Modelling Impacts of Socio-Economic Factors on Temporal Diffusion of PV-Based Communal Grids
Author(s)
Nicholas Opiyo
ABSTRACT
Impacts of socio-economic factors on temporal diffusions of solar electricity microgeneration systems in a rural developing community are modelled and simulated using an agent-based model (ABM). ABMs seek to capture the overall macro-effects of different micro-decisions in a virtual world; they model individual entities within a complex system and the rules that govern them to capture the overall effects of their interactions. Results showed that falling PV costs coupled with generally increasing grid electricity costs would lead to increased uptake of PV systems in such communities. On the other hand, high lending rates in most developing nations would stifle use of credit facilities in purchases of PV systems and thus diminishing their uptakes. Results also showed that introduction of favourable government policies in forms of subsidies would strongly stimulate PV installations in such communities. Social acceptance is important for diffusion of any new technology into a given market and more so with solar systems; results show that neighbourhood influence plays major roles in PV diffusions with many households installing PV systems if their neighbours within a given sensing radius do the same. Results also showed that requiring a certain percentage of neighbours to have installed PV before a household considered doing the same could have negative effects on PV installations as decisions to install PV are influenced by many independent and dependent factors and not by neighbourhood threshold alone.
Impacts of socio-economic factors on temporal diffusions of solar electricity microgeneration systems in a rural developing community are modelled and simulated using an agent-based model (ABM). ABMs seek to capture the overall macro-effects of different micro-decisions in a virtual world; they model individual entities within a complex system and the rules that govern them to capture the overall effects of their interactions. Results showed that falling PV costs coupled with generally increasing grid electricity costs would lead to increased uptake of PV systems in such communities. On the other hand, high lending rates in most developing nations would stifle use of credit facilities in purchases of PV systems and thus diminishing their uptakes. Results also showed that introduction of favourable government policies in forms of subsidies would strongly stimulate PV installations in such communities. Social acceptance is important for diffusion of any new technology into a given market and more so with solar systems; results show that neighbourhood influence plays major roles in PV diffusions with many households installing PV systems if their neighbours within a given sensing radius do the same. Results also showed that requiring a certain percentage of neighbours to have installed PV before a household considered doing the same could have negative effects on PV installations as decisions to install PV are influenced by many independent and dependent factors and not by neighbourhood threshold alone.
Cite this paper
Opiyo, N. (2015) Modelling Impacts of Socio-Economic Factors on Temporal Diffusion of PV-Based Communal Grids. Smart Grid and Renewable Energy, 6, 317-332. doi: 10.4236/sgre.2015.612026.
Opiyo, N. (2015) Modelling Impacts of Socio-Economic Factors on Temporal Diffusion of PV-Based Communal Grids. Smart Grid and Renewable Energy, 6, 317-332. doi: 10.4236/sgre.2015.612026.
References
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http://dx.doi.org/10.1111/j.1083-6101.2001.tb00119.x [27] Rode, J. and Weber, A. (2011) Knowledge Does Not Fall Far from the Tree—A Case Study on the Diffusion of Solar Cells in Germany. ERSA Conference Papers ERSA 11 p 497, European Regional Science Association. [28] Bass, F. (1969) A New Product Growth Model for Consumer Durables. Management Science, 15, 215-227.
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http://dx.doi.org/10.1111/j.1540-5885.2009.00658.x [30] Islam, T. and Meade, N. (2012) The Impact of Competition, and Economic Globalization on the Multinational Diffusion of 3G Mobile Phones. Technological Forecasting and Social Change, 79, 843-850.
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http://www.earth-policy.org/data_center/C23
[1] Parshall, L., Pillai, D., Mohan, S., et al. (2009) National Electricity Planning in Settings with Low Pre-existing Grid Coverage: Development of a Spatial Model and Case Study of Kenya. Energy Policy, 37, 2395-2410.
http://dx.doi.org/10.1016/j.enpol.2009.01.021 [2] Sanoh, A., Parshall, L. Sarr, O., et al. (2012) Local and National Electricity Planning in Senegal: Scenarios and Policies. Energy for Sustainable Development, 16, 13-25.
http://dx.doi.org/10.1016/j.esd.2011.12.005 [3] Levin, T. and Thomas, V. (2012) Least-Cost Network Evaluation of Centralized and Decentralized Contributions to Global Electrification. Energy Policy, 41, 286-302.
http://dx.doi.org/10.1016/j.enpol.2011.10.048 [4] Foster, V. and Steinbuks, J. (2009) Paying the Price for Unreliable Power Supplies In-House Generation of Electricity by Firms in Africa. WPS. [5] Bhattacharyya, S. and Timilsina, G. (2010) Modelling Energy Demand of Developing Countries: Are the Specific Features Adequately Captured? Energy Policy, 38, 1979-1990.
http://dx.doi.org/10.1016/j.enpol.2009.11.079 [6] Urban, F., Benders, R. and Moll, H. (2007) Modelling Energy Systems for Developing Countries. Energy Policy, 35, 3473-3482.
http://dx.doi.org/10.1016/j.enpol.2006.12.025 [7] Zhao, J., Mazhari, E., Celik, N. and Son, Y. (2011) Hybrid Ahent-Based Simulation for Policy Evaluation of Solar Power Generation Systems. Simulation Modelling Practice and Theory, 19, 2189-2205.
http://dx.doi.org/10.1016/j.simpat.2011.07.005 [8] Beise, M. (2004) The International Adoption of Photovoltaic Energy Conversion: Is Japan a Lead Market? Discussion Paper Series 153, Research Institute for Economics & Business Administration, Kobe University. [9] Guidolin, M. and Mortarino, C. (2010) Cross-Country Diffusion of Photovoltaic Systems: Modelling Choices and Forecasts for National Adoption Patterns. Technological Forecasting and Social Change, 77, 279-296.
http://dx.doi.org/10.1016/j.techfore.2009.07.003 [10] Wustenhagen, R. and Bilharz, M. (2006) Green Energy Market Development in Germany: Effective Public Policy and Emerging Customer Demand. Energy Policy, 34, 1681-1696.
http://dx.doi.org/10.1016/j.enpol.2004.07.013 [11] Zhang, Y., Song, J. and Hamori, S. (2011) Impact of Subsidy Policies on Diffusion of Photovoltaic Power Generation. Energy Policy, 39, 1958-1964.
http://dx.doi.org/10.1016/j.enpol.2011.01.021 [12] Rogers, E. (2003) Diffusion of Innovations. Free Press, New York. [13] Sauter, R. and Watson, J. (2007) Strategies for the Deployment of Micro-Generation Implications for Social Acceptance. Energy Policy, 35, 2770-2779.
http://dx.doi.org/10.1016/j.enpol.2006.12.006 [14] Semadeni, M., Hansmann, R. and Flueeler, T. (2004) Public Attitudes in Relation to Risk and Novelty of Future Energy Options. Energy & Environment, 15, 755-777.
http://dx.doi.org/10.1260/0958305042886787 [15] Kaldellis, J. (2005) Social Attitude towards Wind Energy Applications in Greece. Energy Policy, 33, 595-602.
http://dx.doi.org/10.1016/j.enpol.2003.09.003 [16] Fischer, C. (2004) Users as Pioneers: Transformation in the Electricity System, Micro CHP and the Role of the Users. In: Jacob, K., Binder, M. and Wieczorek, A., Eds., Governance for Industrial Transformation, Proceedings of the 2003 Berlin Conference on Human Dimensions of Global Environmental Change, Environmental Policy Research Centre, Berlin, 319-337. [17] Fischer, C. (2006) From Consumers to Operators: the Role of Micro CHP Users. In: Pehnt, M., Cames, M., Fischer, C., et al., Eds., Microgeneration: Towards a Decentralized Energy Supply, Springer, Heidelberg, 117-143. [18] London Renewables (2003) Attitudes to Renewable Energy in London: Public and Stakeholder Opinion and the Scope for Progress. A Report Commission by London Renewables and Carried out by Brook Lyndhurst Ltd in Association with MORI and Upstream, Greater London Authority. [19] Ellison, G. (2004) Renewable Energy Survey: Draft Summary Report of Findings. ORC International, London. [20] Young, H. (2009) Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning. The American Economic Review, 99, 1899-1924.
http://dx.doi.org/10.1257/aer.99.5.1899 [21] Bollinger, B. and Gillingham, K. (2012) Peer Effects in the Diffusion of Solar Photovoltaic Panels. Marketing Science, 31, 800-812.
http://dx.doi.org/10.1287/mksc.1120.0727 [22] Narayanan, S. and Nair, H. (2012) Estimating Causal Installed-Base Effects: A Bias-Correction Approach. [23] Rai, V. and Robinson, S. (2013) Effective Information Channels for Reducing Costs of Environmentally Friendly Technologies: Evidence from Residential PV Markets. Environmental Research Letters, 8, Article ID: 014044.
http://dx.doi.org/10.1088/1748-9326/8/1/014044 [24] Jaffe, A., Newell, R. and Stavins, R. (2005) A Tale of Two Market Failures: Technology and Environmental Policy. Ecological Economics, 54, 164-174.
http://dx.doi.org/10.1016/j.ecolecon.2004.12.027 [25] Brown, J. and Reingen, P. (1987) Social Ties and Word-of-Mouth Referral Behaviour. Journal of Consumer Research, 14, 350-362.
http://dx.doi.org/10.1086/209118 [26] Krishnamurthy, S. (2001) A Comprehensive Analysis of Permission Marketing. Journal of Computer Mediated Communication, 6.
http://dx.doi.org/10.1111/j.1083-6101.2001.tb00119.x [27] Rode, J. and Weber, A. (2011) Knowledge Does Not Fall Far from the Tree—A Case Study on the Diffusion of Solar Cells in Germany. ERSA Conference Papers ERSA 11 p 497, European Regional Science Association. [28] Bass, F. (1969) A New Product Growth Model for Consumer Durables. Management Science, 15, 215-227.
http://dx.doi.org/10.1287/mnsc.15.5.215 [29] Kim, S.-H. and Srinivasan, V. (2009) A Conjoint-Hazard Model of the Timing of Buyers’ Upgrading to Improved Versions of High-Technology Products. Journal of Product Innovation Management, 26, 278-290.
http://dx.doi.org/10.1111/j.1540-5885.2009.00658.x [30] Islam, T. and Meade, N. (2012) The Impact of Competition, and Economic Globalization on the Multinational Diffusion of 3G Mobile Phones. Technological Forecasting and Social Change, 79, 843-850.
http://dx.doi.org/10.1016/j.techfore.2012.01.002 [31] Earth Policy Institute (2015).
http://www.earth-policy.org/data_center/C23