A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets

Singh, Nitin; Mohanty, Soumya R.

doi:10.4236/jpee.2015.39001

Cited by 24 publications

(9 citation statements)

References 63 publications

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“…A large number of competing methods exist to measure, model and predict energy price volatility, and it is important to note that no research has suggested any universal superiority of one method over the others. Instead, each method has merit and may work well in certain circumstances (Nomikos & Andriosopoulos, 2012;Sisodiaa et al, 2015;Singh & Mohanty, 2015). The results indicate a large variation in scientists' methodologies to address the issues in different countries (Sisodiaa et al, 2015).…”

Section: Literature Reviewmentioning

confidence: 98%

Modelling and Forecasting Volatility on Electric Power Exchange SEEPEX

Terzić

Jeremić

Latas³

2023

Manag: J. Sustain. Bus. Manag. Solut. Emerg. Econ.

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Research Question: The launch and the beginning of trade on the South East Electric Power Exchange (SEEPEX) in Belgrade, early in 2016, opened the issue of forecasting volatility and price movements in the market. Motivation: The issue is of vital importance for all market actors for the purpose of maximising profits, reducing risks, planning production and making investment decisions. Forecasting volatility and price movements in electric power markets is important for traders with profit maximisation and yield-to-risk ratio optimisation in mind and, equally, for producers, large industrial consumers, investors and portfolio managers. Idea: Exploring models and techniques to forecast volatility in electricity markets and subsequently testing statistical methods based on time series data, the ARMA-GARCH being the preferred model, with a view to identifying optimal methods for this market. The volatility of the power market and price movements have been tested during a given period. The results can be used to gauge market parameters and opportunities to extrapolate future volatility and movements in electricity prices. Data: For the purposes of this analysis, a time series involving price movements and trade volumes were used, covering a period between the SEEPEX trade launch and the end of 2019. Tools: In the empirical part of the paper, "Stata 13" statistical and econometric software was used to explore stylised facts and model the volatility of SEEPEX electricity price returns. Findings: The authors offer an overview of different methods used in the research, having selected different specifications of the ARMA-GARCH model as the most reliable in predicting volatility in the given market. The exponential GARCH model with student-t error distribution is believed to have provided the best overall performance in modelling the SEEPEX return volatility, as well as the best volatility forecast. Contribution: This is one of the first empirical studies of the Serbian power market that deals with risk modelling. Forecasting time-varying electricity exchange volatility is important for all market participants interested in variance forecasts to be used to calculate risk and hedging measures.

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Section: Literature Reviewmentioning

confidence: 98%

Modelling and Forecasting Volatility on Electric Power Exchange SEEPEX

Terzić

Jeremić

Latas³

2023

Manag: J. Sustain. Bus. Manag. Solut. Emerg. Econ.

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“…The ARIMA forecasting model is the most common type of linear time series model based on Box-Jenkins methodology as presented in [11]. In most cases, the methodology does not require the estimation of many parameters to make the final choice of model [12].…”

Section: Related Previous Work Of Forecasting Modelmentioning

confidence: 99%

A Single Buyer Model of Imbalance Cost Pass-Through Pricing Forecasting under Malaysia Electricity Supply Industry

Khairuddin,

Zaini,

Sulaima

et al. 2024

Preprint

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The Imbalance Cost Pass-Through (ICPT) is a flexible component of the Incentive Regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. However, the cost of electricity generation is significantly influenced by the volatility of commodity prices, including coal, gas and LNG. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Due to that reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed by a Mean Absolute Percentage Error (MAPE). In findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the Moving Average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July-December 2022 and January-June 2023 is MYR 0.21/ kWh for ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB’s ICPT tariff. As for the forecasting, the ICPT price is expected to drop for the next announcement. The findings of this study may contribute to a positive impact on the sustainability of the energy sector in Malaysia.

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“…Note, that the problem of forecasting processes is relevant in solving many problems related to optimizing power systems. The extensive scientific literature is devoted to this problem [41][42][43][44][45][46][47][48]. The approach we use is only one of the possible approaches.…”

Section: Implementation Of the First Taskmentioning

confidence: 99%

Estimating the Economic Impacts of Net Metering Schemes for Residential PV Systems with Profiling of Power Demand, Generation, and Market Prices

et al. 2018

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This article analyses the influence of supporting scheme variants on the profitability of a projected investment of residential photovoltaic systems. The focus of the paper lies in evaluating the feasibility for the power system of solar power generation technologies to achieve a balance between energy generation and support costs in a more efficient way. The case study is based on a year-long time series of examples with an hourly resolution of electricity prices from the Nord Pool power market, in addition to the power demand and solar generation of Latvian prosumers. Electric energy generation and the consumption of big data from more than 100 clients were collected. Based on these data, we predict the processes for the next 25 years, and we estimate economic indicators using a detailed description of the net metering billing system and the Monte-Carlo method. A recommendation to change the current net system to a superior one, taking into account the market cost of energy, concludes the paper.

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A Review of Price Forecasting Problem and Techniques in Deregulated Electricity Markets

Cited by 24 publications

References 63 publications

Modelling and Forecasting Volatility on Electric Power Exchange SEEPEX

Modelling and Forecasting Volatility on Electric Power Exchange SEEPEX

A Single Buyer Model of Imbalance Cost Pass-Through Pricing Forecasting under Malaysia Electricity Supply Industry

Estimating the Economic Impacts of Net Metering Schemes for Residential PV Systems with Profiling of Power Demand, Generation, and Market Prices

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