or platforms from displacement, its thrusters are used automatically to control and stabilize the position and heading of vessels in sea state disturbances. The theory of dynamic positioning has been studied and developed in terms of control techniques to achieve greater accuracy and reduce ship movement caused by environmental disturbance for more than 30 years. This paper reviews the control strategies and architecture of the DPS in marine vessels. In addition, it suggests possible control principles and makes a comparison between the advantages and disadvantages of existing literature. Some details for future research on DP control challenges are discussed in this paper