scholarly journals ON THE FINANCIAL ASSESSMENT OF THE NKR ELECTRICITY SYSTEM

2021 ◽  
Author(s):  
Mikayelyan Yevgenya
Keyword(s):  
Factor Analysis ◽  
Shadow Economy ◽  
Electricity Consumption ◽  
Energy System ◽  
Financial Strategy ◽  
Financial Assessment ◽  
Electricity System ◽  
Real Gdp

The article aims to calculate the separate elements of the financial strategy of the NKR energy system and to carry out a factor analysis. Using the Kaufmann-Calibardi method, the coefficients of flexibility of electricity consumption by GDP were estimated, showing the causes of the shadow economy in the Artsakh Republic depending on the volume of electricity. Based on the annual statistics of electricity consumption and real GDP in the period of 2000-2019, the years were emphasized, as a result of which the fact that it is a calculated value of 1 substantiates the fact that the higher the electricity consumption, the higher the GDP should be, but obtained the results are not equal to 1 (greater than or less than 1), so the size of the shadow was calculated in those years.

Investigating a Hampered NRE Utilization in Kaltim’s Energy System: Is there an Energy Policy with a Syndrome of the Energy-abundant Area?

2021 ◽  
Vol 10 (4) ◽  
pp. 653-666
Author(s):  
Novena Damar Asri ◽  
Purnomo Yusgiantoro
Keyword(s):  
Energy Policy ◽  
Population Distribution ◽  
Electricity Consumption ◽  
Energy System ◽  
Fuel Oil ◽  
Linear Regression Method ◽  
Energy Potential ◽  
Electricity System ◽  
The Cost ◽  
Geographical Characteristics

Kaltim presumably experiences an energy paradox, where the energy system is unreliable and unsustainable, despite energy-rich. This study presumes that the paradox is caused by the ‘ill-advised energy policy’ shown by ‘energy-area incompatibility’ that is exacerbated by the ‘energy-rich syndrome’ (a mindset of feeling secure due to energy-abundance leading to a wasteful behavior). This study investigates the indication of the syndrome in Kaltim energy policy by first investigating ‘the incompatibility’ and its impacts by examining Kaltim’s geographical characteristics, energy potential, population-distribution, electricity system, and infrastructure. Also, the impacts of retaining the syndrome through cost analyses. This study finds the incompatibility between energy-sources utilization and geographical characteristics, by conducting a descriptive method with data collection and analyses. Kaltim is forest-dominated with scattered-population, suitable with an off-grid system. However, the electricity development is mostly on-grid, fossil-based designed, explaining the difficulties of electrifying the entire Kaltim, although electricity is surplus. While off-grid should be applied to NRE, the massive use of diesel-gen-sets shows wasteful behavior. By conducting a linear-regression method, this study finds that Kaltim’s electricity consumption (indicating the infrastructure sufficiency) is lower than it should be, given its incredible economic performance. The incompatibility causes infrastructure insufficiency. The cost analysis finds that the massively-used fuel oil is the most expensive. The subsidy would be around 0.003%-0.275% of Kaltim GDRP or 17 billion-1.55 trillion IDR. As the new Capital location, NRE is a must for Kaltim. To conclude, NRE utilization is very low, although its potential is huge, and Kaltim’s forested characteristics suit it. NRE only covers 3% of Kaltim’s electricity, while the potential (hydro alone) is more than 6,900MW. The incompatibility causes an unreliable electricity system, although electricity is surplus. Following Kaltim’s geographical characteristics, NRE should be optimized. This study intends to aware the policy-makers of the syndrome, thereby develop a ‘proper energy policy’.

Electricity consumption and economic growth in ASEAN

2017 ◽  
Vol 5 (2) ◽  
pp. 16
Author(s):  
Ahmad Ghazali Ismail ◽  
Arlinah Abd Rashid ◽  
Azlina Hanif
Keyword(s):  
Economic Growth ◽  
Energy Use ◽  
Electricity Consumption ◽  
Panel Estimation ◽  
Real Gdp ◽  
Long Run ◽  
Bidirectional Relationship ◽  
Short Run ◽  
Using Data ◽  
The Relationship

The relationship and causality direction between electricity consumption and economic growth is an important issue in the fields of energy economics and policies towards energy use. Extensive literatures has discussed the issue, but the array of findings provides anything but consensus on either the existence of relations or direction of causality between the variables. This study extends research in this area by studying the long-run and causal relations between economic growth, electricity consumption, labour and capital based on the neo-classical one sector aggregate production technology mode using data of electricity consumption and real GDP for ASEAN from the year 1983 to 2012. The analysis is conducted using advanced panel estimation approaches and found no causality in the short run while in the long-run, the results indicate that there are bidirectional relationship among variables. This study provides supplementary evidences of relationship between electricity consumption and economic growth in ASEAN.

Study of the operation of an artificial neural network that works in the electric network in order to prevent emergency conditions.

2020 ◽  
Vol 14 (1) ◽  
pp. 48-54
Author(s):  
D. Ostrenko ◽  
Keyword(s):  
Neural Networks ◽  
Learning Algorithm ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Energy System ◽  
Electrical Networks ◽  
Electric Networks ◽  
Electric Network ◽  
Time Dynamics ◽  
Emergency Situations

Emergency modes in electrical networks, arising for various reasons, lead to a break in the transmission of electrical energy on the way from the generating facility to the consumer. In most cases, such time breaks are unacceptable (the degree depends on the class of the consumer). Therefore, an effective solution is to both deal with the consequences, use emergency input of the reserve, and prevent these emergency situations by predicting events in the electric network. After analyzing the source [1], it was concluded that there are several methods for performing the forecast of emergency situations in electric networks. It can be: technical analysis, operational data processing (or online analytical processing), nonlinear regression methods. However, it is neural networks that have received the greatest application for solving these tasks. In this paper, we analyze existing neural networks used to predict processes in electrical systems, analyze the learning algorithm, and propose a new method for using neural networks to predict in electrical networks. Prognostication in electrical engineering plays a key role in shaping the balance of electricity in the grid, influencing the choice of mode parameters and estimated electrical loads. The balance of generation of electricity is the basis of technological stability of the energy system, its violation affects the quality of electricity (there are frequency and voltage jumps in the network), which reduces the efficiency of the equipment. Also, the correct forecast allows to ensure the optimal load distribution between the objects of the grid. According to the experience of [2], different methods are usually used for forecasting electricity consumption and building customer profiles, usually based on the analysis of the time dynamics of electricity consumption and its factors, the identification of statistical relationships between features and the construction of models.

scholarly journals Analysis of Energy System in Norway with Focus on Energy Consumption Prediction

2017 ◽  
Vol 9 (1) ◽  
pp. 5-14 ◽  
Author(s):  
Maryam Hamlehdar ◽  
Alireza Aslani
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Electricity Consumption ◽  
Energy System ◽  
High Energy ◽  
Regression Result ◽  
Industry Growth ◽  
The Status

Abstract Today, the fossil fuels have dominant share of energy supply in order to respond to the high energy demand in the world. Norway is one of the countries with rich sources of fossil fuels and renewable energy sources. The current work is to investigate on the status of energy demand in Norway. First, energy and electricity consumption in various sectors, including industrial, residential are calculated. Then, energy demand in Norway is forecasted by using available tools. After that, the relationship between energy consumption in Norway with Basic economics parameters such as GDP, population and industry growth rate has determined by using linear regression model. Finally, the regression result shows a low correlation between variables.

scholarly journals Impacts of Electric Road Systems on the German and Swedish Electricity Systems—An Energy System Model Comparison

2021 ◽  
Vol 9 ◽  
Author(s):  
Johanna Olovsson ◽  
Maria Taljegard ◽  
Michael Von Bonin ◽  
Norman Gerhardt ◽  
Filip Johnsson
Keyword(s):  
Wind Power ◽  
Peak Power ◽  
Large Scale ◽  
Solar Power ◽  
Energy System ◽  
Transport Sector ◽  
Electricity System ◽  
Road Systems ◽  
The Impact ◽  
Electricity Systems

This study analyses the impacts of electrification of the transport sector, involving both static charging and electric road systems (ERS), on the Swedish and German electricity systems. The impact on the electricity system of large-scale ERS is investigated by comparing the results from two model packages: 1) a modeling package that consists of an electricity system investment model (ELIN) and electricity system dispatch model (EPOD); and 2) an energy system investment and dispatch model (SCOPE). The same set of scenarios are run for both model packages and the results for ERS are compared. The modeling results show that the additional electricity load arising from large-scale implementation of ERS is mainly, depending on model and scenario, met by investments in wind power in Sweden (40–100%) and in both wind (20–75%) and solar power (40–100%) in Germany. This study also concludes that ERS increase the peak power demand (i.e., the net load) in the electricity system. Therefore, when using ERS, there is a need for additional investments in peak power units and storage technologies to meet this new load. A smart integration of other electricity loads than ERS, such as optimization of static charging at the home location of passenger cars, can facilitate efficient use of renewable electricity also with an electricity system including ERS. A comparison between the results from the different models shows that assumptions and methodological choices dictate which types of investments are made (e.g., wind, solar and thermal power plants) to cover the additional demand for electricity arising from the use of ERS. Nonetheless, both modeling packages yield increases in investments in solar power (Germany) and in wind power (Sweden) in all the scenarios, to cover the new electricity demand for ERS.

scholarly journals Raising the Accuracy of Shadow Economy Measurements

2021 ◽  
Vol 239 (4) ◽  
pp. 71-125
Author(s):  
Vicente Ríos ◽  
◽  
Antonio Gómez ◽  
Pedro Pascual ◽  
◽  
...  
Keyword(s):  
Bayesian Model ◽  
Latent Variable ◽  
Bayesian Model Averaging ◽  
Shadow Economy ◽  
Electricity Consumption ◽  
Model Averaging ◽  
Hidden Sector ◽  
Multiple Indicators ◽  
Econometric Methods ◽  
Currency Demand

This article estimates the size of the shadow economy in a Spanish region (Navarre) for the period 1986- 2016. To this end, we employ indirect macro-econometric methods such as the Currency Demand approach, Electricity Consumption (Physical Input) methods and the multiple indicators multiple causes (MIMIC) approach. A differential feature of our empirical analysis is that we incorporate various methodological innovations (e..g. Bayesian Model Averaging, a Time-Varying Parameter model, normalization of the latent variable) to refine and increase the measurement accuracy of each of the indirect methods considered. The temporal pattern of the shadow economy’s size that emerges from the different approaches is similar, which suggests that the estimates obtained are robust and capture the underlying dynamics of the hidden sector. After quantifying the shadow economy, we analyze its determinants by means of Bayesian Model Averaging techniques. We find that the evolution of the shadow economy in Navarre can be explained by a small and robust set of factors, specifically the tax burden, the share of employment in the construction sector, the inflation rate, euro area membership and the ratio of currency outside the banks to M1.

scholarly journals Evaluation of the Shadow Economy of the Russian Federation with the Use of Electricity Consumption

2017 ◽  
Vol 17 (4) ◽  
pp. 84-93
Author(s):  
A. V. Kostin ◽  
◽  
A. V. Martel ◽  
A. D. Kashnikova ◽  
◽  
...  
Keyword(s):  
Russian Federation ◽  
Shadow Economy ◽  
Electricity Consumption ◽  
The Russian Federation

scholarly journals Monthly Electricity Consumption Forecasting Method Based on X12 and STL Decomposition Model in an Integrated Energy System

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Tianhe Sun ◽  
Tieyan Zhang ◽  
Yun Teng ◽  
Zhe Chen ◽  
Jiakun Fang
Keyword(s):  
Power Consumption ◽  
Rapid Development ◽  
Electricity Consumption ◽  
Energy System ◽  
Small Scale ◽  
Power Demand ◽  
Distributed Energy ◽  
Strategic Competition ◽  
Integrated Energy System ◽  
Monthly Electricity Consumption

With the rapid development and wide application of distributed generation technology and new energy trading methods, the integrated energy system has developed rapidly in Europe in recent years and has become the focus of new strategic competition and cooperation among countries. As a key technology and decision-making approach for operation, optimization, and control of integrated energy systems, power consumption prediction faces new challenges. The user-side power demand and load characteristics change due to the influence of distributed energy. At the same time, in the open retail market of electricity sales, the forecast of electricity consumption faces the power demand of small-scale users, which is more easily disturbed by random factors than by a traditional load forecast. Therefore, this study proposes a model based on X12 and Seasonal and Trend decomposition using Loess (STL) decomposition of monthly electricity consumption forecasting methods. The first use of the STL model according to the properties of electricity each month is its power consumption time series decomposition individuation. It influences the factorization of monthly electricity consumption into season, trend, and random components. Then, the change in the characteristics of the three components over time is considered. Finally, the appropriate model is selected to predict the components in the reconfiguration of the monthly electricity consumption forecast. A forecasting program is developed based on R language and MATLAB, and a case study is conducted on the power consumption data of a university campus containing distributed energy. Results show that the proposed method is reasonable and effective.

ASSESSING THE LINKAGES BETWEEN THE ELECTRICITY CONSUMPTION AND ECONOMIC FACTORS IN A LANDLOCKED ECONOMY: CASE STUDY OF LAO PDR

2020 ◽  
pp. 1-25
Author(s):  
NISIT PANTHAMIT ◽  
CHUKIAT CHAIBOONSRI
Keyword(s):  
Bayesian Inference ◽  
Lao Pdr ◽  
Energy Resource ◽  
Electricity Consumption ◽  
Energy System ◽  
Relationship Analysis ◽  
Explanatory Factors ◽  
Long Run ◽  
Short Run

This research paper aims to investigate linkages of electricity consumption representing energy security with estimated factors — GDP, population and foreign direct investment (FDI) during 1998–2018 for Laos People Democratic Republic (Lao PDR) by using ARDLbased Bayesian inference. This study provided empirical evidence on a long-run linear relationship analysis under ARDL-based Bayesian inference, which concludes that they have performed real relationships between electricity consumption, GDP, population and FDI. In addition, in the short-run, it was found that explanatory factors have both negative and positive impacts on Laos’ electricity consumption. The results confirm the hypothesis that although Lao PDR has access to domestic energy resources, only relying on one energy resource will make the energy system insecure. Thus, Lao PDR must develop substantial infrastructures and alternative renewable energies to support the campaign of Lao PDRs electricity security in the long-run.

The cost of undisturbed landscapes: on the valuation of wind turbines in Austria

2020 ◽  
Author(s):  
Sebastian Wehrle ◽  
Johannes Schmidt
Keyword(s):  
Power System ◽  
Wind Energy ◽  
Wind Turbines ◽  
Electricity Market ◽  
House Prices ◽  
Electricity Consumption ◽  
Negative Externalities ◽  
System Cost ◽  
Electricity System ◽  
Set Up

<p>In Europe, the system cost minimizing highly renewable power system set-up predominantly relies on wind energy, with minor shares of photovoltaics.</p><p>Yet, minimizing system cost neglects negative externalities of wind turbines, such as their impact on wildlife, noise emissions, landscape aesthetics, manifesting in local economic impacts such as a decline of house prices in the vicinity of wind turbines.</p><p>To better understand the trade-off between electricity system cost and the negative externalities from wind turbines, we quantify the increase in electricity system cost when the system cost minimizing deployment of wind turbines is reduced in the favor of photovoltaics.</p><p>Methodologically, we rely on the power system model medea, an open, techno-economic, numerical model of hourly dispatch and investment, set up to resemble the electricity market in Austria and its largest electricity trading partner Germany in 2030, when Austria aims to generate 90% of its electricity consumption from domestic renewable sources on annual balance.</p><p>Depending on the capital cost of renewable energy technologies, the marginal system cost from displaced wind turbines can reach up to 40.000 EUR per MW and year or approximately 20 EUR per MWh. Moreover, CO2 emissions can increase by up to 1.2 million tons per year when wind energy is fully displaced. Producer surplus could increase by up to 220 million EUR per annum at intermediate wind energy displacement but falls back towards initial levels when wind energy is fully displaced.</p><p>These numbers compare to estimates of property price declines between 2% and 16% caused by wind turbines, depending on the proximity to, and the visibility of the turbine. For illustration, adding a 3.5 MW wind turbine to a total installed wind power capacity of 12.6 GW in Austria over its lifetime (assuming a 3% discount rate) would generate sufficient social value to compensate affected property worth between 0.8 and 6.7 million EUR.</p>

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