Modified Dragonfly Optimisation for Distributed Energy Mix in Distribution Networks

This article presents a two-stage optimization model aiming to determine optimal energy mix in distribution networks, i.e., battery energy storage, fuel cell, and wind turbines. It aims to alleviate the impact of high renewable penetration on the systems. To solve the proposed complex optimization model, a standard variant of the dragonfly algorithm (DA) has been improved and then applied to find the optimal mix of distributed energy resources. The suggested improvements are validated before their application. A heuristic approach has also been introduced to solve the second stage problem that determines the optimal power dispatch of battery energy storage as per the size suggested by the first stage. The proposed framework was implemented on a benchmark 33-bus and a practical Indian 108-bus distribution network over different test cases. The proposed model for energy mix and modified DA technique has significantly enhanced the operational performance of the network in terms of average annual energy loss reduction, node voltage profiles, and demand fluctuation caused by renewables.

Simplified Event-Based Load Shedding Scheme for Frequency Stability in an Isolated Power System With High Renewable Penetration. El Hierro: A Case Study

This study presents a novel approach to calculate the load to be shed in El Hierro isolated power system in generation tripping events. The proposed shedding law is based on a linear regression model. The regression model is obtained by means of offline dynamic simulations in a set of representative test cases. The proposed shedding law has been compared to the under-frequency load shedding scheme currently utilized in El Hierro, considering three different implementation schemes. The results of this study demonstrate that the proposed shedding law can contribute to reduce the average amount of load shed in generation tripping events and meet the requirements of the system’s frequency with a moderate investment in smart power management devices.

Construction of a Frequency Compliant Unit Commitment Framework Using an Ensemble Learning Technique

Frequency control is essential to ensure reliability and quality of power systems. North American Electric Reliability Corporation’s (NERC) Control Performance Standard 1 (CPS1) is widely adopted by many operating authorities to examine the quality of the frequency control. The operating authority would have a strong interest in knowing how the frequency-sensitive features affect the CPS1 score and finding out more effective unit-dispatch schedules for reaching the CPS1 goal. As frequency-sensitive features usually possess multi-variable and high-correlated characteristics, this paper employed an ensemble learning technique (the Gradient Boosting Decision Tree algorithm, GBDT) to construct Frequency Response Model (FRM) of the Taipower system in Taiwan to evaluate by CPS1 score. The proposed CPS1 model was then integrated with Unit Commitment (UC) program to determine the unit-dispatch that achieves the targeted CPS1 score. The feasibility and effectiveness of the proposed CPS1-UC platform were validated and compared with the other benchmark model-based UC methods by two operating cases. The proposed model shows promising results: the system frequency could be maintained well, especially in the periods of the early morning or the high renewable penetration.