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Post-Optimality Analysis of Energy Consumption Model and Utility Application

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Problems associated with energy distribution, consumption and management are undoubtedly some of the most significant problems that energy utilities face globally. For instance, when development takes place, the demand for electrical power and in particular domestic electrical energy also increases. Thus improvement of energy distribution policies becomes important for utilities and energy decision making agencies. The authors had earlier [1] [2] provided a mixed strategy 2-player game model for a residential energy consumption profile for winter and summer seasons of the year using a dual-occupancy high-rise (11-storey) building located within the Polytechnic of Namibia, Windhoek. The optimum energy values and the corresponding probabilities obtained from the model extend the usual simple statistical analyses of minimum and maximum energy values and their associated percentages. The time-block and the week-day strategies depict critical probabilistic values worth considering for decision purposes, especially, the necessity and justification for a dual tariff regime for the residential and workplace residents of the building as against the existing institutional uniform energy tariff policy. However, this paper presents extended results of post-optimality analyses for the winter and summer seasons, and thus provides the optimal range of energy values over which the energy consumption can change without changing the optimal tariff estimate parameters obtained from the mixed strategy of critical energy game values. The post-optimality analyses also provide extended information on the mixed strategy of non-optimal week-day solutions obtained from the game model, hence validating one of the essential roles of sensitivity analysis, namely, investigation of sub-optimal solutions. From application point of view, the post-optimality model provides a useful tool for Utilities, especially for identifying flexibility range of optimal break-even energy values for consumers, such as in the informal settlements where metering is rather a challenge to determine varied or non-uniform tariffs.
Cite this paper
Reju, S. and Gope, G. (2014) Post-Optimality Analysis of Energy Consumption Model and Utility Application. Open Journal of Applied Sciences, 4, 533-546. doi: 10.4236/ojapps.2014.414052. 
 

[1] Gope, G., Reju, S.A. and Kanyimba, K. (2012) Residential Time of Use Energy Modelling and Tariff Evaluation. Canadian Journal on Electrical and Electronics Engineering, 3, 115-120.
[2] Gope, G., Reju, S.A. and Kamanja, T. (2013) A Review of Energy Modelling Methods and the Application of Wireless Sensor Networks (WSNs).
Proceedings of Pan African International Conference on Information Science, Computing and Telecommunications (PACT 2013), 15-18 July 2013, Lusaka, 50-55.
[3] Arsham, H. (2011) Construction of General Sensitivity Regions.
http://home.ubalt.edu/ntsbarsh/Business-stat/opre/PartVII.htm
[4] Bradley, S.P., Hax, A.C. and Magnanti, T.L. (1977) Applied Mathematical Programming. Addison-Wesley, Boston.
http://web.mit.edu/15.053/www/AMP-Chapter-03.pdf          eww141225lx
[5] Li, Y. and Cui, J. (2008) A Method of Designing Energy Tax Rate Based on Game Theory. The 7th International Symposium on Operations Research and Its Applications (ISORA’08), Lijiang, 31 October-3 November 2008.
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