Abstract
Load factor (LF) is a critical parameter that can greatly affect the efficiency of the power system and hence the cost of produced energy. For a power plant to operate economically, it must have a high LF. It is therefore considered useful and necessary to search for ways to match Demand and Supply as much as possible by implementing Demand Side Management (DSM) strategies on the consumer demand loads such as load profile reshaping. DSM practices have been implemented by the industry many years ago, mainly to manage large and predictable loads. Technical losses and other supply constraints caused by geopolitical changes, and the high cost of imported oil, may encourage the power utilities to find smart solutions within DSM strategies. The objective of this paper is to develop new universal algorithms for the load demand shifting. These algorithms were tested on real data and a comparison with Genetic Algorithms (GA) is shown to prove the validity of the obtained results. The study further aims at increasing the LF for different consuming sectors by exploring some applicable cases. Saving on the electricity bill is ultimately analyzed by referring to more than one sample of demand load objective and analyzing the cost reduction from the end user side.
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