scholarly journals COSTOS DE GENERACIÓN, INVERSIÓN Y PRECIOS DEL SECTOR ELÉCTRICO EN MÉXICO

2019 ◽  
Vol 78 (309) ◽  
pp. 58
Author(s):  
Alejandra Enríquez ◽  
José Carlos Ramírez ◽  
Juan Rosellón
Keyword(s):  
Electricity Market ◽  
Electricity Sector ◽  
Cost Curve ◽  
Transmission Congestion ◽  
Price Increases ◽  
Daily Data ◽  
Generation Cost ◽  
Generation Costs ◽  
La Red ◽  
The Relationship

<p>Los precios de la electricidad han registrado una marcada tendencia al alza desde la implementación de la reforma en el mercado eléctrico (RME) de México, que ha sido citada por algunos como evidencia de su fracaso. En este artículo estudiamos los determinantes de esa alza mediante la deducción de la curva de costos de generación de la Comisión Federal de Electricidad, antes de la entrada en vigor de la reforma, la construcción de datos horarios sobre precios promedios y el estudio de la relación entre precios y rentas de congestión. Los resultados principales del documento muestran que los tipos de tecnología de generación más rentables son los resultantes de la RME y que el aumento de los precios registrado durante los primeros años de la RME se explica, principalmente, por una creciente congestión de la red nacional de transmisión eléctrica más que por un diseño ineficiente de la competencia en el sector de generación.</p><p> </p><p align="center">GENERATION COSTS, INVESTMENT AND PRICES IN MEXICO’S ELECTRICITY SECTOR<strong></strong></p><p align="center"><strong>ABSTRACT</strong><strong> </strong><strong></strong></p>Electricity prices have seen a consistent upward trend since the implementation of Mexico’s electricity market reform (EMR). This has been interpreted by some as a failure of the EMR. In this paper we study the determinants of such price increases. We calculate the generation cost curve of the Federal Electricity Commission prior to the entry into force of the reform. We then construct daily data on average prices during the EMR. We also finally study the relationship between prices and transmission congestion rents. Our main results indicate that the most profitable types of generation technology are the ones resulting from the EMR. Likewise, price increases have taken place despite the existence of a considerably larger number of competitors in the generation sector. Lastly, the strong correlation between prices and congestion revenues is evidence that the increase in prices under the EMR is mainly due to the growing congestion in the national electricity transmission network rather than due to an inefficient competitive market design in the generation sector.

PENGARUH PRODUK DOMESTIK BRUTO, INFLASI DAN RASIO GINI TERHADAP PENGANGGURAN TERBUKA DI PROVINSI DKI JAKARTA

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Sri Mahendra Putra Wirawan
Keyword(s):  
Economic Growth ◽  
Significant Influence ◽  
Economic Conditions ◽  
Research Model ◽  
Economic Region ◽  
Regression Methods ◽  
Price Increases ◽  
Comprehensive Picture ◽  
Development Goals ◽  
The Relationship

Gross Regional Domestic Product (GRDP) which provides a comprehensive picture of the economic conditions of a region is indicator for analyzing economic region development. Another indicator that is no less important is inflation as an indicator to see the level of changes in price increases due to an increase in the money supply that causes rising prices. The success of development must also look at the income inequality of its population which is illustrated by this ratio. One of the main regional development goals is to improve the welfare of its people, where to see the level of community welfare, among others, can be seen from the level of unemployment in an area. To that end, in order to get an overview of the effects of GRDP, inflation and the ratio of gini to unemployment in DKI Jakarta for the last ten years (2007-2016), an analysis was carried out using multiple linear regression methods. As a result, together the relationship between GRDP, inflation and the Gini ratio is categorized as "very strong" with a score of 0.936, and has a significant influence on unemployment. Partially, the GRDP gives a significant influence, but inflation and gini ratio do not have a significant influence. GDP, inflation and the Gini ratio together for the last ten years have contributed 81.4% to unemployment in DKI Jakarta, while the remaining 18.6% is influenced by other variables not included in this research model, so for reduce unemployment in DKI Jakarta, programs that are oriented to economic growth, suppressing inflation and decreasing this ratio need to be carried out simultaneously. Keywords: GRDP, inflation, unemployment, DKI Jakarta, GINI ratio  

scholarly journals Changes in ambient temperature increase hospital outpatient visits for allergic rhinitis in Xinxiang, China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jianhui Gao ◽  
Mengxue Lu ◽  
Yinzhen Sun ◽  
Jingyao Wang ◽  
Zhen An ◽  
...  
Keyword(s):  
Allergic Rhinitis ◽  
Ambient Temperature ◽  
Positive Association ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Hospital Outpatient ◽  
Distributed Lag ◽  
Outpatient Visits ◽  
Daily Data ◽  
The Relationship

Abstract Background The effect of ambient temperature on allergic rhinitis (AR) remains unclear. Accordingly, this study aimed to explore the relationship between ambient temperature and the risk of AR outpatients in Xinxiang, China. Method Daily data of outpatients for AR, meteorological conditions, and ambient air pollution in Xinxiang, China were collected from 2015 to 2018. The lag-exposure-response relationship between daily mean temperature and the number of hospital outpatient visits for AR was analyzed by distributed lag non-linear model (DLNM). Humidity, long-time trends, day of the week, public holidays, and air pollutants including sulfur dioxide (SO2), and nitrogen dioxide (NO2) were controlled as covariates simultaneously. Results A total of 14,965 AR outpatient records were collected. The relationship between ambient temperature and AR outpatients was generally M-shaped. There was a higher risk of AR outpatient when the temperature was 1.6–9.3 °C, at a lag of 0–7 days. Additionally, the positive association became significant when the temperature rose to 23.5–28.5 °C, at lag 0–3 days. The effects were strongest at the 25th (7 °C) percentile, at lag of 0–7 days (RR: 1.32, 95% confidence intervals (CI): 1.05–1.67), and at the 75th (25 °C) percentile at a lag of 0–3 days (RR: 1.15, 95% CI: 1.02–1.29), respectively. Furthermore, men were more sensitive to temperature changes than women, and the younger groups appeared to be more influenced. Conclusions Both mild cold and mild hot temperatures may significantly increase the risk of AR outpatients in Xinxiang, China. These findings could have important public health implications for the occurrence and prevention of AR.

scholarly journals Linearity extensions of the market model: a case of the top 10 cryptocurrency prices during the pre-COVID-19 and COVID-19 periods

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Serdar Neslihanoglu
Keyword(s):  
State Space Model ◽  
Additive Model ◽  
Market Model ◽  
Time Varying ◽  
Daily Data ◽  
Forecasting Performance ◽  
Modeling And Forecasting ◽  
Crypto Currency ◽  
Beta Risk ◽  
The Relationship

AbstractThis research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods. Two extensions are offered to compare the performance of the linear specification of the market model (LMM), which allows for the measurement of the cryptocurrency price beta risk. The first is the generalized additive model, which permits flexibility in the rigid shape of the linearity of the LMM. The second is the time-varying linearity specification of the LMM (Tv-LMM), which is based on the state space model form via the Kalman filter, allowing for the measurement of the time-varying beta risk of the cryptocurrency price. The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization, using the Crypto Currency Index 30 (CCI30) as a market proxy and 1-day and 7-day forward predictions. Such a comparison of cryptocurrency prices has yet to be undertaken in the literature. The empirical findings favor the Tv-LMM, which outperforms the others in terms of modeling and forecasting performance. This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear, especially during the COVID-19 period.

scholarly journals Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Electricity Market ◽  
Optimization Methods ◽  
Evolutionary Programming ◽  
Congestion Management ◽  
Transmission Congestion ◽  
Nsga Ii ◽  
Line Overload ◽  
Nondominated Sorting

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

The relationship between spot and futures prices in the Nord Pool electricity market

2010 ◽  
Vol 32 (5) ◽  
pp. 967-978 ◽  
Author(s):  
Audun Botterud ◽  
Tarjei Kristiansen ◽  
Marija D. Ilic
Keyword(s):  
Electricity Market ◽  
Futures Prices ◽  
The Relationship

scholarly journals Electricity market efficiency and voltage stability

2021 ◽  
Author(s):  
Ayman Helmy Mostafa Elkasrawy
Keyword(s):  
Power System ◽  
Market Efficiency ◽  
Electricity Markets ◽  
Electricity Market ◽  
Voltage Stability ◽  
Electric Power System ◽  
Maximum Efficiency ◽  
Best Interest ◽  
The Relationship ◽  
System Layout

Several electricity markets were created in the last two decades by deregulation and restructuring vertically integrated utilities. In order to serve the best interest of participating entities, it is important to operate electricity markets at their maximum efficiency. In most cases, electricity markets were formed to operate on existing physical power systems that had evolved over several decades as vertically integrated utilities. Location of generating stations, large urban load centers and enabling transmission systems were unique to every power system and followed the 'lay of the land'. Depending upon a power system layout, voltage stability and margin to voltage collapse are unique to it. While an electricity market is to be operated efficiently, its optimal generation schedule to supply energy through an electric power system has to be reliable and meet the strict standards including those that relate to voltage stability. This work elicits the relationship between market efficiency and voltage stability. To this end, a formulation and a solution algorithm are presented. Two contrasting 5-bus cases illustrate how the transmission system layout influences the relationship between voltage stability and market efficiency. The IEEE 118-bus system is also used to illustrate this relationship.

scholarly journals Hybrid Forecasting Model for Short-Term Electricity Market Prices with Renewable Integration

2018 ◽  
Vol 11 (1) ◽  
pp. 57 ◽  
Author(s):  
Gerardo Osório ◽  
Mohamed Lotfi ◽  
Miadreza Shafie-khah ◽  
Vasco Campos ◽  
João Catalão
Keyword(s):  
Electricity Market ◽  
Wavelet Transforms ◽  
Fuzzy Inference ◽  
Electricity Sector ◽  
Forecasting Model ◽  
Probabilistic Forecasting ◽  
Short Term ◽  
Market Prices ◽  
Inference System ◽  
Hybrid Particle Swarm Optimization

In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards of robustness and the quality of power grids. One of the main challenges hindering this progress are uncertainties and stochasticity associated with the electricity sector and especially renewable generation. In this paradigm shift, forecasting tools are indispensable, and their utilization can significantly improve system operation and minimize costs associated with all related activities. Thus, forecasting tools have an essential key role in all decision-making stages. In this work, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP) combining wavelet transforms (WT), hybrid particle swarm optimization (DEEPSO), adaptive neuro-fuzzy inference system (ANFIS), and Monte Carlo simulation (MCS). The proposed hybrid probabilistic forecasting model (HPFM) was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets. The proposed model exhibited favorable results and performance in comparison with previously published work considering electricity market prices (EMP) data, which is notable.

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