Energies

Research

12 pages, 419 KiB

Open AccessArticle

Electricity Prices and Consumer Behavior, Case Study Serbia—Randomized Control Trials Method

by Iztok Podbregar, Sanja Filipović, Mirjana Radovanović, Olga Mirković Isaeva and Polona Šprajc

Energies 2021, 14(3), 591; https://doi.org/10.3390/en14030591 - 25 Jan 2021

Cited by 6 | Viewed by 5620

The aim of this research was to identify energy saving instructions effect on household’s electricity consumption. The research was conducted using Randomized Control Trials, which implied defining a treatment and control group on a sample of 330 households. The research was carried [...] Read more.

The aim of this research was to identify energy saving instructions effect on household’s electricity consumption. The research was conducted using Randomized Control Trials, which implied defining a treatment and control group on a sample of 330 households. The research was carried out in Republic of Serbia, where electricity prices are the lowest in Europe and electricity is used inefficiently. For quantitative analysis of data, the Difference in Difference method was used, which compares the changes in electricity consumption over time between the treatment and control group and estimates the overall impact of the energy saving instructions. The research showed that in situations where electricity price is very low, energy saving information does not have the significant impact on change in consumer behavior. However, inefficient use of electricity might be due to the different efficiency of heating devices used. Not only that the low impact of information on energy saving habits may be a consequence of the low will to change habit, but also of the impossibility to change the habit (unless changing the heating device, but this implies expenditures). Results can be used for consideration of changes in organization and regulation of the electricity market in all South Eastern European countries (SEE). Full article

(This article belongs to the Special Issue Energy Demand and Prices)

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17 pages, 504 KiB

Open AccessArticle

The Impact of Forecasting Jumps on Forecasting Electricity Prices

by Maciej Kostrzewski and Jadwiga Kostrzewska

Energies 2021, 14(2), 336; https://doi.org/10.3390/en14020336 - 9 Jan 2021

Cited by 7 | Viewed by 2151

Abstract

The paper is devoted to forecasting hourly day-ahead electricity prices from the perspective of the existence of jumps. We compare the results of different jump detection techniques and identify common features of electricity price jumps. We apply the jump-diffusion model with a double [...] Read more.

The paper is devoted to forecasting hourly day-ahead electricity prices from the perspective of the existence of jumps. We compare the results of different jump detection techniques and identify common features of electricity price jumps. We apply the jump-diffusion model with a double exponential distribution of jump sizes and explanatory variables. In order to improve the accuracy of electricity price forecasts, we take into account the time-varying intensity of price jump occurrences. We forecast moments of jump occurrences depending on several factors, including seasonality and weather conditions, by means of the generalised ordered logit model. The study is conducted on the basis of data from the Nord Pool power market. The empirical results indicate that the model with the time-varying intensity of jumps and a mechanism of jump prediction is useful in forecasting electricity prices for peak hours, i.e., including the probabilities of downward, no or upward jump occurrences into the model improves the forecasts of electricity prices. Full article

(This article belongs to the Special Issue Energy Demand and Prices)

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18 pages, 3751 KiB

Open AccessArticle

Analysing the Drivers of Electricity Demand in Spain after the Economic Crisis

by Javier Bueno, Desiderio Romero-Jordán and Pablo del Río

Energies 2020, 13(20), 5336; https://doi.org/10.3390/en13205336 - 13 Oct 2020

Cited by 9 | Viewed by 1914

Abstract

Electricity provides a crucial service in our daily lives. However, in electricity systems mostly based on conventional, fossil-fuel fired technologies, an increase in electricity demand also leads to higher greenhouse gas emissions and, in countries without fossil-fuel resources, also increases their dependence on [...] Read more.

Electricity provides a crucial service in our daily lives. However, in electricity systems mostly based on conventional, fossil-fuel fired technologies, an increase in electricity demand also leads to higher greenhouse gas emissions and, in countries without fossil-fuel resources, also increases their dependence on foreign energy sources. In more decarbonised electricity systems, with a high penetration of variable renewable energy sources, strong increases in electricity demand lead to higher system costs, given the need for back-up. Therefore, identifying the drivers of electricity demand is an academically-relevant, but also a policy-relevant exercise, since specific policy measures can be linked to those drivers. The aim of this paper is to assess the drivers of electricity demand in Spain in the period immediately after the economic crisis (2013–2017), with the help of a unique database of Spanish households and econometric modeling. Our results show that electricity demand in this period has mostly been driven by price changes. Demand has been highly price-elastic, with price elasticities being much higher (in absolute values) than in previous studies and periods. It is also negatively driven by the features of the household and its breadwinners (whether they are single-parent households or its members are foreign residents) and positively driven by income, the hours of sun and temperature changes, although the influence of these variables is much lower. In contrast, other variables do not seem to have an influence on demand, including the age of the breadwinners and their working situation (whether they are unemployed or not). These results suggest that price-based instruments, i.e., measures with an impact on electricity prices, would be the most effective to curb electricity demand. Full article

(This article belongs to the Special Issue Energy Demand and Prices)

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20 pages, 621 KiB

Open AccessArticle

Evaluating Public Policies for Fair Social Tariffs of Electricity in Brazil by Using an Economic Market Model

by Leticia dos Santos Benso Maciel, Benedito Donizeti Bonatto, Hector Arango and Lucas Gustavo Arango

Energies 2020, 13(18), 4811; https://doi.org/10.3390/en13184811 - 14 Sep 2020

Cited by 11 | Viewed by 2534

Abstract

This paper presents an evaluation of public policies for fare social tariffs of electricity in Brazil by using an economic model of the electricity market (TAROT-Optimized Tariff) that represents the regulated market of distribution of electrical energy. It was considered the scenario of [...] Read more.

This paper presents an evaluation of public policies for fare social tariffs of electricity in Brazil by using an economic model of the electricity market (TAROT-Optimized Tariff) that represents the regulated market of distribution of electrical energy. It was considered the scenario of an increasing number of prosumers (residential consumers who self-generate energy) in two of the five major regions of Brazil which have quite different socioeconomic characteristics. However, the current electricity regulation is the same for all concessionaires. In this work a new public policy is proposed, allowing the use of regulation in a different way aiming for a best result for Brazil and particularly for the poor population that today are not able to enjoy the benefits of electricity due to high tariff values. It is also discussed how this can contribute in a positive way to improve the income distribution in these regions, which is evaluated by using the GINI index. Full article

(This article belongs to the Special Issue Energy Demand and Prices)

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15 pages, 1711 KiB

Open AccessArticle

Short-Term Electricity Price Forecasting Based on Similar Day-Based Neural Network

by Chun-Yao Lee and Chang-En Wu

Energies 2020, 13(17), 4408; https://doi.org/10.3390/en13174408 - 26 Aug 2020

Cited by 8 | Viewed by 1865

Abstract

This paper presents four refined distance models to the application of forecasting short-term electricity price namely Euclidean norm, Manhattan distance, cosine coefficient, and Pearson correlation coefficient. The four refined models were constructed and used to select the days, which are like a reference [...] Read more.

This paper presents four refined distance models to the application of forecasting short-term electricity price namely Euclidean norm, Manhattan distance, cosine coefficient, and Pearson correlation coefficient. The four refined models were constructed and used to select the days, which are like a reference day in electricity prices and loads, called similar days in this study. Using the similar days, the electricity prices of a forecast day were further obtained by similar day regression (SDR) and similar day based artificial neural network (SDANN). The simulation results of the case of the PJM (Pennsylvania, New Jersey and Maryland) interchange energy market indicate the superiority and availability of the selection 45 framework days and three similar days based on Pearson correlation coefficient model. Full article

(This article belongs to the Special Issue Energy Demand and Prices)

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Graphical abstract

18 pages, 1958 KiB

Open AccessArticle

Research on the Hybrid Recommendation Method of Retail Electricity Price Package Based on Power User Characteristics and Multi-Attribute Utility in China

by Yongxiu He, Meiyan Wang, Jinxiong Yu, Qing He, Huijun Sun and Fengyu Su

Energies 2020, 13(11), 2693; https://doi.org/10.3390/en13112693 - 27 May 2020

Cited by 10 | Viewed by 2556

Abstract

With the deregulation of the retail electricity market and the increase of the types of electricity price packages, electricity retail companies provide the recommended service of price packages for users, so as to improve the market competitiveness and user stickiness of enterprises. The [...] Read more.

With the deregulation of the retail electricity market and the increase of the types of electricity price packages, electricity retail companies provide the recommended service of price packages for users, so as to improve the market competitiveness and user stickiness of enterprises. The existing research does not fully consider the impact of user characteristics and package attributes on recommendation results. This paper proposes a hybrid recommendation method of retail electricity price package based on the characteristics of power users and the multi-attribute utility of price package. Firstly, the hierarchical model of hybrid characteristics of power users in retail electricity market is constructed based on the tree structure, and all characteristics are analyzed quantitatively by proximity measurement method. Then, based on the multi-attribute utility theory, the utility model of retail electricity price package to users is constructed. Secondly, the accurate recommendation of the package is realized according to the characteristics of power users and the multi-attribute utility of price package. Finally, the rationality of the hybrid recommendation method of the retail electricity price package is verified by empirical analysis. This study provides valuable support for user to choose the retail electricity price package and improve the competitiveness of power retail companies. Full article

(This article belongs to the Special Issue Energy Demand and Prices)

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29 pages, 2190 KiB

Open AccessArticle

Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand

by Kamal Chapagain, Somsak Kittipiyakul and Pisut Kulthanavit

Energies 2020, 13(10), 2498; https://doi.org/10.3390/en13102498 - 15 May 2020

Cited by 28 | Viewed by 5118

Abstract

Accurate electricity demand forecasting for a short horizon is very important for day-to-day control, scheduling, operation, planning, and stability of the power system. The main factors that affect the forecasting accuracy are deterministic variables and weather variables such as types of days and [...] Read more.

Accurate electricity demand forecasting for a short horizon is very important for day-to-day control, scheduling, operation, planning, and stability of the power system. The main factors that affect the forecasting accuracy are deterministic variables and weather variables such as types of days and temperature. Due to the tropical climate of Thailand, the marginal impact of weather variables on electricity demand is worth analyzing. Therefore, this paper primarily focuses on the impact of temperature and other deterministic variables on Thai electricity demand. Accuracy improvement is also considered during model design. Based on the characteristics of demand, the overall dataset is divided into four different subgroups and models are developed for each subgroup. The regression models are estimated using Ordinary Least Square (OLS) methods for uncorrelated errors, and General Least Square (GLS) methods for correlated errors, respectively. While Feed Forward Artificial Neural Network (FF-ANN) as a simple Deep Neural Network (DNN) is estimated to compare the accuracy with regression methods, several experiments conducted for determination of training length, selection of variables, and the number of neurons show some major findings. The first finding is that regression methods can have better forecasting accuracy than FF-ANN for Thailand’s dataset. Unlike much existing literature, the temperature effect on Thai electricity demand is very interesting because of their linear relationship. The marginal impacts of temperature on electricity demand are also maximal at night hours. The maximum impact of temperature during night hours happens at 11 p.m., is 300 MW/

^{°}

C, about

4 %

rise in demand while during day hours, the temperature impact is only 10 MW/

^{°}

C to 200 MW/

^{°}

C about

1.4 %

to

2.6 %

rise. Full article

(This article belongs to the Special Issue Energy Demand and Prices)

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18 pages, 2867 KiB

Open AccessArticle

A Generalized Unit Commitment and Economic Dispatch Approach for Analysing the Polish Power System under High Renewable Penetration

by Marcin Pluta, Artur Wyrwa, Wojciech Suwała, Janusz Zyśk, Maciej Raczyński and Stanisław Tokarski

Energies 2020, 13(8), 1952; https://doi.org/10.3390/en13081952 - 15 Apr 2020

Cited by 13 | Viewed by 3057

Abstract

The achievement of carbon neutrality requires a deep transformation of the Polish power sector. This paper analyses the impact of increased electricity generation from wind and solar technologies envisaged in the newest version of the Energy Policy of Poland until 2040 on the [...] Read more.

The achievement of carbon neutrality requires a deep transformation of the Polish power sector. This paper analyses the impact of increased electricity generation from wind and solar technologies envisaged in the newest version of the Energy Policy of Poland until 2040 on the operation of dispatchable generators in 2030. The analysis was carried out using the Model of Economic Dispatch and Unit commitment for System Analysis (MEDUSA) model, which solves a mixed integer problem related to unit commitment and economic dispatch in electrical power production. At first, the model was validated based on the real operation data from 2018. Next, five scenarios were built to analyse the operation of the system in 2030. The overall result of the study is that the safest solution from the point of view of power system stability is to extend the decommissioning of coal units of 200 and 300 MW classes, to invest in renewable energy sources (RES) according to the energy policy, to build new gas power plants with the total capacity of ca. 4 GW, and to enforce Demand Side Management (DSM) programs for shifting the electrical load. The proposed framework for the optimization of power system planning helps to avoid wrong investment decisions that would have a negative impact on energy prices. Full article

(This article belongs to the Special Issue Energy Demand and Prices)

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35 pages, 446 KiB

Open AccessArticle

Quantifying Electricity Supply Resilience of Countries with Robust Efficiency Analysis

by Patrick Gasser, Marco Cinelli, Anna Labijak, Matteo Spada, Peter Burgherr, Miłosz Kadziński and Božidar Stojadinović

Energies 2020, 13(7), 1535; https://doi.org/10.3390/en13071535 - 25 Mar 2020

Cited by 8 | Viewed by 2717

Abstract

The interest in studying energy systems’ resilience is increasing due to a rising awareness of the importance of having a secure energy supply. This growing trend is a result of a series of recent disruptions, among others also affecting electricity systems. Therefore, it [...] Read more.

The interest in studying energy systems’ resilience is increasing due to a rising awareness of the importance of having a secure energy supply. This growing trend is a result of a series of recent disruptions, among others also affecting electricity systems. Therefore, it is of crucial importance for policymakers to determine whether their country has a resilient electricity supply. Starting from a set of 12 indicators, this paper uses data envelopment analysis (DEA) to comprehensively evaluate the electricity supply resilience of 140 countries worldwide. Two DEA models are applied: (1) the original ratio-based Charnes, Cooper, and Rhodes (CCR) model and (2) a novel hybrid framework for robust efficiency analysis incorporating linear programming and Monte Carlo simulations. Results show that the CCR model deems 31 countries as efficient and hence lacks the capability to differentiate them. Furthermore, the CCR model considers only the best weight vectors for each country, which are not necessarily representative of the overall performance of the countries. The robustness analysis explores these limitations and identifies South Korea, Singapore and Canada as the most resilient countries. Finally, country analyses are conducted, where Singapore’s and Japan’s performances and improvement potentials are discussed. Full article

(This article belongs to the Special Issue Energy Demand and Prices)

25 pages, 3035 KiB

Open AccessArticle

A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm

by Jun Hao, Xiaolei Sun and Qianqian Feng

Energies 2020, 13(3), 550; https://doi.org/10.3390/en13030550 - 23 Jan 2020

Cited by 17 | Viewed by 2489

Abstract

Accurate forecasting of the energy demand is crucial for the rational formulation of energy policies for energy management. In this paper, a novel ensemble forecasting model based on the artificial bee colony (ABC) algorithm for the energy demand was proposed and adopted. The [...] Read more.

Accurate forecasting of the energy demand is crucial for the rational formulation of energy policies for energy management. In this paper, a novel ensemble forecasting model based on the artificial bee colony (ABC) algorithm for the energy demand was proposed and adopted. The ensemble model forecasts were based on multiple time variables, such as the gross domestic product (GDP), industrial structure, energy structure, technological innovation, urbanization rate, population, consumer price index, and past energy demand. The model was trained and tested using the primary energy demand data collected in China. Seven base models, including the regression-based model and machine learning models, were utilized and compared to verify the superior performance of the ensemble forecasting model proposed herein. The results revealed that (1) the proposed ensemble model is significantly superior to the benchmark prediction models and the simple average ensemble prediction model just in terms of the forecasting accuracy and hypothesis test, (2) the proposed ensemble approach with the ABC algorithm can be employed as a promising framework for energy demand forecasting in terms of the forecasting accuracy and hypothesis test, and (3) the forecasting results obtained for the future energy demand by the ensemble model revealed that the future energy demand of China will maintain a steady growth trend. Full article

(This article belongs to the Special Issue Energy Demand and Prices)

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17 pages, 3748 KiB

Open AccessArticle

The Impact of International Oil Prices on the Stock Price Fluctuations of China’s Renewable Energy Enterprises

by Cody Yu-Ling Hsiao, Weishun Lin, Xinyang Wei, Gaoyun Yan, Siqi Li and Ni Sheng

Energies 2019, 12(24), 4630; https://doi.org/10.3390/en12244630 - 5 Dec 2019

Cited by 29 | Viewed by 3842

Abstract

In order to address a series of issues, including energy security, global warming, and environmental protection, China has ranked first in global renewable investment for the seventh consecutive year. However, developing a renewable energy industry requires a significant capital investment. Also, the international [...] Read more.

In order to address a series of issues, including energy security, global warming, and environmental protection, China has ranked first in global renewable investment for the seventh consecutive year. However, developing a renewable energy industry requires a significant capital investment. Also, the international oil price fluctuations have an important impact on the stock prices of renewable energy firms. Thus, in order to provide implications for market investment as well as policy recommendations, this paper studied the spillover effect of international oil prices on the stock prices of China’s renewable energy listed companies. We used a Vector Autoregressive (VAR) model with innovations using a Factor-GARCH (Generalized Autoregressive Conditional Heteroskedasticity) process to evaluate the impact of market co-movements and time-varying volatility and correlation between the international oil price and China’s renewable energy market. The results show that the international oil price has a significant price spillover effect on the stock prices of China’s renewable energy listed companies. Moreover, the fluctuations of international oil prices have an influence on the stock price variations of Chinese renewable energy listed companies. Full article

(This article belongs to the Special Issue Energy Demand and Prices)

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17 pages, 429 KiB

Open AccessArticle

Policy Makers’ Perspectives on the Expansion of Renewable Energy Sources in Chile’s Electricity Auctions

by Shahriyar Nasirov, Eugenio Cruz, Claudio A. Agostini and Carlos Silva

Energies 2019, 12(21), 4149; https://doi.org/10.3390/en12214149 - 30 Oct 2019

Cited by 13 | Viewed by 3271

Abstract

Chile has become one of the first few countries where renewable sources compete directly with conventional generation in price-based auctions. Moreover, the results of energy auctions during the last few years show a remarkable transition from conventional fossil fuels to renewable energies. In [...] Read more.

Chile has become one of the first few countries where renewable sources compete directly with conventional generation in price-based auctions. Moreover, the results of energy auctions during the last few years show a remarkable transition from conventional fossil fuels to renewable energies. In fact, the energy auction in 2017, to provide energy to customers from distribution companies, achieved a massive expansion in renewable technology at one of the lowest prices in the world. These positive results prompted the question if such results were permanent or temporal due to factors with limited effects. In this regard, this paper studies the key factors that drove the significant rise of renewable technologies in Chilean energy auctions, obtaining valuable lessons for regulators, not only in Chile, but also in the region and the world. For this purpose, we considered a well-proven method based on a hybrid multicriteria decision-making model to examine and prioritize the main drivers of the expansion of renewables in auctions. The results showed that some specific characteristics of the auction design, particularly the hourly supply blocks, the lead time for project construction, and contract duration, were the most significant drivers for the expansion of renewables in energy auctions. Moreover, the results showed that, provided that the auction design accommodates for such drivers, solar energy ends up as the most attractive technology in the Chilean auctions. The research also shows the main findings are robust by the application of a probabilistic sensitivity analysis. Full article

(This article belongs to the Special Issue Energy Demand and Prices)

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