Demand Elasticity in the Italian Power Market: a Bayesian Experiment under dual pricing scheme

Maria Chiara D'errico

Abstract


This study run experiment aiming to provide a flexible and analytically elegant framework to reliably estimate the price elasticity of electricity demand. Inference pertains to the demand at hourly level in the Italian wholesale electricity market and uses individual demand bid data. Individuals' bids represent the ex-ante willingness to pay and thus allows for constructing a market demand grounded in the consumer behavior theory, by exploiting the duality approach.

Bayesian econometric estimation is applied, relaxing homoskedasticity assumptions of the traditional linear regression model. It allows to identify robust results, showing that elasticity varies significantly among hours of the day, zone segmentation as well as the level of equilibrium price. Bayesian inference provides also the opportunity to include prior information sourced from previous studies and the institutional struc- ture governing the agents' behavior. This prior information involves some degree of uncertainty, for this reason Bayesian approach assigns it a probability distribution. Using Bayes rule, prior information are then updated according to the observed data. Results validate the market reform designed to foster competition and increase wel- fare even through the time-varying pricing schemes that trigger the consumers' price reaction.


Keywords


Demand Elasticity, Bayesian Heteroskedastic SUR Model, Power Market

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References


Bigerna, S., Bollino, C.A., 2014. Electricity demand in wholesale italian market. The Energy Journal 35.

Caves, D.W., Christensen, L.R., 1980. Residential substitution of off-peak for peak electricity usage under time-of-use pricing. The Energy Journal 1.

Chib, S., Greenberg, E., 1995. Hierarchical analysis of sur models with extensions to correlated serial errors and time-varying parameter models. Journal of Econo- metrics 68, 339-360.

De Finetti, B., 1993. On the subjective meaning of probability. Probabilità e in- duzione. Clueb, Bologna , 291-321.

Dubin, J.A., 2014. Consumer durable choice and the demand for electricity. Elsevier.

Espey, J.A., Espey, M., 2004. Turning on the lights: A meta-analysis of residential electricity demand elasticities. Journal of Agricultural and Applied Economics 36, 65-81.

Faruqui, A., George, S., 2005. Quantifying customer response to dynamic pricing. The Electricity Journal 18, 53-63.

Filippini, M., 1995. Electricity demand by time of use an application of the household aids model. Energy Economics 17, 197-204.

Gelfand, A.E., Dey, D.K., 1994. Bayesian model choice: asymptotics and exact calculations. Journal of the Royal Statistical Society: Series B (Methodological) 56, 501-514.

Geweke, J., 1993. Bayesian treatment of the independent student-t linear model. Journal of applied econometrics 8, S19S40.

Kamyab, F., Bahrami, S., 2016. Efficient operation of energy hubs in time-of-use and dynamic pricing electricity markets. Energy 106, 343355.

King, C., Chatterjee, S., 2003. Predicting california demand response .

Koop, G.M., 2003. Bayesian econometrics. John Wiley & Sons Inc.

Labandeira, X., Labeaga, J.M., López-Otero, X., 2017. A meta-analysis on the price elasticity of energy demand. Energy Policy 102, 549568.

Lin, B., Liu, J., 2011. Principles, effects and problems of di􏰃erential power pricing policy for energy intensive industries in china. Energy 36, 111118.

Maskin, E., Tirole, J., 1992. The principal-agent relationship with an informed principal, ii: Common values. Econometrica: Journal of the Econometric Society , 142.

Mountain, D.C., 1993. An overall assessment of the responsiveness of households to time-of-use electricity rates: The ontario experiment. Energy Studies Review 5.

Nahata, B., Izyumov, A., Busygin, V., Mishura, A., 2007. Application of ramsey model in transition economy: a russian case study. Energy Economics 29, 105 125.

Parti, M., Parti, C., 1980. The total and appliance-specific conditional demand for electricity in the household sector. The Bell journal of economics , 309321.

Patrick, R.H., Wolak, F.A., 2001. Estimating the customer-level demand for electricity under real-time market prices. Technical Report. National Bureau of Economic Research.

Reiss, P.C., White, M.W., 2005. Household electricity demand, revisited. The Review of Economic Studies 72, 853883.

Taylor, T.N., Schwarz, P.M., Cochell, J.E., 2005. 24/7 hourly response to electricity real-time pricing with up to eight summers of experience. Journal of regulatory economics 27, 235262.

Wolak, F.A., 2003. Measuring unilateral market power in wholesale electricity markets: the california market, 1998-2000. American Economic Review 93, 425430.

Wolak, F.A., 2010. An experimental comparison of critical peak and hourly pricing: the powercentsdc program. Department of Economics Stanford University .

Wolak, F.A., et al., 2001. Designing a competitive wholesale electricity market that benefits consumers. Stanford University.




DOI: http://dx.doi.org/10.5202/rei.v11i1-2.326



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