To reduce peak electricity demand and hence reduce capacity
costs due to added investment of generating additional power to meet short intervals
of peak demand, can enhance energy efficiency. Where it is possible to adjust timing
and the quantity of electricity consumption and at the same time achieve the same
useful effect, the value of the energy service itself remains unchanged. Peak demand
management is viewed as the balance between demand and generation of energy hence
an important requirement for stabilized operation of power system. Therefore, the
purpose of this study was to establish the correlation between peak electricity
demand management strategies and energy efficiency among large steel manufacturing
firms in Nairobi, Kenya. The strategies investigated were demand scheduling, Peak
shrinking and Peak shaving. Demand scheduling involves shifting predetermined loads
to low peak periods thereby flattening the demand curve. Peak shrinking on the other
hand involves installation of energy efficient equipment thereby shifting the overall
demand curve downwards. Peak shaving is the deployment of secondary generation on
site to temporarily power some loads during peak hours thereby reducing demand during
the peak periods of the plant. The specific objectives were to test the relationship
between demand scheduling and energy efficiency among large steel manufacturing
firms in Nairobi Region; to test the correlation between peak shrinking and energy
efficiency among large steel manufacturing firms in Nairobi Region; and to test
the association between peak shaving and energy efficiency among large steel manufacturing
firms in Nairobi Region. The study adopted a descriptive research design to determine
the relationship between each independent variable namely demand scheduling, peak
shrinking, peak shaving and the dependent variable, the energy efficiency. The target
population was large steel manufacturing firms in Nairobi Region, Kenya. The study
used both primary and secondary data. The primary data was from structured questionnaires
while secondary data was from historical electricity consumption data for the firms
under study. The results revealed that both peak shrinking and peak shaving were
statistically significant in influencing energy efficiency among the steel manufacturing
firms in Nairobi Region, each with Pearson correlation coefficient of 0.903, thus
a strong linear relationship between the investigated strategy and the dependent
variable, energy efficiency. The obtained results are significant at probability
value of 0.005
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