Electricity Price Instability over Time: Time Series Analysis and Forecasting

Wang, Diankai; Gryshova, Inna; Kyzym, Mykola; Salashenko, Tetiana; Khaustova, V. Y.; Shcherbata, Maryna

doi:10.3390/su14159081

Cited by 16 publications

(6 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CPP peak-valley division is to add critical peak hours to the TOU peak hours [21], i.e., the day is divided into four periods: critical-peak, peak, flat and valley. Existing research is mainly based on membership function, power supply cost or user demand response for time segmentation.…”

Section: Time Segmentation Modelingmentioning

confidence: 99%

Research on Critical Peak Price Decision Optimization Considering Industrial Consumer’s Risk Appetite under the Carbon Neutrality Goal

Dong

Zheng

2023

Sustainability

View full text Add to dashboard Cite

The existing research on critical peak price (CPP) decision-making ignores the difference in risk appetite between industries within the consumer population, resulting in a serious lag in the enthusiasm of some users to respond to CPP, and unsatisfactory improvement of power systems and carbon emission reduction on the supply and demand side. Firstly, the problem of consumer risk appetite was comprehensively analyzed, and the industrial consumer population was secondarily stratified according to the influencing factors and the enthusiasm of responding to CPP, namely: stubborn, active and conformist, and quantitatively verified by cluster analysis. Secondly, by combing the relevant paths of CPP decision-making, the critical-peak window determination model and CPP multi-objective optimization model were constructed, and the calculation of relevant indicators was introduced. Finally, taking 10 industrial enterprises in a city in Sichuan Province as an example, the clustering method was used to verify the stratification results, and the index analysis method was used to measure the load and carbon emission improvement of two typical enterprises after CPP optimization. The results showed that the stubborn users insist on using electricity, the improvement effect of load and carbon emission reduction was poor, and additional production costs will be caused. The conformist and active users had high sensitivity to electricity price fluctuations, good load and carbon emission reduction improvement effects, and significantly reduced electricity costs.

show abstract

Section: Time Segmentation Modelingmentioning

confidence: 99%

Research on Critical Peak Price Decision Optimization Considering Industrial Consumer’s Risk Appetite under the Carbon Neutrality Goal

Dong

Zheng

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…The forecasting results show reasonable accuracy in the mean and scaled mean absolute errors. According to Wang et al [25], the competition in electricity markets leads to volatile conditions that cause persistent price fluctuations over time. Their work explores the problem of electricity price fluctuations from October 2018 to March 2022 by applying time series analysis.…”

Section: Related Workmentioning

confidence: 99%

Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico

et al. 2023

View full text Add to dashboard Cite

The energy price influences the interest in investment, which leads to economic development. An estimate of the future energy price can support the planning of industrial expansions and provide information to avoid times of recession. This paper evaluates adaptive boosting (AdaBoost), bootstrap aggregation (bagging), gradient boosting, histogram-based gradient boosting, and random forest ensemble learning models for forecasting energy prices in Latin America, especially in a case study about Mexico. Seasonal decomposition of the time series is used to reduce unrepresentative variations. The Optuna using tree-structured Parzen estimator, optimizes the structure of the ensembles through a voter by combining several ensemble frameworks; thus an optimized hybrid ensemble learning method is proposed. The results show that the proposed method has a higher performance than the state-of-the-art ensemble learning methods, with a mean squared error of 3.37 × 10−9 in the testing phase.

show abstract

“…Developing issues in the electric power sector have received a lot of attention in research. A wide range of these problems have formed a relevant research field in economics, which includes studying the sustainability of electric power sector development [9][10][11][12][13], the liberalization of electricity markets [14][15][16][17][18], electricity pricing policy and price fluctuations [19][20][21][22], and the forecasting of energy mix and fuel consumption [23,24]. Today, the electric power sector's development is considered relevant to ensuring EU energy security [25][26][27][28] and its energy transition to decarbonization [29][30][31][32][33][34][35].…”

Section: Literature Reviewmentioning

confidence: 99%

Fundamental Shifts in the EU’s Electric Power Sector Development: LMDI Decomposition Analysis

et al. 2023

Self Cite

View full text Add to dashboard Cite

The electric power sector plays a central role in changing the EU’s energy landscape and establishing Europe as the first climate-neutral continent in the world. This paper investigates fundamental shifts in the EU’s electric power sector by carrying out its logarithmic mean Divisia index decomposition by stages of electricity flows on a large-scale basis (for both the entire EU and its 25 member states) for the period 1995–2021 and identifies the individual contribution of each EU member state to these shifts. In this study, four decomposition models were proposed and 14 impact factors (extensive, structural, and intensive) affecting the development of the EU electric power sector were evaluated in absolute and relative terms. It was found that the wind–gas transition, which took place in the EU’s electric power sector, was accompanied by an increase in the transformation efficiency of inputs in electricity generation and a drop in the intensity of final energy consumption. The non-industrial reorientation of the EU’s economy also resulted in a decrease in the final electricity consumption. At the same time, this transition led to negative shifts in the structure and utilization of its generation capacities. The fundamental shifts occurred mainly at the expense of large economies (Germany, France, Spain, and Italy), but smaller economies (Romania, Poland, Croatia, the Netherlands, and others) made significant efforts to accelerate them, although their contributions on a pan-European scale were less tangible.

show abstract

Electricity Price Instability over Time: Time Series Analysis and Forecasting

Cited by 16 publications

References 38 publications

Research on Critical Peak Price Decision Optimization Considering Industrial Consumer’s Risk Appetite under the Carbon Neutrality Goal

Research on Critical Peak Price Decision Optimization Considering Industrial Consumer’s Risk Appetite under the Carbon Neutrality Goal

Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico

Fundamental Shifts in the EU’s Electric Power Sector Development: LMDI Decomposition Analysis

Contact Info

Product

Resources

About