Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.2
Journals
Energies
Special Issues
Forecasting and Decision Support Systems for Energy Market Development
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Forecasting and Decision Support Systems for Energy Market Development

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 26138

Special Issue Editors

Prof. Dr. Lean Yu

E-Mail Website
Guest Editor
School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China
Interests: business intelligence; big data mining; decision support systems; energy forecasting; financial management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaofeng Xu

E-Mail Website
Guest Editor
College of Economics and Management, China University of Petroleum, Qingdao 266580, China
Interests: Industry 4.0; intelligent transportation systems and logistics; big data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the double problems of energy shortage and environment deterioration have shaped the energy market, which has taken an increasingly predominant part in the global economic system and begun to exhibit more and more uncertainty, complexities, and chaotic traits. Meanwhile, rapid advances in computing technology and significant growth in Internet access have supported forecasting and decision making for the energy market. The emergence of a series of artificial intelligence and machine learning technologies provides more possibilities for prediction and decision making in global energy markets.

Recent research into forecasting and decision support systems has both provided optimal plans and also ensured the environmentally friendly and sustainable operation of the energy market. The rapid development of many emerging technologies, such as artificial intelligence, machine learning, big data computing, cloud computing, and blockchain, has laid the technical foundation for energy forecasting. Accurate prediction results enable managers to make optimal and efficient decisions.

Based on the above, this Special Issue calls for papers broadly related to forecasting and decision support systems, especially for energy market development. Recent theoretical and methodological advancements, case studies, applications, technical contributions, and applications of tools and techniques to improve forecasting and decision making for energy market development are all welcomed. Specific topics of interest include but are not limited to the following:

  • Smart energy market
  • Energy modeling and simulation
  • Energy security, climate change and sustainability
  • Effective energy market efficiency measurement
  • Evaluation of energy market operation efficiency
  • Big data analysis in energy market
  • Intelligent forecasting and decision for energy market
  • Energy market timing and forecasting
  • Energy factor identification and optimization
  • Energy market anomalies and inefficiencies
  • Energy risk management
  • Energy logistics and supply chain optimization

text

Prof. Dr. Lean Yu
Prof. Dr. Xiaofeng Xu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Energy market 
  • Smart energy market 
  • Decision support systems 
  • Forecasting 
  • Big data analytics

Published Papers (12 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 1440 KiB  
Article
Predicting Electricity Consumption in the Kingdom of Saudi Arabia
by Marwa Salah EIDin Fahmy, Farhan Ahmed, Farah Durani, Štefan Bojnec and Mona Mohamed Ghareeb
Energies 2023, 16(1), 506; https://0-doi-org.brum.beds.ac.uk/10.3390/en16010506 - 02 Jan 2023
Cited by 5 | Viewed by 2420
Abstract
Forecasting energy consumption in Saudi Arabia for the period from 2020 until 2030 is investigated using a two-part composite model. The first part is the frontier, and the second part is the autoregressive integrated moving average (ARIMA) model that helps avoid the large [...] Read more.
Forecasting energy consumption in Saudi Arabia for the period from 2020 until 2030 is investigated using a two-part composite model. The first part is the frontier, and the second part is the autoregressive integrated moving average (ARIMA) model that helps avoid the large disparity in predictions in previous studies, which is what this research seeks to achieve. The sample of the study has a size of 30 observations, which are the actual consumption values in the period from 1990 to 2019. The philosophy of this installation is to reuse the residuals to extract the remaining values. Therefore, it becomes white noise and the extracted values are added to increase prediction accuracy. The residuals were calculated and the ARIMA (0, 1, 0) model with a constant was developed both of the residual sum of squares and the root means square errors, which were compared in both cases. The results demonstrate that prediction accuracy using complex models is better than prediction accuracy using single polynomial models or randomly singular models by an increase in the accuracy of the estimated consumption and an improvement of 18.5% as a result of the synthesizing process, which estimates the value of electricity consumption in 2030 to be 575 TWh, compared to the results of previous studies, which were 365, 442, and 633 TWh. Full article
(This article belongs to the Special Issue Forecasting and Decision Support Systems for Energy Market Development)
►▼ Show Figures

Figure 1

12 pages, 2534 KiB  
Article
A Study on the Vulnerability Cascade Propagation of Integrated Energy Systems in the Transportation Industry Based on the Petri Network
by Xiaohong Yin, Lin Li and Qiang Liu
Energies 2022, 15(12), 4320; https://0-doi-org.brum.beds.ac.uk/10.3390/en15124320 - 13 Jun 2022
Cited by 3 | Viewed by 1200
Abstract
In order to solve the vulnerability problem of the integrated energy system in the transportation industry, a vulnerability cascade propagation model based on the Petri net is proposed. The article comprehensively considers the configuration of the energy system, constructs the cascade propagation-based function [...] Read more.
In order to solve the vulnerability problem of the integrated energy system in the transportation industry, a vulnerability cascade propagation model based on the Petri net is proposed. The article comprehensively considers the configuration of the energy system, constructs the cascade propagation-based function Petri net model using a hierarchical modelling approach, and performs vulnerability cascade propagation analysis using Matlab on this basis. However, the integrated energy system of the transportation industry is complex and extensive, and it is not easy to model the Petri network of the whole transportation industry, which will be continued in-depth in the subsequent research. The study results show that the energy system’s vulnerability keeps changing with the growth of time, and the factors in maintaining the equilibrium vary from one subsystem to another. In addition, the ringed structure is more vulnerable compared to the acyclic structure, and the vulnerability cascade propagates faster for the ringed structure than the acyclic structure. The results of the study contribute to the scientific development of integrated energy system planning and construction for the transportation industry and provide a reference for the rehabilitation and construction of energy systems. Full article
(This article belongs to the Special Issue Forecasting and Decision Support Systems for Energy Market Development)
►▼ Show Figures

Figure 1

19 pages, 2405 KiB  
Article
Prediction-Driven Sequential Optimization for Refined Oil Production-Sales-Stock Decision-Making
by Jindai Zhang and Jinlou Zhao
Energies 2022, 15(12), 4222; https://0-doi-org.brum.beds.ac.uk/10.3390/en15124222 - 08 Jun 2022
Viewed by 1244
Abstract
This paper proposes a prediction-driven sequential optimization methodology for joint decision-making problems of production-sales-stock in refined oil enterprises. In the proposed prediction-driven sequential optimization methodology, three dynamic nonlinear programming models are first constructed to model the production-sales-stock decision-making problems in refined oil enterprises. [...] Read more.
This paper proposes a prediction-driven sequential optimization methodology for joint decision-making problems of production-sales-stock in refined oil enterprises. In the proposed prediction-driven sequential optimization methodology, three dynamic nonlinear programming models are first constructed to model the production-sales-stock decision-making problems in refined oil enterprises. Then, the analytical solutions to sequential optimization for production-sales-stock decision-making issues are presented by using the inverse inference method in dynamic programming. Finally, the impact of price and demand prediction of refined oil products on sequential optimization for production-sales-stock decision-making are analyzed using a numerical analysis method. Numerical results demonstrated the significant impact of forecasting results of price and demand of refined oil products on sequential optimization decision-making, indicating that the prediction-driven sequential optimization methodology can be used as an effective tool for joint decision-making of production-sales-stock. Full article
(This article belongs to the Special Issue Forecasting and Decision Support Systems for Energy Market Development)
►▼ Show Figures

Graphical abstract

15 pages, 2099 KiB  
Article
Trend- and Periodicity-Trait-Driven Gasoline Demand Forecasting
by Jindai Zhang and Jinlou Zhao
Energies 2022, 15(10), 3553; https://0-doi-org.brum.beds.ac.uk/10.3390/en15103553 - 12 May 2022
Cited by 1 | Viewed by 1078
Abstract
In order to make reasonable production-sales-stock decision-making for gasoline production enterprises, it is necessary to make an accurate prediction of the gasoline demand. However, gasoline demand is often affected by many factors, which makes it very difficult to predict. Therefore, this paper tries [...] Read more.
In order to make reasonable production-sales-stock decision-making for gasoline production enterprises, it is necessary to make an accurate prediction of the gasoline demand. However, gasoline demand is often affected by many factors, which makes it very difficult to predict. Therefore, this paper tries to construct a trend- and periodicity-trait-driven decomposition-ensemble forecasting model in terms of trend and periodicity characteristics of gasoline demand data. In order to verify the effectiveness of the proposed model, the demand data of a typical gasoline product-93# gasoline in China, is used. The empirical results show that the proposed trend- and periodicity-trait-driven decomposition-ensemble forecasting model can achieve better prediction results than the single models, indicating that the proposed methodology can be used as a feasible solution to predict the gasoline demand series with trend and periodicity traits. Full article
(This article belongs to the Special Issue Forecasting and Decision Support Systems for Energy Market Development)
►▼ Show Figures

Figure 1

16 pages, 4227 KiB  
Article
Experiment and Model of Conductivity Loss of Fracture Due to Fine-Grained Particle Migration and Proppant Embedment
by Weidong Zhang, Qingyuan Zhao, Xuhui Guan, Zizhen Wang and Zhiwen Wang
Energies 2022, 15(7), 2359; https://0-doi-org.brum.beds.ac.uk/10.3390/en15072359 - 24 Mar 2022
Cited by 2 | Viewed by 1368
Abstract
In weakly cemented reservoirs or coal-bed methane reservoirs, the conductivity of hydraulic fractures always declines after a period of production, which greatly influences gas production. In this paper, a comprehensive model considering fine-grained particle migration and proppant embedment is proposed to give a [...] Read more.
In weakly cemented reservoirs or coal-bed methane reservoirs, the conductivity of hydraulic fractures always declines after a period of production, which greatly influences gas production. In this paper, a comprehensive model considering fine-grained particle migration and proppant embedment is proposed to give a precise prediction for conductivity decline. Then, an experiment was conducted to simulate this process. A published experiment using coal fines was also tested and simulated. The results indicate that both fine-grained particle migration and proppant embedment have great negative effect on conductivity of fractures in weakly cemented sandstone and coal-bed methane reservoirs. The formulation we proposed matches the experimental data smoothly and can be widely used in the prediction of conductivity decline in weakly cemented sandstone and coal-bed methane reservoirs. In order to discuss the influencing factors of the filtration coefficient in the particle transport model, a porous media network model was established based on the theoretical model. The simulation results show that the filtration coefficient increases with the increase in particle size and/or throat size, and the filtration coefficient increases with the decrease in the fluid velocity. At the same time, it was found that the large larynx did not easily cause particle retention. Large size particles tend to cause particle retention. Full article
(This article belongs to the Special Issue Forecasting and Decision Support Systems for Energy Market Development)
►▼ Show Figures

Figure 1

14 pages, 1163 KiB  
Article
Can Clean Heating in Winter in Northern China Reduce Air Pollution?—Empirical Analysis Based on the PSM-DID Method
by Si Wang, Qiaojie Huang, Qiang Liu and Demei Sun
Energies 2022, 15(5), 1839; https://0-doi-org.brum.beds.ac.uk/10.3390/en15051839 - 02 Mar 2022
Cited by 10 | Viewed by 2663
Abstract
In northern China, a large volume of coal is consumed for heating in winter, resulting in frequent smog and air pollution, which has a serious effect on people’s health and quality of life. Clean heating is a national important livelihood strategy related to [...] Read more.
In northern China, a large volume of coal is consumed for heating in winter, resulting in frequent smog and air pollution, which has a serious effect on people’s health and quality of life. Clean heating is a national important livelihood strategy related to residents’ warmth. Whether haze weather can be effectively reduced and whether the heating energy structure can really be improved needs to be verified. This study takes 115 heating cities in the northern area as the research objects, takes 12 pilot cities implementing clean heating policy as the treatment group, employs the Propensity Score Matching method to match the non-pilot cities to get the reasonable control group city, uses the double-difference method to carry out the quantitative test on the implementation effect of the clean heating policy. The results show that clean heating in northern China reduces the air pollution in winter, and the air pollution level in winter decreases 46.6% after the implementation of the policy. Clean heating is an effective environmental regulation method that can control the level of winter air pollution from the source. Full article
(This article belongs to the Special Issue Forecasting and Decision Support Systems for Energy Market Development)
►▼ Show Figures

Figure 1

18 pages, 1103 KiB  
Article
Universal Model to Predict Expected Direction of Products Quality Improvement
by Grzegorz Ostasz, Dominika Siwiec and Andrzej Pacana
Energies 2022, 15(5), 1751; https://0-doi-org.brum.beds.ac.uk/10.3390/en15051751 - 26 Feb 2022
Cited by 21 | Viewed by 1711
Abstract
Improving the quality of products remains a challenge. This is due to the turbulent environment and the dynamics of changing customer requirements. Hence, the key action is to predict beneficial changes in products, which will allow one to achieve customer satisfaction and reduce [...] Read more.
Improving the quality of products remains a challenge. This is due to the turbulent environment and the dynamics of changing customer requirements. Hence, the key action is to predict beneficial changes in products, which will allow one to achieve customer satisfaction and reduce the waste of resources. Therefore, the purpose of this article was to develop a universal model to predict the expected direction of quality improvement. Initially, the purpose of the research was determined by using the SMART(-ER) method. Then, during the brainstorming method (BM), the product criteria and range states of these criteria were determined. Next, a survey with the Likert scale was used to obtain customers’ expectations, i.e., assessing the importance of criteria and customers’ satisfaction with ranges of product criteria states. Based on customer assessments, quality product levels were calculated using the Weighted Sum Model (WSM). Then, the initial customer satisfaction from the product quality level was identified according to the relative state’s scale. Based on this, the direction of product quality improvement was anticipated using the Naïve Bayesian Classifier (NBC). A test of the model was carried out for photovoltaic panels (PV) of a key EU producer. However, the proposed model is universal, because it can be used by any entity to predict the direction of improvement of any kind of product. The originality of this model allows the prediction of the destination of product improvement according to customers’ assessments for weights of criteria and satisfaction from ranges of quality-criterion states. Full article
(This article belongs to the Special Issue Forecasting and Decision Support Systems for Energy Market Development)
►▼ Show Figures

Figure 1

15 pages, 35799 KiB  
Article
Using Finite Element Method for Stress-Strain Evaluation of Commonly Used Buried Pipelines in Fault
by Ning Tan, Liang Zhou, Weibo Zheng, Honglin Song, Zhibin Sun, Zhiyin Wang, Guisheng Wang, Guanjun Wang, Liming Zhang and Xingyu Zhou
Energies 2022, 15(5), 1655; https://0-doi-org.brum.beds.ac.uk/10.3390/en15051655 - 23 Feb 2022
Cited by 3 | Viewed by 1832
Abstract
In different kinds of buried pipelines, L245 and L360 are the most used which are chosen by the China Pipeline Design Institute. For studying the stress and deformation characteristics of buried pipelines with different specifications across faults, this paper established a physical model [...] Read more.
In different kinds of buried pipelines, L245 and L360 are the most used which are chosen by the China Pipeline Design Institute. For studying the stress and deformation characteristics of buried pipelines with different specifications across faults, this paper established a physical model of cross-fault buried pipelines and a finite element model of pipelines crossing the fault zone, which adopts the finite element method and ANSYS software. The models take pipeline material, soil material, grid division, load application method, and other factors into consideration, concentrating on the nonlinear solution of L245 and L360 buried pipelines under the condition of strike-slip fault soil. The results illustrate that pipelines with larger diameters are more conducive to resisting the stress and deformation caused by faults. Moreover, the strain and dislocation amount of the pipeline increases with the increase of the dislocation amount when a fault occurs. Furthermore, the resistance is optimal when the angle of intersection between the fault and the pipe is 60, while further research and analysis are needed for special cases. This work can provide a direction for the optimization of parameters for pipeline design especially strain-based design. Full article
(This article belongs to the Special Issue Forecasting and Decision Support Systems for Energy Market Development)
►▼ Show Figures

Figure 1

25 pages, 1198 KiB  
Article
How Environmental Regulation, Digital Development and Technological Innovation Affect China’s Green Economy Performance: Evidence from Dynamic Thresholds and System GMM Panel Data Approaches
by Lijiang Jia, Xiaoli Hu, Zhongwei Zhao, Bin He and Weiming Liu
Energies 2022, 15(3), 884; https://0-doi-org.brum.beds.ac.uk/10.3390/en15030884 - 26 Jan 2022
Cited by 39 | Viewed by 4188
Abstract
Based on the background of China’s “carbon neutral” policy and the booming digitalization, how does environmental regulation affect green economy performance? The existing literature has studied the impact of energy consumption on green economic performance. However, the literature has ignored the impact of [...] Read more.
Based on the background of China’s “carbon neutral” policy and the booming digitalization, how does environmental regulation affect green economy performance? The existing literature has studied the impact of energy consumption on green economic performance. However, the literature has ignored the impact of carbon dioxide emissions on China’s green economy performance. In this regard, this research uses the non-radial distance function (NDDF) to calculate the green economic performance of China’s prefecture-level cities, and uses the dynamic panel threshold model and the systematic GMM method to study the nonlinear impacts and mechanisms of environmental regulation, digital development, technological innovation, and industrial structure upgrade on green economic performance. The panel data set contains 228 Chinese cities from 2003 to 2019. The following findings are established: first, after adding carbon dioxide emissions to China’s green economy performance, the environmental performance was reduced, and the green economy performance was also reduced. Second, the impact of environmental regulations on green economic performance has a double-threshold effect, with threshold values of −0.267 and 3.602, and this double-threshold effect has temporal and regional heterogeneity. Third, environmental regulations of different intensities have a single-threshold effect between digital development, technological innovation, and industrial structure upgrade, with threshold values of 2.955, 3.957, and 2.249, respectively. Fourth, digital development, technological innovation, and industrial structure upgrade promote green economic performance. Fifth, environmental regulation acts on green economic performance through the transmission of digitalization, technological innovation, and industrial structure upgrade. Based on these empirical findings, this research suggests that Chinese local governments should appropriately increase the intensity of environmental regulations, strengthen the digital application and technological innovation, and promote the upgrading of industrial structure to achieve the improvement of urban green economic performance. Full article
(This article belongs to the Special Issue Forecasting and Decision Support Systems for Energy Market Development)
►▼ Show Figures

Figure 1

16 pages, 407 KiB  
Article
Prospect Theory with Bounded Temporal Horizon for Modeling Prosumer Behavior in the Smart Grid
by Mohsen Rajabpour, Mohammad Yousefvand, Robert Mulligan and Narayan B. Mandayam
Energies 2021, 14(21), 7134; https://0-doi-org.brum.beds.ac.uk/10.3390/en14217134 - 01 Nov 2021
Viewed by 1255
Abstract
We study prosumer decision-making in the smart grid in which a prosumer must decide whether to make a sale of solar energy units generated at her home every day or hold (store) the energy units in anticipation of a future sale at a [...] Read more.
We study prosumer decision-making in the smart grid in which a prosumer must decide whether to make a sale of solar energy units generated at her home every day or hold (store) the energy units in anticipation of a future sale at a better price. Specifically, we enhance a Prospect Theory (PT)-based behavioral model by taking into account bounded temporal horizons (a time window specified in terms of the number of days) that prosumers implicitly impose on their decision-making in arriving at “hold” or “sell” decisions of energy units. The new behavioral model for prosumers assumes that in addition to the framing and probability weighting effects imposed by classical PT, humans make decisions that will affect their lives within a bounded temporal horizon regardless of how far into the future their units may be sold. Modeling the utility of the prosumer with parameters such as the offered price on a day, the available energy units on a day, and the probabilities of the forecast prices, we fit the PT-based proposed behavioral model with bounded temporal horizons to prosumer data collected over 10 weeks from 57 homeowners who generated surplus units of solar power and had the opportunity to sell those units to the local utility at the price set that day by the utility or hold the units for sale in the future. For most participants, a model with a bounded temporal horizon in the range of 1–6 days provided a much better fit to their responses than was found for the traditional EUT-based model, thus validating the need to model PT effects (framing and probability weighting) and bounded temporal horizons imposed in prosumer decision-making. Full article
(This article belongs to the Special Issue Forecasting and Decision Support Systems for Energy Market Development)
►▼ Show Figures

Figure 1

26 pages, 12420 KiB  
Article
A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting
by Lean Yu and Yueming Ma
Energies 2021, 14(15), 4604; https://0-doi-org.brum.beds.ac.uk/10.3390/en14154604 - 29 Jul 2021
Cited by 2 | Viewed by 1797
Abstract
In order to predict the gasoline consumption in China, this paper propose a novel data-trait-driven rolling decomposition-ensemble model. This model consists of five steps: the data trait test, data decomposition, component trait analysis, component prediction and ensemble output. In the data trait test [...] Read more.
In order to predict the gasoline consumption in China, this paper propose a novel data-trait-driven rolling decomposition-ensemble model. This model consists of five steps: the data trait test, data decomposition, component trait analysis, component prediction and ensemble output. In the data trait test and component trait analysis, the original time series and each decomposed component are thoroughly analyzed to explore hidden data traits. According to these results, decomposition models and prediction models are selected to complete the original time series data decomposition and decomposed component prediction. In the ensemble output, the ensemble method corresponding to the decomposition method is used for final aggregation. In particular, this methodology introduces the rolling mechanism to solve the misuse of future information problem. In order to verify the effectiveness of the model, the quarterly gasoline consumption data from four provinces in China are used. The experimental results show that the proposed model is significantly better than the single prediction models and decomposition-ensemble models without the rolling mechanism. It can be seen that the decomposition-ensemble model with data-trait-driven modeling ideas and rolling decomposition and prediction mechanism possesses the superiority and robustness in terms of the evaluation criteria of horizontal and directional prediction. Full article
(This article belongs to the Special Issue Forecasting and Decision Support Systems for Energy Market Development)
►▼ Show Figures

Graphical abstract

21 pages, 512 KiB  
Article
Electricity Markets during the Liberalization: The Case of a European Union Country
by Štefan Bojnec and Alan Križaj
Energies 2021, 14(14), 4317; https://0-doi-org.brum.beds.ac.uk/10.3390/en14144317 - 17 Jul 2021
Cited by 17 | Viewed by 3343
Abstract
This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations [...] Read more.
This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations between electricity prices on the one hand, and primary energy prices, variation in air temperature, daily maximum electricity power, and cross-border grid prices on the other hand, are analyzed separately for industrial and household consumers. Monthly data are used in a regression analysis during the period of Slovenia’s electricity market deregulation and price liberalization. Empirical results show that electricity prices achieved in the EEX market were significantly associated with primary energy prices. In Slovenia, the prices for daily maximum electricity power were significantly associated with electricity prices achieved on the EEX market. The increases in electricity prices for households, however, cannot be explained with developments in electricity prices on the EEX market. As the period analyzed is the stage of market deregulation and price liberalization, this can have important policy implications for the countries that still have regulated and monopolized electricity markets. Opening the electricity markets is expected to increase competition and reduce pressures for electricity price increases. However, the experiences and lessons learned among the countries following market deregulation and price liberalization are mixed. For industry, electricity prices affect cost competitiveness, while for households, electricity prices, through expenses, affect their welfare. A competitive and efficient electricity market should balance between suppliers’ and consumers’ market interests. With greening the energy markets and the development of the CO2 emission trading market, it is also important to encourage use of renewable energy sources. Full article
(This article belongs to the Special Issue Forecasting and Decision Support Systems for Energy Market Development)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-12
Back to TopTop