Efficacy of Wind Power in Mitigation of CO2 Emission from the Irish Electrical Grid

The results in this paper are based on a data set containing system demand, wind generation and CO2 emission between Jan 2010 and Sep 2013. The data was recorded at 15 minute intervals and reflects the macroscopic operation of the Republic of Ireland's electrical grid. The data was analyzed by investigating how daily wind generation effected daily CO2 emission across multiple days with equivalent daily demand. A figure for wind turbine efficiency was determined by dividing the CO2 mitigation potential of wind power by the CO2 intensity of the grid; both in units of Tonnes of CO2 per MWh. The yearly wind power efficiency appears to have increased by 5.6% per year, now standing around 90%. Over the four years significant regularity was observed in the profiles of wind turbine efficiency against daily demand. It appears that the efficiency profile has moved in recent years so that maximum efficiency coincides with most frequent demand.

A new battery modelling method based on simulation error minimization

A new battery modelling method is presented based on the simulation error minimization criterion rather than the conventional prediction error criterion. A new integrated optimization method to optimize the model parameters is proposed. This new method is validated on a set of Li ion battery test data, and the results confirm the advantages of the proposed method in terms of the model generalization performance and long-term prediction accuracy.

Islanding Detection Based on Probabilistic PCA with Missing Values in PMU Data

This paper proposes a probabilistic principal component analysis (PCA) approach applied to islanding detection study based on wide area PMU data. The increasing probability of uncontrolled islanding operation, according to many power system operators, is one of the biggest concerns with a large penetration of distributed renewable generation. The traditional islanding detection methods, such as RoCoF and vector shift, are however extremely sensitive and may result in many unwanted trips. The proposed probabilistic PCA aims to improve islanding detection accuracy and reduce the risk of unwanted tripping based on PMU measurements, while addressing a practical issue on missing data. The reliability and accuracy of the proposed probabilistic PCA approach are demonstrated using real data recorded in the UK power system by the OpenPMU project. The results show that the proposed methods can detect islanding accurately, without being falsely triggered by generation trips, even in the presence of missing values.