A Demand-Side Perspective on Developing a Future Electricity Generation Mix: Identifying Heterogeneity in Social Preferences

Public support is an important factor in failure or success of the government decisions with respect to the electricity generation mix, which highlights the necessity of developing an electricity mix that reflects social preferences and acceptance. This study explores heterogeneity in social preferences for power sources and develops an electricity mix from a demand-side perspective. The study utilizes the choice-based conjoint survey and latent class model, and bases its empirical analysis on South Korea’s electric power sector. Results demonstrate that preferences for power sources in Korean society consist of two classes: one that is sensitive to the environment and one that is sensitive to risk. An electricity mix for Korea that reflects social preferences is 16.5–19.8% coal-fired, 13.3–24.9% liquefied natural gas (LNG), 9.0–11.2% oil, 22.3–32.9% nuclear, and 18.5–38.9% renewables, depending on the scenario. The study confirms that renewables are the power source with the least potential to cause social conflict, compared to nuclear and coal-fired sources. Moreover, increasing the proportion of renewables (currently only 3.9%) while decreasing the proportion of coal-fired power sources (currently 39.9%) to less than half its current level will result in an electricity mix that is accordance with social preferences in the long run.

[1]  Jung-hua Wu,et al.  A portfolio risk analysis on electricity supply planning , 2008 .

[2]  Jianguo Liu,et al.  Effects of attitudinal and sociodemographic factors on pro-environmental behaviour in urban China , 2011, Environmental Conservation.

[3]  Ross Baldick,et al.  A strategic review of electricity systems models , 2010 .

[4]  JongRoul Woo,et al.  What do customers want from improved residential electricity services? Evidence from a choice experiment , 2015 .

[5]  Elisabetta Strazzera,et al.  Modeling individual preferences for energy sources: the case of IV generation nuclear energy in Italy , 2016 .

[6]  Allison M. Borchers,et al.  Does willingness to pay for green energy differ by source? A contingent choice experiment , 2007 .

[7]  Jan Weinzettel,et al.  A consumption-based indicator of the external costs of electricity , 2012 .

[8]  Joongha Ahn,et al.  Optimal allocation of energy sources for sustainable development in South Korea: Focus on the electric power generation industry , 2015 .

[9]  S. Awerbuch.  Portfolio-Based Electricity Generation Planning: Policy Implications For Renewables And Energy Security , 2006 .

[10]  Wonjoon Kim,et al.  An international comparative analysis of public acceptance of nuclear energy , 2014 .

[11]  Kyunam Kim,et al.  Electricity-generation mix considering energy security and carbon emission mitigation: Case of Korea and Mongolia , 2014 .

[12]  David A. Hensher,et al.  A latent class model for discrete choice analysis: contrasts with mixed logit , 2003 .

[13]  Carsten Schröder,et al.  Eliciting Public Support for Greening the Electricity Mix Using Random Parameter Techniques , 2011 .

[14]  T. Biegler.  The hidden costs of electricity: externalities of power generation in Australia , 2009 .

[15]  Anindya Bhattacharya,et al.  Power sector investment risk and renewable energy: A Japanese case study using portfolio risk optimization method , 2012 .

[16]  R. Balasubramanian,et al.  Optimization of India’s electricity generation portfolio using intelligent Pareto-search genetic algorithm , 2014 .

[17]  N. Bronfman,et al.  Understanding social acceptance of electricity generation sources , 2012 .

[18]  Zibin Zhang,et al.  On Using Individual Characteristics in the MNL Latent Class Conjoint Analysis: An Empirical Comparison of the Nested Approach versus the Regression Approach , 2009 .

[19]  Hyunsuk Byun,et al.  Analyzing Korean consumers’ latent preferences for electricity generation sources with a hierarchical Bayesian logit model in a discrete choice experiment , 2017 .

[20]  Juozas Augutis,et al.  Energy mix optimization from an energy security perspective , 2015 .

[21]  Mark Lijesen,et al.  Valuing the preferences for micro-generation of renewables by househoulds , 2014 .

[22]  Jungwoo Shin,et al.  Analyzing public preferences and increasing acceptability for the Renewable Portfolio Standard in Korea , 2014 .

[23]  D. Georgakellos,et al.  Incorporating life cycle external cost in optimization of the electricity generation mix , 2014 .

[24]  F. J. Rijnsoever,et al.  Public acceptance of energy technologies : The effects of labeling, time, and heterogeneity in a discrete choice experiment , 2015 .

[25]  Jay Magidson,et al.  Latent class models for clustering : a comparison with K-means , 2002 .

[26]  Chris Van Atten,et al.  North American Power Plant Air Emissions , 2005 .

[27]  T. Cockerill,et al.  Developing an optimal electricity generation mix for the UK 2050 future , 2016 .

[28]  Z. Geem,et al.  Optimal Energy Mix with Renewable Portfolio Standards in Korea , 2016 .

[29]  Poul Alberg Østergaard,et al.  Optimal energy mix for transitioning from fossil fuels to renewable energy sources – The case of the Mexican electricity system , 2015 .

[30]  Florian Heiss,et al.  Discrete Choice Methods with Simulation , 2016 .

[31]  Iftekhar A. Karimi,et al.  Long-term optimal energy mix planning towards high energy security and low GHG emission , 2015 .

[32]  Kenneth Train,et al.  Customers' Choice Among Retail Energy Suppliers: The Willingness-to-Pay for Service Attributes , 2000 .

[33]  Rolf Wüstenhagen,et al.  Whatever the customer wants, the customer gets? Exploring the gap between consumer preferences and default electricity products in Germany , 2013 .

[34]  Azucena Gracia,et al.  Can renewable energy be financed with higher electricity prices? Evidence from a Spanish region , 2012 .

[35]  Neveen Hamza,et al.  Renewable energy adoption in an ageing population: Heterogeneity in preferences for micro-generation technology adoption , 2011 .

[36]  René Bañares-Alcántara,et al.  A knowledge representation model for the optimisation of electricity generation mixes , 2012 .

[37]  Aviel Verbruggen,et al.  Renewable and nuclear power: A common future? , 2008 .

[38]  Richard C. Ready,et al.  Preference heterogeneity for renewable energy technology , 2014 .

[39]  Heinz Welsch,et al.  Electricity supply preferences in Europe: Evidence from subjective well-being data , 2014 .

[40]  Mariano Luque,et al.  A multiobjective interactive approach to determine the optimal electricity mix in Andalucía (Spain) , 2014 .

[41]  Deendarlianto,et al.  Multi-objective optimization model for sustainable Indonesian electricity system: Analysis of economic, environment, and adequacy of energy sources , 2015 .

[42]  Roberto Aguilera,et al.  The role of natural gas in a low carbon Asia Pacific , 2014 .