Estimating sectoral demands for electricity using the pooled mean group method

Gautam, Tej K.; Paudel, Krishna P.

doi:10.1016/j.apenergy.2018.09.023

Cited by 23 publications

(10 citation statements)

References 52 publications

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“…The Equations (21) and (22), which allow to state that the energy efficiencies values of the two-zone tariff, depend only on the mutual relations of parameters of standard load profiles for tariffs G11 and G12 taking into account that the relation between E aG11 and E aG12 is given by relationship (3).…”

Section: Results Concerning Residential Zonal Tariffs' Efficiencymentioning

confidence: 99%

“…Both self elasticity values and cross elasticity values concerning other energy carriers or other time zones during the day are considered. Filippini [20] Switzerland Self -peak: −0.778/−0.835 -off-peak: −0.652/ −0.758 -peak: −1.608/−2.266 -off-peak: −1.273/−1.652 Cross (peak/off-peak) -peak: 0.793/0.917 -off-peak: 0.363/0.407 -peak: 1.767/2.311 -off-peak: 0.684/0.919 Gautam et al [21] USA Self −0.0523 −0.110 Cross (natural gas) -0.0952 Ishaque [22] Pakistan Self −0.10 −0.462 Kwon et al [23] South Korea Self −0.051 −0.207 Labandeira et al [24] Various Self −0.126 −0.365 Lijesen [25] The Netherlands Self −0.0014/−0.0043 -Loi et al [ Analyses regarding customer responses using different pricing schemes have already started in the second half of the previous century, beginning with the identification of problems related to determining the price elasticity of demand [38] and attempts to determine the response of customers using multi-zone tariffs for price changes [39,40]. More detailed analyses of electricity demand in multi-zone tariffs, determining price elasticity of demand, were later carried out for time-of-use (TOU) tariffs [20,27,41], for progressive tariffs [27], for tariffs with a critical peak pricing (CPP) [36,42], and for real-time pricing (RTP) tariffs [36].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, the improved AIC (Akaike's Information Criterion) can be used to find the most appropriate specifications in the load models under consideration [52]. Furthermore, when using the panel data, it is possible to use the PMG (Pooled Mean Group) method to determine price elasticity of demand [21]. A different approach to the estimation of results is presented in [37], where the ITSUR (Iterated Seemingly Unrelated Regression) method is used.…”

Section: Literature Reviewmentioning

confidence: 99%

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Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles

2019

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The concept of price elasticity of demand has been widely used for the assessment of the consumers’ behavior in the electricity market. As the residential consumers represent a significant percentage of the total load, price elasticity of their demand may be used to design desirable demand side response programs in order to manage peak load in a power system. The method presented in this study proposes an alternative approach towards price elasticity determination for zonal tariff users, based on comparisons of load profiles of consumers settled according to flat and time-of-use electricity tariffs. A detailed explanation of the proposed method is presented, followed by a case-study of price elasticity determination for residential electricity consumers in Poland. The forecasted values of price elasticity of demand for the Polish households using time-of-use (TOU) tariff vary between −1.7 and −2.3, depending on the consumers’ annual electricity consumption. Moreover, an efficiency study of residential zonal tariff is performed to assess the operation of currently applicable electricity tariffs. Presented analysis is based on load profiles published by Distribution System Operators and statistical data, but the method can be applied to the real-life measurements from the smart metering systems as well when such systems are accessible for residential consumers.

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Section: Results Concerning Residential Zonal Tariffs' Efficiencymentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles

2019

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“…Few of the studies include the work by Gautam and Paudel (2018) that investigate the cost of sectoral demands of electricity. The authors documented that electricity for the residential sector is responsive to its own price in the long-run but irresponsive to own price in the short run.…”

Section: Introductionmentioning

confidence: 99%

Electricity Consumption and Manufacturing Sector Performance: Evidence From Nigeria

Asaleye

Lawal²,

Inegbedion³

et al. 2021

IJEEP

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Adequate supply of energy is important for sustainable growth in an economy. The rate of the growth of the Nigeria's electricity development is very slow and not effective compare to other emerging economies; this on the other hands has discouraged production, most especially in the manufacturing sector. In light of this, the study investigates the long-run impact of electricity consumption on manufacturing sector performance proxy by output, employment and capital using Canonical Cointegrating Regression for the period of 1981-2019. Evidence from the result in the output equation shows that electricity consumption and credit to manufacturing sector have a negative relationship with output. In the employment equation, consumption in electricity and interest rate have negative effects on employment. In the capital equation, electricity consumption is not statistically significant. In conclusion, effects of electricity consumption as input in the manufacturing sector have not improved the performance in the sector. To improve the situation, the study recommends among others the need to create a framework to promote energy efficiency by maximizing output from the power sector and minimize wastage.

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“…Mohammadi (2009) accepts the cointegration hypothesis to examine the relationship between retail power prices and gas prices at wellhead in the US. Gautam and Paudel (2018) examine the demand for power in the residential, commercial, and industrial sectors after the acceptance of cointegration with gas prices.…”

Section: Introductionmentioning

confidence: 99%

Expectations for Statistical Arbitrage in Energy Futures Markets

Nakajima

2019

JRFM

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Energy futures have become important as alternative investment assets to minimize the volatility of portfolio return, owing to their low links with traditional financial markets. In order to make energy futures markets grow further, it is necessary to expand expectations of returns from trading in energy futures markets. Therefore, this study examines whether profits can be earned by statistical arbitrage between wholesale electricity futures and natural gas futures listed on the New York Mercantile Exchange. On the assumption that power prices and natural gas prices have a cointegration relationship, as tested and supported by previous studies, the short-term deviation from the long-term equilibrium is regarded as an arbitrage opportunity. The results of the spark-spread trading simulations using historical data from 2 January 2014 to 29 December 2017 show about 30% yield at maximum. This study shows the possibility of generating earnings in energy futures market.

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Estimating sectoral demands for electricity using the pooled mean group method

Cited by 23 publications

References 52 publications

Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles

Demand Price Elasticity of Residential Electricity Consumers with Zonal Tariff Settlement Based on Their Load Profiles

Electricity Consumption and Manufacturing Sector Performance: Evidence From Nigeria

Expectations for Statistical Arbitrage in Energy Futures Markets

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