Performing price scenario analysis and stress testing using quantile regression: A case study of the Californian electricity market

Westgaard, Sjur; Fleten, Stein–Erik; Negash, Ahlmahz I.; Botterud, Audun; Bogaard, Katinka; Verling, Trude Haugsvær

doi:10.1016/j.energy.2020.118796

Cited by 17 publications

(10 citation statements)

References 27 publications

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“…Energies 2021, 14, x FOR PEER REVIEW 7 of 24 The studies concentrating on merit-order effect for wind power on electricity market price are viable among researchers. Positive merit order effects were found with OLS analysis and time series regressions for Italy [31,65] and for US (California) [66], with time series analysis for Australia [67], and Germany [68], and with ARDL model and demand/supply framework for Australia [69,70], and with quantile regression model for Germany [71] and for US (California) [72]. A different type of time series analysis with panel data analysis through fixed effect regression was applied in [31] for Germany, and a dampening effect of wind power with reduced forecasting errors, which led to decreased price volatility.…”

Section: Electricity Market Price and Load Forecasting Through Wind Energy Productionmentioning

confidence: 86%

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Acaroğlu

Márquez

2021

Energies

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Forecasting the electricity price and load has been a critical area of concern for researchers over the last two decades. There has been a significant economic impact on producers and consumers. Various techniques and methods of forecasting have been developed. The motivation of this paper is to present a comprehensive review on electricity market price and load forecasting, while observing the scientific approaches and techniques based on wind energy. As a methodology, this review follows the historical and structural development of electricity markets, price, and load forecasting methods, and recent trends in wind energy generation, transmission, and consumption. As wind power prediction depends on wind speed, precipitation, temperature, etc., this may have some inauspicious effects on the market operations. The improvements of the forecasting methods in this market are necessary and attract market participants as well as decision makers. To this end, this research shows the main variables of developing electricity markets through wind energy. Findings are discussed and compared with each other via quantitative and qualitative analysis. The results reveal that the complexity of forecasting electricity markets’ price and load depends on the increasing number of employed variables as input for better accuracy, and the trend in methodologies varies between the economic and engineering approach. Findings are specifically gathered and summarized based on researches in the conclusions.

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Section: Electricity Market Price and Load Forecasting Through Wind Energy Productionmentioning

confidence: 86%

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Acaroğlu

Márquez

2021

Energies

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“…Scenario analysis is often used by scientists and international organizations to forecast the future development of various sectors of the economy and to solve problems of its sustainable development. For example, it has been used in publications concerning: the natural gas market [47][48][49][50], the reduction of greenhouse gas emissions [51][52][53][54][55], waste management [56][57][58], electricity [59], renewable energy [60][61][62], transport [63][64][65] and others [66][67][68].…”

Section: Swot and Scenario Analysismentioning

confidence: 99%

Turkmenistan’s Gas Sector Development Scenarios Based on Econometric and SWOT Analysis

2021

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Turkmenistan is currently one of the key suppliers in the global natural gas market, and it is important to examine its competitive position in the world. The aim of this article is to forecast the development strategy of its gas sector. To achieve this aim, the authors first examined: (1) Turkmenistan’s position in the global gas market and the structure of its oil and gas sector; (2) the share of gas in Turkmenistan’s exports. They then conducted an analysis of the causal relationship between FDI and GDP. The above research made it possible to conduct a comprehensive SWOT analysis. Based on the SWOT analysis, three sector development scenarios were developed: pessimistic, realistic, and optimistic. On their basis, the gas sector in Turkmenistan can build strategies for its development, considering the impact of market and non-market factors.

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“…According to the above analysis, some key elements are missing from the current studies on electricity price forecasting, namely: (a) Many current studies ignore the useless information brought by the large amount of electricity price data when screening data features, which not only causes a reduction in forecasting accuracy but also affects the operational efficiency of forecasting models [34]. (b) The existing prediction models are generally based on a single sample set composed of features, which leads to the extraction of too much data, resulting in poor prediction accuracy [35,36].…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

et al. 2022

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With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information is examined using the Spearman correlation coefficient to eliminate characteristics that have a weak link with the price of power. Secondly, a similar day-screening model with weighted grey correlation degree is constructed based on the grey correlation theory (GRA) to exclude superfluous samples. Thirdly, the regularized limit learning machine (RELM) is tuned using the Marine Predators Algorithm (MPA) to increase RELM parameter accuracy. Finally, the proposed forecasting model is applied to the Shanxi spot market, and other forecasting models and error computation methodologies are compared. The results demonstrate that the model suggested in this paper has a specific forecasting effect for power price forecasting technology.

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Performing price scenario analysis and stress testing using quantile regression: A case study of the Californian electricity market

Cited by 17 publications

References 27 publications

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy

Turkmenistan’s Gas Sector Development Scenarios Based on Econometric and SWOT Analysis

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

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