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Uncertainties and Risk Management in Competitive Energy Markets

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 38168

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Special Issue Editors

Prof. Christoph Weber

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Guest Editor

House of Energy Markets & Finance, University of Duisburg-Essen, Berliner Platz 6-8, 45127 Essen, Germany
Interests: energy risk management; energy market liberalization; application of operations research methods to energy issues

Prof. Dr. Florian Ziel

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Guest Editor

House of Energy Markets and Finance, University of Duisburg-Essen, Universitätsstraße 2, 45141 Essen, Germany
Interests: energy analytics; electricity price and load forecasting; energy markets; renewable energy; forecasting in water and environmental sciences; high-dimensional time series; seasonal time series; probabilistic forecasting; evaluation; risk and portfolio management
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Special Issue Information

Dear colleagues,

We are pleased to call for papers for a Special Issue on “Uncertainties and Risk Management in Competitive Energy Markets”.

In recent years, the quantitative methods for analyzing energy systems have improved substantially. This allows for better understanding and modeling of the uncertainty and risk in energy markets, which includes the long-term perspective for decision making as investment projects, but also short-term problems concerning operational management of uncertainties. Due to the continuing expansion of renewable energy supply, such as storage facilities, the relevance of incorporating uncertainties and risk appropriately into energy system models will increase substantially. New and innovative approaches for describing uncertainty-related aspects of energy systems and related problems are welcome in this Special Issue.

This Special Issue is dedicated to all areas of energy markets, including electricity, heat, cooling, and mobility.

The main topics of interest for this Special Issue include but are not limited to:

- Risk measurement and management for energy systems;

- Energy forecasting and modeling;

- Energy innovations and energy markets;

- Energy policies and economic impact;

- Fluctuation of renewables and load flows;

- Energy data and digitization;

- Stochastic processes in energy;

- Financing energy infrastructure;

- Regulation and regulatory risk;

- Valuation of physical assets.

Prof. Christoph Weber
Prof. Florian Ziel
Guest Editors

Manuscript Submission Information

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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.

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Keywords

forecasting
energy risk
energy markets
innovation
uncertainties
regulatory risk

Published Papers (14 papers)

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Research

19 pages, 4821 KiB

Open AccessArticle

Self-Reinforcing Electricity Price Dynamics under the Variable Market Premium Scheme

by Ulrich J. Frey, Martin Klein, Kristina Nienhaus and Christoph Schimeczek

Energies 2020, 13(20), 5350; https://0-doi-org.brum.beds.ac.uk/10.3390/en13205350 - 14 Oct 2020

Cited by 6 | Viewed by 1851

Abstract

We report a potential self-reinforcing design flaw in the variable market premium scheme that occurs if variable renewable energy power plants receiving a premium become price-setting in the market. A high share of renewable energy is a goal of many countries on their transformation path to a sustainable future. Accordingly, policies like feed-in tariffs have been in place for many years in many countries to support investment. To foster market alignment, variable market premia have been introduced in at least 12 European countries and a further dozen additional countries world-wide. We demonstrate both with a mathematical model and different scenarios of an agent-based simulation that the combination of variable premia and a high share of hours in which renewables are price-setting may lead to a self-reinforcing downward spiral of prices if unchecked. This is caused by the market premium opening up the bidding space towards negative prices. We discuss possible objections and countermeasures and evaluate the severity of this market design flaw. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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22 pages, 8236 KiB

Open AccessArticle

Comparison of Electricity Spot Price Modelling and Risk Management Applications

by Ethem Çanakoğlu and Esra Adıyeke

Energies 2020, 13(18), 4698; https://0-doi-org.brum.beds.ac.uk/10.3390/en13184698 - 10 Sep 2020

Cited by 6 | Viewed by 2065

Abstract

In dealing with sharp changes in electricity prices, contract planning is considered as a vital risk management tool for stakeholders in deregulated power markets. In this paper, dynamics of spot prices in Turkish electricity market are analyzed, and predictive performance of several models are compared, i.e., time series models and regime-switching models. Different models for derivative pricing are proposed, and alternative portfolio optimization problems using mean-variance optimization and conditional value at risk (CVaR) are solved. Expected payoff and risk structure for different hedging strategies for a hypothetical electricity company with a given demand are analyzed. Experimental studies show that regime-switching models are able to capture electricity characteristics better than their standard counterparts. In addition, evaluations with various risk management models demonstrate that those models are highly competent in providing an effective risk control practice for electricity markets. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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18 pages, 692 KiB

Open AccessArticle

Forecasting Electricity Prices Using Deep Neural Networks: A Robust Hyper-Parameter Selection Scheme

by Grzegorz Marcjasz

Energies 2020, 13(18), 4605; https://0-doi-org.brum.beds.ac.uk/10.3390/en13184605 - 04 Sep 2020

Cited by 16 | Viewed by 2664

Abstract

Deep neural networks are rapidly gaining popularity. However, their application requires setting multiple hyper-parameters, and the performance relies strongly on this choice. We address this issue and propose a robust ex-ante hyper-parameter selection procedure for the day-ahead electricity price forecasting that, when used jointly with a tested forecast averaging scheme, yields high performance throughout three-year long out-of-sample test periods in two distinct markets. Being based on a grid search with models evaluated on long samples, the methodology mitigates the noise induced by local optimization. Forecast averaging across calibration window lengths and hyper-parameter sets allows the proposed methodology to outperform a parameter-rich least absolute shrinkage and selection operator (LASSO)-estimated model and a deep neural network (DNN) with non-optimized hyper-parameters in terms of the mean absolute forecast error. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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14 pages, 352 KiB

Open AccessEditor’s ChoiceArticle

Intraday Electricity Pricing of Night Contracts

by Marcel Kremer, Rüdiger Kiesel and Florentina Paraschiv

Energies 2020, 13(17), 4501; https://0-doi-org.brum.beds.ac.uk/10.3390/en13174501 - 01 Sep 2020

Cited by 11 | Viewed by 3953

Abstract

This paper investigates the intraday electricity pricing of 15-min. contracts in night hours. We tailor a recently introduced econometric model with fundamental impacts, which is successful in describing the pricing of day contracts. Our estimation results show that the mean reversion and the positive price impact of neighboring contracts are generic features of the price formation process on the intraday market, independent of the time of day. Intraday auction prices have higher explanatory power for the pricing of night than day contracts, particularly, for the first and last 15-min. contract in a night hour. Intradaily updated forecasts of wind power infeed are the only significant fundamental factors for intraday electricity prices at night. Neither expected conventional capacities nor the slope of the merit order curve contribute to explaining price dynamics. Overall, we conclude that fundamentals lose in importance in night hours and the 15-min. intraday market is rather driven by price information. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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24 pages, 3856 KiB

Open AccessArticle

Predictive Trading Strategy for Physical Electricity Futures

by Claudio Monteiro, L. Alfredo Fernandez-Jimenez and Ignacio J. Ramirez-Rosado

Energies 2020, 13(14), 3555; https://0-doi-org.brum.beds.ac.uk/10.3390/en13143555 - 10 Jul 2020

Cited by 6 | Viewed by 2711

Abstract

This article presents an original predictive strategy, based on a new mid-term forecasting model, to be used for trading physical electricity futures. The forecasting model is used to predict the average spot price, which is used to estimate the Risk Premium corresponding to electricity futures trade operations with a physical delivery. A feed-forward neural network trained with the extreme learning machine algorithm is used as the initial implementation of the forecasting model. The predictive strategy and the forecasting model only need information available from electricity derivatives and spot markets at the time of negotiation. In this paper, the predictive trading strategy has been applied successfully to the Iberian Electricity Market (MIBEL). The forecasting model was applied for the six types of maturities available for monthly futures in the MIBEL, from 1 to 6 months ahead. The forecasting model was trained with MIBEL price data corresponding to 44 months and the performances of the forecasting model and of the predictive strategy were tested with data corresponding to a further 12 months. Furthermore, a simpler forecasting model and three benchmark trading strategies are also presented and evaluated using the Risk Premium in the testing period, for comparative purposes. The results prove the advantages of the predictive strategy, even using the simpler forecasting model, which showed improvements over the conventional benchmark trading strategy, evincing an interesting hedging potential for electricity futures trading. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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19 pages, 1087 KiB

Open AccessArticle

PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices

by Katarzyna Maciejowska, Bartosz Uniejewski and Tomasz Serafin

Energies 2020, 13(14), 3530; https://0-doi-org.brum.beds.ac.uk/10.3390/en13143530 - 08 Jul 2020

Cited by 20 | Viewed by 3197

Abstract

Recently, the development in combining point forecasts of electricity prices obtained with different length of calibration windows have provided an extremely efficient and simple tool for improving predictive accuracy. However, the proposed methods are strongly dependent on expert knowledge and may not be directly transferred from one to another model or market. Hence, we consider a novel extension and propose to use principal component analysis (PCA) to automate the procedure of averaging over a rich pool of predictions. We apply PCA to a panel of over 650 point forecasts obtained for different calibration windows length. The robustness of the approach is evaluated with three different forecasting tasks, i.e., forecasting day-ahead prices, forecasting intraday ID3 prices one day in advance, and finally very short term forecasting of ID3 prices (i.e., six hours before delivery). The empirical results are compared using the Mean Absolute Error measure and Giacomini and White test for conditional predictive ability (CPA). The results indicate that PCA averaging not only yields significantly more accurate forecasts than individual predictions but also outperforms other forecast averaging schemes. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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15 pages, 461 KiB

Open AccessArticle

Solving the Stochastic Generation and Transmission Capacity Planning Problem Applied to Large-Scale Power Systems Using Generalized Shift-Factors

by Victor H. Hinojosa and Joaquín Sepúlveda

Energies 2020, 13(13), 3327; https://0-doi-org.brum.beds.ac.uk/10.3390/en13133327 - 30 Jun 2020

Cited by 2 | Viewed by 1502

Abstract

In this study, we successfully develop the transmission planning problem of large-scale power systems based on generalized shift-factors. These distribution factors produce a reduced solution space which does not need the voltage bus angles to model new transmission investments. The introduced formulation copes with the stochastic generation and transmission capacity expansion planning problem modeling the operational problem using a 24-hourly load behaviour. Results show that this formulation achieves an important reduction of decision variables and constraints in comparison with the classical disjunctive transmission planning methodology known as the Big M formulation without sacrificing optimality. We test both the introduced and the Big M formulations to find out convergence and time performance using a commercial solver. Finally, several test power systems and extensive computational experiments are conducted to assess the capacity planning methodology. Solving deterministic and stochastic problems, we demonstrate a prominent reduction in the solver simulation time especially with large-scale power systems. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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21 pages, 699 KiB

Open AccessArticle

Impact of Long-Term Water Inflow Uncertainty on Wholesale Electricity Prices in Markets with High Shares of Renewable Energies and Storages

by Heike Scheben, Nikolai Klempp and Kai Hufendiek

Energies 2020, 13(9), 2347; https://0-doi-org.brum.beds.ac.uk/10.3390/en13092347 - 08 May 2020

Cited by 5 | Viewed by 1762

Abstract

Renewable energy shares in electricity markets are increasing and therefore also require an increase in flexibility options. Conventional electricity price modelling with optimisation models in thermally dominated markets is not appropriate in markets with high shares of renewable energies and storages because price structures are not adequately represented. Previous research has already identified the impact of uncertainty in renewable energy feed-in on investment and dispatch decisions. However, we are not aware of any work that investigates the influence of uncertainties on price structures by means of optimisation models. Appropriate modelling of electricity price structures is important for investment and policy decisions. We have investigated the influence of uncertainty concerning water inflow by applying a second stage stochastic dual dynamic programming approach in a linear optimisation model using Norway as an example. We found that the influence of uncertainty concerning water inflow combined with high shares of storages has a strong impact on the electricity price structures. The identified structures are highly influenced by seasonal water inflow, electricity demand, wind, and export profiles. Additionally, they are reinforced by seasonal primary energy source prices and import prices. Incorporating uncertainties in linear optimisation models improves the price modelling and provides, to a large extent, an explanation for the seasonal patterns of Norwegian electricity market prices. The paper explains the basic pricing mechanisms in markets with high shares of storages and renewable energies which are subject to uncertainty. To identify these fundamental mechanisms, we focused on uncertainty regarding water inflow, but the basic results hold true for uncertainties regarding other renewable energies as well. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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35 pages, 4015 KiB

Open AccessArticle

Pan-European CVaR-Constrained Stochastic Unit Commitment in Day-Ahead and Intraday Electricity Markets

by Moritz Nobis, Carlo Schmitt, Ralf Schemm and Armin Schnettler

Energies 2020, 13(9), 2339; https://0-doi-org.brum.beds.ac.uk/10.3390/en13092339 - 08 May 2020

Cited by 3 | Viewed by 3211

Abstract

The fundamental modeling of energy systems through individual unit commitment decisions is crucial for energy system planning. However, current large-scale models are not capable of including uncertainties or even risk-averse behavior arising from forecasting errors of variable renewable energies. However, risks associated with uncertain forecasting errors have become increasingly relevant within the process of decarbonization. The intraday market serves to compensate for these forecasting errors. Thus, the uncertainty of forecasting errors results in uncertain intraday prices and quantities. Therefore, this paper proposes a two-stage risk-constrained stochastic optimization approach to fundamentally model unit commitment decisions facing an uncertain intraday market. By the nesting of Lagrangian relaxation and an extended Benders decomposition, this model can be applied to large-scale, e.g., pan-European, power systems. The approach is applied to scenarios for 2023—considering a full nuclear phase-out in Germany—and 2035—considering a full coal phase-out in Germany. First, the influence of the risk factors is evaluated. Furthermore, an evaluation of the market prices shows an increase in price levels as well as an increasing day-ahead-intraday spread in 2023 and in 2035. Finally, it is shown that intraday cross-border trading has a significant influence on trading volumes and prices and ensures a more efficient allocation of resources. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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15 pages, 5154 KiB

Open AccessArticle

Probabilistic Load Flow Approach Considering Dependencies of Wind Speed, Solar Irradiance, Electrical Load and Energy Exchange with a Joint Probability Distribution Model

by Marie-Louise Kloubert

Energies 2020, 13(7), 1727; https://0-doi-org.brum.beds.ac.uk/10.3390/en13071727 - 04 Apr 2020

Cited by 10 | Viewed by 1931

Abstract

The modelling of stochastic feed-ins and demands becomes essential for transmission grid operation and planning due to the extension of renewable energy sources (RES). Neglecting the correlation between uncertain variables and/or oversimplifying the distribution through the assumption of Normal distributions leads to the inaccurate determination of future network states. Therefore, the uncertainties need to be accurately modelled in order to be used in a probabilistic load flow approach. This paper analyses the characteristics of wind speed and solar irradiance for different locations throughout countries and models the dependencies between them. In addition, the total electrical load and the energy exchange between neighbouring countries are analysed. All of these uncertainties are modelled together in a high-dimensional joint probability distribution using pair-copula constructions. The model is applied to generate samples and determine the probability of extreme events, e.g. high RES production and low demand. The probability for rather high load (>65 GW) and low RES production with wind speed less than 3 m/s and solar irradiance less than

100 \frac{W}{m ²}

at 90% of all stations is e.g. 0.064%. In addition, the model is integrated in a probabilistic load flow approach in order to analyse the German transmission grid for a future scenario of the year 2025. With the copula, samples are generated as an input for the Monte Carlo simulation approach. The approach enables the assessment of planned HVDC lines. When considering the HVDC lines, the load on the AC lines can be significantly reduced. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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43 pages, 10395 KiB

Open AccessArticle

Small-Scale Modelling of Individual Greenhouse Gas Abatement Measures in Industry

by Tobias Hübner

Energies 2020, 13(7), 1619; https://0-doi-org.brum.beds.ac.uk/10.3390/en13071619 - 02 Apr 2020

Cited by 1 | Viewed by 2209

Abstract

The dynamic bottom-up modelling of greenhouse gas (GHG) abatement measures in industry makes it possible to derive consistent transformation paths on the basis of heterogeneous, process-specific developments. The main focus is on the development of a transparent methodology for small-scale modelling and combination of individual GHG abatement measures. In this way, interactions between GHG abatement measures are taken into account when deriving industrial transformation paths. The presented three-part methodological approach comprises the preparation (1) and implementation (2) of GHG abatement measures as well as the resulting effects on the output parameters (3) in a technology mix module. In order to consider interactions in the measures implementation, year-specific overall measure matrices are created and prioritised based on the GHG abatement costs. Finally, the three-part methodology is tested in a consistent technology mix scenario. The results show that the methodology enables integrated industrial technology mix scenarios with a high level of climate ambition based on a plausible development of energy consumption and emissions. Compared to the reference scenario, the process-and energy-related emissions decrease by 90 million tCO₂ (77% of the 1990 level in 2050). The developed methodology and the related technology mix scenario within the framework of the bottom-up industry model SmInd can support strategic decision-making in politics and an efficient transition to a greenhouse gas neutral industry. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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19 pages, 1358 KiB

Open AccessArticle

Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand

by Qi Zhang, Shaohua Zhang, Xian Wang, Xue Li and Lei Wu

Energies 2020, 13(6), 1308; https://0-doi-org.brum.beds.ac.uk/10.3390/en13061308 - 11 Mar 2020

Cited by 8 | Viewed by 1968

Abstract

This paper investigates the problem of how to deploy customers’ shiftable load (SL) for electricity retailers’ risk management under uncertainty of the day-ahead (DA) wholesale market price. The robust profit (RP) and the conditional robust profit (CRP) are introduced for a risk-averse retailer’s risk-reward trade-off analysis in its decision-making of electricity procurement from various options. A CRP-based bi-level optimization model is proposed for the risk-averse retailer to determine its electricity procurement strategy taking into consideration customers’ shiftable load. In the upper problem, the retailer decides its electricity procurement from various options and the SL incentive prices to maximize its CRP under a given confidence level, and in the lower problem, the customers shift their load according to the SL incentive prices to minimize their comprehensive costs including the discomfort cost caused by rescheduling electricity consumption. Finally, a case study is used to verify the effectiveness of this model. It is shown that the retailer can achieve larger profit and less risk by utilizing customers’ SL and the retailer’s risk-aversion level has an important impact on its electricity procurement and SL incentive strategies. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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21 pages, 1758 KiB

Open AccessArticle

Weather Risk Management in Energy Sector: The Polish Case

by Monika Wieczorek-Kosmala

Energies 2020, 13(4), 945; https://0-doi-org.brum.beds.ac.uk/10.3390/en13040945 - 20 Feb 2020

Cited by 16 | Viewed by 3053

Abstract

The energy sector is perceived as one of the most exposed sectors to the consequences of weather risk both directly (damages of its infrastructure) and indirectly (frictions to the energy supply–demand balance). The main aim of this paper is to provide an insight into the impact of weather risk on economic activity of companies operating in the energy sector in Poland. The empirical objective is to examine whether energy companies: (i) identify their relevant weather risk exposures; (ii) evaluate the impact of weather risk in the cost-revenues dimension; and (iii) implement weather risk management tools, in this case—weather derivatives. In a methodical context, this study relies on a unique research approach and derives from works that examine companies’ risk disclosures in annual reports, by applying textual content analysis. The results indicate that Polish energy companies recognize the impact of weather risk on their performance, also in the cost-revenues dimension. However, although the reported weather risk management methods were diversified, the examined companies did not use weather derivatives to hedge their weather risk exposures. In the overall dimension, the companies leading with the perception and management of weather risk were diversified regarding performance and market size. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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34 pages, 2255 KiB

Open AccessArticle

Changes in Energy Consumption, Economic Growth and Aspirations for Energy Independence: Sectoral Analysis of Uses of Natural Gas in Ukrainian Economy

by Olexandr Yemelyanov, Anastasiya Symak, Tetyana Petrushka, Olena Zahoretska, Myroslava Kusiy, Roman Lesyk and Lilia Lesyk

Energies 2019, 12(24), 4724; https://0-doi-org.brum.beds.ac.uk/10.3390/en12244724 - 11 Dec 2019

Cited by 29 | Viewed by 4959

Abstract

The main objective of the research is to assess the ability of the Ukrainian economy and its individual industries to ensure, in the conditions of economic growth, a stable reduction of natural gas consumption and, consequently, to reduce dependence on its imports. Six types of relationships were identified between the change in sectoral added value and the change in the consumption of certain energy resources, in particular natural gas. The conditions are established under which the growth of sectoral added value is accompanied by a decrease in the consumption of certain energy resources. The index of sectoral efficiency of the use of certain energy resources was proposed and a model of the decomposition of the growth rate of this indicator was constructed. Quantitative indicators of measuring economic barriers on the way to introduction of energy-saving technologies are presented. Conditions under which economic growth is accompanied by a decrease in the level of dependence of the economy on imports of energy resources are modeled. The dynamics of natural gas consumption by sectors of the Ukrainian economy is analyzed. It is proved that reduction of natural gas consumption due to increased energy efficiency occurs mainly in industries with an average value of share of the cost of purchasing this energy in the total operating expenses. An estimation is undertaken of the possibility of achieving independence of the Ukrainian economy from the import of natural gas in different scenarios of changing main parameters that determine the probability of such an achievement. Full article

(This article belongs to the Special Issue Uncertainties and Risk Management in Competitive Energy Markets)

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