全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Satisfying the Energy Demand of a Rural Area by Considering the Investment on Renewable Energy Alternatives and Depreciation Costs

DOI: 10.1155/2014/907592

Full-Text   Cite this paper   Add to My Lib

Abstract:

In this paper, a fuzzy multiobjective model which chooses the best mix of renewable energy options and determines the optimal amount of energy to be transferred from each resource to each end use is proposed. The depreciation of equipment along with time value of money has been taken into account in the first objective function while the second and the third objective functions minimize the greenhouse gas emissions and water consumption, respectively. Also, this study is one of the pioneer works that has considered demand-side management (DSM) as a competitive option against supply-side alternatives for making apt energy planning decisions. Moreover, the intrinsic uncertainty of demand parameter is considered and modeled by fuzzy numbers. To convert the proposed fuzzy multiobjective formulation to a crisp single-objective formulation the well-known fuzzy goal programming approach together with Jimenez defuzzifying technique is employed. The model is validated through setting up a diversity of datasets whose data were mostly derived from the literature. The obtained results show that DSM programs have greatly contributed to cost reductions in the network. Also, it is concluded that the model is capable of solving even large-scaled instances of problems in negligible central processing unit (CPU) times using Lingo 8.0 software. 1. Introduction Although using renewable energy resources is one of the primary solutions for overcoming poverty and achieving sustainable development, for centuries, the focus has been solely on traditional sources of energy. Nonetheless, there are some difficulties with employing the newly introduced sources of energy such as cultural barriers, lack of proper mentality, and budget estimation difficulties to inhibit the growth of renewable energy in the countryside. Thus, it is imperative for mankind to seek a way for solving such challenges (Kazemi and Rabbani) [1]. Shifting from nonrenewable energies to renewable energy technologies (RETs) should be of the top preferences in the direction toward acquiring a satisfactory energy system. Increasing the penetration of RETs not only contributes to meeting the ongoing increasing energy demand but also decreases the adverse environmental effects of burning fossil fuels. Recent studies offer that renewable energy sources are capable of meeting a remarkable portion of the energy demand even at the current level of technological development. However, as discussed before, this may not occur unless the issues that obstruct the penetration of RETs are properly addressed [2–4]. One of the

References

[1]  S. M. Kazemi and M. Rabbani, “An integrated decentralized energy planning model considering demand-side management and environmental measures,” Journal of Energy, vol. 2013, Article ID 602393, 6 pages, 2013.
[2]  T. Nakata, K. Kubo, and A. Lamont, “Design for renewable energy systems with application to rural areas in Japan,” Energy Policy, vol. 33, no. 2, pp. 209–219, 2005.
[3]  M.-L. Barry, H. Steyn, and A. Brent, “Selection of renewable energy technologies for Africa: eight case studies in Rwanda, Tanzania and Malawi,” Renewable Energy, vol. 36, no. 11, pp. 2845–2852, 2011.
[4]  M. M. Ardehali, “Rural energy development in Iran: non-renewable and renewable resources,” Renewable Energy, vol. 31, no. 5, pp. 655–662, 2006.
[5]  S. J. Benson and S. Asgarpoor, “A fuzzy expert system for evaluation of demand-side management alternatives,” Electric Machines and Power Systems, vol. 28, no. 8, pp. 749–760, 2000.
[6]  J. J. Hain, G. W. Ault, S. J. Galloway, A. Cruden, and J. R. McDonald, “Additional renewable energy growth through small-scale community orientated energy policies,” Energy Policy, vol. 33, no. 9, pp. 1199–1212, 2005.
[7]  A. Bergmann, N. Hanley, and R. Wright, “Valuing the attributes of renewable energy investments,” Energy Policy, vol. 34, no. 9, pp. 1004–1014, 2006.
[8]  D. R. Thiam, “An energy pricing scheme for the diffusion of decentralized renewable technology investment in developing countries,” Energy Policy, vol. 39, no. 7, pp. 4284–4297, 2011.
[9]  A. Bergmann, S. Colombo, and N. Hanley, “Rural versus urban preferences for renewable energy developments,” Ecological Economics, vol. 65, no. 3, pp. 616–625, 2008.
[10]  B. Mainali and S. Silveira, “Renewable energy markets in rural electrification: country case Nepal,” Energy for Sustainable Development, vol. 16, no. 2, pp. 168–178, 2012.
[11]  W. Wolde-Ghiorgis, “Renewable energy for rural development in Ethiopia: the case for new energy policies and institutional reform,” Energy Policy, vol. 30, no. 11-12, pp. 1095–1105, 2002.
[12]  B. Zhu, W. Zhang, J. Du, W. Zhou, T. Qiu, and Q. Li, “Adoption of renewable energy technologies (RETs): a survey on rural construction in China,” Technology in Society, vol. 33, no. 3-4, pp. 223–230, 2011.
[13]  R. B. Hiremath, B. Kumar, P. Balachandra, N. H. Ravindranath, and B. N. Raghunandan, “Decentralised renewable energy: scope, relevance and applications in the Indian context,” Energy for Sustainable Development, vol. 13, no. 1, pp. 4–10, 2009.
[14]  W. Liu, C. Wang, and A. P. J. Mol, “Rural public acceptance of renewable energy deployment: the case of Shandong in China,” Applied Energy, vol. 102, pp. 1187–1196, 2013.
[15]  H. Liming, “Financing rural renewable energy: a comparison between China and India,” Renewable and Sustainable Energy Reviews, vol. 13, no. 5, pp. 1096–1103, 2009.
[16]  H. Winkler, A. Hughes, and M. Haw, “Technology learning for renewable energy: implications for South Africa's long-term mitigation scenarios,” Energy Policy, vol. 37, no. 11, pp. 4987–4996, 2009.
[17]  P. Purohit, A. Kumar, S. Rana, and T. C. Kandpal, “Using renewable energy technologies for domestic cooking in India: a methodology for potential estimation,” Renewable Energy, vol. 26, no. 2, pp. 235–246, 2002.
[18]  L. Zhang, Z. Yang, B. Chen, and G. Chen, “Rural energy in China: pattern and policy,” Renewable Energy, vol. 34, no. 12, pp. 2813–2823, 2009.
[19]  M. Jiménez, M. Arenas, A. Bilbao, and M. V. Rodríguez, “Linear programming with fuzzy parameters: an interactive method resolution,” European Journal of Operational Research, vol. 177, no. 3, pp. 1599–1609, 2007.
[20]  O. Ak?z and D. Petrovic, “A fuzzy goal programming method with imprecise goal hierarchy,” European Journal of Operational Research, vol. 181, no. 3, pp. 1427–1433, 2007.
[21]  M. A. Yaghoobi and M. Tamiz, “A method for solving fuzzy goal programming problems based on MINMAX approach,” European Journal of Operational Research, vol. 177, no. 3, pp. 1580–1590, 2007.
[22]  C.-F. Hu, C.-J. Teng, and S.-Y. Li, “A fuzzy goal programming approach to multi-objective optimization problem with priorities,” European Journal of Operational Research, vol. 176, no. 3, pp. 1319–1333, 2007.
[23]  M. G. Iskander, “A fuzzy weighted additive approach for stochastic fuzzy goal programming,” Applied Mathematics and Computation, vol. 154, no. 2, pp. 543–553, 2004.
[24]  J. S. Kim and K.-S. Whang, “A tolerance approach to the fuzzy goal programming problems with unbalanced triangular membership function,” European Journal of Operational Research, vol. 107, no. 3, pp. 614–624, 1998.
[25]  C.-C. Lin, “A weighted max-min model for fuzzy goal programming,” Fuzzy Sets and Systems, vol. 142, no. 3, pp. 407–420, 2004.
[26]  O. M. Saad, “An iterative goal programming approach for solving fuzzy multiobjective integer linear programming problems,” Applied Mathematics and Computation, vol. 170, no. 1, pp. 216–225, 2005.
[27]  J.-M. Martel and B. Aouni, “Diverse imprecise goal programming model formulations,” Journal of Global Optimization, vol. 12, no. 2, pp. 127–138, 1998.
[28]  M. G. Iskander, “Exponential membership function in stochastic fuzzy goal programming,” Applied Mathematics and Computation, vol. 173, no. 2, pp. 782–791, 2006.
[29]  L.-H. Chen and F.-C. Tsai, “Fuzzy goal programming with different importance and priorities,” European Journal of Operational Research, vol. 133, no. 3, pp. 548–556, 2001.
[30]  J. Ramík, “Fuzzy goals and fuzzy alternatives in goal programming problems,” Fuzzy Sets and Systems, vol. 111, no. 1, pp. 81–86, 2000.
[31]  S. R. Arora and R. Gupta, “Interactive fuzzy goal programming approach for bilevel programming problem,” European Journal of Operational Research, vol. 194, no. 2, pp. 368–376, 2009.
[32]  R. Narasimhan, “Goal programming in a fuzzy environment,” Decision Sciences, vol. 11, pp. 325–326, 1980.
[33]  F. Kreith and D. Y. Goswami, Energy Management and Conservation Handbook, CRC, 2008.
[34]  D. Silva Herran and T. Nakata, “Renewable technologies for rural electrification in Colombia: a multiple objective approach,” International Journal of Energy Sector Management, vol. 2, no. 1, pp. 139–154, 2008.
[35]  A. Evans, V. Strezov, and T. J. Evans, “Assessment of sustainability indicators for renewable energy technologies,” Renewable and Sustainable Energy Reviews, vol. 13, no. 5, pp. 1082–1088, 2009.
[36]  A. B. Kanase-Patil, R. P. Saini, and M. P. Sharma, “Integrated renewable energy systems for off grid rural electrification of remote area,” Renewable Energy, vol. 35, no. 6, pp. 1342–1349, 2010.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133