European Power Exchange Research Articles

A potential framework for cross-zonal reserve procurement on European power exchanges based on robust optimization

In order to prepare for unexpected events in the power system, regulation reserves are routinely procured in advance. Economic considerations dictate that it is advantageous to perform reserve procurement and energy allocation on a joint market platform using a single clearing algorithm. Furthermore, it is also beneficial if the option exists to allocate resources between different bidding zones to alleviate local shortages and corresponding price pressures. The present paper proposes a solution algorithm for this task in a European market environment without unit commitment and uplift payments to non-convex bidders. The main challenge of cross-zonal reserve allocation arises from the fact that deployment decisions are not yet known, therefore the actual deployment flows are uncertain at the time of procurement. The proposed algorithm handles this issue with robust optimization techniques and provides a guarantee of feasible transmission limits for deployment i.e. reserve deliverability. The clearing model is formalized as a computationally efficient Mixed Integer Linear Problem; its exact equations are presented along with case studies for the purpose of demonstration. The research is part of the FARCROSS H2020 project of the European Union.

Electricity Market Dynamics and Regional Interdependence in the Face of Pandemic Restrictions and the Russian–Ukrainian Conflict

Electricity constitutes a significant part of the consumption basket of European households and companies. Since energy products are essential components of almost all products and services, any change in energy prices directly impacts the general price level of those products and services. Therefore, this study aims to conduct a comprehensive analysis of power exchange data between 2019 and 2022. For the analysis, we examined the data of 15 countries. In the research, we compared electricity prices in European power exchanges using the Jaccard similarity index and the overlap coefficient, using the DAM hourly prices between 1 January 2019 and 31 December 2022. We transformed the time series into networks using the visibility graph procedure and compared the networks of the studied countries using the two comparison methods with the degree distribution functions. Our aim is to examine how the market anomalies caused by the COVID-19 pandemic and the Russian–Ukrainian conflict affect European electricity markets and how quickly the repercussions spread across the studied countries’ exchanges, and whether they show persistent or anti-persistent characteristics. The results support that similar market effects significantly influence the pattern of price changes among the countries. The methods forming the basis of the research can provide significant assistance in analyzing market trends and contribute to a better understanding of market processes.

Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows

Electricity is traded on various markets with different time horizons and regulations. Short-term intraday trading becomes increasingly important due to the higher penetration of renewables. In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the European Power EXchange (EPEX) spot markets in a distinct hourly pattern. This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts. The model captures the emerging hourly pattern by considering the four 15min intervals in each day-ahead price interval as a four-dimensional joint probability distribution. The resulting nontrivial, multivariate price difference distribution is learned using a normalizing flow, i.e., a deep generative model that combines conditional multivariate density estimation and probabilistic regression. Furthermore, this work discusses the influence of different external impact factors based on literature insights and impact analysis using explainable artificial intelligence (XAI). The normalizing flow is compared to an informed selection of historical data and probabilistic forecasts using a Gaussian copula and a Gaussian regression model. Among the different models, the normalizing flow identifies the trends with the highest accuracy and has the narrowest prediction intervals. Both the XAI analysis and the empirical experiments highlight that the immediate history of the price difference realization and the increments of the day-ahead price have the most substantial impact on the price difference.

An integrated model for electricity market coupling simulations: Evidence from the European power market crossroad

Motivated by the project of European Power Exchanges to develop a single price-coupling solution to calculate electricity prices across Europe that respects the cross-border capacity constraints on a day-ahead basis, we empirically examine and quantify market inefficiencies in non-coupled day-ahead electricity markets. These result from inefficient cross-border capacity allocation and its underlying effect on the market clearing prices. Efficient cross-border capacity allocation and new market clearing prices are simulated using a social welfare maximisation algorithm for the capacity of relevant network elements, whereas the order book generation process is reproduced by the econometrically estimated supply price elasticity functions. The estimated vector autoregression model and the underlying impulse response functions examine price shock transmission under different market regimes. The market coupling process is simulated on the historical non-coupled day-ahead market realisations at the junction of three regional power markets: Central Western Europe, Northern Italian, and South Eastern European markets. Simulation results confirm steady, efficient cross-border capacity utilisation, reduced price variance, improved overall price convergence and amplified price shock transmission in coupled electricity markets. Furthermore, in the simulated period, we have estimated an increase in the overall suppliers' and consumers’ surplus of almost EUR 16 million. The proposed simulation framework is a clear choice for applied simulation in coupled day-ahead electricity markets, offering valuable visual insights into the cross-border capacity allocation and its implications on electricity prices.

Pricing and Hedging Bond Power Exchange Options in a Stochastic String Term-Structure Model

We study power exchange options written on zero-coupon bonds under a stochastic string term-structure framework. Closed-form expressions for pricing and hedging bond power exchange options are obtained and, as particular cases, the corresponding expressions for call power options and constant underlying elasticity in strikes (CUES) options. Sufficient conditions for the equivalence of the European and the American versions of bond power exchange options are provided and the put-call parity relation for European bond power exchange options is established. Finally, we consider several applications of our results including duration and convexity measures for bond power exchange options, pricing extendable/accelerable maturity zero-coupon bonds, options to price a zero-coupon bond off of a shifted term-structure, and options on interest rates and rate spreads. In particular, we show that standard formulas for interest rate caplets and floorlets in a LIBOR market model can be obtained as special cases of bond power exchange options under a stochastic string term-structure model.

Complexity and Persistence of Price Time Series of the European Electricity Spot Market

The large variability of renewable power sources is a central challenge in the transition to a sustainable energy system. Electricity markets are central for the coordination of electric power generation. These markets rely evermore on short-term trading to facilitate the balancing of power generation and demand and to enable systems integration of small producers. Electricity prices in these spot markets show pronounced fluctuations, featuring extreme peaks as well as occasional negative prices. In this article, we analyze electricity price time series from the European Power Exchange market, in particular the hourly day-ahead, hourly intraday, and 15-min intraday market prices. We quantify the fluctuations, correlations, and extreme events and reveal different time scales in the dynamics of the market. The short-term fluctuations show remarkably different characteristics for time scales below and above 12 h. Fluctuations are strongly correlated and persistent below 12 h, which contributes to extreme price events and a strong multifractal behavior. On longer time scales, they get anticorrelated and price time series revert to their mean, witnessed by a stark decrease of the Hurst coefficient after 12 h. The long-term behavior is strongly influenced by the evolution of a large-scale weather pattern with a typical time scale of four days. We elucidate this dependence in detail using a classification into circulation weather types. The separation in time scales enables a superstatistical treatment, which confirms the characteristic time scale of four days, and motivates the use of q-Gaussian distributions as the best fit to the empiric distribution of electricity prices.3 MoreReceived 6 December 2021Revised 21 February 2022Accepted 28 February 2022DOI:https://doi.org/10.1103/PRXEnergy.1.013002Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasComplex systemsEconophysicsEnergy sourcesFractional Brownian motionPatterns in complex systemsSolar energyStochastic processesWind energyPhysical SystemsFractalsMultiple time scale dynamicsTechniquesNon-Markovian processesTime series analysisStatistical PhysicsInterdisciplinary PhysicsGeneral Physics

Optimal pool composition of commercial electric vehicles in V2G fleet operation of various electricity markets

The market ramp-up of electromobility is shifting vehicle-to-grid (V2G) issues into the focus of research and industry. Electric vehicles (EVs) have the potential to support the trend towards renewable energies in their role as storage units during idle times. To participate in balancing power and energy markets, EVs are pooled via aggregators. Instead of a random composition, aggregators can smartly compose their pools and add only those vehicles that actually contribute to the pool’s performance, gaining advantages over competitors. The optimization methods presented in this paper form optimized pool combinations based on the power and energy capability profiles of commercial EVs. Genetic algorithms are used to determine the revenues of the possible pools per participating EV. The use cases analyzed are the provision of balancing power on the frequency containment reserve (FCR) market of Central Europe and energy arbitrage trading on the European power exchange intraday continuous and day-ahead auction spot markets. The results show that through smart pool composition, an aggregator can increase revenue per vehicle by up to seven-fold across the markets compared to randomly assembled pools. In the Central European market, for example, the potential V2G revenues on the FCR market (380 €) exceeded those of arbitrage trading (28 € − 203 €) in 2020. In a simulation, we show the increased degradation of the vehicle battery in V2G operation compared to sole use for mobility with a smart charging strategy. However, the additional revenue can make V2G financially worthwhile, depending on costs for measuring equipment, bidirectional charging stations, and aggregator costs.

An effective Two-Stage Electricity Price forecasting scheme

With the development of the global power market reform, the monopoly of the power sector and government control pattern has gradually broken. Due to the unique properties of electricity, electricity prices show high volatility and uncertainty, bringing significant challenges to the accurate prediction of electricity prices. The sudden occurrence of a few spike prices in the electricity spot market has significantly affected electricity price forecasting accuracy. We propose a novel two-stage electricity price forecasting scheme (TSEP). A multi-source data-based spike occurrence prediction scheme is presented in the first stage, which adopts a deep neural network (DNN) to predict whether the price to be forecasted is a spike or not. Specifically, to alleviate the impact of low spike price samples, the oversampling method is used to synthesize some spikes at the data level. A loss function with a misclassification penalty to increase the cost of missing price spikes is designed at the algorithm level. Based on the outputs of the first stage, in the second stage, TSEP exploits the variance stabilizing transformations respectively suitable for pre-processing spike and normal prices and combines an artificial neural network (ANN) based spike calibration model to improve the accuracy of electricity price forecasting further. The experimental results on the European Power Exchange for France (EPEX-FR) demonstrate that TSEP increases spike occurrence prediction accuracy compared with the conventional models and significantly improves the accuracy of spike electricity price forecasting without affecting the accuracy of forecasting normal electricity price.

Investigating minimum income condition orders on European power exchanges: Controversial properties and enhancement proposals

One of the most difficult challenges of deregulated electricity markets is the proper management and operation planning of economically non-convex generators. The origin of economic non-convexity is twofold. Firstly, generators generally have non-zero minimum output power (MOP) levels, below which they cannot sustain operation. Secondly, they might have substantial fixed costs. The direct consideration of these characteristics leads to very hard non-convex mathematical problems with the increased probability of computational difficulties and long solution time for the market clearing process.Complex orders with explicit Minimum Income Conditions (MICs) have been invented to provide a method to represent non-convex generators on European day-ahead power exchanges. The rationale is that a MIC can be attached to a set of simple supply bids, which means that these bids can be accepted only if their acquired incomes surpass bidder-specified minimal values.The present research is conducted to investigate several controversial issues concerning the current use of MICs from the viewpoint of market participants. Firstly, no direct mechanism is provided to handle the MOP of generators. Secondly, simple bid price constraints and simultaneously attached income criteria can have contradictory implications that lead to sub-optimal clearing results. Thirdly, the current use of MICs contributes to cheating opportunities, because it entails distortions in the social welfare calculation. The paper defines new ways of MIC attachment to circumvent the revealed hindrances. For this purpose, several modification techniques are considered such as the discretization of bid quantities, the relaxation of elementary bid pricing rules and the revision of social welfare calculation. Two novel order types are proposed, supported by comparative numerical simulations using realistic market data.

Assessing new block order options of the EUPHEMIA algorithm: An optimization model for the economic dispatch problem in power exchanges

To implement the process of European power markets’ integration, a market-clearing algorithm has been developed among European power exchanges under the title EUPHEMIA (Pan-European Hybrid Electricity Market Integration Algorithm), being a strictly economic dispatch algorithm and providing several options and products to market participants. This paper proposes an optimization-based methodological framework for the optimal economic dispatch problem in power exchanges, further enhancing the EUPHEMIA algorithm’s block order module. More specifically, through the development of a mixed-integer linear programming (MILP) model and utilizing an iterative process, it quantifies the impacts of the proposed new options to the optimal energy mix, the wholesale prices, and on the market players’ economic performance. The model considers all the current market products of the EUPHEMIA algorithm, as well as introduces new market products such as the flexibility provision of the main aspects of block orders (minimum acceptance ratio, price offer, and block time limits), the modification of existing block orders (exclusive groups), the development of new linkage structure (linked block orders with parallel relationship), as well as the activation of mixed schemes combining existing types of block orders into new integrated forms. The developed optimization framework has been assessed in the Greek interconnected power system, including its interconnections with neighboring power systems. The proposed methodological approach suggests a robust and systematic methodological formation to provide useful insights on policy issues and ongoing debate regarding the shaping of more efficient market designs and structures to deal with the new operational challenges of low-carbon flexible power systems.

Dynamic Modeling of a Decarbonized District Heating System with CHP Plants in Electricity-Based Mode of Operation

The targets of global CO2 reduction outline the importance of decarbonizing the heating and cooling sector, which consume half of the final energy in the European Union (EU). Consequently, heating network operators must adapt to growing requirements for carbon neutrality. Energy system modeling allows the simulation of individual network compositions and regulations, while considering electricity market signals for a more efficient plant operation. The district heating model, programmed for this work, covers a measured heat demand with peak load boiler, biomass-fired combined heat and power (CHP) plant, and biomass heating plant supply. The CHP plant reacts to electricity prices of the European Power Exchange market and uses a long-term heat storage to decouple heat and electricity production. This paper presents the results of three annual simulation scenarios aimed at carbon neutrality for the analyzed heating network. Two scenarios achieve a climate-neutral system by replacing the peak load boiler generation. The exclusive storage capacity expansion in the first scenario does not lead to the intended decarbonization. The second scenario increases the output of the CHP plant, while the third simulation uses the biomass heating plant supply. This additional heat producer enables a significant reduction in storage capacity and a higher CHP plant participation in the considered electricity market.

Modeling Intraday Markets under the New Advances of the Cross-Border Intraday Project (XBID): Evidence from the German Intraday Market

The intraday cross-border project (XBID) allows intraday market participants to trade based on a shared order book independent of countries or local energy exchanges. This theoretically leads to an efficient allocation of cross-border capacities and ensures maximum market liquidity across European intraday markets. If this postulation holds, the technical implementation of XBID might mark a regime switch in any intraday price series. We present a regression-based model for intraday markets with a particular focus on the German European Power Exchange (EPEX) intraday market and evaluate if the introduction of XBID influence prices, volume or volatility. We analyze partial volume-weighted average prices and standard deviations as well as cross-border volumes at different trading times. We are able to falsify our initial hypothesis assuming a measurable influence of changes caused by XBID. Thus, this paper contributes to the ongoing discussion on appropriate modeling of intraday markets and demonstrates that XBID does not necessarily need to be included in any model.

Emissions Trading System of the European Union: Emission Allowances and EPEX Electricity Prices in Phase III

The Emissions Trading System in the European Union was introduced to achieve the climate goal of reducing emissions by around 43% between 1990 and 2030. Accordingly, the costs of emission allowances are part of power generation and, by extension, the price of electricity. Theoretical works thus suggest a positive relationship between the price of emission allowances and electricity. However, this has not been validated empirically for phase III of the Emissions Trading System in the short run as part of the price setting mechanism of electricity producers. Our evidence suggests an opposite effect: According to our empricial results, both European Power Exchange (EPEX) day-ahead and intraday markets are negatively affected during phase III. We further test for a potentially asymmetric influence with the help of quantile regressions. Altogether, the outcome has implications for policy-makers and calls for further attention by academics and policy-makers in the future design of the Emissions Trading System, especially under larger amount of renewables in the electricity system.

Market design of an energy exchange: The case of Greece

Driven by the liberalization of the energy market that began in the 1990s, the European Union aims to unify its internal market and achieve price convergence among all European economies. The majority of European countries have successfully established power exchanges, aiming to conduct cross-border transactions in a transparent and reliable manner. This paper provides a comprehensive overview of prior literature on the market design of the European Power Exchanges. It also identifies recent developments in the electricity market in Greece and describes the structure of the Hellenic Energy Exchange and the markets that will be formed in the future. These upcoming markets are expected to provide greater flexibility to all market participants. At the same time, the Hellenic Energy Exchange is expected to facilitate the integration of Greece into the rest South East European electricity markets.

BRIM: An Accurate Electricity Spot Price Prediction Scheme-Based Bidirectional Recurrent Neural Network and Integrated Market

For the benefit from accurate electricity price forecasting, not only can various electricity market stakeholders make proper decisions to gain profit in a competitive environment, but also power system stability can be improved. Nevertheless, because of the high volatility and uncertainty, it is an essential challenge to accurately forecast the electricity price. Considering that recurrent neural networks (RNNs) are suitable for processing time series data, in this paper, we propose a bidirectional long short-term memory (LSTM)-based forecasting model, BRIM, which splits the state neurons of a regular RNN into two parts: the forward states (using the historical electricity price information) are designed for processing the data in positive time direction and backward states (using the future price information available at inter-connected markets) for the data in negative time direction. Moreover, due to the fact that inter-connected power exchange markets show a common trend for other neighboring markets and can provide signaling information for each other, it is sensible to incorporate and exploit the impact of the neighboring markets on forecasting accuracy of electricity price. Specifically, future electricity prices of the interconnected market are utilized both as input features for forward LSTM and backward LSTM. By testing on day-ahead electricity prices in the European Power Exchange (EPEX), the experimental results show the superiority of the proposed method BRIM in enhancing predictive accuracy in comparison with the various benchmarks, and moreover Diebold-Mariano (DM) shows that the forecast accuracy of BRIM is not equal to other forecasting models, and thus indirectly demonstrates that BRIM statistically significantly outperforms other schemes.

A hybrid electricity price forecasting model with Bayesian optimization for German energy exchange

Electricity price forecasting affects the operation of the entire electricity market and it is extremely important to every market participant. In this paper, a novel hybrid method, with using empirical wavelet transform (EWT), support vector regression (SVR), Bi-directional long short-term memory (BiLSTM) and Bayesian optimization (BO), is proposed to increase the accuracy of electricity price forecasting. First, EWT is used as a processing tool to decompose the original signal into specific modal components according to the characteristics of the signal itself. Then, considering the complexity of forecasting nonlinear subseries, SVR and BiLSTM are used as basic framework to forecast the nonlinear subseries. At the same time, BO is introduced to adjust parameters and optimize model performance. In last, the final prediction results are combined by the prediction results of different models. The proposed hybrid model is employed on the data gathered from the European Power Exchange Spot (EPEXSPOT). Five different case studies are adopted to verify the effectiveness of BO, EWT and hybrid model respectively. Statistical tests of experimental results compared with other situations demonstrated the proposed hybrid model can achieve a better forecasting performance.

Pricing vulnerable power exchange options in an intensity based framework

In this article, we study the valuation of a European vulnerable power exchange option in an intensity based framework. We assume that the default by the counter-party is the time of the first jump of a doubly stochastic Poisson process whose intensity is modeled by a jump–diffusion process. The dynamics of the two assets are assumed to be driven by correlated jump–diffusion processes. All the three processes are assumed to be correlated in continuous part as well as in the jump part. In the proposed framework, employing the measure-change technique, we obtain the explicit formula for the price of the power exchange option with counter-party risk. Furthermore, sensitivity analysis is given to illustrate the effects of counterparty risk on the price of the option and effect of various parameters on the option prices.

Incorporating unit commitment aspects to the European electricity markets algorithm: An optimization model for the joint clearing of energy and reserve markets

The European electricity markets’ integration aims at the market coupling among interconnected power systems and the enhancement of market competitive forces. This process is facilitated by the adoption of a common clearing algorithm among European power exchanges, entitled EUPHEMIA (Pan-European Hybrid Electricity Market Integration Algorithm), which however lacks to capture critical technical aspects of power systems, as done by the unit commitment problem including start-up and shut-down decisions, time constraints (minimum on- and off-times), as well as the consideration of ancillary services. This paper presents an optimization-based framework for the optimal joint energy and reserves market clearing algorithm, further utilizing the hourly offers module of the EUPHEMIA algorithm. In particular, through the formulation of a mixed integer linear programming (MILP) model and employing an iterative approach, it determines the optimal energy and reserves mix, the resulting market clearing prices, and it calculates the welfares of the market participants. The model incorporates intra-hourly power reserve constraints, as well as introduces new market products such as the option of forming linked groups of power units, aiming at supplying additional flexibility in the decision-making of the market participants. The model applicability has been assessed in the Greek power system and its interconnections with neighboring power systems in Southeast Europe. The proposed optimization framework can provide useful insights on the determination of the optimal generation and interconnection portfolios that address the new market-based operational challenges of contemporary power systems subject to technical and economic constraints.

Economic Optimal Implementation of Virtual Power Plants in the German Power Market

The burden of excess energy from the high renewable energy sources (RES) share creates a significant reduction of residual load for the future, resulting in reduced market prices. The higher the share of stochastic RES, the more often the price will be 0 €/MWh. The power market needs new methods to solve these problems. The development of virtual power plants (VPPs) is aimed at solving techno-economic problems with an increasing share of RES in the power market. This study analyses a possible implementation of stochastic and deterministic RES in a VPP to generate secured power, which can be implemented in the European Power Exchange (EPEX)/European Energy Exchange (EEX) power market using existing market products. In this study, the optimal economic VPP configuration for an RES-based power plant is investigated and implemented into standard power market products. The results show that the optimal economic VPP configuration for different market products varies, depending on the energy availability and the marginal costs of the VPP components. The size of the VPP components is positively correlated to the components’ share of the energy generated. It was also found that projecting or implementing VPPs in Germany at current market prices (EPEX/EEX prices) is not yet economically feasible for a small share of market products. However, the secured power can be marketed on the SPOT and in the futures market with higher and more stable prices compared with the status quo.

Risk premia in the German day-ahead electricity market revisited: The impact of negative prices

This paper replicates the study by Viehmann (2011) that investigated risk premia in the German day-ahead electricity market from October 2005 to September 2008. While estimated sizes of risk premia can be replicated, this paper does not reproduce respective standard errors, leading to remarkable differences between the reported significance levels. An extension with data of preceding years points to further differences with respect to size and statistical significance. In addition, this paper analyzes the impact of negative prices on risk premia. Negative electricity prices were introduced in 2008 at the European Power Exchange (EPEX), and in 2013 at the Energy Exchange Austria (EXAA). The results of an econometric analysis suggest that the introduction of negative prices has led to a decrease in risk premia when compared to the period of a positive price regime.