Domestic load management based on integration of MODE and AHP-TOPSIS decision making methods. (October 2019)

• Record Type: Journal Article
• Authors: Muhsen, Dhiaa Halboot; Haider, Haider Tarish; Al-Nidawi, Yaarob Mahjoob; Khatib, Tamer
• Is Part Of: Sustainable cities and society. Volume 50(2019)
• Publication Date: 2019-10
• Subjects: Smart grid -- Demand response -- Load management -- AHP -- TOPSIS -- Differential evolution -- Multi-criteria decision making; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2019.101651
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Multi-objective optimization DE algorithm to optimize load scheduling is proposed. Energy cost and customer's inconvenience as optimization constrains is used. Results show that there are saving in the cost of energy is 32% for 10 min time slot. Results also show that the peak load saving is 33% for 10 min time slot. Abstract: The increasing in energy demand leads to wide range of blackout crises around the worldwide. Load management is represented as one of the most important solutions to balance the energy demand with the available generation resource. Dynamic and adaptive method is required to sort all multi-objective sets of optimal solutions of customer load scheduling. A multi-objective optimization differential evolution (MODE) algorithm in this paper is used to obtain a set of optimal customer load management by minimizing the energy cost and customer's inconvenience simultaneously. The obtained optimal set of solutions are sorted from the best to the worst using multi-criteria decision making (MCDM) methods. An integration of analytic hierarchy process (AHP) and technique for order preferences by similarity to ideal solution (TOPSIS) are used as MCDM methods. The effect of different time slots on the given optimal solutions are addressed for real customer's data of a typical household. Results of simulation indicate that the proposed method manages to realize energy cost saving of 44%, 44% and 32% for 1, 5 and 10 min time slots, respectively. Moreover, the peak load savings are 42%, 31% and 41% for 1, 5 and 10 min time slots, respectively. Furthermore, the results are validated by other approaches presented earlier in literature to support the findings of the proposed method. The proposed method provides superior saving for energy cost and peak consumption as well as maintains an acceptable range of customer inconvenience.