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Yaarob Mahjoob Al-Nidawi
Department of Computer Engineering, University of Mustansiriyah, Baghdad, Iraq

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Journal article
Published: 11 June 2019 in Sustainable Cities and Society
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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.

ACS Style

Dhiaa Halboot Muhsen; Haider Tarish Haider; Yaarob Mahjoob Al-Nidawi; Tamer Khatib. Domestic load management based on integration of MODE and AHP-TOPSIS decision making methods. Sustainable Cities and Society 2019, 50, 101651 .

AMA Style

Dhiaa Halboot Muhsen, Haider Tarish Haider, Yaarob Mahjoob Al-Nidawi, Tamer Khatib. Domestic load management based on integration of MODE and AHP-TOPSIS decision making methods. Sustainable Cities and Society. 2019; 50 ():101651.

Chicago/Turabian Style

Dhiaa Halboot Muhsen; Haider Tarish Haider; Yaarob Mahjoob Al-Nidawi; Tamer Khatib. 2019. "Domestic load management based on integration of MODE and AHP-TOPSIS decision making methods." Sustainable Cities and Society 50, no. : 101651.

Journal article
Published: 10 May 2019 in Electronics
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From the growth of residential energy demands has emerged new approaches for load scheduling to realize better energy consumption by shifting the required demand in response to cost changes or incentive offers. In this paper, a hybrid method is proposed to optimize the load scheduling problem for cost and energy saving. The method comprises a multi-objective optimization differential evolution (MODE) algorithm to obtain a set of optimal solutions by minimizing the cost and peak of a load simultaneously, as a multi-objective function. Next, an integration of the analytic hierarchy process (AHP) and a technique for order preferences by similarity to ideal solution (TOPSIS) methods are used as multi-criteria decision making (MCDM) methods for sorting the optimal solutions’ set from the best to the worst, to enable the customer to choose the appropriate schedule time. The solutions are sorted based on the load peak and energy cost as multi-criteria. Data are for ten appliances of a household used for 24 h with a one-minute time slot. The results of the proposed method demonstrate both energy and cost savings of around 47% and 46%, respectively. Furthermore, the results are compared with other recent methods in the literature to show the superiority of the proposed method.

ACS Style

Dhiaa Halboot Muhsen; Haider Tarish Haider; Yaarob Al-Nidawi; Tamer Khatib. Optimal Home Energy Demand Management Based Multi-Criteria Decision Making Methods. Electronics 2019, 8, 524 .

AMA Style

Dhiaa Halboot Muhsen, Haider Tarish Haider, Yaarob Al-Nidawi, Tamer Khatib. Optimal Home Energy Demand Management Based Multi-Criteria Decision Making Methods. Electronics. 2019; 8 (5):524.

Chicago/Turabian Style

Dhiaa Halboot Muhsen; Haider Tarish Haider; Yaarob Al-Nidawi; Tamer Khatib. 2019. "Optimal Home Energy Demand Management Based Multi-Criteria Decision Making Methods." Electronics 8, no. 5: 524.