Economic energy management in textile industry using meta-heuristic algorithms incorporating solar distributed generation

Among the various industrial businesses, the textile industry is one of the most energy-intensive. Energy cost reductions in these industries are one of the prime intentions of the industrialists. Energy Management System (EMS) gains importance with the increase in production and higher market space. Distribution Companies (DISCOMS) charge higher tariff while industries use power in the peak loading condition of the distribution system. This paper proposes a load scheduling method among the loads that are uninterrupted loads, fixed loads, and the shiftable loads. Optimization of load scheduling algorithm for minimizing the total energy cost is developed. Load scheduling using optimization algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for lowest energy cost is developed. Analysis by adding the solar based power and finding the break-even point due to the introduction of a capital cost due to Photovoltaic (PV) installation. The proposed algorithm is applied with the energy tariff that is charged by the Bangalore Electricity Supply Company Limited (BESCOM) for different industrial loading levels. MATLAB based implementation of these optimization techniques is compared with the Bat algorithm for performance evaluation. Cost benefits of around 24.4% are evident from the cost analysis while PV installation of 300 kW is incorporated in the textile industry premises. Break even for the industry because the PV installation will be obtained after 3.5 years.