ETOU electricity tariff for manufacturing load shifting strategy using ACO algorithm

Abstract

This paper presents load shifting strategy for cost reduction on manufacturing electricity demand side, by which a real test load profile had been used to prove the concept. Superior bio-inspired algorithm, Ant Colony Optimization (ACO) had been implemented to optimize the upright load profile of load shifting strategy in the Malaysia Enhance Time of Use (ETOU) tariff condition. Subsequently, significant simulation results of operation profit gain through 24 hours electricity consumption had been analyzed properly. The proposed method had shown reduction of approximately 6% of the electricity cost at peak and mid peak zones, when 20%, 40%, 60%, 80% and 100% load shifting weightages were applied to the identified 10% controlled loads consequently. It is hoped that the finding of this study can help poise the manufacturers to switch to ETOU tariff as well as support the national Demand Side Management (DSM) program

Investigation of electricity load shifting under various tariff design using ant colony optimization algorithm

“…For example, in reducing the energy cost in the peak zone, the load at the peak zone is shifted to the off-peak time zone, which has a lower cost of energy [23], [24]. The percentage of load shifting weightage depends on the type of load limitation, as illustrated by [25]- [27]. However, in this article, there were given demonstration the classification of appliance types for non-shift able and shift able due to load shifting strategy when dealing with TOU tariff structure.…”

Simultaneous Load Management Strategy for Electronic Manufacturing Facilities by using EPSO Algorithm

“…Thus, the best way to put LS is after VF and PC selection, while the rest of the time segmentations will be the location for LS to perform randomly. The process of the proposed LS procedure is written as in Equations ( 9), (10), and (11) accordingly.…”

“…The authors in [9] have promoted the bubble chart solution to identify the appropriate percentage of the load to be transferred in peak and mid-peak zones. Meanwhile, the application of the optimization algorithm proposed by [10] only focuses on the load shifting strategy for the manufacturing operation. On the other hand, the application of the optimization algorithm in the perspective of the price-based program for the demand-side was divided into three purposes: i) for the tariff design, ii) for the operation schedule, and iii) for the load management [11].…”

Industrial Energy Load Profile Forecasting under Enhanced Time of Use Tariff (ETOU) using Artificial Neural Network

“…The application of the Particle Swarm Optimization (PSO) algorithm has been presented in reference [11] to find the appropriate load categories under six groups of the sectors in peninsular Malaysia. The powerful load management strategies to reduce total electricity under ETOU tariff have been decreased by using Ant Colony Optimization (ACO) [12] and [13]. The authors in [2] present an Evolutionary Algorithm (EA) as the optimal search of load profile in tackling the ETOU price.…”