Industrial Electricity Demand Research Articles

Modeling and Forecasting Industrial Electricity Demand for Saudi Arabia: Uncovering Regional Characteristics

Modeling and Forecasting Industrial Electricity Demand for Saudi Arabia: Uncovering Regional Characteristics

National level assessment of using existing airport infrastructures for photovoltaic deployment

The airport is one of the most fundamental infrastructures in our society, its terminal buildings and parking lots are ideal locations for photovoltaic (PV) installation. The future trend of the electrification of the aviation and automobile industries will increase the electricity demand at airports calls for the integration of PV systems with airport infrastructure. Understanding the benefits of airport PV systems is crucial for the stakeholders including investors, project developers and policymakers to promote PV sector development. Thus, evaluation of the PV potential and relevant economic performance of airports is essential. Combined with the geographic information system (GIS), the PV potential of the civil airports in China is investigated. The results show that potential PV capacity of China’s 239 civil airports reaches up to 2.50 GW. The annual electricity demand of the aviation industry in 8 provinces can be self-satisfied via airport PV generation. Detailed economic analysis shows that all airport PV systems are profitable through appropriate investment and operation strategies.

The future of coal investment, trade, and stranded assets

The future of coal investment, trade, and stranded assets

Industry electricity price and output elasticities for high-income and middle-income countries

Energy planning and climate policy require understanding long-run energy demand patterns. Electricity demand further is important because energy services derived from electricity typically do not have substitution possibilities from other fuels. By employing dynamic panel models, we estimate the long-run price and output elasticities of aggregate industrial electricity demand for high-income (mostly OECD) and middle-income (mostly non-OECD) countries. The unbalanced data span 1978–2016 and include 35 high-income countries and 30 middle-income countries. Our dynamic panel estimates address nonstationarity, heterogeneity, and cross-sectional dependence. We believe these are the first such panel estimates for middle-income/non-OECD countries and among the few such estimates for high-income/OECD countries to appear in the literature. The output elasticity for high-income countries typically was significantly below unity, around 0.5, and the price elasticity was around − 0.25 (and was statistically significant). For middle-income countries, the output elasticity was greater than unity and was likely significantly larger than the output elasticity for high-income countries, whereas the price elasticity was small and insignificant for middle-income countries.

Impact of climate change on sectoral electricity demand in Turkey

ABSTRACT This paper analyzes the impact of climate change on Turkey’s residential, industrial and commercial electricity demand by applying the Structural Time Series Model. The model consists of explanatory variables and uses annual time series data for the period from 1980 to 2017. The variables included are electricity price, income per capita, urbanization rate, manufacturing added value, population-weighted cooling degree days (as a climate variable) and, finally, stochastic Underlying Energy Demand Trend. The effect of cooling degree days on the residential, industrial and commercial electricity demand is found to be significantly elastic. Moreover, residential, industrial and commercial electricity demands in 2031 are expected to increase considerably, and at different ratios, depending on the chosen climate change scenario. The findings of this study are significant for efforts to establish regulations to shape energy security policies in the presence of climate change.

How Price Responsive is Industrial Electricity Demand in the US?

How Price Responsive is Industrial Electricity Demand in the US?

Analysing the Industrial Electricity Demand for Turkey

This study analyses the effects of some selected parameters on Turkey’s industrial electricity demand by using annual data between 1978 and 2018. In this regard, the Autoregressive Distributed Lag (ARDL) Bounds Testing method is utilized for establishing the models. The variables that used in the models of this study are electricity consumption, industrial value added, price of electricity, urbanization rate and average air temperature. According to the empirical findings, the price elasticities are estimated negative as expected which are -0.14 and -0.18 for the short and long term, respectively. On the other hand, the income elasticities have positive signs and computed as statistically significant. The short and long run income elasticities of industrial electricity demand are found as 0,15 and 0,35, respectively. Additionally, the urbanization rate and air temperature positively affect the industrial electricity demand of Turkey. These results indicate that the estimated price and income elasticities for the Turkish industrial electricity demand are very low and smaller than 1 in absolute terms. Therefore, it can be said that an increase and/or decrease in price and income as percentage is more than increase in electricity consumption for the industrial sector. In addition, these results imply that since the electricity usage in Turkey’s industrial sector is a necessity, consumers are not changing their consumption behaviour easily with respect to the price and income movements.

Energy consumption modeling of production process for industrial factories in a day ahead scheduling with demand response

Abstract Industrial electricity demand is growing rapidly, whereby, energy consumption modeling and optimization techniques in industries has attracted significant attention in recent years. In this paper, a new model of energy consumption in the production process of aluminum, steel and cement is presented in accordance with a linear piece-wise approximation (LPWA) method. The proposed model is subsequently implemented in the day ahead energy management scheduling of a Microgrid (MG) (involving industrial factories). In order to increase efficiency and give industries an opportunity to contribute in the energy and ancillary services markets, demand response (DR) programs are implemented. The proposed scheduling model considers all the constraints of industrial factories and the MG to maximize their revenue. The performance of the proposed model is evaluated using three case studies. The first and second case studies respectively investigate the effectiveness of the proposed model with and without the implementation of DR programs. In the third case study, the coordination between industrial factories and a MG is investigated. Finally, the results show that the implementation of DR programs and participation of industrial factories in the energy and ancillary services markets, have improved the demand curve, hence increasing the revenue of the MG and industrial factories.

THE CAUSAL RELATIONSHIP BETWEEN, ELECTRICITY CONSUMPTION AND ECONOMIC GROWTH IN KINGDOM OF SAUDI ARABIA: A DYNAMIC CAUSALITY TEST

The main objective of this study is to empirically test whether there exist short run and long run causality between, Residential Electricity Consumption (REC), Industrial Electricity Consumption (IEC) and economic growth in Kingdom of Saudi Arabia (KSA). Time series data for this study spans from 1990 to 2019. The study adopts granger causality and co. Integration analysis to estimate a Vector Error Correction Model (VECM). Results from error correction model show that there exist long run co. integration relationship between targeted variables. In addition, VECM results indicates that, industrial electricity consumption is inelastic to the changes in electricity prices with respect to economic growth, while residential electricity consumption shows elastic relationship. Granger causality test indicates there is unidirectional relationship, running from economic growth to industrial electricity consumption, which lead to accept, proactive (conservative) hypothesis. In this case, energy conservative policy will have little or no effect on economic growth. Nevertheless, results proof acceptance of neutrality hypothesis in the case of residential electricity consumption and economic growth. The study therefore, recommends that in Saudi Arabia, policy makers should consider expanding their energy-mix alternatives, in order to cope with the future industrial electricity demand arising from increased economic growth.

Unsupervised grouping of industrial electricity demand profiles: Synthetic profiles for demand-side management applications

Demand side management is a promising alternative to offer flexibility to power systems with high shares of variable renewable energy sources. Numerous industries possess large demand side management potentials but accounting for them in energy system analysis and modelling is restricted by the availability of their demand data, which are usually confidential. In this study, a methodology to synthetize anonymized hourly electricity consumption profiles for industries and to calculate their flexibility potential is proposed. This combines different partitioning and hierarchical clustering analysis techniques with regression analysis. The methodology is applied to three case studies in Chile: two pulp and paper industry plants and one food industry plant. A significant hourly, daily and annual flexibility potential is found for the three cases (15%–75%). Moreover, the resulting demand profiles share the same statistical characteristics as the measured profiles but can be used in modelling exercises without confidentiality issues.

Effect of an Electric Vehicle Promotion Policy on China’s Islands: A Case Study of Hainan Island

Despite the abundant clean energy resources on islands, energy supply on the islands in many countries and regions still depends on high-voltage power grids in neighboring continents and high-cost fossil energy such as oil. In China, increasing demand for private gasoline vehicles calls for an abundant gasoline supply, which is harmful to the renewable energy transformation on Chinese islands. The promotion of electric vehicles offers a potential solution. However, on large islands with large populations, insufficient electricity supply restricts residents’ behavior, which has led to difficulties in promoting clean energy vehicles. To solve this problem, in 2019, the government formulated an 11-year plan to comprehensively promote clean energy vehicles from the energy supply side and user demand side to transform Hainan Island into a clean energy island. However, some predictions in the report are doubtful. As a result, this study takes Hainan Island in China as an example. Based on data on energy and electric vehicles, the greenhouse gas emissions and electricity demand of the transportation industry in Hainan Island under policy and non-policy scenarios are calculated using a generation substitution accounting model. Based on the results, this study evaluates the expected effect of Hainan Province’s 11-year plan to develop clean energy vehicles and compares the results with those in the report. Policy implications based on the results are also proposed. This study combines the island’s energy structure with the application of electric vehicles and expands the evaluation of government policies on the impact of large-scale energy transformation from the perspective of the energy supply chain. It also provides a reference for energy transformation on other large islands.

Revisiting Energy Demand Relationship: Theory and Empirical Application

In this paper, we revisit the theoretical framework for energy demand. We then use this theoretical framework to empirically model the Saudi Arabian industrial electricity demand. We show, in the case of Saudi Arabian data, that imposing parsimonious energy demand specification on data without testing relevant assumptions can lead to biased estimations and noticeably poor approximations, while imposing general energy demand specification without accounting for the data properties can lead to redundant estimations and lower approximation than what could be obtained otherwise. Combining the theory with the data can provide unbiased and irredundant estimations with high levels of approximations. Hence, this paper recommends, based on the empirical findings, that a better strategy would be the combination of theoretical coherence with data coherence in the General to Specific Modeling (GtSM) framework for the empirical analyses of energy demand.

Econometric Model for Forecasting Electricity Demand of Industry and Construction Sectors in Vietnam to 2030

An accurate forecasting for long-term electricity demand makes a major role in the planning of the power system in any country. Vietnam is one of the most economically developing countries in the world, and its electricity demand has been increased dramatically high of about 15%/y for the last three decades. Contribution of industry and construction sectors in GDP has been increasing year by year, and are currently holding the leading position of largest consumers with more than 50% sharing in national electricity consumption proportion. How to estimate the electricity consumption of these sectors correctly makes a crucial contribution to the planning of the power system. This paper applies an econometric model with Cobb Douglas production function - a top-down method to forecast electricity demand of the industry and construction sectors in Vietnam to 2030. Four variables used are the value of the sectors in GDP, income per person, the proportion of electricity consumption of the sectors in total, and electric price. Forecasted results show that the proposed method has a quite low MAPE of 7.66% for long-term forecasting. Variable of electric price does not affect the demand. This is a very critical result of the study for authority governors in Vietnam. In the base scenario of the GDP and the income per person, the forecasted electricity demands of the sectors are 112,853 GWh, 172,691 GWh, and 242,027 GWh in 2020, 2025, 2030, respectively. In high scenario one, the demands are 115,947 GWh, 181,591 GWh, and 257,272 GWh, respectively. The above values in the high scenario are less than from 9.0% to 15.8 % of that of the based on in the Revised version of master plan N0. VII.

Price and income elasticities of residential and industrial electricity demand in the European Union

This study examines the short- and long-run price and income elasticities of residential and industrial electricity demand in the European Union between 1996 and 2016. Instrumental variable models using the between estimator, as well as dynamic panel models are employed to present robust estimates, and to assess the impact of different methodologies on the reported elasticities. The long-run price elasticity of residential electricity consumption is estimated between −0.53 and −0.56. These elasticities are more inelastic than that of industrial electricity use, which is reported between −0.75 and −1.01. The choice of different econometric methodologies has only moderate impact on the estimates. While long-run residential income elasticity estimates are moderately inelastic and estimated around 0.61, industrial electricity use tends to be closely tied to income, with elasticities between 0.76 and 1.08. Electricity demand in all sectors is highly price and income inelastic in the short run. Additionally, population density, temperatures, and policy measures also influence sectoral electricity use. The above estimates can aid the design of European energy and environmental policy.

METROPOLITAN EVIDENCE REGARDING SMALL COMMERCIAL AND INDUSTRIAL ELECTRICITY CONSUMPTION

Small commercial and industrial (CIS) electricity demand is an important category of electric energy consumption. Historically, it has received substantially less research attention than residential usage, potentially due to data constraints. This study seeks to partially fill that gap in the energy economics literature by employing a fairly unique data set for the El Paso, Texas, USA metropolitan economy that includes private capital stock estimates from 1978 through2017. The empirical model is specified using a recently developed analytical framework based on duality theory and a derived input demand function. Parameter estimation is completed using an Autoregressive-Distributed Lag model (ARDL) and an Error Correction Model (ECM). In the long-run, CIS customers in El Paso respond only to own-price and the quantity of capital stock per capita. In the short-run, CIS customers adjust their electricity usage in response to changes in all variables except for the price of electricity. The most unexpected result from this analysis is a short-run income elasticity of -0.32, indicating that CIS electricity usage decreases with economic expansion in El Paso, Texas.Keywords: Electricity usage, Metropolitan economic growth, Small commercial and industrial customers, Capital stocks, Duality theory, Derived input demandJEL Classifications: Q41; R11; M21DOI: https://doi.org/10.32479/ijeep.8233

Demand Function for Industrial Electricity: Evidence from South Korean Manufacturing Sector

Electricity is a crucial input to the industrial production of South Korea. Estimating the demand function for electricity in the manufacturing sector is an important task because electricity consumption in the manufacturing sector accounts for 56.3% of total electricity consumption in South Korea. Thus, this article tries to estimate the demand function for industrial electricity in the manufacturing sector of South Korea using cross-sectional data for analyzing the influence of manufacturing firms’ characteristics. To this end, 946 observations collected from a nationwide survey of manufacturing firms in 2018 are used and analyzed. As a robust approach, the least absolute deviations estimation method is applied to obtaining the demand function. The results show that the price elasticity and the sales amount elasticity of the industrial electricity demand are estimated to be −0.9206 and 0.2568, respectively, which are statistically significant at the 1% level. Furthermore, the economic benefits of industrial electricity consumption are computed to be 1.46 times as great as the price of electricity. The results of this study can be utilized in policy planning, making, and evaluation.

WIPSO, PSO and GA Techniques to Locate UPFC Effectively in Power System to Improve Voltage Stability and Reduce Losses

The domestic and industrial demand for electricity has been increasing extensively making the power system more expensive. With this increases in demand for electricity, the losses also increase in demand for electricity the losses also increase from power generation to distribution Flexible alternating currents transmission system (FACTS) is used to maintain flexible operation of the power system from power generation level to the distribution level. The reliability of the network system can be enhanced by using FACTs devices in the power system more reliably, the inventions in the advanced power electronics devices can be implemented in the design of FACTs, serices, shunt, serices-shunt and shunt-shunt are some of the FACTs devices. One way to operate the power system with less power losses and improved system voltage profile is to use FACTs. Unified power flow controller (UPFC) is one of the serices-shunt FACTs systems. This paper throws light on how UPFC can be used to improve the voltage profile and reduce the installation cost of UPFC, the system loss, in the electrical power system. Analyst and soft computing methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Weight Enhanced Particle Swarm Optimization (WIPSO) are used to determine ideal FACTs device settings and FACTs device place.

An Innovative Technique for Optimization of Efficiency of Transformer and Inductor

In modern industrial era demand for electricity is increasing, as a result there is a rapid increase in demand for power generation and transmission. Various researches are going on in order to deliver the efficient energy systems. One such effective method for optimizing the efficiency of inductive devices such as transformer and inductors in cost efficient way has been discussed in this paper. Inductive circuits are used in all electronic devices; the values of impedance of this circuit determine the total efficiency of an instrument or a device. The significant loss in the transformer is the leakage of magnetic flux and Eddy current. By introducing a filler material, which is abundantly available in the earth’s crust; these losses can be minimized. An amorphous filler material which has high permeability and lower electrical conductivity is introduced in the air gap of soft-core iron transformer which significantly reduces the magnetic flux leakage through the lateral sides of the transformer, in turn, enhances the magnetic coupling between the primary coil and the secondary coil of the transformer. Thus, the varying magnetic field around the coil is utilized to the possible extent, as a result the efficiency of a device is optimized. A prototype of a step-down transformer using this filler material has been designed in which the efficiency is increased by 8.306% when compared to the traditional transformers used today. The main contribution of this research is the introduction of a technique for optimizing the magnetic coupling between the primary and secondary coils, which imparts greater efficiency to the transformer and minimizes the overall transformer materials and production cost.

Exploring drivers of sectoral electricity demand in Indonesia

ABSTRACTThis study estimates the influence of income, electricity price, oil price, urbanization, and Asian economic crisis to electricity demands in residential, industrial, and commercial sectors of Indonesia using autoregressive distributed lag (ARDL) method. Our estimations reveal that electricity demands in all sectors during the period of 1969–2015 are significantly affected by income and urbanization in which the demands in all sectors are elastic to urbanization but inelastic to income. On the other hand, electricity price only significantly affects residential electricity demand while oil price does not have significant influence on electricity demands in all sectors. The electricity demands in residential and commercial sectors are also significantly inelastic to the number of electricity customers while Asian economic crisis in 1998 only affects industrial electricity demand. These findings are like most previous studies for other country cases but not to previous Indonesian studies that had suffered from the cointegration issues.

Household and industrial electricity demand in Europe

This paper examines the electricity demand, and its determinants, in 29 European countries during the liberalization of the electricity market. Based on panel data for these countries for the years 1995–2015 and using a dynamic partial adjustment model, price elasticities are estimated for both residential and industrial electricity demand. These elasticities and effects of other variables on electricity consumption are estimated using both GMM (generalized method of moments) and ML (maximum likelihood) approaches. It is found that the price elasticities are very small, especially in the short run, while the income elasticities are relatively large, especially for households and in the long run.