Muhamad Rasydan Mokhtar, Md Pauzi Abdullah, Mohammad Yusri Hassan, Faridah Hussin


Demand Side Management (DSM) is a method used to modify the electrical load profile of a consumer to reduce its electricity bill. There are various types of DSM options available but mostly involve costs to be incurred by consumers. Moreover, the effectiveness of a DSM option depends on various factors including investment cost, saved energy, payback period and more. Multi Criteria Decision Analysis (MCDA) is a tool that can be applied to make decision when a lot of factors to be taken into account. In DSM, Analytical Hierarchy Process (AHP) is one MCDA technique that is widely used in ranking the DSM options. However, AHP requires additive aggregation that may cause lost in detailed information. This paper presents another MDCA method; Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) to perform the ranking of DSM options. PROMETHEE (I and II) were used in a case study and the results shows that PROMETHEE give the same result as AHP. PROMETHEE has an advantage over AHP as it does not require additive aggregation even the problem is multi-dimensional and could provide visual analysis.  


Demand side management; multi criteria decision analysis,; analytical hierarchy process; preference ranking organization method for enrichment evaluation

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Albadi, M. H., El-Saadany E. F. 2007. Demand Response in Electricity Markets: An Overview. IEEE Power Engineering Society General Meeting. 1-5.

Gellings, C. W. 1987. Demand Side Management: Concepts and Methods, Fairmont.

Palensky, P. and Dietmar, D. 2011. Demand Side Management: Demand Response, Intelligent Energy Systems And Smart Loads. Industrial Informatics. IEEE Transactions. 3: 381-388.

Attia, H. A. 2010. Mathematical Formulation Of Demand Side Management (DSM) And Its Optimal Solution. Proc 14th Int Middle East Power Syst Conf (MEPCON’ 10) Cairo University, Cairo, Egypt.

Strbac, G. 2008. Demand Side Management: Benefits And Challenges”. Energy Policy. 36(12): 4419-4426.

Connell, N. O’ Pinson, P., Madsen, H., O’Malley, M. 2011. Benefits And Challenges Of Electrical Demand Response: A Critical Review. Renew Sustain Energy Rev. 39: 686-699.

Triantapphyllou, E. 2000. Multi-Criteria Decision Making Methods: A Comparative Study,Kluwer.

Hwang C. L. and Yoon, K. 1981. Multiple Attribute Decision Making: Methods And Applications, Springer.

Massam, B. H.1988. Multi Criteria Decision Making Techniques In Planning. Prog Plan. 30: 1-84.

Saaty, T. L. 1988. What is the analytic hierarchy process? Springer.

Brans, J. P., Vincke, P. H. and Mareschal, B. 1986. How To Select And How To Rank Projects: The PROMETHEE method. European Journal of Operational Research. 24: 228-238.

Alajmi. A. 2012. Energy Audit Of An Education Building In A Hot Summer Climate. Energy Build, 47: 122-130.

Blondeau, P., Sperandio M. and Allard. F. 2002. Multicriteria Analysis Of Ventilation In Summer Period. Build Environ 37: 165-176.

Caccavelli, D. and Gugerli, H. 2002. TOBUS-a European Diagnosis And Decision-Making Tool For Office Building Upgrading. Energy Build. 34: 113-119.

Guo-Hua, W., Na, C. and Chun-ling, Z. 2010. Energy Saving And Emission Reducing Evaluation Of Industrial Enterprises based on ANP. IntConfon Management Science and Engineering 2010 (ICMSE), Melbourne.

Al-Enezi, A. N. 2010. DSM for Efficient Use of Energy in the Residential Sector in Kuwait: Analysis of Options and Priorities. PhD dissertation.



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