Evolutionary algorithm based optimum scheduling of processing units in rice industry to reduce peak demand

• Energy consumption in each section of the rice industry is analyzed.
• The operation of pre milling units is optimized using evolutionary algorithms.
• DE, PSO and ABC algorithms are applied to minimize the peak demand and hence cost.
• Feeder peak demand is reduced hence the gap between generation and demand reduces.

In India, power shortage is a major issue for economical growth. According to the data provided by National Load Despatch Centre, peak power shortage in Tamilnadu during the year 2014 varies between 3000 MW and 4000 MW. This power shortage can be reduced by increasing installed capacity of conventional and non-conventional energy sources. But the construction of new generation plants is cost-effective and also power generation is not assured throughout the year. This power shortage can also be minimized by implementing load management in the consumer side. This paper focuses on the reduction of peak demand by the proper operating schedule of major equipments. For this analysis, three rice industries have been considered. The major operating sections in the rice industries are pre-cleaning, soaking, pre-milling and milling. In this proposed work, to reduce the peak demand, the operating time of pre-milling section is rescheduled using the optimization techniques, DE (Differential Evolution), PSO (Particle Swarm Optimization) and ABC (Artificial Bee Colony). The rescheduled results given by DE, PSO and ABC algorithms reduce the peak demand cost of the energy consumed in three rice industries. However, the optimum scheduling obtained by ABC reduces the feeder power flow than the DE and PSO scheduling.