An analysis of the role of solar energy in residential land uses (Case study: Tehran Urban)

Document Type : Review Article

Authors

MSc, Geography and Urban Planning, University of Tabriz, Tabriz, Iran.

Abstract

The role of energy in the global economy may highlight the importance of energy. Accordingly, development and expansion of theories as well as the energy applications have led to novel methods for the issues associated with the energy and environment. A number of scholars have addressed solar energy and considered it an appropriate alternative for the fossil fuels. Therefore, using solar systems as one of the components of the buildings has become more common. According to 2015 census, Tehran urban have 20×106 residential buildings consuming a tremendous amount of energy derived from fossil fuels. Consequently, using solar energy could provide a part of energy consumed by residential buildings in Tehran urban. In the present study, residential buildings of Tehran city were considered as the sample, and the energy consumed for heating and hot water was calculated. The results indicate that the energy consumed by such buildings for heating and hot water accounts for 559.88 kW which is equivalent to 4904111 h saved electrical energy. Therefore, the reduction of CO2, SO2, NOx, CO, and SPM pollutants as the result of saving in electrical energy for residential buildings is respectively 28.08 Ton, 490 kg, 438.4 kg, 0.49 kg, and 52.96 kg. Notably, using solar energy may lead to saving in fossil fuel consumption; furthermore, the costs associated with their production and handling are reduced.

Keywords


[3] A. M. Makarieva, V. G. Gorshkov, B. L. Li, Energy budget of the biosphere and civilization: Rethinking environmental security of global renewable and non-renewable resources, Ecological complexity, Vol. 5, No. 4, pp. 281-288, 2008.
[8] J. Ortiga, J. C.  Bruno, A. Coronas, I. E. Grossman, Review of optimization models for the design of polygeneration systems in district heating and cooling networks, In Computer Aided Chemical Engineering, Vol. 24, pp. 1121-1126, 2007.
[9] C. Diakaki, E. Grigoroudis, D. Kolokotsa, Performance study of a multi-objective mathematical programming modelling approach for energy decision-making in buildings, Energy, Vol. 59, pp. 534-542, 2013
[10] E. Asadi, M. G. da Silva, C. H. Antunes, L. Dias, L. Glicksman, Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application, Energy and Building, Vol. 81, pp. 444-456, 2014.
 [20] AWEA, AWEA 2008 Annual Rankings Report, American Wind Energy Association, Annual Rankings Report, 2008.
[21] A. Hainoun, M. S. Aldin, S. Almoustafa, Formulating an optimal long-term energy supply strategy for Syria using MESSAGE model, Energy policy, Vol. 38, No. 4, pp. 1701-1714, 2010.
[22] S. M. C. Fairuz, M. Y. Sulaiman, C. H. Lim, S. Mat, B. Ali, O. Saadatian, M. H. Ruslan, E. Salleh, K. Sopian, Long term strategy for electricity generation in Peninsular Malaysia–Analysis of cost and carbon footprint using MESSAGE, Energy policy, Vol 62, pp 493-502. 2013.
 [23] G. Klaassen, K. Riahi, Internalizing externalities of electricity generation: An analysis with MESSAGE-MACRO, Energy Policy, Vol. 35, No. 2, pp. 815-827, 2007.
 [24] A. Laxson, M. M. Hand, N. Blair, High wind penetration impact on us wind manufacturing capacity and critical resources (No. NREL/TP-500-40482), National Renewable Energy Lab.(NREL), Golden, CO (United States), 2006
[25] J. Carlsson, M. D. M. P. Fortes, G. de Marco, J. Giuntoli, M. Jakubcionis, A. Jäger-Waldau, ... & B.   Sigfusson, ETRI 2014-Energy technology reference indicator projections for 2010-2050, European Commission, Joint Research Centre, Institute for Energy and Transport, Luxembourg: Publications Office of the European Union, 2014.
[27] I. Lewis, R. H. Wiser, Fostering a renewable energy technology industry: An international comparison of wind industry policy support mechanisms, Energy policy, Vol. 35, No. 3, pp. 1844-1857, 2007.
[28] Y. I. Topcu, F.   Ulengin, Energy for the future: An integrated decision aid for the case of Turkey, Energy, Vol. 29, No. 1, pp. 137-154, 2004.
[29] M. Rogner, K. Riahi, Future nuclear perspectives based on MESSAGE integrated assessment modeling, Energy Strategy Reviews,Vol. 1, No. 4, pp. 223-232, 2013.
[30] F. Ugranlı, E.  Karatepe, Multiple-distributed generation planning under load uncertainty and different penetration levels, International Journal of Electrical Power & Energy Systems, Vol. 46, pp .132-144, 2013.
 [31] D. Diakoulaki, G. Mavrotas, D. Orkopoulos, L. A. Papayannakis, bottom-up decomposition analysis of energy-related CO2 emissions in Greece, Energy, Vol. 31, No. 14, pp. 2638-2651, 2006.
[33] L. W. Barnthouse, G. F. Cada, M. D. Cheng, C. E. Easterly, R. L. Kroodsma, R. Lee, ... & R. S. Turner, Estimating Fuel Cycle Externalities: Analytical Methods and Issues, Report 2 (No. Number), Oak Ridge National Lab., TN (US), 1994.
[35] International Energy Agency, Energy Technology Systems Analysis Programme (IEA/AIE), (2010a), “Coal-Fired Power”, 2010.
[39] E. Koutroulis, D. Kolokotsa, A. Potirakis, K.  Kalaitzakis, Methodology for optimal sizing of stand-alone photovoltaic/wind-generator systems using genetic algorithms, Solar energy, Vol. 80, No. 9, pp. 1072-1088, 2006.
[40] OPEC, “ OPEC Annual Statistical Bulletin", Organization of the  P      Petroleum Exporting   Countries, Vienna, Austria, 2015.