Women and work-life balance: a case of austerity and authenticity

Recent evidence suggests that support for gender equality appears to be declining amid concern that women who play a full role in the work force do so at the expense of family life. Although this has been exacerbated by the economic downturn there were pre-recessionary signs of concerns that the welfare of children was being compromised when women find themselves with the double burden of employment and family care. These work-life balance issues are examined from the point of view of looking at how this characterised for women as a problem of authentic self-realisation in terms of working towards and achieving an optimum balance. This kind of dichotomisation allows for different aspects of the self to be pitted against each other: the professional career-orientated aspect and the personal relationship aspect. The paper argues that work-life balance discourse is counter-productive in that it reinforces the inequalities for women that it seeks to redress.

A genetic algorithm based economic dispatch (GAED) with environmental constraint optimisation

The role of renewable energy in power systems is becoming more significant due to the increasing cost of fossil fuels and climate change concerns. However, the inclusion of Renewable Energy Generators (REG), such as wind power, has created additional problems for power system operators due to the variability and lower predictability of output of most REGs, with the Economic Dispatch (ED) problem being particularly difficult to resolve. In previous papers we had reported on the inclusion of wind power in the ED calculations. The simulation had been performed using a system model with wind power as an intermittent source, and the results of the simulation have been compared to that of the Direct Search Method (DSM) for similar cases. In this paper we report on our continuing investigations into using Genetic Algorithms (GA) for ED for an independent power system with a significant amount of wind energy in its generator portfolio. The results demonstrate, in line with previous reports in the literature, the effectiveness of GA when measured against a benchmark technique such as DSM.